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qfGT-8OoznFJIrIe
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Contemporary Families in the US: An Equity Lens 2e
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5.4 Culture and Families
Monica Olvera and Elizabeth Torres
As discussed in Chapter 2, culture, broadly defined, is the set of beliefs, values, symbols, means of communication, religion, logics, rituals, fashions, etiquette, foods, and art that unite a particular society. Cultural elements are learned behaviors; children learn them while growing up in a particular culture as older members teach them how to live. As such, culture is passed down from one generation to the next.
Culture is intertwined with both ethnicity, religion, and spirituality. Ethnicity refers to the shared social, cultural, and historical experiences stemming from common national, ancestral, or regional backgrounds that make subgroups of a population different from one another. Similarly, an ethnic group is a subgroup of a population with a set of shared social, cultural, and historical experiences; relatively distinctive beliefs, values, and behaviors and some sense of identity or belonging to the subgroup. Pan-ethnicity is the grouping together of multiple ethnicities and nationalities under a single label. For example, people in the United States with Vietnamese, Cambodian, Japanese, and Korean backgrounds could be grouped together under the pan-ethnic label Asian American. The United States has five pan-ethnic groups, including Native Americans, African Americans, Asian Americans, European Americans, and Latinos. The grouping together of multiple ethnicities or nationalities under one umbrella term can be helpful, but it can also be problematic—these groups may share geography, but they have differing values, beliefs, and rituals.
In addition to ethnicity, other terms are used to refer to this aspect of cultures, such as majority and minoritized or marginalized cultures, dominant and nondominant cultures, or macro- and microcultures. Some groups relate to social identities based on regions (the South, the East Coast, urban, rural) or affiliation (street gangs, NASCAR fans, college students). Such groups are not necessarily distinct cultures but rather groups of people who share concerns and who might perceive similarities due to common interests or characteristics (Lustig & Koester, 2010).
Religion is a collection of cultural systems, belief systems, and worldviews that relate humanity to spirituality and, sometimes, to moral values. Many religions have narratives, symbols, traditions, and sacred histories that are intended to give meaning to life or to explain the origin of life or the universe. People may affiliate with religions, beliefs, or a general sense of spirituality.
In Focus: What Brings Us Together
My whole family has such a close relationship and tight bond because we do a lot as a group, including working together. I would say that our religion and culture do play a big role in this.
In our culture, food is a big thing, and we always work together to cook for the family. We are also always willing to help one another whenever one needs a hand, and that’s where religion comes in. Being Catholic, we are just always taught to be nice and respectful toward everyone, especially your family.
Families maintain traditions, rituals, and routines that are heavily influenced by the cultural spaces that any kinship group occupies. But families are also made up of individuals, and while a kinship group may share a culture, individuals may embrace different cultures, ethnic identities, and religious or spiritual beliefs, which creates complexities in family life.
For example, for immigrant and refugee families in the United States, religiosity can be a protective factor when adapting to another culture. Religiosity and spirituality, often integrated with one’s ethnic identity, rituals, and traditions, appear to play a significant role as protective factors in the immigrant paradox among Latino and Somali youth (Areba, 2015; Ruiz & Steffen, 2011). What happens when individuals within a family have differing beliefs? People who grew up in families where parents had different religions from one another report less overall religiosity (McPhail, 2019). Children may grow up with differing religious or spiritual beliefs from those of their parents. Especially in the case of children who identify as LGBTQIA+ within a family whose religious or moral beliefs negate these identities, children can experience dissonance and a lack of connection within their family.
Belonging
While there are many definitions and conceptualizations of belonging, one definition is when a person experiences a subjective feeling that they are an integral part of their surrounding systems, including their friends, family, school and work environments, communities, cultural groups, and physical places (Hagerty et al., 1992). The need for belonging, “to connect deeply with other people and secure places, to align with one’s cultural and subcultural identities, and to feel like one is a part of the systems around them,” is a very basic human need (Allen et al., 2021). Connection with others, physical safety, and well-being are inextricably linked and are crucial for survival (Boyd & Richardson, 2009). A greater sense of belonging is associated with positive psychosocial outcomes.
The benefits and potential protective factors derived from a sense of belonging are especially potent for individuals who identify with marginalized or minoritized groups, including people who identify as sexually or gender diverse, people with disabilities, or those who experience mental health issues (Gardner et al., 2019; Harrist & Bradley, 2002; Rainey et al., 2018; Spencer et al., 2016; Steger & Kashdan, 2009). Among college students from minoritized communities, social belonging interventions are associated with positive impacts on academic and health outcomes (Walton & Cohen, 2011). Other positive effects include having a healthy sense of belonging, including more positive social relationships, academic achievement, occupational success, and better physical and mental health (Allen et al., 2018; Goodenow & Grady, 1993; Hagerty et al., 1992).
In contrast to the benefits of feeling a sense of belonging, a lack of belonging has been linked to an increased risk for mental and physical health problems (Cacioppo et al., 2015). The health risks associated with social isolation can be the equivalent to smoking 15 cigarettes a day and are twice as harmful as obesity (Holt-Lunstad et al., 2015). Social isolation across the lifespan is associated with poor sleep quality, depression, cardiovascular difficulties, rapid cognitive decline, reduced immunity, increased risk for mental illness, lowered immune functioning, antisocial behavior, physical illness, and early mortality (Cacioppo & Hawkley, 2003; Cacioppo et al., 2011; Choenarom et al., 2005; Cornwell & Waite, 2009; Hawkley & Capitanio, 2015; Holt-Lunstad, 2018; Leary, 1990; Slavich et al., 2010; O’Donovan et al., 2010).
Belonging can be fostered at the individual and social level. Figure 5.8 provides a framework for understanding and fostering belonging. A sense of belonging can be impacted by one’s competencies, opportunities, perceptions, and motivations (Allen et al., 2021).
Competency refers to having a set of skills and abilities that are needed to connect and relate to others, develop a sense of identity, and ensure one’s behavior aligns with social norms and cultural values.
Opportunities to belong come from the availability of groups, people, places, times, and spaces to connect with others in ways that allow belonging to occur. Individuals from isolated or rural areas, first- and second-generation immigrants, and refugees may experience circumstances that limit opportunities to foster belonging. The lack of opportunities for belonging was sharply felt during the COVID-19 pandemic when shelter-in-place orders and social distancing measures limited human interactions. But despite opportunities to connect in person, technologies such as gaming and social media quickly became more favored opportunities for connection, especially for youth, those who are shy, or people who experience social anxiety (Allen et al., 2014; Amichai-Hamburger et al., 2002; Davis, 2012; Moore & McElroy, 2012; Seabrook et al., 2016; Seidman, 2013).
Motivations to belong consist of the need or desire to connect with others or the fundamental need to feel accepted, belong, and seek social interactions and connections (Leary & Kelly, 2009).
Individuals have varying perceptions of belonging within their kinship groups and within chosen or assigned cultures. Perceptions of belonging are related to one’s subjective feelings and cognitions regarding their experiences and are informed by past experiences. A person’s negative perceptions of self or others, stereotypes, and negative experiences, such as feeling left out, can affect the desire to connect with others.
Cultural Erasure and Cultural Persistence
Cultural erasure is the practice of a dominant culture contributing to the erasure of a non-dominant or minoritized culture. An example of active cultural erasure would be that of Native American children being forced to attend residential boarding schools, where they might be punished for speaking their heritage language, forced to wear uniforms that were stripped of makers of their community and identity, and harshly mistreated, even to the point of starvation or being beaten (figure 5.9). The strategy of not allowing the children to speak their communities’ languages or learn and practice their communities’ traditions and rituals was an active cultural erasure. Passive cultural erasure could include the histories of communities not being included in historical textbooks or the passing of laws that prohibit people from wearing jewelry, hairstyles, clothing, or other items that are indicators of one’s cultural identity.
Cultural persistence, then, is the very opposite of cultural erasure. Cultural persistence is when elements of culture (such as language, rituals, foodways, and traditions) persist despite efforts to blot out those cultural practices and identities. Among Black Caribbean immigrants, gatherings of family and friends called “liming” sessions reinforce family and cultural identities through storytelling (Brooks, 2013). Another example of cultural persistence is that of language revitalization programs among Indigenous communities, such as the Chinuk Wawa language program supported by Lane Community College (LCC) in Eugene, Oregon. This program consists of a collaboration between Lane Community College, the Confederated Tribes of Grand Ronde, and the Northwest Indian Language Institute of the University of Oregon (UO). This program, which has operated for nearly a decade, provides language classes for tribal members, LCC and UO students, and members of the Grand Ronde Community.
Comprehension Self Check
Licenses and Attributions for Culture and Families
Open Content, Original
“Culture and Families” and all subsections except those noted below by Monica Olvera. License: CC BY 4.0.
“In Focus: What Brings Us Together” By Elizabeth Torres. License: CC BY-NC-ND 4.0.
Figure 5.8 “Four Components of Belonging” designed by Monica Olvera and Michaela Willi Hooper. License: CC BY 4.0. Based on ideas from “Belonging: A Review of Conceptual Issues, an Integrative Framework, and Directions for Future Research” by K.-A. Allen, M. L. Kern, C. S. Rozek, D. M. McInerney, & G. M. Slavich in Australian Journal of Psychology.
Open Content, Shared Previously
The definition of culture in the first paragraph is from “Culture” in “Sociology” by Libre Texts. License: CC BY-SA.
The definitions of ethnicity and ethnic groups in the second paragraph are from “Ethnicity and Religion” in “Social Justice Studies” by Libre Texts. License: CC BY-NC-SA.
The definition of religion in the 5th paragraph is from” The Nature of Religion ” in “Sociology” by Libre Texts. License: CC BY-SA.
Figure 5.7 “Photo“ by Jeswin Thomas on unsplash.com. License: Unsplash License.
Figure 5.9 “Tom Torlino – Navajo” by Carlisle Indian School Digital Resource Center. License: CC BY-NC-SA.
References
Brooks, L. J. (2013). The Black survivors: Courage, strength, creativity and resilience in the cultural traditions of Black Caribbean immigrants. In J.D. Sinnott (Ed.) Positive Psychology (pp. 121-134). New York: Springer.
Lustig, M., & Koester, J. (2010). Intercultural communication: interpersonal communication across cultures. J. Koester.–Boston: Pearson Education.
What Census Calls Us. (2020, May 30). Pew Research Center. https://www.pewresearch.org/interactives/what-census-calls-us/
the shared meanings and shared experiences passed down over time by individuals in a group, such as beliefs, values, symbols, means of communication, religion, logics, rituals, fashions, etiquette, foods, and art that unite a particular society.
the shared social, cultural, and historical experiences, stemming from common national, ancestral, or regional backgrounds, that make subgroups of a population different from one another.
a subgroup of a population with a set of shared social, cultural, and historical experiences; relatively distinctive beliefs, values, and behaviors; and some sense of identity of belonging to the subgroup.
the grouping together of multiple ethnicities and nationalities under a single label.
can include the emotional significance of an action or way of being; the intention or reason for doing something; something that we create and feel; closely linked to motivation.
the geographical location where a person was born and spent (at least) their early years in.
the social structure that ties people together (whether by blood, marriage, legal processes, or other agreements) and includes family relationships.
a sense of self that is derived from a sense of belonging to a group, a culture, and a particular setting.
influenced by personal experiences and opinions.
a socially constructed expression of a person’s sexual identity which influences the status, roles, and norms for their behavior.
the visible or hidden and temporary or permanent conditions that create barriers or challenges in one’s life.
the state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.
a state of mind characterized by emotional well-being, behavioral adjustment, relative freedom from anxiety and disabling symptoms, and a capacity to establish relationships and cope with the ordinary demands and stresses of life.
a wide range of mental health disorders that affect your mood, thinking, and behavior.
the practice of a dominant or hegemonic culture actively or passively contributing to the erasure, or disappearing, of a non-dominant or minoritized culture.
a systematic investigation into a particular topic, examining materials, sources, and/or behaviors.
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2.2: Visible and Near Infrared Optical Spectroscopic Sensors for Biosystems Engineering
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2.2: Visible and Near Infrared Optical Spectroscopic Sensors for Biosystems Engineering
Nathalie Gorretta
University of Montpellier, INRAe and SupAgro
Montpellier, France
Aoife A. Gowen
UCD School of Biosystems and Food Engineering
University College Dublin, Ireland
Variables
Introduction
Optical sensors are a broad class of devices for detecting light intensity. This can be a simple component for notifying when ambient light intensity rises above or falls below a prescribed level, or a highly sensitive device with the capacity to detect and quantify various properties of light such as intensity, frequency, wavelength, or polarization. Among these sensors, optical spectroscopic sensors, where light interaction with a sample is measured at many different wavelengths, are popular tools for the characterization of biological resources, since they facilitate comprehensive, non-invasive, and non-destructive monitoring. Optical sensors are widely used in the control and characterization of various biological environments, including food processing, agriculture, organic waste sorting, and digestate control.
The theory of spectroscopy began in the 17th century. In 1666, Isaac Newton demonstrated that white light from the sun could be dispersed into a continuous series of colors (Thomas, 1991), coining the word spectrum to describe this phenomenon. Many other researchers then contributed to the development of this technique by showing, for example, that the sun’s radiation was not limited to the visible portion of the electromagnetic spectrum. William Herschel (1800) and Johann Wilhelm Ritter (1801) showed that the sun’s radiation extended into the infrared and ultraviolet, respectively. A major contribution by Joseph Fraunhofer in 1814 laid the foundations for quantitative spectrometry. He extended Newton’s discovery by observing that the sun’s spectrum was crossed by a large number of fine dark lines now known as Fraunhofer lines. He also developed an essential element of future spectrum measurement tools (spectrometers) known as the diffraction grating, an array of slits that disperses light. Despite these major advances, Fraunhofer could not give an explanation as to the origin of the spectral lines he had observed. It was only later, in the 1850s, that Gustav Kirchoff and Robert Bunsen showed that each atom and molecule has its own characteristic spectrum. Their achievements established spectroscopy as a scientific tool for probing atomic and molecular structure (Thomas, 1991; Bursey, 2017).
Many terms are used to describe the measurement of electromagnetic energy at different wavelengths, such as spectroscopy, spectrometry, and spectrophotometry. The word spectroscopy originates from the combination of spectro (from the Latin word specere , meaning “to look at”) with scopy (from the Greek word skopia , meaning “to see”). Following the achievements of Newton, the term spectroscopy was first applied to describe the study of visible light dispersed by a prism as a function of its wavelength. The concept of spectroscopy was extended, during a lecture by Arthur Schuster in 1881 at the Royal Institution, to incorporate any interaction with radiative energy according to its wavelength or frequency (Schuster, 1911). Spectroscopy, then, can be summarized as the scientific study of the electromagnetic radiation emitted, absorbed, reflected, or scattered by atoms or molecules. Spectrometry or spectrophotometry is the quantitative measurement of the electromagnetic energy emitted, reflected, absorbed, or scattered by a material as a function of wavelength. The suffix “- photo” (originating from the Greek term phôs , meaning “light”) refers to visual observation, for example, printing on photographic film, projection on a screen, or the use of an observation scope, while the suffix “- metry” (from the Greek term metria , meaning the process of measuring) refers to the recording of a signal by a device (plotter or electronic recording).
Spectroscopic data are typically represented by a spectrum, a plot of the response of interest (e.g. reflectance, transmittance) as a function of wavelength or frequency. The instrument used to obtain a spectrum is called a spectrometer or a spectrophotometer. The spectrum, representing the interaction of electromagnetic radiation with matter, can be analyzed to gain information on the identity, structure, and energy levels of atoms and molecules in a sample.
Two major types of spectroscopy have been defined, atomic and molecular. Atomic spectroscopy refers to the study of electromagnetic radiation absorbed or emitted by atoms, whereas molecular spectroscopy refers to the study of the light absorbed or emitted by molecules. Molecular spectroscopy provides information about chemical functions and structure of matter while atomic spectroscopy gives information about elemental composition of a sample. This chapter focuses on molecular spectroscopy, particularly in the visible-near infrared wavelength region due to its relevance in biosystems engineering.
Concepts
Light and Matter Interaction
Spectroscopy is based on the way electromagnetic energy interacts with matter. All light is classified as electromagnetic radiation consisting of alternating electric and magnetic fields and is described classically by a continuous sinusoidal wave-like motion of the electric and magnetic fields propagating transversally in space and time. Wave motion can be described by its wavelength \(\lambda\) (nm), the distance between successive maxima or minima, or by its frequency ν (Hz), the number of oscillations of the field per second (Figure 2.2.1). Wavelength is related to the frequency via the speed of light c (3 × 10 8 m s −1 ) according to the relationship given in Equation 2.2.1.
\[ \lambda = \frac{c}{v} \]
Sometimes it is convenient to describe light in terms of units called “wavenumbers,” where the wavenumber is the number of waves in one centimeter. Thus, wavenumbers are frequently used to characterize infrared radiation. The wavenumber, \(\bar{\nu}\) is formally defined as the inverse of the wavelength, \(\lambda\) expressed in centimeters:
\[ \bar{\nu}=\frac{1}{\lambda} \]
The wavenumber is therefore directly proportional to frequency, ν:
\[ v = c\bar{\nu} \]
leading to the following conversion relationships:
\[ \bar{\nu} (\text{cm}^{-1}) = \frac{10^{7}}{\lambda{\text{(nm)}}} \]
\[ \lambda{\text{(nm)} = \frac{10^{7}}{\bar{\nu}(\text{cm}^{-1})}} \]
The propagation of light is described by the theory of electromagnetic waves proposed by Christian Huygens in 1878 (Huygens, 1912). However, the interaction of light with matter (emission or absorption) also leads to the particle nature of light and electromagnetic waves as proposed by Planck and Einstein in the early 1900s. In this theory, light is considered to consist of particles called photons, moving at the speed c . Photons are “packets” of elementary energy, or quanta, that are exchanged during the absorption or emission of light by matter.
|
Wavelength
\(\lambda\) |
Wavenumber
\(\bar{\nu}\) |
Relation | |
|---|---|---|---|
|
Unit |
cm |
cm −1 |
\(\bar{\nu}= \frac{1}{\lambda}\) |
|
nm |
cm −1 |
\(\bar{\nu}= \frac{10^{7}}{\lambda}\) |
The energy of photons of light is directly proportional to its frequency, as described by the fundamental Planck relation (Equation 2.2.6). Thus, high energy radiation (such as X-rays) has high frequencies and short wavelengths and, inversely, low energy radiation (such as radio waves) has low frequencies and long wavelengths.
\[ E =h\nu=\frac{hc}{\lambda}=hc\bar{\nu} \]
where E = energy of photons of light (J)
h = Plank’s constant = 6.62607004 × 10 −34 J·s
ν = frequency (Hz)
c = speed of light (3 ×10 8 m s −1 )
\(\lambda\) = wavelength (m)
The electromagnetic spectrum is the division of electromagnetic radiation according to its different components in terms of frequency, photon energy or associated wavelengths, as shown in Figure 2.2.2. The highest energy radiation corresponds to the γ-ray region of the spectrum. At the other end of the electromagnetic spectrum, radio frequencies have very low energy (Pavia et al., 2008). The visible region only makes up a small part of the electromagnetic spectrum and ranges from 400 to about 750 nm. The infrared (IR) spectral region is adjacent to the visible spectral region and extends from about 750 nm to about 5 × 10 6 nm. It can be further subdivided into the near-infrared region (NIR) from about 750 nm to 2,500 nm which contains the short wave-infrared (SWIR) from 1100–2500 nm, the mid-infrared (MIR) region from 2,500 nm to 5 × 10 4 nm, and the far-infrared (FIR) region from 5 × 10 4 nm to 5 × 10 6 nm (Osborne et al., 1993).
When electromagnetic radiation collides with a molecule, the molecule’s electronic configuration is modified. This modification is related to the wavelength of the radiation and consequently to its energy. The interaction of a wave with matter, whatever its energy, is governed by the Bohr atomic model and derivative laws established by Bohr, Einstein, Planck, and De Broglie (Bohr, 1913; De Broguie, 1925). Atoms and molecules can only exist in certain quantified energy states. The energy exchanges between matter and radiation can, therefore, only be done by specific amounts of energy or quanta \( \Delta{E} =h\nu \). These energy exchanges can be carried out in three main ways (Figure 2.2.3): absorption, emission, or diffusion.
In absorption spectroscopy, a photon is absorbed by a molecule, which undergoes a transition from a lower-energy state E i to a higher energy or excited state E j such that E j – E i = h ν. In emission spectroscopy, a photon can be emitted by a molecule that undergoes a transition from a higher energy state E j to a lower energy state E i such that E j – E i = h ν. In diffusion or scattering spectroscopy, a part of the radiation interacting with matter is scattered in many directions by the particles of the sample. If, after an interaction, the photon energy is not modified, the interaction is known as elastic . This corresponds to Rayleigh or elastic scattering, which maintains the frequency of the incident wave. When the photon takes or gives energy to the matter and undergoes a change in energy, the interaction is called inelastic , corresponding, respectively, to Stokes or anti-Stokes Raman scattering. Transitions between energy states are referred to as absorption or emission lines for absorption and emission spectroscopy, respectively.
Absorption Spectrometry
In absorption spectrometry, transitions between energy states are referred to as absorption lines . These absorption lines are typically classified by the nature of the electronic configuration change induced in the molecule (Sun, 2009):
- • Rotation lines occur when the rotational state of a molecule is changed. They are typically found in the microwave spectral region ranging between 100 μm and 1 cm.
- • Vibrational lines occur when the vibrational state of the molecule is changed. They are typically found in the IR, i.e., in the spectral range between 780 and 25,000 nm. Overtones and combinations of the fundamental vibrations in the IR are found in the NIR range (Figure 2.2.2).
- • Electronic lines correspond to a change in the electronic state of a molecule (transitions of the energetic levels of valence orbitals). They are typically found in the ultraviolet (approx. 200–400 nm) and visible region (approx. 200–400 nm). In the visible region (350–800 nm), molecules such as carotenoids and chlorophylls absorb light due to their molecular structure. This visible spectral range is also used to evaluate color (for instance, of food or vegetation). In the ultraviolet spectral range, fluorescence and phosphorescence can be observed. While fluorescence and phosphorescence are both spontaneous emission of electromagnetic radiation, they differ in the way the excited molecule loses its energy after it has been irradiated. The glow of fluorescence stops right after the source of excitatory radiation is switched off, whereas for phosphorescence, an afterglow can last from fractions of a second to hours.
The spectral ranges selected for measurement and analysis depend on the application and the materials to be characterized. Absorption spectroscopy in the visible and NIR is commonly used for the characterization of biological systems due to the many advantages associated with this wavelength range, including rapidity, non-invasivity, non-destructive measurement, and significant incident wave penetration. Moreover, the NIR range enables probing of molecules containing C-H, N-H, S-H, and O-H bonds, which are of particular interest for characterization of biological samples (Pasquini, 2018; 2003). In addition to the chemical characterization of materials, it is possible to quantify the concentration of certain molecules using the Beer-Lambert law, described in detail below.
Beer-Lambert Law
Incident radiation passing through a medium undergoes several changes, the extent of which depends on the physical and chemical properties of the medium. Typically, part of the incident beam is reflected, another part is absorbed and transformed into heat by interaction with the material, and the rest passes through the medium. Transmittance is defined as the ratio of the transmitted light intensity to the incident light intensity (Equation 2.2.7). Absorbance is defined as the logarithm of the inverse of the transmittance (Equation 2.2.8). Absorbance is a positive value, without units. Due to the inverse relationship between them, absorbance is greater when the transmitted light is low.
\[ T= \frac{I}{I_{0}} \]
\[ A=log(\frac{1}{T})=log(\frac{I_{0}}{I}) \]
where T = transmittance
I = transmitted light intensity
I 0 = incident light intensity
A = absorbance (unitless)
The Beer-Lambert law (Equation 2.2.9) describes the linear relationship between absorbance and concentration of an absorbing species. At a given wavelength λ , absorbance A of a solution is directly proportional to its concentration ( C ) and to the length of the optical path ( b ), i.e., the distance over which light passes through the solution (Figure 2.2.4, Equation 2.2.9). When the concentration is expressed in moles per liter (mol L −1 ), the length of the optical path in centimeters (cm), the molar absorptivity or the molar extinction coefficient ε is expressed in L mol −1 cm −1 .
Molar absorptivity is a measure of the probability of the electronic transition and depends on the wavelength but also on the solute responsible for absorption, the temperature and, to a lesser extent, the pressure.
\[ A=\epsilon bC \]
where A = absorbance (unitless)
ε = molar absorptivity or molar extinction coefficient = Beer-Lambert proportionality constant (L mol −1 cm −1 )
b = path length of the sample (cm)
C = concentration (mol L −1 )
Beer-Lambert Law Limitations
Under certain circumstances, the linear relationship between the absorbance, the concentration, and the path length of light can break down due to chemical and instrumental factors. Causes of nonlinearity include the following:
- • Deviation of absorptivity coefficient: The Beer-Lambert law is capable of describing the behavior of a solution containing a low concentration of an analyte. When analyte concentration is too high (typically >10 mM), electrostatic interactions between molecules close to each other result in deviations in absorptivity coefficients.
- • High analyte concentrations can also alter the refractive index of the solution which in turn could affect the absorbance obtained.
- • Scattering: Particulates in the sample can induce scattering of light.
- • Fluorescence or phosphorescence of the sample.
- • Non-monochromatic radiation due to instrumentation used.
Non-linearity can be detected as deviations from linearity when the absorbance is plotted as a function of concentration (see example 1). This is usually overcome by reducing analyte concentration through sample dilution.
Spectroscopic Measurements
Spectrometers are optical instruments that detect and measure the intensity of light at different wavelengths. Different measurement modes are available, including transmission, reflection, and diffuse reflection (Figure 2.2.5). In transmission mode, the spectrometer captures the light transmitted through a sample, while in reflectance mode, the spectrometer captures the light reflected by the sample. In some situations, e.g., for light-diffusing samples such as powders, reflected light does not come solely from the front surface of the object; radiation that penetrates the material can reappear after scattering of reflection within the sample. These radiations are called diffuse reflection.
Spectrometers share several common basic components, including a source of light energy, a means for isolating a narrow range of wavelengths (typically a dispersive element), and a detector. The dispersive element must allow light of different wavelengths to be separated (Figure 2.2.6).
The light source is arguably the most important component of any spectrophotometer. The ideal source is a continuous one that contains radiation of uniform intensity over a large range of wavelengths. Other desirable properties are stability over time, long service life, and low cost. Quartz-tungsten halogen lamps are commonly used as light sources for the visible (Vis) and NIR regions, and deuterium lamps or high-powered light emitting diodes may be used for the ultraviolet region.
The light produced by the light source is then focused and directed to the monochromater by an entrance slit. A grating diffraction element is then used to split the white light from the lamp into its components. The distance between the lines on gratings (“grating pitch”) is of the same order of magnitude as the wavelength of the light to be analyzed. The separated wavelengths then propagate towards the sample compartment through the exit slit.
Depending on the technology used for the detector, the sample can be positioned before or after the monochromater. For simplicity, this chapter describes a positioning of the sample after the monochromater; the entire operation described above is valid regardless of the positioning of the sample.
In some spectrometers, an interferometer (e.g. Fabry-Pérot or Fourier-transform interferometer for UV and IR spectral range, respectively) is used instead of a diffraction grating to obtain spectral measurements. In this case, the initial beam light is split into two beams with different optical paths by using mirror arrangements. These two beams are then recombined before arriving at the detector. If the optical path lengths of the two beams do not differ by too much, an interference pattern is produced. A mathematical operation (Fourier transform) is then applied to the obtained interference pattern (interferogram) to produce a spectrum.
Once the light beams have passed through the samples, they will continue to the detector or photodetector. A photodetector absorbs the optical energy and converts it into electrical energy. A photodetector is a multichannel detector and can be a photodiode array, a charge coupled device (CCD), or a complementary metal oxide semiconductor (CMOS) sensor. While photodetectors can be characterized in many different ways, the most important differentiator is the detector material. The two most common semiconductor materials used in Vis-NIR spectrometers are silicon (Si) and indium gallium arsenide (InGaAs).
Spectral Imaging
Spectral imaging is a technique that integrates conventional imaging and spectroscopy to obtain both spatial and spectral information from an object. Multispectral imaging usually refers to spectral images in which <10 spectral bands are collected, while hyperspectral imaging is the term used when >100 contiguous spectral bands are collected. The term spectral imaging is more general. Spectral images can be represented as three-dimensional blocks of data, comprising two spatial and one wavelength dimension.
Two sensing modes are commonly used to acquire hyperspectral images, i.e., reflectance and transmission modes (Figure 2.2.7). The use of these modes depends on the objects to be characterized (e.g., transparent or opaque) and the properties to be determined (e.g. size, shape, chemical composition, presence of defects). In reflectance mode, the hyperspectral sensor and light are located on the same side of the object and the imaging system acquires the light reflected by the object. In this mode, the lighting system should be designed to avoid any specular reflection. Specular reflection occurs when a light source can be seen as a direct reflection on the surface of an object. It is characterized by an angle of reflection being equal to the angle of incidence of the incoming light source on the sample. Specular reflection appears as bright saturated spots on acquired images impacting their quality. In transmittance mode, the detector is located in the opposite side of the light source and captures the transmitted light through the sample.
Applications
Vegetation Monitoring in Agriculture
The propagation of light through plant leaves is governed primarily by absorption and scattering interactions and is related to chemical and structural composition of the leaves. Spectral characteristics of radiation reflected, transmitted, or absorbed by leaves can thus provide a more thorough understanding of physiological responses to growth conditions and plant adaptations to the environment. Indeed, the biochemical components and physical structure of vegetation are related to its state of growth and health. For example, foliar pigments including chlorophyll a and b, carotenoids, and anthocyanins are strong absorbers in the Vis region and are abundant in healthy vegetation, causing plant reflectance spectra to be low in the Vis relative to NIR wavelength range (Asner, 1998; Ollinger, 2011) (Figure 2.2.8). Chlorophyll pigments absorb violet-blue and red light for photosynthesis, the process by which plants use sunlight to synthesize organic matter. Green light is not absorbed by photosynthesis and reflectance spectra of green vegetation in the visible range are maximum around 550 nm. This is why healthy leaves appear to be green. The red edge refers to the area of the sudden increase in the reflectance of green vegetation between 670 and 780 nm. The reflectance in the NIR plateau (800–1100 nm) is a region where biochemical absorptions are limited and is affected by the scattering of light within the leaf, the extent of which is related to the leaf’s internal structure. Reflectance in the short wave-IR (1100–2500 nm) is characterized by strong water absorption and minor absorptions of other foliar biochemical contents such as lignin, cellulose, starch, protein, and cellulose.
Stress conditions on plants, such as drought and pathogens, will induce changes in reflectance in the Vis and NIR spectral domain due to degradation of the leaf structure and the change of the chemical composition of certain tissues. Consequently, by measuring crop reflectance in the Vis and NIR regions of the spectrum, spectrometric sensors are able to monitor and estimate crop yield and crop water requirements and to detect biotic or abiotic stresses on vegetation. Vegetation indices (VI), which are combinations of reflectance images at two or more wavelengths designed to highlight a particular property of vegetation, can then be calculated over these images to monitor vegetation changes or properties at different spatial scales.
The normalized difference vegetation index (NDVI) (Rouse et al., 1974) is the ratio of the difference between NIR and red reflectance, divided by the sum of the two:
\[ NDVI = \frac{R_{NIR}-R_{R}}{R_{NIR}+R_{R}} \]
where R NIR = reflectance in the NIR spectral region (one wavelength selected over the 750–870 nm spectral range) and R R = reflectance in the red spectral region (one wavelength selected over 580–650 nm spectral range). Dividing by the sum of the two bands reduces variations in light over the field of view of the image. Thus, NDVI maintains a relatively constant value regardless of the overall illumination, unlike the simple difference which is very sensitive to changes in illumination. NDVI values can range between −1 and +1, with negative values corresponding to surfaces other than plant cover, such as snow or water, for which the red reflectance is higher than that in the NIR. Bare soils, which have red and NIR reflectance about the same order of magnitude, NDVI values are close to 0. Vegetation canopies have positive NDVI values, generally in the range of 0.1 to 0.7, with the highest values corresponding to the densest vegetation coverage.
NDVI can be correlated with many plant properties. It has been, and still is, used to characterize plant health status, identify phenological changes, estimate green biomass and yields, and in many other applications. However, NDVI also has some weaknesses. Atmospheric conditions and thin cloud layers can influence the calculation of NDVI from satellite data. When vegetation cover is low, everything under the canopy influences the reflectance signal that will be recorded. This can be bare soil, plant litter, or other vegetation. Each of these types of ground cover will have its own spectral signature, different from that of the vegetation being studied. Other indices to correct NDVI defects or to estimate other vegetation parameters have been proposed, such as the normalized difference water index or NDWI (Gao, 1996), which uses two wavelengths located respectively in the NIR and the SWIR regions (750–2500 nm) to track changes in plant moisture content and water stress (Eq. 2.2.11). Both wavelengths are located in a high reflectance plateau (Fig. 2.2.8) where the vegetation scattering properties are expected to be about the same. The SWIR reflectance is affected by the water content of the vegetation. The combination of the NIR and the SWIR wavelength is thus not sensitive to the internal structure of the leaf but is affected by vegetation water content. The normalized difference water index is:
\[ NDWI=\frac{R_{NIR}-R_{SWIR}}{R_{NIR}+R_{SWIR}} \]
where R NIR is the reflectance in the NIR spectral region (one wavelength selected over the 750–870 nm spectral range) and R SWIR is the reflectance in the SWIR spectral region around 1240 nm (water absorption band). Gao (1996) proposed using R NIR equal to reflectance at 860 nm and R SWIR at 1240 nm.
Absorption spectroscopy is widely used for monitoring and characterizing vegetation at different spatial, spectral, and temporal scales. Sensors are available mainly for broad-band multispectral or narrow-band hyperspectral data acquisition. Platforms are space-borne for satellite-based sensors, airborne for sensors on manned and unmanned airplanes, and ground-based for field and laboratory-based sensors.
Satellites have been used for remote sensing imagery in agriculture since the early 1970s (Bauer and Cipra, 1973; Doraiswamy et al., 2003) when Landsat 1 (originally known as Earth Resources Technology Satellite 1) was launched. Equipped with a multispectral scanner with four wavelength channels (one green, one red and two IR bands), this satellite was able to acquire multispectral images with 80 m spatial resolution and 18-day revisit time (Mulla 2013). Today, numerous multispectral satellite sensors are available and provide observations useful for assessing vegetation properties far better than Landsat 1. Landsat 8, for example, launched in 2013, offers nine spectral bands in the Vis to short-wave IR spectral range (i.e., 400–2500 nm) with a spatial resolution of 15–30 m and a 16-day revisit time. Sentinel-2A and Sentinel-2B sensors launched in 2015 and 2017, respectively, have 13 spectral bands (400–2500 nm) and offer 10–30 m multi-spectral global coverage and a revisit time of less than 10 days. Hyperspectral sensors, however, are still poorly available on satellites due to their cost and their relatively short operating life. Among them, Hyperion (EO-1 platform) has 220 spectral bands over the 400–2500 nm spectral range, a spatial resolution of 30 m, and a spectral resolution of 10 nm. The next generation, such as PRISMA (PRecursore IperSpettrale della Missione Applicativa) with a 30 m spatial resolution and a wavelength range of 400–2505 nm and the EnMAP (Environmental Mapping and Analysis Program) with a 30 m spatial resolution and a wavelength range of 400–2500 nm (Transon et al., 2018), indicate the future for this technology.
Some companies now use satellite images to provide a service to help farmers manage agricultural plots. Farmstar (www.myfarmstar.com/web/en) and Oenoview ( https://www.icv.fr/en/viticulture-oenology-consulting/oenoview ), for example, support management of inputs and husbandry in cereal and vine crops, respectively. However, satellite-based sensors often have an inadequate spatial resolution for precision agriculture applications. Some farm management decisions, such as weed detection and management, require images with a spatial resolution in the order of one centimeter and, for emergent situations (such as to monitor nutrient stress and disease), a temporal resolution of less than 24 hours (Zhang and Kovacs, 2012).
Airborne sensors are today able to produce data from multispectral to hyperspectral sensors with wavelengths ranging from Vis to MIR, with spatial resolutions ranging from sub-meter to kilometers and with temporal frequencies ranging from 30 min to weeks or months. Significant advancements in unmanned aerial vehicle (UAV) technology as well as in hyperspectral and multispectral sensors (in terms of both weight and image acquisition modes) allow for the combination of these tools to be used routinely for precision agricultural applications. The flexibility of these sensors, their availability and the high achievable spatial resolutions (cm) make them an alternative to satellite sensors. Multispectral sensors embedded on UAV platforms have been used in various agricultural studies, for example, to detect diseases in citrus trees (Garcia-Ruiz et al., 2013), grain yield in rice (Zhou et al., 2017) and for mapping vineyard vigor (Primicerio et al., 2012). UAV systems with multispectral imaging capability are used routinely by companies to estimate the nitrogen needs of plants. This information, given in near real-time to farmers, helps them to make decisions about management. Information extracted from airborne images are also used for precision farming to enhance planning of agricultural interventions or management of agricultural production at the scale of farm fields.
Ground-based spectroscopic sensors have also been developed for agricultural purposes. They collect reflectance data from short distances and can be mounted on tractors or held by hand. For example, the Dualex Force A hand-tool leaf clip ( https://www.force-a.com/fr/produits/dualex ) is adapted to determine the optical absorbance of the epidermis of a leaf in the ultraviolet (UV) optical range through the differential measurement of the fluorescence of chlorophyll as well as the chlorophyll content of the leaf using different wavelengths in the red and NIR ranges. Using internal model calibration, this tool calculates leaf chlorophyll content, epidermal UV-absorbance and a nitrogen balance index (NBI). This information could then be used to obtain valuable indicators of nitrogen fertilization, plant senescence, or pathogen susceptibility. Other examples are the nitrogen sensors developed by Yara ( https://www.yara.fr/fertilisation/outils-et-services/n-sensor/ ) that enable adjustment of the nitrogen application rate in real time and at any point of the field, according to the crop’s needs.
Food-Related Applications
Conventional, non-imaging, spectroscopic methods are widely used for routine analysis and process control in the agri-food industry. For example, NIR spectroscopy is commonly used in the prediction of protein, moisture, and fat content in a wide range of raw materials and processed products, such as liquids, gels, and powders (Porep et al., 2015). Ultraviolet-Vis (UV-Vis) spectroscopy is a valuable tool in monitoring bioprocesses, such as the development of colored phenolic compounds during fermentation of grapes in the process of winemaking (Aleixandre-Tudo et al., 2017). The Beer-Lambert law (Equation 2.2.9) can be used to predict the concentration of a given compound given its absorbance at a specific wavelength.
While conventional spectroscopic methods are useful for characterizing homogeneous products, the lack of spatial resolution leads to an incomplete assessment of heterogeneous products, such as many foodstuffs. This is particularly problematic in the case of surface contamination, where information on the location, extent, and distribution of contaminants over a food sample is required. Applications of Vis-NIR spectral imaging for food quality and safety are widespread in the scientific literature and are emerging in the commercial food industry. The heightened interest in this technique is driven mainly by the non-destructive and rapid nature of spectral imaging, and the potential to replace current labor- and time-intensive analytical methods in the production process.
This section provides a brief overview of the range and scope of such applications. For a more comprehensive description of these and related applications, several informative reviews have been published describing advances in hyperspectral imaging for contaminant detection (Vejarano et al., 2017), food authentication (Roberts et al., 2018), and food quality control (Gowen et al. 2007; Baiano, 2017).
Contaminant Detection
The ability of spectral imaging to detect spatial variations over a field of view, combined with chemical sensitivity, makes it a promising tool for contaminant detection. The main contaminants that can be detected in the food chain using Vis-NIR include polymers, paper, insects, soil, bones, stones, and fecal matter. Diffuse reflectance is by far the most common mode of spectral imaging utilized for this purpose, meaning that primarily only surface or peripheral contamination can be detected. Of concern in the food industry is the growth of spoilage and pathogenic microorganisms at both pre-harvest and post-harvest processing stages, since these result in economic losses and potentially result in risks to human health. Vis-NIR spectral imaging methods have been demonstrated for pre-harvest detection of viral infection and fungal growth on plants, such as corn (maize) and wheat. For instance, decreases in the absorption of light in wavebands related to chlorophyll were found to be related to the destruction of chloroplasts in corn ears due to Fusarium infection (Bauriegel et al., 2011). Fecal contamination acts as a favorable environment for microbial growth, thus many studies have focused on the detection of such contamination over a wide variety of foods, including fresh produce, meat, and poultry surfaces. For example, both fluorescence and reflectance modalities have been shown to be capable of detecting fecal contamination on apples with high accuracy levels (Kim et al., 2007). Recent studies have utilized spectral imaging transmittance imaging for insect detection within fruits and vegetables, resulting in high detection levels (>80% correct classification) (Vejarano et al., 2017).
Food Authentication
Food ingredient authentication is necessary for the ever expanding global supply chain to ensure compliance with labeling, legislation, and consumer demand. Due to the sensitivity of vibrational spectroscopy to molecular structure and the development of advanced multivariate data analysis techniques such as chemometrics, NIR and MIR spectroscopy have been used successfully in authentication of the purity and geographical origin of many foodstuffs, including honey, wine, cheese, and olive oil. Spectral imaging, having the added spatial dimension, has been used to analyze non-homogeneous samples, where spatial variation could improve information on the authentication or prior processing of the food product, for example, in the detection of fresh and frozen-thawed meat or in adulteration of flours (Roberts et al., 2018).
Food Quality Control
Vis-NIR spectral imaging has been applied in a wide range of food quality control issues, such as bruise detection in mushrooms, apples, and strawberries, and in the prediction of the distribution of water, protein, or fat content in heterogeneous products such as meat, fish, cheese, and bread (Liu et al., 2017). The dominant feature in the NIR spectrum of high moisture foods is the oxygen-hydrogen (OH) bond-related peak centered around 1450 nm. The shape and intensity of this peak is sensitive to the local environment of the food matrix, and can provide information on changes in the water present in food products. This is useful since many deteriorative biochemical processes, such as microbial growth and non-enzymatic browning, rely on the availability of free water in foods. Vis-NIR spectral imaging has also been applied to quality assessment of semi-solid foods, as reviewed by Baiano (2017). For instance, transmittance spectral imaging has been used to non-destructively assess the interior quality of eggs (Zhang et al., 2015), while diffuse reflectance spectral imaging has been used to study the microstructure of yogurt (Skytte et al., 2015) and milk products (Abildgaard et al., 2015).
Examples
Example \(\PageIndex{1}\)
Example 1: Using the Beer-Lambert law to predict the concentration of an unknown solution
Problem:
Data were obtained from a UV-Vis optical absorption instrument, as shown in Table 2.2.2. Light absorbance was measured at 520 nm for different concentrations of a compound that has a red color. The path length was 1 cm. The goal is to use the Beer-Lambert law to calculate the molar absorptivity coefficient and determine the concentration of an unknown solution that has an absorbance of 1.52.
| Concentration (mol L −1 ) | Absorbance at 520 nm |
|---|---|
|
0.001 |
0.21 |
|
0.002 |
0.39 |
|
0.005 |
1.01 |
|
0.01 |
2.02 |
Solution
The first step required in calculating the molar absorptivity coefficient is to plot a graph of absorbance as a function of concentration, as shown in Figure 2.2.9. The data follow a linear trend, indicating that the assumptions of the Beer-Lambert law are satisfied.
To calculate the molar absorptivity coefficient, it is first necessary to calculate the line of best linear fit to the data. This is achieved here using the “add trendline” function in Excel. The resultant line of best fit is shown in Figure 2.2.10. The equation of this line is y = 201.85x.
Compare this equation to the Beer-Lambert law (Equation 2.2.9):
\( A=\epsilon bC \) (Equation \(\PageIndex{9}\)
where A = absorbance (unitless)
ε = molar absorptivity or molar extinction coefficient = Beer-Lambert proportionality constant (L mol −1 cm −1 )
b = path length of the sample (cm)
C = concentration (mol L −1 )
In this example, ε b = 201.85, where b is the path length, defined in the problem as 1 cm. Consequently, ε = 201.85 (L mol −1 cm −1 ). To calculate the concentration of the unknown solution, substitute the absorbance of the unknown solution (1.52) into the equation of best linear fit, resulting in a concentration of 0.0075 mol L −1 .
This type of calculation can be used for process or quality control in the food industry or for environmental monitoring such as water quality assessment.
Example \(\PageIndex{2}\)
Example 2: Calculation of vegetation indices from a spectral image
Problem:
The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) developed by the National Aeronautics and Space Administration (NASA) is one of the foremost spectral imaging instruments for Earth remote sensing (NASA, n. d.). An agricultural scene was gathered by flying over the Indian Pines test site in northwestern Indiana (U.S.) and consists of 145 × 145 pixels and 224 spectral reflectance bands in the wavelength range 400–2500 nm. The Indian Pines scene (freely available at https://doi.org/10.4231/R7RX991C ; Baumgardner et al., 2015) contains two-thirds agricultural land and one-third forest or other natural perennial vegetation. There are also two major dual lane highways and a rail line, as well as some low-density housing, other structures, and smaller roads present in the scene. The ground truth image shows the designation of various plots and regions in the scene, and is designated into sixteen classes, as shown in Figure 2.2.11. The average radiance spectrum of four classes of land cover in the scene is plotted in Figure 2.2.12. Table 2.2.3 shows the data corresponding to the plots shown in Figure 2.2.11. Using the mean radiance values, calculate the NDVI and NDWI for each class of land cover. Please note: In this example, the mean radiance values are being used for illustration purposes. This simplification is based on the assumption that the radiation receipt is constant across all wavebands so radiance is assumed to be linearly proportional to reflectance (ratio of reflected to total incoming energy). Typically, vegetation indices are calculated from pixel-level reflectance spectra.
| Grass-Pasture | Grass-Trees | Grass-Pasture-Mowed | Hay-Windrowed | Stone-Steel Towers | |
|---|---|---|---|---|---|
|
NDVI |
0.38 |
0.24 |
0.03 |
0.09 |
−0.25 |
|
NDWI |
0.5 |
0.38 |
0.45 |
0.35 |
0.35 |
By applying the calculation to each pixel spectrum in the image, it is possible to create images of the NDVI and NDWI, as shown in Figure 2.2.13. The NDVI highlights regions of vegetation in red, regions of crop growth and soil in light green-blue, and regions of stone in darker blue. The NDWI, sensitive to changes in water content of vegetation canopies, shows regions of high water content in red, irregularly distributed in the wooded regions.
Image Credits
Figure 1. Gorretta, N. (CC By 4.0). (2020). Schematic of a sinusoidal wave described by its wavelength.
Figure 2. Gorretta, N. (CC By 4.0). (2020). Electromagnetic spectrum.
Figure 3. Gorretta, N. (CC By 4.0). (2020). Simplified energy diagram showing (a) absorption, (b) emission of a photon by a molecule, (c) diffusion process.
Figure 4. Gorretta, N. (CC By 4.0). (2020). Absorption of light by a sample.
Figure 5. Gorretta, N. (CC By 4.0). (2020). Schematic diagram showing the path of light for different modes of light measurement, i.e. (a) transmission, (b) reflection, and (c) diffuse reflection.
Figure 6. Gorretta, N. (CC By 4.0). (2020). Spectrometer configuration: transmission diffraction grating.
Figure 7. Gorretta, N. (CC By 4.0). (2020). Hyperspectral imaging sensing mode: (a) reflectance mode, (b) transmission mode.
Figure 8. Gorretta, N. (CC By 4.0). (2020). A green vegetation spectrum.
Figure 9. Gowen, A. A. (CC By 4.0). (2020). Plot of absorbance at 520 nm as a function of concentration.
Figure 10. Gowen, A. A. (CC By 4.0). (2020). Plot of absorbance at 520 nm as a function of concentration showing line and equation of best linear fit to the data.
Figure 11. Gowen, A. A. (CC By 3.0). (2015). Indian Pines ground truth image showing various plots and regions in the scene, designated into sixteen classes. Citation might be: Baumgardner, M. F., L. L. Biehl, and D. A. Landgrebe. 2015. “220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3.” Purdue University Research Repository. doi:10.4231/R7RX991C. This item is licensed CC BY 3.0.
Figure 12. Gowen, A. A. (CC By 4.0). (2020). Indian Pines average reflectance spectrum of four classes of land cover in the scene shown in figure 11.
Figure 13. Gowen, A. A. (CC BY 4.0). (2020). NDVI and NDWI calculation of Indian Pines images.
References
Abildgaard, O. H., Kamran, F., Dahl, A. B., Skytte, J. L., Nielsen, F. D., Thomsen, C. L., . . . Frisvad, J. R. (2015). Non-invasive assessment of dairy products using spatially resolved diffuse reflectance spectroscopy. Appl. Spectrosc. , 69 (9), 1096–1105. https://doi.org/10.1366/14-07529 .
Aleixandre-Tudo, J. L., Buica, A., Nieuwoudt, H., Aleixandre, J. L., & du Toit, W. (2017). Spectrophotometric analysis of phenolic compounds in grapes and wines. J. Agric. Food Chem. , 65 (20), 4009-4026. https://doi.org/10.1021/acs.jafc.7b01724 .
Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing Environ. , 64 (3), 234-253. https://doi.org/10.1016/S0034-4257(98)00014-5 .
Baiano, A. (2017). Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: A review. J. Food Eng. , 214 , 10-15. https://doi.org/10.1016/j.jfoodeng.2017.06.012 .
Bauer, M. E., & Cipra, J. E. (1973). Identification of agricultural crops by computer processing of ERTS MSS Data. Proc. Symp. on Significant Results Obtained from the Earth Resources Technology Satellite. Retrieved from http://agris.fao.org/agris-search/search.do?recordID=US201302721443 .
Baumgardner, M. F., Biehl, L. L., & Landgrebe, D. A. (2015). 220 Band AVIRIS hyperspectral image data set: June 12, 1992 Indian Pine Test Site 3. Purdue University Research Repository. https://doi.org/10.4231/R7RX991C .
Bauriegel, E., Giebel, A., & Herppich, W. B. (2011). Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors , 11 (4), 3765-3779. https://doi.org/10.3390/s110403765 .
Bohr, N. (1913). I. On the constitution of atoms and molecules. London Edinburgh Dublin Philosophical Magazine J. Sci. , 26 (151), 1-25. https://doi.org/10.1080/14786441308634955 .
Bursey, M. M. (2017). A brief history of spectroscopy. Access Science . https://doi.org/10.1036/1097-8542.BR0213171 .
De Broguie, L. V. 1925. On the theory of quanta. Paris, France.
Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from remote sensing. Photogrammetric Eng. Remote Sensing , 69 (6), 665-674. doi.org/10.14358/PERS.69.6.665.
Farmstar. (n. d.). Farmstar: Have everything you need to manage your crops! Retrieved from www.myfarmstar.com/web/en.
Force A. (n. d.). Dualex scientific. Retrieved from https://www.force-a.com/fr/produits/dualex .
Gao, B.-c. (1996). NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environ. , 58 (3), 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3 .
Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput. Electron. Agric. , 91 , 106-115. https://doi.org/10.1016/j.compag.2012.12.002 .
Gowen, A. A., O’Donnell, C. P., Cullen, P. J., Downey, G., & Frias, J. M. (2007). Hyperspectral imaging—An emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. , 18 (12), 590-598. doi.org/10.1016/j.jpgs.2007.06.001.
Huygens, C. (1912). Treatise on light. Macmillan. Retrieved from http://archive.org/details/treatiseonlight031310mbp .
Kim, M. S., Chen, Y.-R., Cho, B.-K., Chao, K., Yang, C.-C., Lefcourt, A. M., & Chan, D. (2007). Hyperspectral reflectance and fluorescence line-scan imaging for online defect and fecal contamination inspection of apples. Sensing Instrumentation Food Qual. Saf. , 1 (3), 151. doi.org/10.1007/s11694-007-9017-x.
Liu, Y., Pu, H., & Sun, D.-W. (2017). Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci. Technol. , 69 , 25-35. doi.org/10.1016/j.jpgs.2017.08.013.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. , 114 (4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009 .
NASA (n. d.). Airborne visible/infrared imaging spectrometer: AVIRIS overview. NASA Jet Propulsion Laboratory, California Institute of Technology. https://www.jpl.nasa.gov/missions/airborne-visible-infrared-imaging-spectrometer-aviris/ .
Ollinger, S. V. (2011). Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. , 189 (2), 375-394. doi.org/10.1111/j.1469-8137.2010.03536.x.
Osborne, B. G., Fearn, T., Hindle, P. H., & Osborne, B. G. (1993). Practical NIR spectroscopy with applications in food and beverage analysis (Vol. 2). Longman Scientific & Technical.
Pasquini, C. (2003). Near infrared spectroscopy: Fundamentals, practical aspects and analytical applications. J. Brazilian Chem. Soc. , 14 (2), 198-219. https://doi.org/10.1590/S0103-50532003000200006 .
Pasquini, C. (2018). Near infrared spectroscopy: A mature analytical technique with new perspectives: A review. Anal. Chim. Acta , 1026 , 8-36. https://doi.org/10.1016/j.aca.2018.04.004 .
Pavia, D. L., Lampman, G. M., Kriz, G. S., & Vyvyan, J. A. (2008). Introduction to spectroscopy. Cengage Learning.
Porep, J. U., Kammerer, D. R., & Carle, R. (2015). On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. , 46 (2, Part A), 211-230. doi.org/10.1016/j.jpgs.2015.10.002.
Primicerio, J., Di Gennaro, S. F., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., & Vaccari, F. P. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precision Agric. , 13 (4), 517-523. doi.org/10.1007/s11119-012-9257-6.
Roberts, J., Power, A., Chapman, J., Chandra, S., & Cozzolino, D. (2018). A short update on the advantages, applications and limitations of hyperspectral and chemical imaging in food authentication. Appl. Sci. , 8 (4), 505. https://doi.org/10.3390/app8040505 .
Rouse Jr., J. W., Haas, R. H., Schell, J. A., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publ. 351.
Schuster, A. (1911). Encyclopedia Britannica, 2 :477.
Skytte, J., Moller, F., Abildgaard, O., Dahl, A., & Larsen, R. (n. d.). Discriminating yogurt microstructure using diffuse reflectance images. Proc. Scandinavian Conf. on Image Analysis (pp. 192-203). Springer. doi.org/10.1007/978-3-319-19665-7_16.
Sun, D.-W. (2009). Infrared spectroscopy for food quality analysis and control. Academic Press.
Thomas, N. C. (1991). The early history of spectroscopy. J. Chem. Education , 68 (8), 631. https://doi.org/10.1021/ed068p631 .
Remote Sensing, 10 (2). https://doi.org/10.3390/rs10020157.
Vejarano, R., Siche, R., & Tesfaye, W. (2017). Evaluation of biological contaminants in foods by hyperspectral imaging: A review. Int. J. Food Properties , 20 (sup2), 1264-1297. doi.org/10.1080/10942912.2017.1338729.
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agric. , 13 (6), 693-712. doi.org/10.1007/s11119-012-9274-5.
Zhang, W., Pan, L., Tu, S., Zhan, G., & Tu, K. (2015). Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. J. Food Eng. , 157 , 41-48. https://doi.org/10.1016/j.jfoodeng.2015.02.013 .
Zhou, X., Zheng, H. B., Xu, X. Q., He, J. Y., Ge, X. K., Yao, X., . . . Tian, Y. C. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogrammetry Remote Sensing , 130 , 246-255. https://doi.org/10.1016/j.isprsjprs.2017.05.003 .
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19.4: Anatomy of Lymphatic Organs and Tissues
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19.4: Anatomy of Lymphatic Organs and Tissues
By the end of this section, you will be able to:
- Describe the structure and function of the primary and secondary lymphatic organs
- Describe the structure, function, and location of lymphoid tissues
Lymphoid organs are distinct structures consisting of multiple tissue types. The category can be further subdivided into primary lymphoid organs, which support lymphocyte production and development, and secondary lymphoid organs, which support lymphocyte storage and function. Lymphoid tissues are concentrations of lymphocytes and other immune cells within other organs of the body.
Primary Lymphoid Organs and Lymphocyte Development
The differentiation and development of B and T cells is critical to the adaptive immune response. When the body is exposed to a new pathogen, lymphocytes of the adaptive immune response must "learn" the new antigen associated with the pathogen, mount an effective response to eradicate the pathogen, and "remember" the antigen in case the body is exposed again in the future by forming memory cells. It is also through this process that the body (ideally) learns to destroy only pathogens and leaves the body’s own cells relatively intact. The primary lymphoid organs are the bone marrow and thymus gland. The lymphoid organs are where lymphocytes proliferate and mature.
Bone Marrow
Yellow bone marrow consists of adipose tissue, consisting largely of fat cells for energy storage (Figure \(\PageIndex{1}\)) and is primarily located in the medullary cavity of adult long bones. Red bone marrow is a loose collection of cells where hematopoiesis (blood cell formation) occurs surrounding a framework of reticular connective tissue. Red bone marrow is primarily located surrounding the trabeculae of spongy bone.
Hematopoiesis is summarized in Figure \(\PageIndex{2}\) and was covered in more detail in the blood chapter. In the embryo, blood cells are made in the yolk sac. As development proceeds, this function is taken over by the spleen, lymph nodes, and liver. Later, the red bone marrow takes over most hematopoietic functions, although the final stages of the differentiation of some cells may take place in other organs. The B cell undergoes nearly all of its development in the red bone marrow, whereas the immature T cell, called a thymocyte , leaves the bone marrow and matures largely in the thymus gland.
Thymus
The thymus gland is a bilobed organ found in the space between the sternum and the aorta of the heart (Figure \(\PageIndex{3}\)) that serves as a specialized structure in which T lymphocytes mature. The thymus is most active early in life as you are exposed to and build immunity to many pathogens. After puberty, the thymus slowly but continuously decreases in activity and size as its tissue is replaced with a mix of fibrous and adipose connective tissues.
In the thymus, the maturation of T lymphocytes begins with thymocytes. Thymocytes travel through the bloodstream from the red bone marrow to the thymus. Thymocytes are precursors to T lymphocytes that lack the surface proteins mature T lymphocytes use to recognize an antigen and coordinate an adaptive immune response. There is a multi-step physiological process by which T lymphocytes mature into naïve T lymphocytes ready to be activated for an adaptive immune response.
Dense irregular connective tissue holds the lobes of the thymus closely together but also separates them and forms a fibrous capsule. The fibrous capsule further divides the thymus into lobules via extensions called trabeculae. Blood vessels and nerves are routed within the trabeculae. The outer region of each lobule is known as the cortex and here the structure of the thymus forms a blood-thymus barrier to prevent the thymocytes from being exposed to antigens from the bloodstream before they mature. The densely packed thymocytes in the cortex stain more intensely than the rest of the thymus (see Figure \(\PageIndex{3}\)). The medulla contains a less dense collection of thymocytes, epithelial cells, macrophages, and dendritic cells, so it appears more lightly stained in the micrograph. Thymocytes move from the cortex to the medulla as they mature where the lack of the blood-thymus barrier allows them to enter the bloodstream and travel to other lymphatic structures to await activation for an adaptive immune response.
AGING AND THE...
Immune System
By the year 2050, 25 percent of the population of the United States will be 60 years of age or older. The CDC estimates that 80 percent of those 60 years and older have one or more chronic diseases associated with deficiencies of the immune systems. This loss of immune function with age is called immunosenescence. To treat this growing population, medical professionals must better understand the aging process. One major cause of age-related immune deficiencies is thymic involution, the shrinking of the thymus gland that begins after puberty, at a rate of about three percent tissue loss per year, and continues until 35–45 years of age, when the rate declines to about one percent loss per year for the rest of one’s life. At that pace, the total loss of thymic epithelial tissue and thymocytes would occur at about 120 years of age. Thus, this age is a theoretical limit to a healthy human lifespan.
Thymic involution has been observed in all vertebrate species that have a thymus gland. Animal studies have shown that transplanted thymic grafts between inbred strains of mice involuted according to the age of the donor and not of the recipient, implying the process is genetically programmed. There is evidence that the thymic microenvironment, so vital to the development of naïve T cells, loses thymic epithelial cells according to the decreasing expression of the FOXN1 gene with age.
It is also known that thymic involution can be altered by hormone levels. Sex hormones such as estrogen and testosterone enhance involution, and the hormonal changes in pregnant women cause a temporary thymic involution that reverses itself, when the size of the thymus and its hormone levels return to normal, usually after lactation ceases. What does all this tell us? Can we reverse immunosenescence, or at least slow it down? The potential is there for using thymic transplants from younger donors to keep thymic output of naïve T cells high. Gene therapies that target gene expression are also seen as future possibilities. The more we learn through immunosenescence research, the more opportunities there will be to develop therapies, even though these therapies will likely take decades to develop. The ultimate goal is for everyone to live and be healthy longer, but there may be limits to immortality imposed by our genes and hormones.
DISORDERS OF THE...
Immune System: Autoimmune Responses
The worst cases of the immune system over-reacting are autoimmune diseases. Somehow, tolerance breaks down and the immune systems in individuals with these diseases begin to attack their own bodies, causing significant damage. The trigger for these diseases is, more often than not, unknown and the treatments are usually based on resolving or minimizing the symptoms using immunosuppressive and anti-inflammatory drugs such as steroids. These diseases can be localized and crippling, as in rheumatoid arthritis, or diffuse in the body with multiple symptoms that differ in different individuals, as is the case with systemic lupus erythematosus (Figure \(\PageIndex{4}\)).
Environmental triggers seem to play large roles in autoimmune responses. One explanation for the breakdown of tolerance is that, after certain bacterial infections, an immune response to a component of the bacterium cross-reacts with a self-antigen. This mechanism is seen in rheumatic fever, a result of infection with Streptococcus bacteria, which causes strep throat. The antibodies to this pathogen’s M protein cross-react with an antigenic component of heart myosin, a major contractile protein of the heart that is critical to its normal function. The antibody binds to these molecules and activates complement proteins, causing damage to the heart, especially to the heart valves. On the other hand, some theories propose that having multiple common infectious diseases actually prevents autoimmune responses. The fact that autoimmune diseases are rare in countries that have a high incidence of infectious diseases supports this idea.
There are genetic factors in autoimmune diseases as well. Overall, there are more than 80 different autoimmune diseases, which are a significant health problem in the elderly. Table \(\PageIndex{1}\) lists several of the most common autoimmune diseases, along with the antigens that are targeted and symptoms of the disease.
| Disease | Autoantigen | Symptoms |
|---|---|---|
| Celiac disease | Tissue transglutaminase | Damage to small intestine |
| Diabetes mellitus type I | Beta cells of pancreas | Low insulin production; inability to regulate serum glucose |
| Graves’ disease | Thyroid-stimulating hormone receptor (antibody blocks receptor) | Hyperthyroidism |
| Hashimoto’s thyroiditis | Thyroid-stimulating hormone receptor (antibody mimics hormone and stimulates receptor) | Hypothyroidism |
| Lupus erythematosus | Nuclear DNA and proteins | Damage to many body systems |
| Myasthenia gravis | Acetylcholine receptor in neuromuscular junctions | Debilitating muscle weakness |
| Rheumatoid arthritis | Joint capsule antigens | Chronic inflammation of joints |
Secondary Lymphoid Organs and their Roles in Immune Responses
Lymphocytes develop and mature in the primary lymphoid organs, but they mount immune responses from the secondary lymphoid organs . A naïve lymphocyte is one that has left the primary organ and entered a secondary lymphoid organ. Naïve lymphocytes are fully functional immunologically, sometimes referred to as immunocompetency, but have yet to encounter an antigen to which to respond. In addition to circulating in the blood and lymph, lymphocytes concentrate in secondary lymphoid organs, which include the lymph nodes, spleen, and lymphoid tissues associated with several organs in the body. All of these tissues have many features in common, including the following:
- An internal structure of reticular connective tissue with associated fixed macrophages
- The presence of lymphoid follicles , collections of lymphocytes, with specific B cell-rich and T cell-rich areas
- Germinal centers , which are the sites of activated (rapidly dividing) B lymphocytes and plasma cells, with the exception of the spleen
- Specialized post-capillary vessels known as high endothelial venules ; the cells lining these venules are thicker and more columnar than normal endothelial cells, which allow cells from the blood to directly enter these tissues
Lymph Nodes
As described in the previous section, lymph nodes are positioned at regular intervals along the length of lymphatic vessels. Lymph nodes function to remove debris and pathogens from the lymph, and are thus sometimes referred to as the “filters of the lymph” (Figure \(\PageIndex{5}\)). Any bacteria that infect the interstitial fluid are taken up by the lymphatic capillaries and transported to a regional lymph node. Dendritic cells and macrophages within this organ internalize and kill many of the pathogens and debris that pass through, thereby removing them from the body. The lymph node is the most common site of activation of adaptive immune responses mediated by T cells, B cells, and accessory cells of the adaptive immune system. This is why swollen lymph nodes can be a sign of infection.
Like the thymus, the bean-shaped lymph nodes are surrounded by a tough capsule of dense connective tissue and are separated into compartments by trabeculae, extensions of the capsule. In addition to the structure provided by the capsule and trabeculae, the structural support of the lymph node is provided by a series of reticular fibers laid down by fibroblasts of its reticular connective tissue framework. Several afferent lymphatic vessels deliver lymph into the convex side of a lymph node and a one-way valve in each vessel near where it connects with the lymph node prevents back-flow of lymph. One or two efferent lymphatic vessels allow lymph to flow out of the concave hilum of the lymph node. The tissue inside the lymph node consists of two generalized regions: the cortex and the medulla. The cortex is nearest the convex side of the node and contains lymphoid follicles where activated lymphocytes proliferate. The medulla, nearest the hilum of each node, is rich with lymphocytes (T cells, B cells, and plasma cells), as well as macrophages and dendritic cells.
Spleen
The spleen is located inferior and medial to the curve of the diaphragm and lateral to the stomach in the upper left quadrant of the abdomen. It is built on a framework of reticular connective tissue and surrounded by a capsule of dense irregular connective tissue that invaginates into trabeculae to divide the spleen into nodules (Figure \(\PageIndex{6}\)). Upon entering the spleen, the splenic artery splits into several arterioles and eventually into sinusoid capillaries. Blood from the capillaries subsequently collects in venules that drain into the splenic vein.
Within each splenic nodule is a large area of red pulp surrounding the sinusoid capillaries that is so-called because it consists mainly of erythrocytes. The red pulp consists of reticular fibers with fixed macrophages attached, free macrophages, and other formed elements of the blood, including some lymphocytes. The red pulp primarily functions in phagocytosis of worn-out erythrocytes and bloodbourne pathogens. Aside from the blood vessels, the remaining tissue of each nodule is called the white pulp , so-called because it lacks the erythrocytes found in the red pulp. The white pulp surrounds the arteriole and resembles the lymphoid follicles of the lymph nodes. It consists of germinal centers of dividing B cells surrounded by T cells and accessory cells, including macrophages and dendritic cells. Germinal centers function as a site of T cell and B cell activation. The marginal zone is the region where the white pulp transitions to the red pulp and their functions mix.
Lymphoid Nodules
The other lymphoid tissues, lymphoid nodules , have a simpler architecture than the spleen and lymph nodes in that they consist of a dense cluster of lymphocytes on a framework of reticular connective tissue without a surrounding fibrous capsule. These nodules are associated with the mucus membranes of the respiratory and digestive tracts, areas routinely exposed to environmental pathogens.
Tonsils are lymphoid nodules located along the inner surface of the pharynx and are important in developing immunity to oral pathogens (Figure \(\PageIndex{7}\)).
- The pharyngeal tonsil , sometimes referred to as the adenoid, is located in the superoposterior of the nasopharynx
- The palatine tonsils are in the lateral wall of the oropharynx
- The lingual tonsil faces the oropharynx in the wall at the posterior of the tongue
Swelling of the tonsils is an indication of an active immune response to infection. Histologically, tonsils do not contain a complete capsule, and the epithelial layer invaginates deeply into the interior of the tonsil to form tonsillar crypts. The crypts allow all sorts of materials taken into the body through eating and breathing to accumulate in the tonsils, actually “encouraging” pathogens to penetrate deep into the tonsillar tissues where T cells and B cells in the germinal centers can be activated for an adaptive immune response. This seems to be the major function of tonsils—to help children’s bodies recognize, destroy, and develop immunity to common environmental pathogens so that they will be protected in their later lives. Tonsils are often removed in those children who have recurring throat infections, especially those involving the palatine tonsils on either side of the throat, whose swelling may interfere with their breathing and/or swallowing.
Mucosa-associated lymphoid tissue (MALT) consists of an aggregate of lymphoid follicles directly associated with the mucous membrane epithelia. MALT makes up dome-shaped structures found underlying the mucosa of the gastrointestinal tract, breast tissue, lungs, and eyes. Peyer’s patches, a type of MALT in the small intestine, are especially important for immune responses against ingested substances (Figure \(\PageIndex{8}\)). Peyer’s patches contain specialized endothelial cells called M (or microfold) cells that sample material from the intestinal lumen and transport it to nearby follicles so that adaptive immune responses to potential pathogens can be mounted.
Concept Review
Lymphoid organs are comprised of multiple tissues forming a distinct structure in the body. Primary lymphoid organs include bone marrow and the thymus. Lymphocytes and other blood cells are produced in red bone marrow while lipids are stored for long-term energy in yellow bone marrow. B lymphocytes remain in red bone marrow to develop into naïve B lymphocytes. The thymus is the organ in which immature T lymphocytes are stored and develop into naïve T lymphocytes. Naïve T and B lymphocytes travel to secondary lymphoid organs, lymph nodes and the spleen, from where they can be activated for the adaptive immune response. Lymph nodes filter lymph for invaders, abnormal cells, and debris as it drains to the bloodstream through lymphatic vessels. The spleen, in the white pulp, filters the blood for invaders, abnormal cells, and debris as it circulates through the bloodstream. The red pulp of the spleen also filters the blood and serves to remove worn-out red blood cells from circulation. Concentrations of lymphocytes and other immune cells within mucus membranes of other organ systems are called lymphoid tissues or nodules. The tonsils surround the pharynx while mucosa-associated lymphoid tissue (MALT) are found in mucus membranes of the gastrointestinal tract, breast tissue, lungs, and eyes.
Review Questions
Q. Which of the lymphoid nodules is most likely to see food-bourne antigens first?
A. tonsils
B. Peyer’s patches
C. bronchus-associated lymphoid tissue
D. mucosa-associated lymphoid tissue
- Answer
-
Answer: A
Critical Thinking Questions
Q. Compare and contrast functions of the lymph nodes and the spleen.
- Answer
-
A. Both lymph nodes and the spleen filter transport fluids in the body for pathogens, abnormal cells, and debris. Lymph nodes are positioned at intervals along lymphatic vessels to filter lymph as it travels through the lymphatic vessels. The spleen filters blood as it travels through the blood stream. Both lymph nodes and the spleen are secondary lymphoid organs meaning they contain concentrations of naïve lymphocytes and other immune cells from which an adaptive immune response can be mounted.
Glossary
- afferent lymphatic vessels
- lead into a lymph node
- bone marrow
- tissue found inside bones; the site of all blood cell differentiation and maturation of B lymphocytes
- efferent lymphatic vessels
- lead out of a lymph node
- germinal centers
- clusters of rapidly proliferating B cells found in secondary lymphoid tissues
- high endothelial venules
- vessels containing unique endothelial cells specialized to allow migration of lymphocytes from the blood to the lymph node
- lingual tonsil
- lymphoid nodule in the anterior wall of the oropharynx posterior to the tongue
- lymph node
- one of the bean-shaped organs found associated with the lymphatic vessels
- lymphoid nodules
- unencapsulated patches of lymphoid tissue found throughout the body
- mucosa-associated lymphoid tissue (MALT)
- lymphoid nodule associated with the mucosa
- palatine tonsils
- lymphoid nodules in the right and left lateral walls of the oropharynx
- pharyngeal tonsil (adenoid)
- lymphoid nodule in the superoposterior wall of the nasopharynx
- primary lymphoid organ
- site where lymphocytes mature and proliferate; red bone marrow and thymus gland
- red pulp
- region of a spleen nodule that is so-called because it is filled with many erythrocytes that surrounds the sinusoid capillaries and functions primarily to remove worn-out erythrocytes from circulation
- secondary lymphoid organs
- sites where lymphocytes mount adaptive immune responses; examples include lymph nodes and spleen
- spleen
- secondary lymphoid organ that filters pathogens from the blood (white pulp) and removes degenerating or damaged blood cells (red pulp)
- thymocyte
- immature T cell found in the thymus
- thymus
- primary lymphoid organ; where T lymphocytes proliferate and mature
- tonsils
- lymphoid nodules associated with the pharynx
- white pulp
- region of a spleen nodule that is filled with germinal centers surrounding the arteriole that functions to activate B cells and T cells
Contributors and Attributions
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OpenStax Anatomy & Physiology (CC BY 4.0). Access for free at https://openstax.org/books/anatomy-and-physiology
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BC Reads: Adult Literacy Fundamental English - Reader 1
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8 Canada’s Tallest Tree
Click play on the following audio player to listen along as you read this section.
A man named Randy liked to hunt trees. He looked for big trees and old trees. He made maps to show where these trees were. He did not want to cut them down. He wanted people to take care of them.
Randy was told of a very tall tree on Vancouver Island. The tree was said to be 314 feet tall. That would make it the tallest tree in Canada. Randy set out to find the tree.
But someone else found it first. It was found by a logger. Loggers wanted to cut down Canada’s tallest tree and all the trees around it.
Randy made a path in the forest so people could see the tall tree. The tree was so big and beautiful it would fill them with awe. More and more people wanted to save that forest. Thanks to these people, that forest is now a park. Canada’s tallest tree is still there.
There may still be a bigger tree out there. Maybe you will find it. But there are only a few old forests left in BC. Many are still at risk of being cut down.
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Chemistry v. 1 backup
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17.4 Shifting Equilibria: Le Châtelier’s Principle
Learning Objectives
- Describe the ways in which an equilibrium system can be stressed
- Predict the response of a stressed equilibrium using Le Châtelier’s principle
As we saw in the previous section, reactions proceed in both directions (reactants go to products and products go to reactants). We can tell a reaction is at equilibrium if the reaction quotient (Q) is equal to the equilibrium constant (K). Next we address what happens when a system at equilibrium is disturbed so that Q is no longer equal to K. If a system at equilibrium is subjected to a perturbance or stress (such as a change in concentration) the position of equilibrium changes. Since this stress affects the concentrations of the reactants and the products, the value of Q will no longer equal the value of K. To re-establish equilibrium, the system will either shift toward the products (if Q < K) or the reactants (if Q > K) until Q returns to the same value as K.
This process is described by Le Châtelier's principle: When a chemical system at equilibrium is disturbed, it returns to equilibrium by counteracting the disturbance. As described in the previous paragraph, the disturbance causes a change in Q; the reaction will shift to re-establish Q = K.
Predicting the Direction of a Reversible Reaction
Le Châtelier’s principle can be used to predict changes in equilibrium concentrations when a system that is at equilibrium is subjected to a stress. However, if we have a mixture of reactants and products that have not yet reached equilibrium, the changes necessary to reach equilibrium may not be so obvious. In such a case, we can compare the values of Q and K for the system to predict the changes.
Effect of Change in Concentration on Equilibrium
A chemical system at equilibrium can be temporarily shifted out of equilibrium by adding or removing one or more of the reactants or products. The concentrations of both reactants and products then undergo additional changes to return the system to equilibrium.
The stress on the system in Figure 17.4a is the reduction of the equilibrium concentration of SCN− (lowering the concentration of one of the reactants would cause Q to be larger than K). As a consequence, Le Châtelier’s principle leads us to predict that the concentration of Fe(SCN)2+ should decrease, increasing the concentration of SCN− part way back to its original concentration, and increasing the concentration of Fe3+ above its initial equilibrium concentration.
The effect of a change in concentration on a system at equilibrium is illustrated further by the equilibrium of this chemical reaction:
The numeric values for this example have been determined experimentally. A mixture of gases at 400 °C with [H2] = [I2] = 0.221 M and [HI] = 1.563 M is at equilibrium; for this mixture, Qc = Kc = 50.0. If H2 is introduced into the system so quickly that its concentration doubles before it begins to react (new [H2] = 0.442 M), the reaction will shift so that a new equilibrium is reached, at which [H2] = 0.374 M, [I2] = 0.153 M, and [HI] = 1.692 M. This gives:
We have stressed this system by introducing additional H2. The stress is relieved when the reaction shifts to the right, using up some (but not all) of the excess H2, reducing the amount of uncombined I2, and forming additional HI.
Effect of Change in Pressure on Equilibrium
Sometimes we can change the position of equilibrium by changing the pressure of a system. However, changes in pressure have a measurable effect only in systems in which gases are involved, and then only when the chemical reaction produces a change in the total number of gas molecules in the system. An easy way to recognize such a system is to look for different numbers of moles of gas on the reactant and product sides of the equilibrium. While evaluating pressure (as well as related factors like volume), it is important to remember that equilibrium constants are defined with regard to concentration (for Kc) or partial pressure (for KP). Some changes to total pressure, like adding an inert gas that is not part of the equilibrium, will change the total pressure but not the partial pressures of the gases in the equilibrium constant expression. Thus, addition of a gas not involved in the equilibrium will not perturb the equilibrium.
Check out this video to see a dramatic visual demonstration of how equilibrium changes with pressure changes.
Watch Volume Effect on Equilibrium – LeChatelier’s Principle Lab Extension (0:43 min)
Video Source: North Carolina School of Science and Mathematics. (2011, December 14). Volume effect on equilibrium – LeChatelier’s Principle lab extension [Video].
As we increase the pressure of a gaseous system at equilibrium, either by decreasing the volume of the system or by adding more of one of the components of the equilibrium mixture, we introduce a stress by increasing the partial pressures of one or more of the components. In accordance with Le Châtelier’s principle, a shift in the equilibrium that reduces the total number of molecules per unit of volume will be favoured because this relieves the stress. The reverse reaction would be favoured by a decrease in pressure.
Consider what happens when we increase the pressure on a system in which NO, O2, and NO2 are at equilibrium:
The formation of additional amounts of NO2 decreases the total number of molecules in the system because each time two molecules of NO2 form, a total of three molecules of NO and O2 are consumed. This reduces the total pressure exerted by the system and reduces, but does not completely relieve, the stress of the increased pressure. On the other hand, a decrease in the pressure on the system favours decomposition of NO2 into NO and O2, which tends to restore the pressure.
Now consider this reaction:
Because there is no change in the total number of molecules in the system during reaction, a change in pressure does not favour either formation or decomposition of gaseous nitrogen monoxide.
Effect of Change in Temperature on Equilibrium
Changing concentration or pressure perturbs an equilibrium because the reaction quotient is shifted away from the equilibrium value. Changing the temperature of a system at equilibrium has a different effect: A change in temperature actually changes the value of the equilibrium constant. However, we can qualitatively predict the effect of the temperature change by treating it as a stress on the system and applying Le Châtelier’s principle.
When hydrogen reacts with gaseous iodine, heat is evolved.
Because this reaction is exothermic, we can write it with heat as a product.
Increasing the temperature of the reaction increases the internal energy of the system. Thus, increasing the temperature has the effect of increasing the amount of one of the products of this reaction. The reaction shifts to the left to relieve the stress, and there is an increase in the concentration of H2 and I2 and a reduction in the concentration of HI. Lowering the temperature of this system reduces the amount of energy present, favours the production of heat, and favours the formation of hydrogen iodide.
When we change the temperature of a system at equilibrium, the equilibrium constant for the reaction changes. Lowering the temperature in the HI system increases the equilibrium constant: At the new equilibrium the concentration of HI has increased and the concentrations of H2 and I2 decreased. Raising the temperature decreases the value of the equilibrium constant, from 67.5 at 357 °C to 50.0 at 400 °C.
Temperature affects the equilibrium between NO2 and N2O4 in this reaction
The positive ΔH value tells us that the reaction is endothermic and could be written
At higher temperatures, the gas mixture has a deep brown colour, indicative of a significant amount of brown NO2 molecules. If, however, we put a stress on the system by cooling the mixture (withdrawing energy), the equilibrium shifts to the left to supply some of the energy lost by cooling. The concentration of colourless N2O4 increases, and the concentration of brown NO2 decreases, causing the brown colour to fade.
| Disturbance | Observed Change as Equilibrium is Restored | Direction of Shift | Effect on K |
|---|---|---|---|
| reactant added | added reactant is partially consumed | toward products | none |
| product added | added product is partially consumed | toward reactants | none |
| decrease in volume/increase in gas pressure | pressure decreases | toward side with fewer moles of gas | none |
| increase in volume/decrease in gas pressure | pressure increases | toward side with more moles of gas | none |
| temperature increase | heat is absorbed | toward products for endothermic, toward reactants for exothermic | changes |
| temperature decrease | heat is given off | toward reactants for endothermic, toward products for exothermic | changes |
Catalysts Do Not Affect Equilibrium
As we learned during our study of kinetics, a catalyst can speed up the rate of a reaction. Though this increase in reaction rate may cause a system to reach equilibrium more quickly (by speeding up the forward and reverse reactions), a catalyst has no effect on the value of an equilibrium constant nor on equilibrium concentrations.
The interplay of changes in concentration or pressure, temperature, and the lack of an influence of a catalyst on a chemical equilibrium is illustrated in the industrial synthesis of ammonia from nitrogen and hydrogen according to the equation
A large quantity of ammonia is manufactured by this reaction. Each year, ammonia is among the top 10 chemicals, by mass, manufactured in the world. About 2 billion pounds are manufactured in the United States each year.
Ammonia plays a vital role in our global economy. It is used in the production of fertilizers and is, itself, an important fertilizer for the growth of corn, cotton, and other crops. Large quantities of ammonia are converted to nitric acid, which plays an important role in the production of fertilizers, explosives, plastics, dyes, and fibres, and is also used in the steel industry.
Fritz Haber
In the early 20th century, German chemist Fritz Haber (Figure 17.4c) developed a practical process for converting diatomic nitrogen, which cannot be used by plants as a nutrient, to ammonia, a form of nitrogen that is easiest for plants to absorb.
The availability of nitrogen is a strong limiting factor to the growth of plants. Despite accounting for 78% of air, diatomic nitrogen (N2) is nutritionally unavailable due the tremendous stability of the nitrogen-nitrogen triple bond. For plants to use atmospheric nitrogen, the nitrogen must be converted to a more bioavailable form (this conversion is called nitrogen fixation).
Haber was born in Breslau, Prussia (presently Wroclaw, Poland) in December 1868. He went on to study chemistry and, while at the University of Karlsruhe, he developed what would later be known as the Haber process: the catalytic formation of ammonia from hydrogen and atmospheric nitrogen under high temperatures and pressures. For this work, Haber was awarded the 1918 Nobel Prize in Chemistry for synthesis of ammonia from its elements. The Haber process was a boon to agriculture, as it allowed the production of fertilizers to no longer be dependent on mined feed stocks such as sodium nitrate. Currently, the annual production of synthetic nitrogen fertilizers exceeds 100 million tons and synthetic fertilizer production has increased the number of humans that arable land can support from 1.9 persons per hectare in 1908 to 4.3 in 2008.
In addition to his work in ammonia production, Haber is also remembered by history as one of the fathers of chemical warfare. During World War I, he played a major role in the development of poisonous gases used for trench warfare. Regarding his role in these developments, Haber said, “During peace time a scientist belongs to the World, but during war time he belongs to his country.”[1] Haber defended the use of gas warfare against accusations that it was inhumane, saying that death was death, by whatever means it was inflicted. He stands as an example of the ethical dilemmas that face scientists in times of war and the double-edged nature of the sword of science.
Like Haber, the products made from ammonia can be multifaceted. In addition to their value for agriculture, nitrogen compounds can also be used to achieve destructive ends. Ammonium nitrate has also been used in explosives, including improvised explosive devices. Ammonium nitrate was one of the components of the bomb used in the attack on the Alfred P. Murrah Federal Building in downtown Oklahoma City on April 19, 1995.
Indigenous Perspective: The Three Sisters
Also in relation to nitrogen-fixation, a number of Indigenous communities have used another method for nitrogen fixation for hundreds of years. Termed “The Three Sisters”, corn, squash and beans are co-planted, and their symbiotic relationship allows for all three plants to produce optimum yields. The corn stalks provide support, giving the bean plants a vertical space to grow upwards. The squash plant’s large leaves help maintain soil moisture and can prevent weeds. The bean plants are a natural source of nitrogen-fixation, which helps supports all of the plants. Briefly, the bean plants host the microbe, rhizobia, that converts nitrogen from the air into ammonia. This ammonia can then be absorbed by the plant roots.
To learn more about the Three Sisters, Watch Three Sisters: Companion Planting of North American Indigenous Peoples (10:54 min):
Video Source: GRIN-U Education. (2021, November 16). Three Sisters: Companion planting of North American Indigenous Peoples [Video]. YouTube.
It has long been known that nitrogen and hydrogen react to form ammonia. However, it became possible to manufacture ammonia in useful quantities by the reaction of nitrogen and hydrogen only in the early 20th century after the factors that influence its equilibrium were understood.
To be practical, an industrial process must give a large yield of product relatively quickly. One way to increase the yield of ammonia is to increase the pressure on the system in which N2, H2, and NH3 are at equilibrium or are coming to equilibrium.
The formation of additional amounts of ammonia reduces the total pressure exerted by the system and somewhat reduces the stress of the increased pressure.
Although increasing the pressure of a mixture of N2, H2, and NH3 will increase the yield of ammonia, at low temperatures, the rate of formation of ammonia is slow. At room temperature, for example, the reaction is so slow that if we prepared a mixture of N2 and H2, no detectable amount of ammonia would form during our lifetime. The formation of ammonia from hydrogen and nitrogen is an exothermic process:
Thus, increasing the temperature to increase the rate lowers the yield. If we lower the temperature to shift the equilibrium to favour the formation of more ammonia, equilibrium is reached more slowly because of the large decrease of reaction rate with decreasing temperature.
Part of the rate of formation lost by operating at lower temperatures can be recovered by using a catalyst. The net effect of the catalyst on the reaction is to cause equilibrium to be reached more rapidly.
In the commercial production of ammonia, conditions of about 500 °C, 150–900 atm, and the presence of a catalyst are used to give the best compromise among rate, yield, and the cost of the equipment necessary to produce and contain high-pressure gases at high temperatures (Figure 17.4d).
Exercise 17.4a
Check Your Learning Exercise (Text Version)
Based on the following chemical equation, how can you increase the equilibrium concentration of hydrazine, N2H4?
N2(g) + 2H2(g) ↔ N2H4(g) ∆H = 95 kJ
- Add more N2
- Add more H2
- Increase temperature
- All of the above
Check Your Answer[2]
Source: “Exercise 17.4a” is adapted from “Exercise 13.3-7” in General Chemistry 1 & 2, a derivative of Chemistry (Open Stax) by Paul Flowers, Klaus Theopold, Richard Langley & William R. Robinson, licensed under CC BY 4.0.
Links to Interactive Learning Tools
Explore LeChatelier’s Principle from the Physics Classroom.
Attribution & References
change to a reaction’s conditions that may cause a shift in the equilibrium
concentrations or partial pressures of components of a reaction at equilibrium (commonly used to describe conditions before a disturbance)
when a chemical system at equilibrium is disturbed, it returns to equilibrium by counteracting the disturbance
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2.22: Introduction to Measures of Center
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2.22: Introduction to Measures of Center
What you’ll learn to do: Use mean and median to describe the center of a distribution.
In this section, we define three different measures of center: mean, median, and mode, all of which are different ways to define an average. Casually speaking, the “typical” value in the distribution can be roughly represented by these measures of center. Depending on the data and its distribution, one measure of center might be most informative or most representative of the “typical” value. In analyzing quantitative data, the measure of center will be one key component.
Contributors and Attributions
CC licensed content, Shared previously
- Concepts in Statistics. Provided by : Open Learning Initiative. Located at : http://oli.cmu.edu . License : CC BY: Attribution
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The Wild Turkey and Its Hunting
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Flint and Feather
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Philosophy-A Short History3
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Curriculum Essentials: A Journey
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