| # Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam | |
| <img width="4271" height="2484" alt="Picture1" src="https://github.com/user-attachments/assets/524db8f0-99a2-414a-85c5-cc3b3d959f6a" /> | |
| This repository accompanies our study on **non-destructive sugar content estimation** in apple jam using **VNIR hyperspectral imaging (HSI)** and machine learning. It includes a reproducible set of Jupyter notebooks covering preprocessing, dataset construction, and model training/evaluation with classical ML and deep learning. | |
| --- | |
| ## Dataset | |
| The Apples_HSI dataset is available on Hugging Face: | |
| [issai/Apples_HSI](https://huggingface.co/datasets/issai/Apples_HSI). | |
| ### Dataset structure | |
| ```text | |
| Apples_HSI/ | |
| ├── Catalogs/ # per-cultivar & sugar-ratio sessions | |
| │ ├── apple_jam_{cultivar}_{sugar proportion}_{apple proportion}_{date}/ # e.g., apple_jam_gala_50_50_17_Dec | |
| │ │ ├── {sample_id}/ # numeric sample folders (e.g., 911, 912, …) | |
| │ │ │ ├── capture/ # raw camera outputs + references | |
| │ │ │ │ ├── {sample_id}.raw # raw hyperspectral cube | |
| │ │ │ │ ├── {sample_id}.hdr # header/metadata for the raw cube | |
| │ │ │ │ ├── DARKREF_{sample_id}.raw # dark reference (raw) | |
| │ │ │ │ ├── DARKREF_{sample_id}.hdr | |
| │ │ │ │ ├── WHITEREF_{sample_id}.raw # white reference (raw) | |
| │ │ │ │ └── WHITEREF_{sample_id}.hdr | |
| │ │ │ ├── metadata/ | |
| │ │ │ │ └── {sample_id}.xml # per-sample metadata/annotations | |
| │ │ │ ├── results/ # calibrated reflectance + previews | |
| │ │ │ │ ├── REFLECTANCE_{sample_id}.dat # ENVI-style reflectance cube | |
| │ │ │ │ ├── REFLECTANCE_{sample_id}.hdr | |
| │ │ │ │ ├── REFLECTANCE_{sample_id}.png # reflectance preview | |
| │ │ │ │ ├── RGBSCENE_{sample_id}.png # RGB scene snapshot | |
| │ │ │ │ ├── RGBVIEWFINDER_{sample_id}.png | |
| │ │ │ │ └── RGBBACKGROUND_{sample_id}.png | |
| │ │ │ ├── manifest.xml # per-sample manifest | |
| │ │ │ ├── {sample_id}.png # sample preview image | |
| │ │ │ └── .validated # empty marker file | |
| │ │ └── … # more samples | |
| │ └── … # more cultivar/ratio/date folders | |
| │ | |
| ├── .cache/ # service files (upload tool) | |
| ├── ._.cache | |
| ├── ._paths.rtf | |
| ├── .gitattributes # LFS rules for large files | |
| └── paths.rtf # path list (RTF) | |
| ``` | |
| ## Repository structure | |
| This repository contains: | |
| - **Pre-processing**: `1_preprocessing.ipynb` (import HSI, calibration, masking (SAM), ROI crop, grid subdivision). | |
| - **Dataset building**: `2_dataset preparation.ipynb` (train/val/test splits, sugar concentration/apple cultivar splits, average spectral vectors extraction). | |
| - **Model training & evaluation**: | |
| - `3_svm.ipynb` — SVM, scaling, hyperparameter search. | |
| - `4_xgboost.ipynb` — XGBoost, tuning & early stopping. | |
| - `5_resnet.ipynb` — 1D ResNet training loops, checkpoints, metrics. | |
| ## Preprocessing → Dataset → Models (How to Run) | |
| ### 1) **Preprocessing** | |
| Inputs to set (near the bottom of the notebook) | |
| ```python | |
| input_root = "path/to/input" # root that contains the dataset folders (e.g., Apples_HSI/Catalogs) | |
| output_root = "path/to/output" # where the NPZ files will be written | |
| paths_txt = "path/to/paths.txt" # text file with relative paths to .hdr files (one per line) | |
| ``` | |
| - Run all cells. The notebook: | |
| - reads `REFLECTANCE_*.hdr` with `spectral.open_image` | |
| - builds a SAM mask (ref pixel `(255, 247)`, threshold `0.19`) | |
| - crops ROI and saves `cropped_{ID}.npz` under `output_root/...` | |
| - Each NPZ contains: `cube` (cropped H×W×Bands), `offset` (`y_min`, `x_min`), `metadata` (JSON). | |
| ### 2) **Dataset building** | |
| Run all cells. The notebook: | |
| - loads each NPZ (`np.load(path)["cube"]`) | |
| - extracts **mean spectra per patch** for grid sizes **1, ..., 5** | |
| - creates tables with columns `band_0..band_(B-1)`, `apple_content`, `apple_type` | |
| - writes splits per grid: | |
| - **apple-based:** `{g}x{g}_train_apple.csv`, `{g}x{g}_val_apple.csv`, `{g}x{g}_test_apple.csv` | |
| - **rule-based:** `{g}x{g}_train_rule.csv`, `{g}x{g}_val_rule.csv`, `{g}x{g}_test_rule.csv` | |
| ### 3) **Model training** | |
| Classical ML — `3_svm.ipynb` | |
| Run all cells. The notebook: | |
| - loads pre-split CSVs (e.g., `{g}x{g}_train_apple.csv`, `{g}x{g}_test_apple.csv`) | |
| - scales inputs and targets with **MinMaxScaler** | |
| - fits **SVR** with hyperparameters: `C=110`, `epsilon=0.2`, `gamma="scale"` | |
| - reports **RMSE / MAE / R²** on Train/Test (targets inverse-transformed) | |
| Classical ML — `4_xgboost.ipynb` | |
| Run all cells. The notebook: | |
| - loads Train/Val/Test CSVs and scales inputs with **MinMaxScaler** | |
| - builds **DMatrix** and trains with: | |
| objective = "reg:squarederror", eval_metric = "rmse", | |
| max_depth = 2, eta = 0.15, subsample = 0.8, colsample_bytree = 1.0, | |
| lambda = 2.0, alpha = 0.1, seed = 42 | |
| num_boost_round = 400, early_stopping_rounds = 40 | |
| - evaluates and prints **RMSE / MAE / R²** (Train/Test) | |
| Deep model — `5_resnet.ipynb` | |
| Run all cells. The notebook: | |
| - builds a **ResNet1D** and DataLoaders (`batch_size=16`) | |
| - trains with **Adam** (`lr=1e-3`, `weight_decay=1e-4`), **epochs=150**, **MAE** loss | |
| - uses target **MinMaxScaler** (inverse-transforms predictions for metrics) | |
| - early-stopping on **Val MAE**; saves best checkpoint to **`best_resnet1d_model.pth`** | |
| - reports **RMSE / MAE / R²** on the Test set | |
| ## Downloading the Repository | |
| ```bash | |
| git clone <THIS_REPO_URL> | |
| cd <THIS_REPO_FOLDER> | |
| ``` | |
| ## If you use the dataset/source code/pre-trained models in your research, please cite our work: | |
| Lissovoy, D., Zakeryanova, A., Orazbayev, R., Rakhimzhanova, T., Lewis, M., Varol, H. A., & Chan, M.-Y. (2025). Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam. Foods, 14(21), 3585. https://doi.org/10.3390/foods14213585 | |