Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam 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. Dataset structure 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) Run all cells. The notebook: reads REFLECTANCE .hdr with…
Runs entirely in your browser via DuckDB-Wasm — this dataset's real data file is loaded once, then queried locally. Nothing is sent to a server.
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy