Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Adulteration in edible oils threatens food safety and regulatory compliance, creating a need for rapid, non-destructive screening. This study presents an integrated hyperspectral imaging and machine learning framework that combines Savitzky–Golay smoothing with multiple band selection techniques (Correlation, Entropy, Manifold Learning, Mutual Information (MI), Signal-to-Noise Ratio (SNR), and Variance-Based methods) were employed to extract the most informative features/wavelengths. Using 240 samples acquired with a Specim FX10 camera, covering pure rose, rosehip, tea tree, and turmeric oils and oils adulterated with lemon, vitamin E, garlic, and orange, the selected wavelengths were used to train decision tree models (C4.5, C5.0, ID3) and gradient boosting models (HistGradient Boosting, XGBoost). HistGradient Boosting achieved the best results, with 100% training accuracy and 93% validation accuracy, and complementary metrics (Cohen kappa, MCC, Jaccard index, precision, recall, F1 score, and Hamming loss) supported robust generalization. The novelty lies in jointly exploiting information-theoretic relevance and redundancy control to yield a compact spectral subset, coupled with rigorous benchmarking of boosting methods across multiple oil/adulterant pairs, enabling fast, accurate, and practical quality control.

Yogurt is a valued dairy product known for its taste and texture, but these qualities also make it prone to adulteration. Common practices include adding starch, milk powders, vegetable oils, sweeteners, preservatives, and, in some cases, even harmful heavy metals, which can reduce quality and harm consumer health. Conventional analysis can be selective but is often destructive, labor-intensive, and slow for routine screening. The rapid, non-destructive alternatives provided by modern image-based spectroscopy can probe both composition and spatial heterogeneity in yogurt. This review summarizes the developments of hyperspectral imaging (HSI) and new spectroscopic methods of detecting yogurt adulterants, including Near-Infrared (NIR), Terahertz spectroscopy, Raman, Laser-Induced Breakdown Spectroscopy (LIBS), Nuclear Magnetic Resonance (NMR), vibrational mid-IR/FTIR, and UV-V are prominent methods. Spectroscopy gives robust chemical fingerprints (NIR/Raman/FTIR), and HSI gives space (LIBS/THz), as well as elemental and bulk properties; the combination of the two with ML enhances the precision and generalization. Key challenges include product variability, fermentation effects, calibration transfer, and the lack of publicly available datasets. To address these, future priorities should focus on developing portable instruments, applying multimodal data fusion and transfer learning, ensuring interpretable AI models, and establishing standardized protocols for routine, non-destructive yogurt authenticity testing.

Hyperspectral Imaging (HSI) offers a powerful non-destructive technique for fruit quality evaluation by simultaneously capturing both spatial and spectral information. In this research, apples and oranges were analyzed using a 224-band visible–near-infrared (400–1000 nm) imaging system with a controlled illumination setup and a standardized region-of-interest (ROI) extraction protocol. To enhance signature quality, preprocessing methods such as Savitzky–Golay smoothing and median filtering were applied, effectively reducing noise while retaining meaningful spectral characteristics. The dataset comprised 34,394 spectra (224 bands each), including 19,595 fresh and 14,799 spoiled samples. Class balance was preserved during training and testing to avoid bias. Several supervised learning algorithms—k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), decision trees (DT), linear discriminant analysis (LDA), and logistic regression (LR)—were employed to classify fruit freshness both within individual species and across a four-class scenario (apple-fresh, apple-spoiled, orange-fresh, orange-spoiled). Model performance was assessed using accuracy, precision, recall, and the F1-score. The top-performing classifiers achieved nearly 99% accuracy with balanced error rates across all categories. These findings demonstrate that the integration of HSI with supervised machine learning (ML) provides a reliable and automated solution for grading apples and oranges. The approach is highly applicable to real-time sorting lines, supporting improved quality assurance, food safety, and waste reduction. Furthermore, the methodology is adaptable to other agricultural commodities and can be optimized for industrial deployment through band selection and lightweight models that reduce computational requirements while maintaining classification accuracy.