Adulteration Detection in Cactus Seed Oil: Integrating Analytical Chemistry and Machine Learning Approaches

Current Research in Food Science, Journal Year: 2025, Volume and Issue: 10, P. 100986 - 100986

Published: Jan. 1, 2025

Economically motivated adulteration threatens both consumer rights and market integrity, particularly with high-value cold-pressed oils like cactus seed oil (CO). This study proposes a machine learning model that integrates analytical measurements, data simulations, classification techniques to detect of CO refined sunflower (SO) determine the detectable limit without measuring huge number different mixtures. First, pure SO samples were analyzed for their fatty acid, triacylglycerol, tocochromanol content using HPLC or GC. The resulting composition served as foundation further simulations. Monte Carlo (MC) simulations outperformed Conditional Tabular Generative Adversarial Networks (CTGAN) in simulating realistic compositions, MC yielding lower Kullback-Leibler Divergence values compared CTGAN. MC-simulated then used simulate larger datasets, critical step training testing two models: Random Forest (RF) Neural (NN), robust cannot be achieved small sample sizes. Both models good accuracies, RF achieving higher accuracy than NN, reaching 94% on simulated datasets 90% real-world levels low 1%. also offers better interpretability is computational less demanding NN which makes it advantageous authenticity verification this study. Therefore, combining simulation method detecting proposed. proposed method, coded Python available open-source, flexible framework continuous adaptation new data.

Language: Английский

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Fingerprinting of Boletus bainiugan: FT-NIR spectroscopy combined with machine learning a new workflow for storage period identification

Food Microbiology, Journal Year: 2025, Volume and Issue: 129, P. 104743 - 104743

Published: Feb. 6, 2025

Language: Английский

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Recent Advancements in Chemometrics based Non-Destructive Analytical Techniques for Rapid Detection of Adulterants in Milk and Dairy Products – A Review

Food Control, Journal Year: 2025, Volume and Issue: unknown, P. 111247 - 111247

Published: Feb. 1, 2025

Language: Английский

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Advances of Vis/NIRS and imaging techniques assisted by AI for tea processing

Critical Reviews in Food Science and Nutrition, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: March 7, 2025

Tea is one of the most popular drinks due to its distinct flavor and numerous health benefits. The quality tea closely related production processing. Human sensory evaluation conventional method for monitoring in However, this subjective susceptible environmental influences. Therefore, visible/near-infrared spectroscopy (Vis/NIRS) hyperspectral imaging (HSI) techniques offer great potential their rapid detection speed, nondestructive, low cost, simple operations. Artificial intelligence (AI) promising methodological approaches spectral analysis decision-making automated production. Vis/NIRS HSI assisted by AI further promote progress This paper reviewed updated applications processing from 2019 2025. In particular, process, theories techniques, algorithms are briefly introduced. Furthermore, recent summarized discussed. Finally, challenges future trends associated with practical application industry presented.

Language: Английский

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Data integrity of food and machine learning: Strategies, advances and prospective

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 143831 - 143831

Published: March 1, 2025

Language: Английский

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