Machine learning-assisted FT-IR spectroscopy for identification of pork oil adulteration in tuna fish oil

A Windarsih, TH Jatmiko, AS Anggraeni… - Vibrational …, 2024 - Elsevier
Tuna fish oil (TO) is a valuable source of omega fatty acids and polyunsaturated fatty acids
required for human growth and development. Triggered by economic reasons, TO can …

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

J Wang, J Qian, M Xu, J Ding, Z Yue, Y Zhang, H Dai… - Food Chemistry, 2025 - Elsevier
Oil adulteration is a global challenge in the production of high value-added natural oils.
Raman spectroscopy combined with mathematical modeling can be used for adulteration …

Enhanced food authenticity control using machine learning-assisted elemental analysis

Y Yang, L Zhang, X Qu, W Zhang, J Shi, X Xu - Food Research International, 2024 - Elsevier
With the increasing attention being paid to the authenticity of food, efficient and accurate
techniques that can solve relevant problems are crucial for improving public trust in food …

[HTML][HTML] Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR

A Herath, T Kretzschmar, N Sreenivasulu, P Mahon… - Food chemistry, 2024 - Elsevier
Pigmented rice contains beneficial phenolic antioxidants but analysing them across
germplasm collections is laborious and time-consuming. Here we utilised rapid surface …

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

FD Covaciu, C Berghian-Grosan, AR Hategan… - Foods, 2023 - mdpi.com
Food composition issues represent an increasing concern nowadays, in the context of
diverse food commodity varieties. The contents and types of fatty acids are a constant …

[HTML][HTML] Hyperspectral identification of oil adulteration using machine learning techniques

M Aqeel, A Sohaib, M Iqbal, HU Rehman… - Current Research in …, 2024 - Elsevier
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 …

QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand

JA Martínez-Trespalacios, DE Polo-Herrera… - …, 2024 - pmc.ncbi.nlm.nih.gov
The development of new methods of identification of active pharmaceutical ingredients (API)
is a subject of paramount importance for research centers, the pharmaceutical industry, and …

[HTML][HTML] Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade …

D Malavi, K Raes, S Van Haute - Current Research in Food Science, 2024 - Elsevier
Detecting adulteration in extra virgin olive oil (EVOO) is particularly challenging with oils of
similar chemical composition. This study applies near-infrared hyperspectral imaging (NIR …

Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Artificial Neural Network …

JCP Alarcon, MIO Souza, VM Pepino, BHV Borges - Authorea Preprints, 2024 - techrxiv.org
(This work has been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be accessible.) In this work …

Comparison of machine learning models for classifying edible oils using Fourier‐transform infrared spectroscopy

H Lim, SY Lee, JY Kim, YJ Shin, Y Jang… - Bulletin of the Korean … - Wiley Online Library
Accurate classification and authentication of edible oils are essential for maintaining product
quality, ensuring consumer safety, and preserving market integrity. Therefore, this study …