What is it about?

Raman spectroscopy is an emerging technique for the rapid detection of oil qualities. But the spectral analysis is time-consuming and low-throughput, which has limited its broad adoption. Edible oils are an indispensable source of nutrition and, accordingly, are widely present in food. Oil adulteration has been a chronic issue for many years. Therefore, in this study, Raman spectroscopy combined with machine learning was explored in pursuit of finding a rapid, greener way to analyze the purity of food products.

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Why is it important?

We described a protocol that combined machine learning algorithms with Raman spectroscopy or fatty acid composition to characterize edible oils. Our method yielded a high accuracy (96.7% ) in classifying edible oil types and, accordingly, is an effective means of detecting adulterated oils (R2=0.984).


Our study demonstrated the potential and value of machine learning assisted Raman spectra analysis for the rapid authentication and detection of contaminants in food products, or identification of origin of agricultural products based on their chemical compositions.

Dr. Hefei Zhao
University of California Davis

Read the Original

This page is a summary of: The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration, Food Chemistry, March 2022, Elsevier,
DOI: 10.1016/j.foodchem.2021.131471.
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