化学
食用油
食品科学
拉曼光谱
向日葵
葵花籽油
制浆造纸工业
农业工程
园艺
工程类
生物
光学
物理
作者
Camelia Berghian‐Grosan,Dana Alina Măgdaş
出处
期刊:Talanta
[Elsevier]
日期:2020-10-01
卷期号:218: 121176-121176
被引量:60
标识
DOI:10.1016/j.talanta.2020.121176
摘要
Because of the important advantages as rapidity, cost effectiveness and no sample preparation necessity, encountered in most of the cases, Raman spectroscopy gained more and more attention during the last years with regard to its application in food and beverages authenticity. Vegetable cold-pressed oils obtained from: sesame, hemp, walnut, linseed, pumpkin and sea buckthorn have gained increased attention in consumer interest due to their high nutrient value and health benefits. The high commercial value of these, brought the temptation from some unfair producers and sellers to gain an illegal profit by replacing the raw material of these oils by cheaper ones (i.e. sunflower). Here a new approach based on the rapid processing of Raman spectra using Machine Learning algorithms, for edible oil authentication was developed and successfully tested. Through this approach, not only the adulteration detection was achieved but also an initial estimation of its magnitude.
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