文章基本信息

标题：Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification
本地全文：下载
作者：PRANOTO Wawan Joko ; AL-SHAWI Sarmad Ghazi ; CHETTHAMRONGCHAI Paitoon 等
期刊名称：Food Science and Technology (Campinas)
印刷版ISSN：0101-2061
电子版ISSN：1678-457X
出版年度：2022
卷号：42
DOI：10.1590/fst.80921
语种：English
出版社：Sociedade Brasileira de Ciência e Tecnologia de Alimentos
摘要：Vegetable oils (VOs) come in a wide range of flavors and trademarks. VOs are very similar in appearance, flavor, and taste, and it's frequently difficult to tell them from just by looking at them. Approaches for classifying these oils are sometimes expensive and time-intensive, and they frequently include analytical chemical techniques as well as mathematical algorithms like as Artificial Neural Networks (ANNs), Properties of Partial Least Squares (PLS), Principal Components Regression (PCR), and Principal Component Analysis (PCA) to enhance their effectiveness. Because of the large range of goods available, more productive techniques for qualifying, characterizing, and classifying these substances are required, as the ultimate cost should indicate the quality of the commodity that reaches the user. This study provides a technique for classifying VOs such as different manufacturers' soybean, corn, sunflower, and canola. This method utilized a Charge-Coupled Device (CCD) array sensor, a light emission diode, and a straightforward mathematical approach to capture the generated fluorescence spectrum (FS) in diluted oil. The spectrum classifications are performed using an ANN with three layers, each having four neurons. The approach can categorize VO and enables rapid network training with a 72% success rate utilizing only a few mathematical changes in the spectra data.
关键词：vegetable oils quality;spectrometry;food;mathematical treatment