胡椒粉
食品科学
色调
红壤
化学
园艺
数学
生物
人工智能
计算机科学
生态学
土壤水分
作者
Jong‐Jin Park,Jeong‐Seok Cho,Gyuseok Lee,Donkyu Yun,Seulki Park,Kee Jai Park,Jeong‐Ho Lim
出处
期刊:Foods
[MDPI AG]
日期:2023-09-18
卷期号:12 (18): 3471-3471
标识
DOI:10.3390/foods12183471
摘要
This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.
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