AI Article Synopsis

  • - This study explored the use of shortwave infrared (SWIR) technology to identify whether red pepper powder was mixed with Allura Red dye and red pepper seeds, manipulating the ratio of pericarp to seeds.
  • - Different combinations of pericarp and seeds were analyzed, revealing that pure red pepper powder was often indistinguishable from certain adulterated samples using traditional analysis methods.
  • - However, machine learning techniques combined with SWIR data achieved 100% accuracy in detecting adulteration by classifying samples into categories based on the type of adulterant present.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528317PMC
http://dx.doi.org/10.3390/foods12183471DOI Listing

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