Sang Seop Kim, Ji-Young Choi, Jeong-Ho Lim, Jeong-Seok Cho
{"title":"Non-destructive quality prediction of domestic, commercial red pepper\n powder using hyperspectral imaging","authors":"Sang Seop Kim, Ji-Young Choi, Jeong-Ho Lim, Jeong-Seok Cho","doi":"10.11002/kjfp.2023.30.2.224","DOIUrl":null,"url":null,"abstract":"\n \n We analyzed the major quality characteristics of red pepper powders from various\n regions and predicted these characteristics nondestructively using shortwave\n infrared hyperspectral imaging (HSI) technology. We conducted partial least\n squares regression analysis on 70% (n=71) of the acquired\n hyperspectral data of the red pepper powders to examine the major quality\n characteristics. Rc2 values of >0.8 were obtained\n for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The\n developed quality prediction model was validated using the remaining 30%\n (n=35) of the hyperspectral data; the highest accuracy was achieved for\n the ASTA color value (Rp2=0.8488), and similar\n validity levels were achieved for the capsaicinoid and moisture contents. To\n increase the accuracy of the quality prediction model, we conducted spectrum\n preprocessing using SNV, MSC, SG-1, and SG-2, and the model’s accuracy\n was verified. The results indicated that the accuracy of the model was most\n significantly improved by the MSC method, and the prediction accuracy for the\n ASTA color value was the highest for all the spectrum preprocessing methods. Our\n findings suggest that the quality characteristics of red pepper powders, even\n powders that do not conform to specific variables such as particle size and\n moisture content, can be predicted via HSI.\n","PeriodicalId":17875,"journal":{"name":"Korean Journal of Food Preservation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Korean Journal of Food Preservation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11002/kjfp.2023.30.2.224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
We analyzed the major quality characteristics of red pepper powders from various
regions and predicted these characteristics nondestructively using shortwave
infrared hyperspectral imaging (HSI) technology. We conducted partial least
squares regression analysis on 70% (n=71) of the acquired
hyperspectral data of the red pepper powders to examine the major quality
characteristics. Rc2 values of >0.8 were obtained
for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The
developed quality prediction model was validated using the remaining 30%
(n=35) of the hyperspectral data; the highest accuracy was achieved for
the ASTA color value (Rp2=0.8488), and similar
validity levels were achieved for the capsaicinoid and moisture contents. To
increase the accuracy of the quality prediction model, we conducted spectrum
preprocessing using SNV, MSC, SG-1, and SG-2, and the model’s accuracy
was verified. The results indicated that the accuracy of the model was most
significantly improved by the MSC method, and the prediction accuracy for the
ASTA color value was the highest for all the spectrum preprocessing methods. Our
findings suggest that the quality characteristics of red pepper powders, even
powders that do not conform to specific variables such as particle size and
moisture content, can be predicted via HSI.
期刊介绍:
This journal aims to promote and encourage the advancement of quantitative improvement for the storage, processing and distribution of food and its related disciplines, theory and research on its application. Topics covered include: Food Preservation and Packaging Food and Food Material distribution Fresh-cut Food Manufacturing Food processing Technology Food Functional Properties Food Quality / Safety.