Application of multivariate machine learning methods to investigate organic compound content of different pepper spices
Yükleniyor...
Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of this study was to uncover all aspects and extract comprehensive and valuable information from the data obtained from different pepper varieties using machine learning (ML) methods. The red pepper (RP), fabricated isot (FI), and customary isot (CI) spices were stored for 12 months and the variations in the organic compound content were monitored every 3 months. The data set has been subjected to a supervised ML method Random Forest (RF), unsupervised ML methods principal component analysis (PCA), t-Distributed stochastic neighbor embedding (t-SNE), and hierarchical cluster analysis (HCA). The classification accuracy yielded by the RF model was 100%. RF model showed that terpenoids, acids, and alkanes were ineffective in identifying the differences between pepper spices, but glucose, succinic acid, citric acid, and fructose were primarily responsible for the variations between pepper spices. FI peppers differed significantly from other pepper spices in terms of their chemical compositions. Although most organic compounds exhibited positive correlations; furan-fructose, furan-glucose, furan-citric acid, and glucose-malic acid showed negative correlations. RP peppers were mostly stable for the first 6 months of storage, but after this month, due to changes in malic acid, aldehyde, glucose, and fructose, they displayed similar properties as CI. The organic compound content of CI peppers rapidly changed in the first 3 months of storage and stayed almost stable for the remaining 9 months. Various ML methods were effectively employed in this study to examine the changes that different pepper spices exhibited in association with storage.
Açıklama
Anahtar Kelimeler
Pepper spices, Machine learning, PCA, t-SNE, Hierarchical clustering
Kaynak
Food Bioscience
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
Sayı
51
Künye
Durmuş, Y., & Atasoy, A. F. (2023). Application of multivariate machine learning methods to investigate organic compound content of different pepper spices. Food Bioscience, 51, 102216.
