Abstract
Almost all fruits and vegetables sold in modern society are sorted and labeled, making it easier for customers to recognize the quality of the product, leading to more regular distribution and supply. Consequently, it facilitates the initial packaging and transportation of the product, and farmers will benefit from the added value. Therefore, it is necessary to develop sorting via affordable machines and easy to operate at the current technology level. Since electronic nose technology is new-emerging, it can be used in food quality control systems. In this study, the variety Padrón (Capsicum annuum L.) was evaluated. PCA, LDA, SVM and ANN methods were used to classify sweet and hot peppers. According to PCA, 98% of the variance in the data was detected by the first three components. SVM, ANN, and LDA all showed 100% accuracy in classification. The amounts of capsaicin in two types of sweet and hot peppers were predicted well and with high accuracy by three different methods: MLR, PCR, and PLSR. With this method, it is possible to reliably separate sweet and hot peppers based on odor parameters, and it is also possible to develop sorting machines according to the characteristics of odor.
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