Abstract
The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is made by using ‘CIE Lab’ color model and spectral characteristics in the VIS range. Neural networks are BPN and PNN, and classifiers are kernel and linear SVM. The spectral characteristics are pre-processed with SNV, MSC, First derivative and PCA. According to the results for color features, BPN and SVM with “rbf” kernel have the best performance while PNN has the worst performance. When using spectral characteristics the BPN network performs well: eavg = 4.1% and emax = 12.1% but the SVM linear (eavg = 3.4%, emax =5.3%) and SVM with “rbf” kernel (eavg = 2.4%, emax =5.2%) classifiers have better results. As a conclusion, it could be said that classifiers using spectral features perform well with errors at about 2-5%. Classification with color features is an alternative method, which is less complex, cheaper and with acceptable errors.
Highlights
It is well known that the quality is a critical issue facing the modern food industry because consumers always expect food products with excellent quality and reasonable price
The classification is done by using color features obtained from the ‘CIE Lab’ color model and spectral characteristics obtained in a VIS range (200 -1000nm)
The spectral characteristics are obtained in the same way of the drying process as color features, and some pre-processing methods are applied to spectral data before it can be used for model preparation
Summary
It is well known that the quality is a critical issue facing the modern food industry because consumers always expect food products with excellent quality and reasonable price. They said that the LDA and QDA could categorize the apricots with the accuracy of 0.904 and 0.923, respectively based on color features. Sani et al [18] show the potential of using FT-NIR spectroscopy for the rapid and non-destructive measurement of the moisture, water activity, firmness and SO2 content in sulphureddried apricots They demonstrate that the major quality parameters of sulphured-dried apricots can be measured in a short time by FT-NIR spectroscopy without any need for the sample preparation. Camps et al [19] studied the ability of portable Near Infrared Spectroscopy to determine apricot fruit quality Their calibration models allow determination of different apricot’s quality features with variable precisions. They claim that NIRS technology could be applicable to apricot quality evaluation
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