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Apple Variety Identification Using Near-Infrared Spectroscopy 时间:2018-09-07 22:52 来源:未知作者:admin 点击: 次 Abstract:Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. Experimental results show that ELM models performed better on identifying apple variety and geographical origin than others. Especially, the SPA-ELM model could reach 98.33% identification accuracy on the calibration set and 96.67% on the prediction set. This study suggests that it is feasible to identify apple variety and cultivation region by using NIR spectroscopy.
1. Introduction
China is one of the main fruit-producing and fruit-consuming countries in the world. In recent years, both the area for fruit cultivation and the yield of fruits were increased continuously. Since apple has advantages of high nutrient value, high storage, and short supply cycle, it has become one of the four major fruits in the world. In 2014, the apple cultivation area and production in China reached 22724 km2 and 40.93 million tons, respectively, according to the Food and Agriculture Organization (FAO) report. The overall quality of apple could be determined by external attributes (such as size, colour, and texture) and internal attributes (such as soluble solid content (SSC), total acid content (TAC), and vitamins). These attributes are greatly affected by the variety and cultivation region of apple. Different apple has specific firmness, crispness, juiciness, and taste. Apple fruits that are firm, crispy, juicy, and tasteful are more popular among consumers [1]. Moreover, apples of different varieties or geographical origins could be easily mixed during harvesting and marketing. Therefore, effective and reliable technologies that could identify apple variety and geographical origin are demanded urgently by sellers and consumers.
To determine the variety of fruits, many methodologies have been explored, such as deoxyribonucleic acid (DNA) analysis [2, 3], gas chromatography (GC) analysis [4], and amino acid composition [5]. However, these methods are always containing a considerable amount of time, manual work, and sample preparation [6].
NIR spectroscopy, as a rapid nondestructive detection method, has been proven effective in determining the internal quality attributes of various agricultural products and food [7–11]. Compared with conventional chemical and physical analytical technologies, NIR spectroscopy has the advantages of easy operation, fast detecting speed, and nondestructive measurement. NIR spectral data have information that are concerning the relative proportions of C-H, N-H, and O-H bands which are the main structural constituents of organic molecules [12, 13]. Many studies explored the application of NIR spectroscopy on SSC measurement of fruits, such as apple [14, 15], kiwifruit [16], and grape [17].