Apple Variety Identification Using Near-Infrared Spectroscopy. (27th August 2018)

• Record Type: Journal Article
• Authors: Li, Caihong; Li, Lingling; Wu, Yuan; Lu, Min; Yang, Yi; Li, Lian
• Other Names: Chew Wee Academic Editor.
• Is Part Of: Journal of spectroscopy. Volume 2018(2018)
• Publication Date: 2018-08-27
• Subjects: Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1155/2018/6935197
• Languages: English
• ISSNs: 2314-4920

Abstract

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.