Detection of sugar content in apple by near infrared spectroscopy based on variable optimization

Authors

ZHANG Li-xin, College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China;School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
YANG Cui-fang, College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China
CHEN Jie, College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China
WANG Ya-ming, College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China
ZHANG Xiao, College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China

Abstract

Objective: The sugar content in apple directly affects its taste. The near infrared spectrum data of apples were collected in the wavelength range of 900~1 700 nm to detect the sugar content nondestructively in conjunction with the chemometrics method. Methods: Firstly, the spectral data were corrected by baseline, scattered, smoothed, and scaled in turn, and then the best preprocessing method was selected by minimizing the root mean square error of cross-validation. Secondly, 7 and 52 feature variables were selected by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) respectively. Finally, the linear PLS model and nonlinear ELM model were established with the feature variables selected by SPA method, CARS method and their combination as input variables respectively. Results: The results showed that the modeling effect of the combined feature variables was better than that of the single method, and the nonlinear models were better than that of the linear models. Conclusion: ELM model established by using combined characteristic variables has the best prediction effect, with RMSEC=0.710 1, R2c=0.883 8, RMSEP=0.637 5, R2p=0.894 5, which can provide theoretical reference for the development of apple hyperspectral detection device.

Publication Date

10-28-2021

First Page

112

Last Page

118

DOI

10.13652/j.issn.1003-5788.2021.10.020

Recommended Citation

Li-xin, ZHANG; Cui-fang, YANG; Jie, CHEN; Ya-ming, WANG; and Xiao, ZHANG (2021) "Detection of sugar content in apple by near infrared spectroscopy based on variable optimization," Food and Machinery: Vol. 37: Iss. 10, Article 20.
DOI: 10.13652/j.issn.1003-5788.2021.10.020
Available at: https://www.ifoodmm.cn/journal/vol37/iss10/20

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