Non destructive detection of kiwifruit sugar content based on improved WOA-LSSVM and hyperspectral analysis

Authors

ZHANG Kai, Nanyang Vocational College, Nanyang, Henan 473000, China;Nanyang Eucommia Ulmoides Gum Extraction Engineering Technology Research Center, Nanyang, Henan 473000, ChinaFollow
ZHU Lifang, Nanyang Vocational College, Nanyang, Henan 473000, China;Nanyang Eucommia Ulmoides Gum Extraction Engineering Technology Research Center, Nanyang, Henan 473000, China
LI Rulin, Nanyang Institute of Technology, Nanyang, Henan 473000, China
WANG Ziyi, Henan Agricultural University, Zhengzhou, Henan 450046, China

Abstract

Objective: Addressing the issues of poor accuracy and low efficiency in non-destructive testing methods for kiwifruit sugar content. Methods: Proposing a non-destructive testing method for kiwifruit sugar content that combined hyperspectral detection technology, least squares support vector machine, and improved whale algorithm. By collecting hyperspectral information of kiwifruit through a hyperspectral detection system, after preprocessing and feature wavelength screening, and then input into an improved whale algorithm optimized least squares support vector machine model to achieve rapid and non-destructive detection of kiwifruit sugar content, and verify its performance. Results: The proposed method could achieve rapid and non-destructive detection of kiwifruit sugar content, with a determination coefficient of 0.965 2 for the test set, a root mean square error of 0.880 5 for the test set, and an average detection time of 1.06 seconds. Conclusion: Combining machine learning algorithms with hyperspectral detection technology can achieve rapid and non-destructive detection of kiwifruit sugar content.

Publication Date

7-22-2024

First Page

107

Last Page

112,226

DOI

10.13652/j.spjx.1003.5788.2024.60010

Recommended Citation

Kai, ZHANG; Lifang, ZHU; Rulin, LI; and Ziyi, WANG (2024) "Non destructive detection of kiwifruit sugar content based on improved WOA-LSSVM and hyperspectral analysis," Food and Machinery: Vol. 40: Iss. 5, Article 16.
DOI: 10.13652/j.spjx.1003.5788.2024.60010
Available at: https://www.ifoodmm.cn/journal/vol40/iss5/16

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