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Prediction of apple moisture content based on hyperspectral imaging combined with neural network modeling

文献类型： 外文期刊

作者： Chen, Yueyue 1 ; Li, Shuiping 1 ; Zhang, Xiaobo 2 ; Gao, Xuanxiang 1 ; Jiang, Yunhong 3 ; Wang, Junbo 4 ; Jia, Xiaoyu 5 ; Ban, Zhaojun 1 ;

作者机构： 1.Zhejiang Univ Sci & Technol, Zhejiang Prov Collaborat Innovat Ctr Agr Biol Reso, Sch Biol & Chem Engn, Zhejiang Prov Key Lab Chem & Biol Proc Technol Far, Hangzhou 310023, Peoples R China

2.Huazhong Univ Sci & Technol, Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China

3.Northumbria Univ, Fac Engn & Environm, Dept Appl Sci, Newcastle Upon Tyne NE1 8ST, England

4.Aksu Youneng Agr Technol Co Ltd, Aksu 843001, Peoples R China

5.Tianjin Acad Agr Sci, Inst Agr Prod Preservat & Proc Technol, Natl Engn Technol Res Ctr Preservat Agr Prod, Key Lab Postharvest Physiol & Storage Agr Prod,Min, Tianjin 300384, Peoples R China

关键词： Hyperspectral; BPNN; MSC; CARS; Nondestructive detection

期刊名称：SCIENTIA HORTICULTURAE （ 影响因子：4.2； 五年影响因子：4.6 ）

ISSN： 0304-4238

年卷期： 2024 年 338 卷

页码：

收录情况： SCI

摘要： The moisture content (MC) of apples directly affects their flavor and market value. In this study, the MC of apples was successfully predicted using hyperspectral imaging combined with neural network modeling. Experiments were conducted to compare seven spectral preprocessing methods, two feature extraction methods and to establish classification models respectively. The results show that the back-propagation neural network (BPNN) model built based on multiplicative scatter correction (MSC) preprocessing and competitive adaptive reweighted sampling (CARS) algorithm extraction of characteristic wavelengths is the most effective. The determination coefficients of the correction (RC) and prediction (RP) sets are 0.9875 and 0.9850, respectively. The root mean square error of correction set (RMSEC) and prediction set (RMSEP) are 0.4106, 0.4256, respectively. The relative percent difference (RPD) is 5.8026. Consequently, hyperspectral images with small sample combined with appropriate regression models can be used for prediction of MC of apples. This study can serve as a valuable reference for the nondestructive detection of fresh fruit MC.

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