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Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed

文献类型： 外文期刊

作者： Wang, Zheli 1 ; Fan, Shuxiang 1 ; Wu, Jingzhu 3 ; Zhang, Chi 1 ; Xu, Fengying 4 ; Yang, Xuhai 2 ; Li, Jiangbo 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China

3.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China

4.South China Agr Univ, Key Lab Key Technol Agr Machine & Equipment, Minist Educ, Guangzhou 510642, Peoples R China

关键词： Hyperspectral imaging; Maize seed; Moisture content; Wavelength selection

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：3.232； 五年影响因子：2.848 ）

ISSN： 1386-1425

年卷期： 2021 年 254 卷

页码：

收录情况： SCI

摘要： Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is R-pre = 0.9311 +/- 0.0094 and RMSEP = 1.2131 +/- 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed. (C) 2021 Elsevier B.V. All rights reserved.

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