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Weng S, Tang L, Wang J, Zhu R, Wang C, Sha W, Zheng L, Huang L, Liang D, Hu Y, Chu Z. Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation. Spectrochim Acta A Mol Biomol Spectrosc 2023; 290:122311. [PMID: 36608516 DOI: 10.1016/j.saa.2022.122311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/19/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Rui Zhu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Cong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Wen Sha
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China
| | - Yimin Hu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, China
| | - Zhaojie Chu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111, Jiulong Road, Hefei, China.
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