Abstract
In this work, two methods, which were visible near-infrared spectroscopy (VNIRS) and visible near-infrared spectroscopy combined with colorimetric sensor array (VNIRS-CSA), were used to identify the volatile compound changes of rice samples stored for 0 to 6 months. Principal component analysis (PCA), interval partial least squares (iPLS), and synergy interval partial least squares (SiPLS) were used for qualitative classification. A prediction model was established by linear discriminant analysis (LDA), which was compared with the traditional VNIRS detection technology. The results revealed that the VNIRS-CSA got better performance than VNIRS and exhibited a good result based on iPLS/SiPLS-PCA/LDA models. Furthermore, spectral data from VNIRS-CSA were the best for LDA with a high prediction value of 0.925 after standard normal variate (SNV) processing and variable selection by SiPLS. The research demonstrated that VNIRS-CSA is a quick, accurate, and non-destructive method for monitoring the storage time of rice. The strategy also has the potential for volatile organic components analysis.
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Funding
This work has been financially supported by the National Natural Science Foundation of China (31972154), the Jiangsu Agricultural independent innovation fund (SCX203321), the Project of Faculty of Agricultural Equipment of Jiangsu University (NZXB20200214), and the Traceability Technology and Application of Quality and Safety of Agricultural Products Open Project of National Engineering Laboratory (AQT-2019-YB8).
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Lin, H., Jiang, H., Lin, J. et al. Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array. Food Anal. Methods 14, 1305–1314 (2021). https://doi.org/10.1007/s12161-021-01963-z
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DOI: https://doi.org/10.1007/s12161-021-01963-z