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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
