Study on the Identification of Brown Rice Storage Year Based on Fluorescence Spectral Fusion Technique

Yingying Zhou, Yixin Qiu, Zhipeng Li, Zhuang Miao, Changming Li, Chunyu Liu () and Yong Tan ()
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Yingying Zhou: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Yixin Qiu: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Zhipeng Li: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Zhuang Miao: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Changming Li: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Chunyu Liu: School of Physics, Changchun University of Science and Technology, Changchun 130022, China
Yong Tan: School of Physics, Changchun University of Science and Technology, Changchun 130022, China

Agriculture, 2024, vol. 14, issue 11, 1-12

Abstract: The storage time of rice determines its quality and nutritional value, and the longer the storage time, the greater the impact. In this study, different excitation wavelengths (405 nm, 365 nm, 310 nm) were used to detect the fluorescence spectrum of “Dongdao 12” brown rice. Support vector machine (SVM), K-nearest neighbor (KNN), and wide neural network (WNN) were used for modeling and analysis. Under the excitation of 310 nm, the accuracy of WNN classification is up to 99.2%. In order to reduce the scattering effect and other interference in the data, multiplicative scatter correction (MSC), standard normal variable (SNV), and Savitzky–Goray smoothing (SG) preprocessing methods were used. The results showed that SG + KNN classification achieved an accuracy of 99.3% under 310 nm excitation. In order to further improve the classification accuracy, the original spectrum and the preprocessed spectrum under different excitation light sources were fused. The classification accuracy of all methods was improved, and the original data fusion was combined with the WNN model to reach 100%. It shows that fluorescence spectroscopy has excellent potential in identifying rice storage years.

Keywords: fluorescence spectroscopy; brown rice; classification modeling; spectral analysis; spectral fusion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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