Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Xiaohui Wang, Lijia Xu, Heng Chen, Zhiyong Zou, Peng Huang and Bo Xin
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Xiaohui Wang: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China
Lijia Xu: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China
Heng Chen: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China
Zhiyong Zou: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China
Peng Huang: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China
Bo Xin: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Xinkang Road 46, Ya’an 625000, China

Agriculture, 2022, vol. 12, issue 2, 1-18

Abstract: Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with R P 2 , R C 2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

Keywords: kiwifruit; MK-SVR; MASS; hyperspectral fluorescence; nondestructive detection (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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