Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM

Yuanyuan Liu, Tongzhao Wang, Rong Su, Can Hu, Fei Chen and Junhu Cheng
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Yuanyuan Liu: College of Mechanicaland Electrical Engineering, Tarim University, Alar 843300, China
Tongzhao Wang: College of Mechanicaland Electrical Engineering, Tarim University, Alar 843300, China
Rong Su: College of Mechanicaland Electrical Engineering, Tarim University, Alar 843300, China
Can Hu: College of Mechanicaland Electrical Engineering, Tarim University, Alar 843300, China
Fei Chen: College of Mechanicaland Electrical Engineering, Tarim University, Alar 843300, China
Junhu Cheng: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China

Agriculture, 2021, vol. 11, issue 8, 1-16

Abstract: Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ 2 , for the same parameters measure (8.67 × 10 4 , 1.21 × 10 3 ), (1.45 × 10 4 , 2.93 × 10 4 ), and (2.37 × 10 5 , 3.80 × 10 3 ), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade.

Keywords: IRIV; LS-SVM; Korla fragrant pear; quality parameter; evaluation (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: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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