Title: Determination of SSC and TA content of pear by Vis-NIR spectroscopy combined CARS and RF algorithm

Authors: Baishao Zhan; Xu Xiao; Fan Pan; Wei Luo; Wentao Dong; Peng Tian; Hailiang Zhang

Addresses: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' External Liaison Office, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China ' School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Abstract: Soluble Solid Content (SSC) and Total Acid (TA) are the indicators of fruit maturity and taste, which impact pear fruit quality. Therefore, it is of great significance for pear quality grading to quickly and accurately detect soluble solids and total acids. This work focuses on the visible and near-infrared spectroscopy measurement model. Savitzky-Golay (SG) smoothing, Standard Normal Variable (SNV), and Multiplicative Scatter Correction (MSC) were used to eliminate the error effects. Competitive adaptive weighted sampling (CARS) and Random Frog (RF) algorithms were used to select the characteristic wavelength spectrum to eliminate redundant information and improve measurement speed and accuracy. Partial Least Squares (PLS) regression model and Multiple Linear Regression (MLR) models were built to verify the pre-processing method's performance and prediction model. The results show that SG smoothing had the most significant effect on the error elimination of the original spectra, the CARS-PLS model has the best prediction effect on SSC, R² is 0.9012, CARS-MLR model is the best predictive performance of TA, and R² is 0.8557. Research shows that Vis-NIR spectroscopy as a method to detect SSC content and TA in pear fruit has potential application value.

Keywords: visible and near-infrared spectroscopy; soluble solids; total acid; pear; competitive adaptive reweighted sampling; random frog.

DOI: 10.1504/IJWMC.2021.119061

International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.1, pp.41 - 51

Received: 04 Jan 2021
Accepted: 01 Apr 2021
Published online: 19 Nov 2021 *

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