Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection. Issue 4 (25th February 2022) - Zhang, Xinxin; Li, Shangke; Shan, Yang; Li, Pao; Jiang, Liwen; Liu, Xia; Fan, Wei
Abstract: This study employs a near‐infrared diffuse reflectance spectroscopy (NIRDRS) system to accurate nondestructive determine citrus soluble solids content (SSC). The penetration experiment results showed that the interference of thick peel is large and NIRDRS light has the ability to penetrate the peel to a certain extent. Partial least squares with different characteristic variable selection methods were used to establish the quantitative model of SSC. The results demonstrated that characteristic variable selection methods can select targeted characteristic variables and improve the accuracy with few variables. Monte Carlo‐uninformative variable elimination method was selected as the optimal prediction performance. In the best prediction model, the correlation coefficient and root mean square error of prediction of the prediction set are 0.854 and 0.7%Brix, respectively, while the variable number decreases to 440 from 1557. Furthermore, the models using the average spectra of four points on the equator are the most appropriate. Novelty impact statement: The penetrating ability of near‐infrared diffuse reflectance spectroscopy (NIRDRS) light to thick peel and the prediction of internal quality of citrus is still unsatisfactory with traditional partial least squares (PLS) algorithm due to the interference of thick peel. In this study, a nondestructive method for the analysis of soluble solids content (SSC) in citrus was established by NIRDRS with characteristic variable selection algorithms. The results demonstrated that NIRDRS light has the ability to penetrate the peel to a certain extent, while characteristic variable selection methods can select targeted characteristic variables and improve the accuracy of quantitative analysis models with fewer variables.