Non-destructive prediction of soluble solid contents in Fuji apples using visible near-infrared spectroscopy and various statistical methods

The increasing awareness regarding dietary diversity and pattern has increased the demand for quality over quantity. Numerous non-destructive measurements, including visible near-infrared spectroscopy, have been used in assessing the soluble solid content (SSC) in foods. With advances in statistics, various statistical methods have been developed. These methods need to be verified for their application in effective SSC prediction models. This study aims to review the utility of various statistical methods for the SSC prediction of apples. In this study, we constructed a sorting device for Fuji apples. The spectra of the apples were measured, and the potential of the SSC prediction model was evaluated using various pre-processing methods and machine learning techniques. A developed support vector regression model with a first-order derivative method exhibited the highest prediction accuracy (R2 = 0.8503, RMSEP = 0.4781). Therefore, the developed efficient spectrum pre-processing method coupled with a robust machine learning model was useful for improving the prediction performance of the sorting device.