Quality monitoring method for apples of different maturity under long-term cold storage

Abstract

As the apple industry has developed, non-destructive testing technology for checking the internal quality of apples based on visible/near infrared spectroscopy has attracted widespread attention. Because apples are usually refrigerated for a long time then sold at a suitable time, a reliable and rapid non-destructive testing method is needed to monitor their internal quality. However, establishing a non-destructive testing model for apples during cold storage is affected by two factors that change over time: the quality from different maturity, and the cell structure of apples during cold storage changes, affecting the scattering of the spectra. A model for predicting the soluble solids content (SSC) and firmness of apples during cold storage was established taking into account the time in cold storage and three levels of maturity at harvest. Two wavelength ranges, visible-near-infrared (Vis-NIR) and long-wave near-infrared (LWIR), were used and regression models established using partial least squares (PLS). LWIR provided a better prediction of SSC and Vis-NIR of firmness. The quality prediction model for apples at three maturity levels during cold storage was highly accurate (SSC: 0.86 < Rp < 0.94, 0.37 < RMSEP < 0.56, firmness: 0.82 < Rp < 0.86, 0.63 < RMSEP < 0.69), and the accuracy of a model combining all samples was satisfactory (SSC: Rp = 0.87, RMSEP = 0.59, firmness: Rp = 0.82, RMSEP = 0.71). This study provides an important theoretical basis for improving the industrial quality inspection and quality classification of apples during cold storage.