Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm

Summary Total soluble solids (TSS) and titratable acidity (TA) are essential quality properties for postharvest commercialisation of grapes. This study aimed to estimate the TSS and TA in grapes using hyperspectral imaging (HSI) technique in the range of 400–1001 nm. A deep learning‐based stacked auto‐encoders (SAE) algorithm was developed to extract deep spectral features from pixel‐level spectra. Then, these features with a compensation factor (i.e. size of fruits) were fed into partial least squares (PLS) and least squares support vector machine (LSSVM) for predicting TSS and TA in grapes. Additionally, competitive adaptive reweighed sampling and successive projections algorithm as conventional wavelength selection approaches were also investigated for comparison. The optimal prediction accuracy was achieved by the SAE‐LSSVM model with size compensation, where , RMSEP = 0.5041% and for TSS; , RMSEP = 0.1091 g L −1 and for TA. The results suggested that SAE has great potential for extracting features from pixel‐level hyperspectral image data; the well‐performed deep learning model SAE‐LSSVM with size compensation can be used for rapid and non‐destructive predicting TSS and TA in grapes, which may provide a valuable reference for internal quality evaluation of postharvest fruits via HSI technique.