Online detection of internal quality of strawberries presents challenges particularly concerning fruit damage, detection accuracy, and processing efficiency. This study explores the feasibility of using Vis/NIRS for online detection of SSC in strawberries during hanging transportation. After analyzing SSC distribution in strawberries, an optical sensing system was developed, and optimal configurations were identified using PLSR models. When employing a horizontal optical beam through the strawberry center, the PLSR model combined with SNV preprocessing and CARS feature selection achieved the best conventional chemometric results (RPD of 4.793). Additionally, three 1D-CNN approaches were investigated, with the 1D-CNN-LSTM method exhibiting superior performance (Rp2 of 0.963, RMSEP of 0.209°Brix, RPD of 5.332). These findings demonstrate the excellent capability of our developed system, enhanced by deep learning methods, for online detection of SSC in strawberries. This work may open new avenues for the online assessment of internal quality in small and delicate fruits.
Keywords: Convolutional neural network; Hanging transportation; Online detection; Soluble solids content; Strawberry; Visible-near infrared spectroscopy.
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