NIRS-based fresh grape ripeness prediction with SPA-LASSO spectral feature selection

Abstract

A rapid and non-destructive maturity evaluation model based on Near-Infrared Spectroscopy (NIRS) is proposed for monitoring quality parameter changes during the ripening process of fresh grapes and determining the optimal harvest period. Initially, physicochemical parameter variations of Cabernet Sauvignon grapes across twelve growth stages were studied to support predictions. Subsequently, SPA-LASSO was used to select feature wavelengths from five preprocessed full spectra, and Partial Least Squares Regression (PLSR) was employed to establish models predicting Soluble Solid Content (SSC) and Total Acid (TA) levels. Based on experimental results, the best-performing model for maturity prediction was selected. Results indicate that SSC increases and TA decreases from fruit enlargement to ripening stages. In late maturity, SSC slightly decreases and TA slightly increases. The SG + SPA-LASSO + PLSR model performed best for both SSC and TA, with SSC prediction model coefficients of determination (R_C² and R_P²) at 0.982 and 0.983 respectively, and root mean square errors (RMSEC and RMSEP) of 1.010 and 0.978. TA prediction model coefficients were R_C² = 0.954, R_P² = 0.944, RMSEC = 2.347, and RMSEP = 2.618. Overall, SPA-LASSO proved effective in feature selection, enhancing model generalization for spectroscopic screening in non-destructive grape maturity assessment.