Monitoring and prediction of sensory shelf‐life in strawberry with ultraviolet‐visible‐near‐infrared (UV‐VIS‐NIR) spectroscopy

Abstract

The suitability of UV-VIS-NIR reflectance for predicting the sensory shelf-life (SSL) and the number of days under refrigerated storage (DS) for strawberries was examined. The performance of different classification methods for predicting the days of storage was compared. Partial least squares regression (PLSR) models were calibrated and evaluated for predicting the number of days under storage resulting in similar performance with NIR (R2 = 0.870), UV-VIS (R2 = 0.874), and UV-VIS-NIR (R2 = 0.877) datasets in evaluation sets. The shelf-life of strawberries were estimated from the visual sensory scores from a panel. Based on the sensory shelf life of each strawberry and their days under storage, remaining days till rejection (DTR) was calculated. PLSR models were trained to predict the remaining DTR from UV-VIS-NIR reflectance datasets resulting in performance up to R2 = 0.712 in evaluation sets. The influence of the number of days of storage on the remaining DTR was examined with simple regression models. The PLSR models were trained to predict the residuals from the simple regression models to improve the prediction of the remaining DTR. The PLSR models were built from the predictor dataset containing PCA denoised and Savistzky-Golay (SG) second derivate transformed UV-VIS reflectance data and extended multiplicative signal corrected (EMSC) and first derivate SG transformed NIR reflectance data. The resulting models for predicting the remaining DTR achieved the performance up to R2 = 0.818 in evaluation sets. Repeated feature selection was carried out using Uninformative Variable Elimination (UVE-PLS) in the PLSR models predicting days of storage and remaining shelf-life. The wavebands selected more frequently by the models predicting DS and DTR with feature selection methods were recorded, visualized, and interpreted.

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