Prediction of Soluble Solids Content by Means of NIR Spectroscopy and Relation with <i>Botrytis cinerea</i> Tolerance in Strawberry Cultivars

Abstract

Strawberry fruits are particularly appreciated by consumers for their sweet taste related to their soluble solids content (SSC). However, strawberries are characterized by a short shelf-life and high susceptibility to tissue infection, mainly by Botrytis cinerea. The SSC determination of strawberry fruit through traditional destructive techniques has some limitations related to the applicability, timing, and number of samples. The aims of this study are (i) to verify if any relation between SSC and B. cinerea susceptibility in the fruits of five strawberry cultivars occurs and (ii) to determine the SSC of strawberry fruits through near infrared spectroscopy (NIR). Principal component analysis was used to search for spectral differences among the strawberry genotypes. The partial least squares regression technique was computed in order to predict the SSC of the fruits collected during two harvesting seasons. Moreover, variable selection methods were tested in order to improve the models and get better predictions. The results demonstrated that there was a high correlation between SSC and B. cinerea susceptibility (R2 up to 0.87). The SSC was predicted with a standard error of 0.84 °Brix and R2p 0.75 (for the best model), which indicated the possibility to use the models for screening applications. NIR spectroscopy represents an important non-destructive alternative and finds remarkable applications in the agro-food market.

Keywords

• post-harvest
• grey mold infection
• PLS regression
• non-destructive technique
• sugar analysis