Non-destructive characterization and quality control of intact strawberries based on NIR spectral data

Abstract

External and internal quality parameters of strawberries ( Fragaria x ananassa Duch.) were studied at harvest and during postharvest refrigerated storage using a handheld micro-electro-mechanical system (MEMS) near-infrared spectrophotometer. A total of 189 strawberry punnets were used to develop calibration models using various spectral signal pretreatments and linear and non-linear regression algorithms; the sampling unit for both NIRS analysis and reference methods comprised 5 strawberries from each punnet. Modified partial least squares analysis confirmed the feasibility of NIRS for predicting color-related external quality parameters ( L ∗, a ∗ and C ∗) as well as firmness, soluble solid content and titratable acidity. For other tested parameters ( b ∗, h ∗ and pH), the results suggested that NIRS prediction was not feasible. However, the application of a LOCAL algorithm considerably improved the ability of models to predict all the internal quality parameters studied. Use of the LOCAL algorithm proved valuable in minimizing the error in NIRS models for predicting complex internal quality parameters, mainly those related to texture and acidity. Subsequently, the ability of NIR technology to classify individual strawberries as a function of variety was tested using partial least squares discriminant analysis (PLS-DA), which yielded percentages of correctly classified samples (ratio of correctly classified samples to total samples) ranging from 57% for the Camarosa variety to 78% for Antilla Fnm.