Abstract
Aleatico (Vitis vinifera L.) grapes were harvested at 21.3 Brix and dehydrated at 20°C, 45% relative humidity, and 1.5 m/s air flow in a small scale thermo-conditioned tunnel. Postharvest grape drying was performed until the fruit lost an average of 40% of its initial weight. During dehydration, single destemmed grape berries were analyzed non-destructively using an Acousto Optically Tunable Filter (AOTF) Near-Infrared (NIR) spectrophotometer (1100–2300 nm) in reflectance. Total soluble solids (TSS, Brix) and moisture content (%) were measured on the same berries with the objective of using spectral and non-spectral information to develop regression models for predicting these parameters. Partial Least Square (PLS) applied after different statistical pre-treatments (Multiplicative Scatter Correction, Savitzky-Golay 1st or 2nd derivative filter) was tested on absorbance spectra in order to define the most effective approach. Two prediction models were obtained (n = 450 for TSS, and n = 600 for water loss) in which the coefficient of determination in cross-validation (r2) and the root mean standard error of cross-validation (RMSECV) were 0.93 and 0.89 Brix for TSS and 0.92 and 2.16% for water loss, respectively. A model validation procedure was performed using separate sample sets (n = 170 for TSS, and n = 200 for water loss) with the following results: coefficient of determination (R2) and standard error of prediction (SEP) of 0.92 and 0.72 Brix for TSS and 0.9 and 1.89% for water loss, respectively.
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