Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy

The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend. Conventional methods for drying carrots include hot-water blanching followed by hot-air drying, which is usually uncontrolled and therefore prone to product quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, the efficacy of NIR spectroscopy for the non-destructive monitoring of physicochemical changes and drying behaviour in organic carrot slices during 8-h hot-air drying at 40 A degrees C was demonstrated using Partial least squares (PLS) regression and PLS discriminant analysis (PLS-DA). The impact of hot-water blanching pre-treatment (at 95 A degrees C for 1.45 min) for enzyme inactivation on performances of both regression and classification models was also evaluated. PLS regression models were successfully developed to monitor changes in water activity (R (2) = 0.91-0.96), moisture content (R (2) = 0.97-0.98), total carotenoids content (R (2) = 0.92-0.96), lightness for unblanched carrots (R (2) = 0.80-0.83) and hue angle for blanched samples (R (2) = 0.85-0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43-4.40). Classification models were developed to recognise dehydration phases of carrot slices on the basis of their NIR spectral profile using K-means and PLS-DA algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided from good (> 0.85) to excellent (> 0.95) sensitivity and specificity for the predefined drying phases. Feature selection procedures yielded both regression and classification models with performances very similar to models computed from the full spectrum.