Real-time monitoring of organic carrot (var. Romance) during hot-air drying using near-infrared spectroscopy

Authors: MOSCETTI, ROBERTO - University Of Tuscia; Haff, Ronald - Ron; FERRI, SERENA - University Of Tuscia; RAPONI, FLAVIO - University Of Tuscia; MONARCA, DANILO - University Of Tuscia; Liang, Peishih; MASSANTINI, RICCARDO - University Of Tuscia

Submitted to: Food and Bioprocess Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/28/2017
Publication Date: 8/8/2017
Citation: Moscetti, R., Haff, R.P., Ferri, S., Raponi, F., Monarca, D., Liang, P., Massantini, R. 2017. Real-time monitoring of organic carrot (var. Romance) during hot-air drying using near-infrared spectroscopy. Food and Bioprocess Technology. 10(11):2046.

Interpretive Summary: Dried carrots are seeing increasing use in organic snacks, integral breakfast foods, chips, etc. Conventional methods for drying carrots include hot-air drying and freeze-drying, which are usually uncontrolled and thus susceptible to quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, NIR spectroscopy was explored as a tool for non-destructive monitoring of physicochemical changes in organic carrot slices during the hot-air drying process, and the impact of hot-water blanching pre-treatments for enzyme inactivation on model performances was evaluated. Partial least squares regression models based on NIR reflection spectra were developed to monitor changes in water activity, moisture content, total carotenoids content, lightness (for unblanched carrots) and hue angle (for blanched samples). Soluble solids content prediction was poor for both treatments. Classification analysis was performed to recognize dehydration phases of carrot slices on the basis of their spectral profile. The classification models were computed using K-means and Partial Least Squares Discriminant Analysis (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 to excellent 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. Results suggest that hot-water blanching negatively impacted the feature selection procedure in terms of selected wavelengths due to pronounced effects on both water loss and the microstructure of carrot tissue.

Technical Abstract: The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend due to its increasing use as a raw material for organic snacks, integral breakfast foods, chips, etc. Conventional methods for drying carrots include hot-air drying and freeze-drying, which are 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 in organic carrot slices during 8-h hot-air drying at 40°C was demonstrated and the impact of hotwater blanching pre-treatment (at 95°C for 1.45 min) for enzyme inactivation on model performances was evaluated. Partial least squares (PLS) regression models based on NIR reflection spectra were developed to monitor changes in water activity (R2 = 0.91-0.96), moisture content (R2 = 0.97-0.98), total carotenoids content (R2 = 0.92-0.96), lightness for unblanched carrots (R2 = 0.80-0.83) and hue angle for blanched samples (R2 = 0.85-0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43-4.40). Classification analysis was performed for the development of discriminant models able to recognize dehydration phases of carrot slices on the basis of their spectral profile. The classification models were computed using K-means and Partial Least Squares Discriminant Analysis (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. Results suggest that hotwater blanching negatively impacted the feature selection procedure in terms of selected wavelengths due to pronounced effects on both water loss and the microstructure of carrot tissue.