A nondestructive method to evaluate carrot quality using visible-near infrared spectroscopy

Carrot is a root crop that has relatively high content of Vitamin C and carotenoids. Carrot is usually consumed fresh, juiced or cooked. It is also a major source of industrial natural carotenoids, a precursor to vitamin A. Rapid determination of quality parameters indeed helps process engineers and storage managers to track the condition of root crops such as carrots during storage and/or before processing. The objectives of this study were to apply VIS-NIR spectroscopy to noninvasively assess and predict the various quality attributes of carrot (cv. Nectar) including color parameters (L^∗a^∗b^∗), moisture content (MC), total soluble solids (TSS), firmness, Vitamin C content, and β-carotene. Two spectroscopic sensors with different range were used (400-1000 nm and 900-1700 nm). Samples included whole roots and sliced discs. The best prediction models using partial least squares regression (PLSR) yielded correlation coefficient, r and ratio of performance to deviation or r(RPD) with values of 0.92 (2.46), 0.85(1.65), 0.94(2.85), 0.96(3.55), 1.00(17.92), 0.90(2.16), 0.98(4.73), and 0.97(3.88) for L^∗, a^∗, b^∗, firmness, MC, TSS, Vitamin C, and β-carotene, respectively. Generally, whole roots and VIS-NIR sensor have high prediction (r = 0.69-1.00). Results obtained in this research could be enhanced with more samples to develop a better and higher accuracy predictive multispectral model for carrot quality prediction with possible extension to other similar vegetables in post-harvest losses reduction.