Assessment of Melon Fruit Nutritional Composition Using VIS/NIR/SWIR Spectroscopy Coupled with Chemometrics

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Abstract
The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. A total of 175 fully ripened melons were analyzed for soluble solids content (SSC), dry matter (DM), pH, and titratable acidity (TA) using partial least squares regression (PLSR), principal components regression (PCR), and multilinear regression (MLR) models. Reflectance spectra were captured at three fruit locations (pedicel, equatorial, and blossom end) in the 350–2500 nm range. The PLSR models yielded the highest accuracy, particularly for SSC (R = 0.80) and SSC/TA (R = 0.79), using equatorial zone data. Variable selection using the genetic algorithm (GA) successfully identified the spectral regions critical for each nutritional parameter at the pedicel, equatorial, and blossom end areas. Key wavelengths for SSC were found around 670–720 nm and 900–1100 nm, with important wavelengths for pH prediction located near 1450 nm, and, for dry matter, in the ranges 1900–1950 nm. Variable importance in projection (VIP) analysis confirmed that specific wavelengths between 680 and 720 nm, 900 and 1000 nm, 1400 and 1500 nm, and 1900 and 2000 nm were consistently critical in predicting the SSC, DM, and SSC/TA ratio. The highest VIP scores for SSC prediction were noted around 690 nm and 950 nm, while dry matter prediction was influenced most by wavelengths in the 1450 nm to 1950 nm range. This study demonstrates the potential of VIS/NIR/SWIR spectroscopy for rapid, non-destructive melon quality assessment, with implications for commercial postharvest management.

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