Evaluation of Total Soluble Solid in Sonya Watermelon Fruits Using Near Infrared Spectroscopy Hyperspectral Imaging Technique
DOI:
https://doi.org/10.14456/thaidoa-agres.2025.19Keywords:
total soluble solid, watermelon fruits, near infrared spectroscopy, hyperspectral imaging techniqueAbstract
Near-infrared hyperspectral imaging (NIR-HSI) is a rapid accurate, and non-destructive technique for analyzing the composition and internal quality of agricultural products. This study aimed to develop a method for assessing the internal quality of the 'Sonya' watermelon cultivar using NIR-HSI by constructing a predictive model for total soluble solids (TSS) content based on 100 watermelon samples. The reflectance spectra were measured in the wavelength range of 900–1700 nm. The obtained data were in the form of hypercube spectral data, and an equation was created to predict the total soluble solids content using the partial least squares regression technique. The best prediction equation had a correlation coefficient of 0.977, a root mean square error of 0.231°Bx and a bias of 0.047, indicating that it was effective for quality assessment. Additionally, the absorbance values from each pixel of the hypercube data were used to create a map showing the distribution of TSS in the watermelon samples. The findings of this study demonstrate that NIR-HSI can effectively predict TSS content and visualize its spatial distribution within whole watermelons, highlighting its potential application in future automated systems for watermelon quality grading.
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