Using Visible/Near-Infrared Reflectance Spectroscopy and Chemometrics for the Rapid Evaluation of Two Panamanian Watermelon (Citrullus lanatus) Varieties

In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy measurement in the 300-900 nm wavelength range, are used as external parameter to evaluate the internal parameters (Brix content and pH) of two local watermelon varieties, Quetzaly (N=8) and Mickylee (N=6), in a noninvasive manner. To assess this relationship three Chemometrics and machine learning methods were used: Principal Component Regression (PCR), Partial Least Squares (PLS) and Multilayer Perceptron Regression (MLPR). Spectral Data augmentation was used to generate more observations from the original spectra. Our results suggest Quetzaly has a higher Brix content and that all three methods can be used to predict it with confidence. However, PLS shows a higher correlation coefficient of 0.98 and standard errors of prediction of <= 0.1, closely followed by PCR with 0.96 and errors of <= 0.01 and <= 0.04, respectively. In the case of pH all methods produce good results, with a PLS achieving correlation coefficient of 0.91-0.94 with errors of prediction on the <= 0.05; and lower results for the other two methods. This study demonstrates the value of non-invasive Vis/NIR Reflectance spectroscopy as quality assessment in a watermelon classification system. More importantly, this system can be later implemented in local watermelon fruit packing centers in Panama. This can also be used in other export fruits or vegetables, helping local producers.