Global model for in-field monitoring of sugar content and color of melon pulp with comparative regression approach

Abstract

The development of the global model is an important part of research involving the quality prediction of agricultural commodities using visible/near-infrared (Vis/NIR) spectroscopy due to its efficiency and effectiveness. The Vis/NIR was used in this study to develop a global model and to evaluate the sugar content and pulp color, which are the main determinants of ripeness and quality of melons. Furthermore, it also provides a comparison between linear and nonlinear regression using partial least squares regression (PLSR) and support vector machine regression (SVMR), respectively. The model accuracy was determined by ratio of performance to deviation (RPD). The results showed that there were good model accuracy values in some parameters, such as SSC (2.14), glucose (1.59), sucrose (2.31), a* (2.97), and b* (2.49), while the fructose (1.35) and L* (1.06) modeling showed poor prediction accuracy. The best model for SSC was developed using PLSR, while that of fructose, glucose, sucrose, L*, a*, and b* were obtained from SVMR. Therefore, Vis/NIR spectroscopy can be used as an alternative method to monitor sugar content and pulp color of a melon, but with some limitations, such as the low accuracy in predicting certain variables, such as the L* and fructose.

Keywords

• contactless
• machine learning
• multivariate analysis
• on-tree
• pulp color