Kernel partial least squares model for pectin content in peach using near-infrared spectroscopy

Abstract

Pectin is a class of complex galacturonic acid-rich polysaccharides that are related to the texture of fruit and vegetables. The objective of this study was to develop, using near-infrared (NIR) spectroscopy, the best model for the determination of pectin content in peach fruit. A total of 100 samples divided into lossy and lossless samples were used to collect NIR raw spectra in the range of 1000–2500 nm. NIR absorption spectra were then obtained after pre-processing. Finally, four methods were used to establish lossy and lossless spectral models. The 10-fold cross-validation coefficient of determination R² of the lossy model was between 0.364 and 0.628, whereas that of the lossless model was between 0.187 and 0.288, indicating that the lossy model was better than the lossless model. Among all samples, the kernel partial least squares (KPLS) lossy model was better, with coefficient of determination R² = 0.628, root mean square error (RMSE) = 0.069 and mean absolute error (MAE) = 0.061. This is the first study to evaluate the prediction of peach pectin content using NIR spectroscopy, and the model can be used for rough screening.