NDT model study of crown pear based on near infrared spectroscopy

Abstract

With the growing area and production of pear, as well as the people for the fruit quality requirements are growing. The traditional methods for pear quality inspection are expensive and less accurate, and cannot be done for every pear. In response to the current production environment and market demand, a rapid and non-destructive method of pear quality testing is explored, using near infrared spectroscopy to establish the quality prediction model of crown pear. The main quality indicators for crown pears are soluble solids (SSC) and hardness, using NIR spectroscopy to establish a hardness prediction model and NIR spectroscopy to establish a soluble solids prediction model to build a spectral measurement system. The method was to measure pear spectral images in the NIR band (400nm to 1050nm). The spectra were pre-processed with multiple scattering correction (MSC) and standard normal variables exchange (SNV) to eliminate the influence of extraneous factors, and then downscaled by continuous projection algorithm (SPA) and principal component analysis (PCA) to extract the number of principal factors respectively. Partial Least Squares (PLS) was used to build the regression model and to predict the soluble solids (SSC) and hardness. The best model developed was a hardness prediction model by SNV + principal component analysis with a correlation coefficient of 0.64116 and a soluble solids prediction model by SNV + principal component analysis with a correlation coefficient of 0.73566.