Nondestructive determination of lignin content in Korla fragrant pear based on near-infrared spectroscopy

摘要

The lignin content is a key index affecting the quality of Korla fragrant pear. This study applied a method of near-infrared diffuse reflectance spectroscopy combined with chemometric method to establish a predictive model of lignin content. Among them, moving average, baseline, standard normal variate transformations and multiplicative scatter correction were used to preprocess the collected near-infrared spectra. then, the backward interval partial least-squares, synergy interval partial least squares and uninformative variable elimination algorithms were used to select characteristic wavelengths from the whole spectral range to establish a partial least-squares prediction model of lignin content. The partial least-squares model based on uninformative variable elimination selected characteristic wavelengths was simplified, and the prediction of lignin content in Korla pear was more accurate. Multiple determination coefficient values, standard prediction errors and residual prediction errors for prediction were 0.87, 1.36 and 2.03%, respectively. Compared with partial least squares based on the whole spectral range and partial least squares based on Uninformative variable elimination selection, the number of characteristic wavelength variables was reduced from 289 to 51, thereby improving the prediction accuracy of the model. The experimental results showed that the uninformative variable elimination by partial least-squares model was the best model. The combination of near-infrared spectroscopy and uninformative variable elimination optimization can effectively determine the lignin content in Korla fragrant pear. The near-infrared diffuse reflectance spectroscopy proved to be a good tool for nondestructive determination of lignin content in Korla fragrant pear. The selection of characteristic wavelengths and appropriate pretreatment methods can improve the accuracy of near-infrared spectroscopy in actual detection.