Robustness of calibration model for prediction of lignin content in different batches of snow pears based on NIR spectroscopy - Wu, Xin; Li, Guanglin; Fu, Xinglan; Wu, Weixin (Frontiers in Plant Science, 2023)
Snow pear is very popular in southwest China because of its good fruit texture and medicinal value. Lignin content (LC) plays a direct and negative role in determining the fruit texture of snow pears as well as consumer purchasing decisions about fresh pear fruit. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using near-infrared (NIR) spectroscopy. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods were collected by a microfiber optic spectrometer. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths. As a result, the SNV-GA-PLSR model had a higher Rp of 0.854 and a lower RMSEP of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp values and higher RMSEP values. The independent SS-FPME method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seq-model ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development of portable detection device.