Wu Xin, Li Guanglin, Fu Xinglan, Wu Weixin
School of Electronics and Internet of Things, Chongqing College of Electronic Engineering, Chongqing, China.
College of Engineering and Technology, Southwest University, Chongqing, China.
Front Plant Sci. 2023 Feb 27;14:1128993. doi: 10.3389/fpls.2023.1128993. eCollection 2023.
Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033-2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. 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, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (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 and higher RMSEP. The independent semi-supervision free parameter model enhancement (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 and practical application of portable detection device.
雪梨因其果实质地和潜在药用价值在中国西南部很受欢迎。木质素含量（LC）在决定雪梨的果实质地以及新鲜梨的消费者购买决策方面起着直接的负面作用（石细胞浓度越高、尺寸越大，果肉越厚，口感越差）。在本研究中，我们使用波长范围为1033 - 2300 nm的便携式近红外（NIR）光谱仪评估了不同批次雪梨中预测LC的校准模型的稳健性。收集了在四个不同时期（批次A、B、C和D）购买的雪梨样品在九个不同测量位置的平均近红外光谱。我们开发了一种标准正态变量变换（SNV）-遗传算法（GA）-偏最小二乘回归（PLSR）模型（主模型A），基于80个选定的有效波长预测批次A的雪梨样品中的LC，预测集相关系数（Rp）较高，为0.854，预测集均方根误差（RMSEP）较低，为0.624，我们将其用作预测模型来检测其他三批雪梨样品中的LC。主模型A直接检测批次B、C和D样品的LC的性能较差，Rp较低，RMSEP较高。使用并比较了独立半监督自由参数模型增强（SS - FPME）方法和顺序SS - FPME方法来更新主模型A以预测雪梨的LC。对于批次B样品，更新后的模型（Ind模型AB）的预测能力有所提高，Rp为0.837，RMSEP为0.614。对于批次C样品，Seq模型ABC的性能有很大提高，Rp为0.952，RMSEP为0.383。对于批次D样品，Seq模型ABCD的性能也有所提高，Rp为0.831，RMSEP为0.309。因此，基于顺序SS - FPME方法对新批次样品进行监督学习的更新模型可以提高用于检测雪梨LC的模型的稳健性和迁移能力，并为便携式检测设备的开发和实际应用提供技术支持。
