两批次番茄炒蛋蛋白质近红外光谱检测模型的维护方法

为解决光谱数据差异导致模型不稳定的问题，研究了不同批次中式菜肴营养素含量预测模型的传递方法。以间隔3个月制作的番茄炒蛋样本为例，采集光谱数据并利用理化方法测定蛋白质含量(每批次120个样本)；选择预测效果较好的第二批次模型作为主模型，将分段直接标准化(Piecewise Direct Standardization, PDS)算法、模型更新(Model Updating, MP)和斜率/截距(Slope/Bias, S/B)修正法联合(PDS-MP-S/B)用于菜肴类模型传递，分析不同PDS窗口数和标准集数对预测结果的影响。当PDS窗口数为11且标准集数为100时，PDS-MP-S/B算法对蛋白质含量的预测结果明显优于无模型传递和单独使用3种算法时，预测模型的预测集决定系数R2(Pred)为0.9628，相对预测偏差(Relative Prediction Deviation, RPD)为5.6731，预测均方根误差(Root Mean Square Error of Prediction, RMSEP)为0.3157。从光谱、模型、结果三个方面实现了模型传递，提高了模型的通用性，减少了建模成本，为中式菜肴的快检提供了理论支持。

Maintenance Method of Protein Detection Model of Two Batches of Scrambled Tomatoes and Eggs Based on Near Infrared Spectroscopy

In order to solve the problem of model instability caused by spectral data difference, the transfer method of nutrient content prediction model of different batches of Chinese dishes is studied in this paper. Taking the samples of scrambled tomatoes and eggs prepared at an interval of 3 months as an example, spectral data are collected and protein content is determined by physical and chemical methods (120 samples per batch). The model of the second batch with better prediction effect is selected as the main model. A combination of PDS, MP and S/B (PDS-MP-S/B) is applied in dish model transfer to analyze the influence of different PDS window numbers and standard set numbers on the predicted results. When the number of PDS Windows is 11 and the number of standard sets is 100, the prediction result of protein content by PDS-MP-S/B algorithm is significantly better than that by no model transfer and by using the three algorithms separately. The absolute coefficient of prediction set (R2(Pred)) of prediction model is 0.9628, the relative prediction deviation is 5.6731, and the root mean square error of prediction is 0.3157. The model transfer is realized from three aspects of spectrum, model and result, which improves the universality of the model, reduces the cost of modeling, and provides theoretical support for the fast inspection of Chinese dishes.