傅里叶变换中红外光谱的牛奶品质无损检测分级

市场上普遍存在“高蛋白”，“高乳脂”等特色牛奶。为了实现对特优优质奶、高蛋白特色奶、高乳脂特色奶和普通奶的无损快速分级，收集了河北省10个牧场不同月份（1月、3月—10月）的5 121份牛奶样本并采集中红外光谱数据，分别测定牛奶中的乳蛋白、乳脂和体细胞数，构建了牛奶品质分级模型。首先，分析牛奶光谱并去除冗余波段，最终选择925~1 597和1 712~3 024 cm-1的敏感波段组合作为全光谱用于建立模型。为了提高模型的性能，采用标准正态变量变换（SNV），多元散射校正（MSC），一阶导数，二阶导数，一阶差分和二阶差分6种算法对光谱进行预处理并建立朴素贝叶斯模型（NB）和随机森林模型（RF），确定二阶差分为最佳预处理方法，其测试集准确率分别为92.11%和96.87%。为了简化模型，利用无信息变量消除法（UVE）、竞争性自适应重加权算法（CARS）与稳定性竞争性自适应重加权采样算法（SCARS）以及UVE-CARS算法和UVE-SCARS算法对二阶差分后的光谱数据提取特征变量。然后，分别基于全光谱和所选特征变量数据，建立NB模型和RF模型。结果表明，SCARS算法为NB模型的最佳特征提取算法，模型的训练集准确率与测试集准确率分别为94.45%，93.94%；UVE-SCARS算法为RF模型的最佳特征提取算法，模型的训练集准确率与测试集准确率分别为99.86%，96.48%。综上，基于傅里叶变换中红外光谱技术建立的二阶差分-UVE-SCARS-RF模型，可以实现特优优质奶、高蛋白特色奶、高乳脂特色奶和普通奶的无损快速分级，通过建立中红外光谱模型，首次将乳蛋白、乳脂含量和体细胞数直接结合进行分级鉴定，这是以往未曾有过的。模型应用方便，只需将获得的牛奶红外光谱数据输入模型即可输出预测类别，在牛奶产业中具有实际应用价值。

Nondestructive Testing and Grading of Milk Quality Based on Fourier Transform Mid-Infrared Spectroscopy

There are “high protein”, “high fat”, and other characteristics of milk in the market. In order to realize the nondestructive and rapid grading of super quality milk, high-protein characteristic milk, high-fat characteristic milk and ordinary milk, 5 121 milk samples from 10 pastures in Hebei Province in different months (January, March to October) were collected. Then the mid-infrared spectroscopy data were collected, the protein and fat content in milk were measured, the somatic cell number was measured, and the mid-infrared spectrum model of milk quality grading was established. Firstly, milk spectral analysis was carried out, and redundant bands were removed. Finally, the sensitive band combinations of 9 925～1 597 and 1 712～3 024 cm-1 were selected as the full spectrum to establish the model. In order to improve the prediction accuracy and efficiency of the model, six spectral pre-processing methods were used to improve the signal-to-noise ratio of the original spectrum, including Standard normal variable transform (SNV), multiple scattering correction (MSC), the first derivative and second derivative, first difference and second-order difference. Comparing the effects of different pretreatment methods by establishing naive Bayes model (NB) and random forest model (RF), the second-order difference obtained the best prediction accuracy. The testing set accuracy was 92.11% and 96.87%, respectively. So second-order difference was identified as the best pretreatment method for further analysis. In order to simplify the models, UVE (Uninformative variable elimination), CARS (Competitive adaptive reweighed sampling), SCARS (Stability Competitive adaptive reweighted sampling) were utilized to extract the characteristic variables from the pre-processed spectrum by second-order difference method. Then, the NB and RF models were established based on the full spectral data and the selected characteristic variable data. The results showed that SCARS was the best feature extraction algorithm for the NB model, and the accuracy rates of the training set and the testing set were 94.45% and 93.94%, respectively.UVE-SCARS is the best feature extraction algorithm of the RF model, and the accuracy of the training set and test set are 99.86% and 96.48%, respectively. In conclusion, the second-order difference-UVE-CARS-RF model established based on Fourier transform the mid-infrared spectroscopy technology can realize the rapid and non-destructive prediction of classification of 4 kinds of milk. Through the establishment of mid-infrared spectrum model, the combination of milk protein, milk fat content and somatic cell number is the first time for direct classification and identification, which is unprecedented in previous studies. In applying the model, we only need to input the obtained milk mid-infrared spectral data into the model to output the prediction category, which has practical application value in the milk industry.