近期，华东渔业一本大学工高校王巧华副教授团队合作实验成绩以“Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics”为题在Food Control先生发表。实验体现了A1和A2鲜牛奶的光谱分析分析的差异，建立联系了无损格式格式在线检测几种奶的分级模式化，反映出中红外光谱分析分析技能适用于为无损格式格式迅速鉴定A1奶和A2奶的新辅助工具。该实验也能让分开筹备A1型和A2型奶牛繁育群保证以及的技能支技。 仅含A2β-酪核蛋清的鲜牛乳（A2奶）因为与众不同的健康的益处而在欧洲大受欢迎辞。经常建国以来，各个企业必须要 先对奶牛来进行科技的染色体检查测量，淘汰出β-酪核蛋清中只其中包含A2β-酪核蛋清的纯种A2奶牛，就用一些奶牛研发的牛乳制造加工成A2奶。染色体检查测量虽准确的高，但直接费用也高且历时长，無法做到乳企建设企业化研发的标准要求。对此，急需用钱规划设计本身低直接费用、高利润的科技如何快捷辨别A1奶（各种类型奶）和A2奶。该设计翻过了传统性染色体检查测量的特殊性，利用中红外光谱分析科技如何快捷鉴定出A1和A2鲜牛乳。

CARS算法筛选特征变量

该探究具体剖析A1和A2牛乳在中红外光谱图剖析图吸光度上的性别文化差异，得到强烈股票波段搭配组合看做全光谱图剖析图，各自合理充分利用基准正态变数转换 、互促散射标定、归一化、一阶导数、二阶导数、一阶差分和二阶差分等7种做法对光谱图剖析图展开预处置，合理充分利用无企业信息变数解决法和之间的竞争性响应式性重权重计算神经网络算法选择出能象征着A1和A2奶性别文化差异的本质特征变数，必将共建偏最短二乘判别具体剖析（PLS-DA）实体建模和可以向量机（SVM）实体建模，PLS-DA实体建模的学习集正确率和测试软件测试软件集正确率各自为96.6%和96.0%，SVM实体建模的学习集正确率和测试软件测试软件集正确率各自为96.0%和95.1%。

UVE算法筛选特征变量

该科研选用PLS-DA模特做为最适宜模特，实用1组独自样表对模特通过外接查验。将新采摘的纯牛奶中红外光谱仪图自动进入保持的模特中，以使用的奶牛dna测量效果做为對照指标值，模特的推测精确度率有95.2%，耐磨性正常。效果取决于，中红外光谱仪图技能还可以改变对A1奶与A2奶的飞速的分类辨认，即将将要在生孩子中受到适用。 华北畜牧业高校工学校本科探析生肖仕杰为小作文一号我，工学校王巧华专家和食草动物数学枝术学校张淑君专家为同样网络通讯我。该探析得到了国内政府部楼盘（2013070204020045）贫困资助。

【英文摘要】

The milk containing only A2 β-casein （called A2 milk） is globally popular because of its unique health benefits. Traditionally, genetic testing （such as gene sequencing） is used to identify the cows with A2 β-casein gene that can only produce A2 milk, which is a time-consuming and costly method. The objective of this study was to directly identify A1 and A2 milk from a large quantity of milk using mid-infrared （MIR） spectroscopy and chemometrics without genotyping cows. Before establishing the predictive model, we firstly genotyped the A1 β-casein and A2 β-casein of cows from blood as reference values. Further, the MIR spectra of the milk collected from these cows were obtained using a dairy product analyzer. The MIR spectroscopy data and the reference values were used as the independent and dependent variables, respectively, to establish a category classification model for A1 and A2 milk. Seven preprocessing methods were combined with two feature extraction algorithms to establish the model. Subsequently, partial least squares discriminant analysis （PLS-DA） and support vector machine （SVM） models were developed. The average accuracy of the test set of the two models were 94.9% and 94.4%, respectively, while the PLS-DA model exhibited better effect, and the accuracy of training set and test set reached 96.6% and 96.0%, respectively. We used a set of independent samples for the external validation of the PLS-DA model, and the prediction accuracy was 95.2%. Overall, the proposed prediction models based on MIR spectroscopy can be used for low-cost, rapid, and large-scale classification of A1 and A2 milk, which may be extremely beneficial in milk production industries.