近日，華中農(nóng)業(yè)大學工學院王巧華教授團隊研究成果以“Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics”為題在Food Control發(fā)表。研究揭示了A1和A2牛奶的光譜差異，建立了無損檢測兩種奶的分類模型，表明中紅外光譜技術(shù)可作為無損快速鑒別A1奶和A2奶的新工具。該研究也可為單獨組建A1型和A2型奶牛育種群提供相應的技術(shù)支持。

僅含A2β-酪蛋白的牛奶（A2奶）因其獨特的健康益處而在全球廣受歡迎。長期以來，企業(yè)需要先對奶牛進行專業(yè)的基因檢測，篩選出β-酪蛋白中只包含A2β-酪蛋白的純種A2奶牛，再用這些奶牛生產(chǎn)的牛乳加工成A2奶?；驒z測雖準確性高，但成本也高且耗時長，無法滿足乳企規(guī)?；a(chǎn)的要求。因此，急需開發(fā)一種低成本、高效益的技術(shù)快速識別A1奶（普通奶）和A2奶。該研究突破了傳統(tǒng)基因檢測的局限，應用中紅外光譜技術(shù)快速鑒別出A1和A2牛奶。

CARS算法篩選特征變量

該研究分析A1和A2牛奶在中紅外光譜吸光度上的差異，找到敏感波段組合作為全光譜，分別利用標準正態(tài)變量變換 、多元散射校正、歸一化、一階導數(shù)、二階導數(shù)、一階差分和二階差分等7種方法對光譜進行預處理，利用無信息變量消除法和競爭性自適應重加權(quán)算法篩選出能代表A1和A2奶差異的特征變量，進而構(gòu)建偏最小二乘判別分析（PLS-DA）模型和支持向量機（SVM）模型，PLS-DA模型的訓練集準確率和測試集準確率分別為96.6%和96.0%，SVM模型的訓練集準確率和測試集準確率分別為96.0%和95.1%。

UVE算法篩選特征變量

該研究選擇PLS-DA模型作為最佳模型，使用一組獨立樣本對模型進行外部驗證。將新采集的牛奶中紅外光譜批量帶入保存的模型中，以對應的奶牛基因檢測結(jié)果作為對照指標，模型的預測準確率為95.2%，性能良好。結(jié)果表明，中紅外光譜技術(shù)可以實現(xiàn)對A1奶與A2奶的快速分類鑒別，有望將來在生產(chǎn)中得到應用。

華中農(nóng)業(yè)大學工學院碩士研究生肖仕杰為論文第一作者，工學院王巧華教授和動物科學技術(shù)學院張淑君教授為共同通訊作者。該研究得到中國政府項目（2013070204020045）資助。

【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.