Chirife S V, Albanell E, Such X, Manuelian C L
Group of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Spain.
Group of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Spain.
J Dairy Sci. 2025 Sep;108(9):9144-9151. doi: 10.3168/jds.2025-26500. Epub 2025 Jul 16.
Due to a genetic variation in β-casein, A2 milk is more easily digestible than regular milk (A1); presence of the amino acid proline instead of histidine in position 67 of the peptide chain prevents the release of β-casomorphin-7 during digestion. This study evaluated the application of mid-infrared (MIR) spectroscopy as a rapid, noninvasive, and routinely large-scale method to authenticate the A2 variant in Holstein cow milk. Spectral, genetic, and milk quality (fat, protein, lactose, and SCC) data from 2,270 milk samples from 2 consecutive routine milk controls were retrieved from 1,356 animals from 6 farms located in the same area that raised both A1 and A2 cows. Genetic information included β-casein, κ-casein, and β-lactoglobulin variants. Milk compositional differences were statistically assessed before the spectral modeling. Then, a preliminary principal component analysis (PCA) on spectra information was conducted, followed by a partial least squares discriminant analysis (PLS-DA) with 30% of the samples as the test set. Results indicated that milk quality was similar across all protein fractions but differed slightly among farms (P < 0.05). The preliminary spectral evaluation revealed that the first 2 components of the PCA explained 73.2% of the variance. Still, it could not segregate A1 and A2 milk samples based on β-casein genetic information. The PLS-DA model revealed the lowest balanced accuracy in the training and testing set for the genotype A1A1 (50%). For genotypes A1A2 and A2A2, a better balanced accuracy was recorded in the training than in the testing set and slightly greater for A2A2 than for A1A2. For A1A2, balanced accuracy was 80% for the training set and 81% for the testing set. For A2A2, the balanced accuracy was 81% for the training set and 82% for the testing set. Moreover, balanced accuracy improved when only considering 2 levels, A1 milk (comprising genotypes A1A1 and A1A2) and A2 milk (genotype A2A2), reaching 94% for the training set and 88% for the testing set. In conclusion, MIR spectral information is a promising method to authenticate A2 milk based on a PLS-DA model.
由于β-酪蛋白存在基因变异，A2牛奶比普通牛奶（A1）更易消化；肽链第67位的氨基酸为脯氨酸而非组氨酸，可防止消化过程中β-酪蛋白衍生吗啡-7的释放。本研究评估了中红外（MIR）光谱法作为一种快速、无创且可常规大规模应用的方法，用于鉴定荷斯坦奶牛牛奶中的A2变体。从同一地区饲养A1和A2奶牛的6个农场的1356头动物的连续2次常规牛奶检测中获取了2270份牛奶样本的光谱、基因和牛奶质量（脂肪、蛋白质、乳糖和体细胞计数）数据。基因信息包括β-酪蛋白、κ-酪蛋白和β-乳球蛋白变体。在进行光谱建模之前，对牛奶成分差异进行了统计学评估。然后，对光谱信息进行了初步主成分分析（PCA），随后进行了偏最小二乘判别分析（PLS-DA），将30%的样本作为测试集。结果表明，所有蛋白质组分的牛奶质量相似，但不同农场之间略有差异（P < 0.05）。初步光谱评估显示，PCA的前两个成分解释了73.2%的方差。然而，它无法根据β-酪蛋白基因信息区分A1和A2牛奶样本。PLS-DA模型显示，在训练集和测试集中，A1A1基因型的平衡准确率最低（50%）。对于A1A2和A2A2基因型，训练集的平衡准确率高于测试集，且A2A2略高于A1A2。对于A1A2，训练集的平衡准确率为80%，测试集为81%。对于A2A2，训练集的平衡准确率为81%，测试集为82%。此外，仅考虑A
