Rysova L, Cejnar P, Hanus O, Legarova V, Havlik J, Nejeschlebova H, Nemeckova I, Jedelska R, Bozik M
Department of Food Science, Czech University of Life Sciences Prague, Kamycka 129, 165 00 Prague-Suchdol, Czech Republic.
Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Technicka 5, 166 28 Prague 6-Dejvice, Czech Republic.
J Dairy Sci. 2022 Jun;105(6):4882-4894. doi: 10.3168/jds.2021-21396. Epub 2022 Apr 2.
Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500-4,000 Da; 4-20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).
检测小反刍动物奶的掺假情况对于健康和商业原因而言都非常重要。需要开发新的分析方法且成本效益高的方法来检测新的掺假行为。在这项工作中,我们旨在利用来自18个奶牛场的样本,探索基质辅助激光解吸电离飞行时间质谱(MALDI-TOF)检测山羊奶和绵羊奶中牛奶掺假的能力。在山羊和绵羊的泌乳期(5月,产奶60至90天;8月,产奶150至180天)分析了不同程度的掺假(0.5%、1%、5%、10%、20%、40%、60%和80%)。使用两个不同范围的肽-蛋白质谱(500 - 4000 Da;4 - 20 kDa),通过偏最小二乘法和带有套索正则化的广义线性模型建立校准模型,以预测掺假物的浓度。光谱的低分子量部分以及带有套索正则化回归模型的广义线性模型似乎在检测小反刍动物奶掺假方面具有更大潜力。后续的预测模型能够预测山羊奶中牛奶的浓度,其均方根误差在山羊奶中为11.4%,在绵羊奶中为17.0%。结果提供了令人信服的证据,表明MALDI-TOF可以检测小反刍动物奶的掺假情况。然而,该方法受到以下因素的严重限制:(1)掺假技术导致的牛奶蛋白质组复杂性;(2)热不稳定蛋白质的潜在降解;(3)测试样本的遗传变异性。此外,仅基于一个个体样本掺假系列的预测均方根误差,对于掺假山羊奶的定量,在所有浓度范围内可降至6.34%,对于掺假绵羊奶可降至6.28%;如果仅限制在低浓度掺假(0、0.5%、1%、5%、10%),则分别降至2.33%和4.00%。