Lehrstuhl für Grünlandlehre, Technische Universität München , D-85350 Freising, Germany.
Fachgebiet Tierernährung, Hochschule Weihenstephan-Triesdorf , D-85350 Freising, Germany.
J Agric Food Chem. 2015 Dec 9;63(48):10500-7. doi: 10.1021/acs.jafc.5b03646. Epub 2015 Nov 23.
Dairy production systems vary widely in their feeding and livestock-keeping regimens. Both are well-known to affect milk quality and consumer perceptions. Stable isotope analysis has been suggested as an easy-to-apply tool to validate a claimed feeding regimen. Although it is unambiguous that feeding influences the carbon isotope composition (δ(13)C) in milk, it is not clear whether a reported feeding regimen can be verified by measuring δ(13)C in milk without sampling and analyzing the feed. We obtained 671 milk samples from 40 farms distributed over Central Europe to measure δ(13)C and fatty acid composition. Feeding protocols by the farmers in combination with a model based on δ(13)C feed values from the literature were used to predict δ(13)C in feed and subsequently in milk. The model considered dietary contributions of C3 and C4 plants, contribution of concentrates, altitude, seasonal variation in (12/13)CO2, Suess's effect, and diet-milk discrimination. Predicted and measured δ(13)C in milk correlated closely (r(2) = 0.93). Analyzing milk for δ(13)C allowed validation of a reported C4 component with an error of <8% in 95% of all cases. This included the error of the method (measurement and prediction) and the error of the feeding information. However, the error was not random but varied seasonally and correlated with the seasonal variation in long-chain fatty acids. This indicated a bypass of long-chain fatty acids from fresh grass to milk.
奶制品生产系统在饲养和牲畜管理方案方面存在很大差异。人们都知道这两者会影响牛奶质量和消费者认知。稳定同位素分析已被提议作为一种易于应用的工具，用于验证所声称的饲养方案。虽然可以明确的是，饲养会影响牛奶中的碳同位素组成（δ(13)C），但尚不清楚是否可以通过测量牛奶中的δ(13)C 而无需采样和分析饲料来验证所报告的饲养方案。我们从分布在中欧的 40 个农场中获得了 671 份牛奶样本，以测量 δ(13)C 和脂肪酸组成。根据农民的饲养方案以及基于文献中 δ(13)C 饲料值的模型，用于预测饲料和随后牛奶中的 δ(13)C。该模型考虑了 C3 和 C4 植物的饮食贡献、浓缩物的贡献、海拔、（12/13）CO2 的季节性变化、Suess 效应和饮食-牛奶的辨别。预测和测量的牛奶 δ(13)C 密切相关（r(2) = 0.93）。分析牛奶中的 δ(13)C 可以验证所报告的 C4 成分，在 95%的情况下，其误差小于 8%。这包括方法（测量和预测）的误差和饲养信息的误差。然而，误差不是随机的，而是随季节变化，并与长链脂肪酸的季节性变化相关。这表明新鲜草中的长链脂肪酸绕过了牛奶。
