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首页> 外文期刊>Biological Agriculture & Horticulture >Differentiation between milk from low-input biodynamic, intermediate-input organic and high-input conventional farming systems using fluorescence excitation spectroscopy (FES) and fatty acids
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Differentiation between milk from low-input biodynamic, intermediate-input organic and high-input conventional farming systems using fluorescence excitation spectroscopy (FES) and fatty acids

机译：使用荧光激发光谱（FES）和脂肪酸的低输入生物动力学，中间输入有机和高输入常规农业系统的牛奶之间的差异化

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摘要

This study evaluated the ability of fluorescence excitation spectroscopy (FES) to differentiate milk samples from different origins. Three different farming systems were chosen: D-samples originating from low-input biodynamic farms (cows fed on hay or pasture); O-samples from intermediate-input organic farms (cows fed mainly on grass silage); and C-samples from high-input conventional farms (indoor housing, cows fed on maize and grass silage). Milk samples were collected every second month between July 2015 and June 2016 from 12 farms (four farms per system), and a total of 70 samples were obtained. Fat-, protein- and urea-concentrations, somatic-cell count and fatty acid levels (FA) were determined. FES-measurements were performed by exciting the sample with light of different wavelengths and detecting delayed luminescence. Differences between farming systems in each season were checked by ANOVA. Factors of season, system and breed were evaluated in a linear regression model. By linear-discriminant analysis, variables contributing to correct classification were analysed. Milk FAs, especially the concentration of omega-3 (n3) and omega-6 (n6) FAs, were different between farming systems, while conjugated linoleic acid (CLA) and C18:1t11 (tVA)-concentration was mainly influenced by season (pasture). FES-parameters showed slight seasonal variations, but strong farming-system impacts. Differentiation between the three farming systems was possible for 81% of the samples by using FAs as variables. FES-parameters discriminated up to 86% of the samples, and, in combination, 93% of the samples were classified correctly. These results indicated that FES-results contributed to correct discrimination and that FES-results may be linked with qualities different to the FA profile.
机译：该研究评估了荧光激发光谱（FES）从不同起源区分牛奶样品的能力。选择了三种不同的农业系统：源自低输入生物动力场的D样品（饲喂干草或牧场的奶牛）;来自中间输入有机农场的O-样品（主要喂养的奶牛）;和来自高投入常规农场的C样本（室内外壳，饲喂玉米和草青贮饲养的母牛）。 2015年7月至2016年6月从12个农场（每种系统的四个农场）收集牛奶样品，并获得了70种样本。测定脂肪，蛋白质和尿素浓度，体细胞计数和脂肪酸水平（FA）。通过用不同波长的光激发样品并检测延迟发光来进行FES测量。 ANOVA检查每个季节的农业系统之间的差异。在线性回归模型中评估了季节，系统和品种的因素。通过线性判别分析，分析了有助于正确分类的变量。牛奶Fas，尤其是ω-3（N3）和ω-6（N6）Fas的浓度不同，而农业系统之间是不同的，而共轭的亚油酸（CLA）和C18：1T11（TVA） - 浓度主要受季节（牧场）。 FES参数显示出轻微的季节性变化，但强烈的农业系统的影响。通过使用FAS作为变量，三种农业系统之间的差异是可能的81％的样本。 FES参数可歧视高达86％的样品，并且组合，93％的样品被正确分类。这些结果表明，FES-结果有助于正确的歧视，并且FES-结果可能与与FA档案不同的品质相关联。

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