Going deeper in lipolysis: Prediction of individual free fatty acid contents in milk by mid-infrared spectroscopy.

IF 4.4 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Octave Christophe, Romain Reding, Julie Leblois, Clément Grelet, Denis Pittois, Hélène Soyeurt, Cedric Guignard, Frédéric Dehareng
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Abstract

The dairy sector deals with a recurring issue: a taste alteration due to degradation of fat, commonly called lipolysis. Lipolysis occurs after milking, through the physical shocks induced by freezing, pumping, transfer, and storage of the milk. Physical breaking of fat globules makes triglycerides accessible to enzymes and degrades into free fatty acids (FFA). Among them, the volatile short-chain FFA lead to organoleptic issues through undesired tastes. Currently, methods to measure FFA are very complex and time-consuming, incomplete, only provide information on the total amount of FFA, or a combination of these. To bring a new way of preventive and corrective action for dairies and farmers, this study aims to develop predictive models based on milk Fourier transform mid-infrared spectroscopy (FT-MIR) to quantify individual FFA, with a special focus on short-chain FFA. A total of 792 milk samples were collected from individual cows and in bulk tanks in 4 different countries (Luxembourg, Belgium, France, and Germany) and analyzed by FT-MIR spectroscopy as well as GC coupled with tandem mass spectrometry. When performing a random sampling of individual cows, and therefore relying only on spontaneous lipolysis, most of the samples showed that short-chain FFA under the limit of quantification and derived FT-MIR models had poor performance. In a second step, lipolysis was induced by 4 different mechanical processes of milk. Among them, time milk homogenization demonstrated a clear increase of short-chain FFA values leading to improved predictive performance of models. The predictions of short-chain FFA could be used for rough screening on the dairy population, especially C4 and total short-chain FFA as their models had a validation R2 above 0.7. Unfortunately, the mid- and long-chain FFA models showed poor predictive performance. However, those results can still be considered positive because they may enable a better monitoring of taste defect mainly occurring from short-chain FFA.

深入脂肪分解:中红外光谱法预测牛奶中个别游离脂肪酸含量。
乳制品行业面临着一个反复出现的问题:由于脂肪降解(通常称为脂解)而导致的味道改变。脂解发生在挤奶后,通过冷冻、泵送、转移和储存牛奶引起的物理冲击。脂肪球的物理分解使甘油三酯可以被酶接触并降解为游离脂肪酸(FFA)。其中,挥发性短链FFA通过不希望的味道导致感官问题。目前,测定游离脂肪酸的方法非常复杂,耗时,不完整,只能提供游离脂肪酸总量的信息,或两者的结合。为了给奶牛场和农民提供一种新的预防和纠正措施,本研究旨在建立基于牛奶傅里叶变换中红外光谱(FT-MIR)的预测模型,以量化个体FFA,特别关注短链FFA。从4个不同国家(卢森堡、比利时、法国和德国)的个体奶牛和散装储罐中采集792份牛奶样品,采用FT-MIR光谱和GC -串联质谱分析。当对奶牛个体进行随机抽样时,因此仅依赖于自发脂肪分解,大多数样本显示,在定量限制和推导的FT-MIR模型下,短链FFA表现不佳。在第二步中,通过4种不同的机械过程诱导牛奶的脂肪分解。其中,牛奶均质时间明显增加了短链FFA值,提高了模型的预测性能。短链游离脂肪酸预测可用于奶牛种群的粗略筛选,特别是C4和总短链游离脂肪酸,其模型的验证R2均在0.7以上。不幸的是,中长链FFA模型的预测性能较差。然而,这些结果仍然可以被认为是积极的,因为它们可以更好地监测主要由短链FFA引起的味觉缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Dairy Science
Journal of Dairy Science 农林科学-奶制品与动物科学
CiteScore
7.90
自引率
17.10%
发文量
784
审稿时长
4.2 months
期刊介绍: The official journal of the American Dairy Science Association®, Journal of Dairy Science® (JDS) is the leading peer-reviewed general dairy research journal in the world. JDS readers represent education, industry, and government agencies in more than 70 countries with interests in biochemistry, breeding, economics, engineering, environment, food science, genetics, microbiology, nutrition, pathology, physiology, processing, public health, quality assurance, and sanitation.
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