Infrared spectroscopic methods for the discrimination of cows' milk according to the feeding system, cow breed and altitude of the dairy farm

Valenti, Bernardo; Martin, Bruno; Andueza, Donato; Leroux, Christine; Labonne, Cyril; Lahalle, F.; Larroque, Hélène; Brunschwig, P.; Lecomte, Christophe; Brochard, Mickaël; Ferlay, Anne

doi:10.1016/j.idairyj.2013.02.014

Cited by 34 publications

(25 citation statements)

References 30 publications

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“…These very high discriminating performances were quite unexpected, especially considering the heterogeneity of the present data set . The results were improved further still when discriminating between pairs of feeding systems (Table 5), reaching performance among the highest found in literature for cow feeding authentication using stable isotopes or IR technologies (Renou et al, 2004;Coppa et al, 2012b, Valenti et al, 2013. When discriminating between fresh herbage-and corn silage-feeding systems (96.8% of samples were correctly classified), the FA contributing the most to the discriminant function were C16:0 and the C16:1 cis-9 + anteiso C17:0-to-iso C16:0 ratio (r > 0.39), positively related to the corn silage-feeding system, and C18:3n-3, iso C17:0 + C16:1 trans-9, C18:2 trans-11,cis-15-to-C18:1 trans-10 + trans-11 ratio, anteiso C15:0, and iso C15:0 (r > 0.35), positively related to fresh herbagefeeding system.…”

Section: Authentication Of Cow-feeding System and Altitude Origin Usimentioning

confidence: 80%

“…However, stable isotopes seem to be less efficient for the authentication of animal feeding (Renou et al, 2004;Engel et al, 2007). Recently proposed global approaches based on milk infrared spectra analysis (IR) are promising rapid and cheap methods to authenticate cow feeding, but failed to identify production altitude (Coppa et al, 2012b;Valenti et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potential of milk fatty acid composition to predict diet composition and authenticate feeding systems and altitude origin of European bulk milk

Coppa

Chassaing

Ferlay

et al. 2015

Journal of Dairy Science

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The aims of this work were to elucidate the potential of using milk fatty acid (FA) concentration to predict cow diet composition and altitude of bulk milk collected in 10 different European countries and to authenticate cow-feeding systems and altitude of the production area using a data set of 1,248 bulk cow milk samples and associated farm records. The predictions based on FA for cow diet composition were excellent for the proportions of fresh herbage [coefficient of determination (R2)=0.81], good for hay, total herbage-derived forages, and total preserved forages (R2>0.73), intermediate for corn silage and grass silage (R2>0.62), and poor for concentrates (R2<0.51) in the cow diet. Milk samples were assigned to groups according to feeding system, level of concentrate supplementation, and altitude origin. Milk FA composition successfully authenticated cow-feeding systems dominated by a main forage (>93% of samples correctly classified), but the presence of mixed diets reduced the discrimination. Altitude prediction reliability was intermediate (R2<0.62). Milk FA composition was not able to authenticate concentrate supplementation level in the diet (<58% of samples correctly classified). Similarly, the altitude origin was not successfully authenticated by milk FA composition (<76% of samples correctly classified). The potential of milk FA composition to authenticate cow feeding was confirmed using a data set representative of the diversity of European production conditions.

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Section: Authentication Of Cow-feeding System and Altitude Origin Usimentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Potential of milk fatty acid composition to predict diet composition and authenticate feeding systems and altitude origin of European bulk milk

Coppa

Chassaing

Ferlay

et al. 2015

Journal of Dairy Science

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“…Many studies have shown the effect of feed on milk composition (e.g., Grummer, 1991;Palmquist et al, 1993;Slots et al, 2009). Valenti et al (2013) demonstrated that based on infrared data it is possible to distinguish milk from hay-and pasture-based systems and those from maize silage-and pasture-based systems.…”

Section: Herdmentioning

confidence: 99%

“…Consumers have increasing interest in the authenticity of milk as they purchase biological and organic products at a higher price. Infrared spectroscopy might be one of the methods that enable discriminating milk samples produced by cows fed different diets (Valenti et al, 2013). Quantifying herd effects will give insight in the extent to which feed and management differences are reflected by individual wavenumbers.…”

Section: Introductionmentioning

confidence: 99%

Genetic and environmental variation in bovine milk infrared spectra

Wang

Hulzebosch

Bovenhuis

2016

Journal of Dairy Science

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Fourier transform infrared (FTIR) spectroscopy is widely used to determine milk composition. In this study, 1,060 milk infrared wavenumbers ranging from 925 to 5,008cm(-1) of 1,748 Holstein Friesian cows on 371 herds in the Netherlands were available. The extent to which infrared wavenumbers are affected by genetic and environmental factors was investigated. Inter-herd heritabilities of 1,060 infrared wavenumbers ranged from 0 to 0.63, indicating that the genetic background of infrared wavenumbers differs considerably. The majority of the wavenumbers have moderate to high inter-herd heritabilities ranging from 0.20 to 0.60. The diacylglycerol O-acyltransferase 1 (DGAT1), stearoyl-CoA desaturase (SCD1), κ-casein (CSN3), and β-lactoglobulin (LGB) polymorphisms are known to have a large effect on milk composition, and therefore we studied the effects of these polymorphisms on infrared wavenumbers. The DGAT1 polymorphism had highly significant effects on many wavenumbers. In contrast, the SCD1 polymorphism did not significantly affect any of the wavenumbers. The SCD1 is known to have a strong effect on the content of C10:1, C12:1, C14:1, and C16:1 fatty acids. Therefore, these results suggest that FTIR spectra contain little direct information on these monounsaturated fatty acids. The CSN3 and LGB polymorphisms had significant effects on a few wavenumbers. Differences between herds explained 10 to 25% of the total variance for most wavenumbers. This suggests that the wavenumbers of milk FTIR spectra are indicative for differences in feeding and management between herds. The wavenumbers between 1,619 and 1,674cm(-1) and between 3,073 and 3,667cm(-1) are strongly influenced by water absorption and usually excluded when setting up prediction equations. However, we found that some of the wavenumbers in the water absorption region are affected by the DGAT1 polymorphism and lactation stage. This suggests that these wavenumbers contain useful information regarding milk composition.

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“…In nutritional metabolomics PLS-DA is the favourite classifi cation method due to its sensitivity to reveal hidden patterns related to diets effects that are diffi cult to be revealed by unsupervised techniques (ANDERSEN et al, 2014). Other recent studies include the authentication of geographical origin of palm oils based on GC-MS profi les of triacylglycerols (RUIZ-SAMBLÁS et al, 2013) and differentiation of NIR spectra of cow milk samples based on farm altitude, different feeding regimes, and different breeding systems (VALENTI et al, 2013). The combination of NIR spectroscopy and PLS-DA classifi cation have also been proposed to augment the control of GMO foods by allowing high-throughput screening and discrimination of transgenic potato line (LeETR2) from its parental non-transgenic lines (XIE et al, 2007).…”

Section: Partial Least Squares -Discriminant Analysis (Pls-da)mentioning

confidence: 99%

Trends in the application of chemometrics to foodomics studies

Khakimov¹,

Gürdeniz²,

Engelsen³

2015

Acta Alimentaria

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There is an ever-increasing trend in advanced food analysis and foodomics to use more and more sophisticated analytical platforms that generate large and complex data structures, which in turn require more and more sophisticated data analysis tools for converting data into information. The choice of multivariate chemometric methods is primarily determined by the design of the study, type of the data, and the conclusions sought. In order to validate multivariate models, scientists are required to have basic chemometric knowledge and to be familiar with the variance structure of the investigated data. This review outlines some of the key aspects of applying common chemometric methods used within foodomics and provides selected examples of current applications. The review aims to provide simple insight into various multivariate methods and to illustrate pros and cons of unsupervised and supervised methods. The main analytical platforms used in foodomics are briefl y discussed from the application point of view and the utilization of the generated data is illustrated. In addition, advanced data pre-processing tools, prior to multivariate analysis, are explained and relevant tools are demonstrated.

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Infrared spectroscopic methods for the discrimination of cows' milk according to the feeding system, cow breed and altitude of the dairy farm

Cited by 34 publications

References 30 publications

Potential of milk fatty acid composition to predict diet composition and authenticate feeding systems and altitude origin of European bulk milk

Potential of milk fatty acid composition to predict diet composition and authenticate feeding systems and altitude origin of European bulk milk

Genetic and environmental variation in bovine milk infrared spectra

Trends in the application of chemometrics to foodomics studies

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