Estimating Fatty Acid Content in Cow Milk Using Mid-Infrared Spectrometry

Soyeurt, Hélène; Dardenne, Pierre; Dehareng, Frédéric; Lognay, Georges; Veselko, Didier; Marlier, M.; Bertozzi, Carlo; Mayeres, Patrick; Gengler, Nicolas

doi:10.3168/jds.s0022-0302(06)72409-2

Cited by 235 publications

(308 citation statements)

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“…In general, FAs with higher content in milk can be predicted more accurately than milk with a lower FA content (Soyeurt et al, 2006 andRutten et al, 2009). In this study, predictions of FA with high content in milk (.1 g/dl milk) were also highly accurate (validation R 2 .0.80); however, 7 of the total 11 FAs with lower content in milk (,1 g/dl milk) were predicted to be highly accurate by means of validation R 2 .…”

Section: Discussionmentioning

confidence: 99%

“…For the other FAs, of which the calibration equations are validated in this study, the separation during the GC analysis was similar. The FA traits were predicted on the basis of milk (g/dl) because these predictions are more accurate than on the basis of fat (g/100 g; Soyeurt et al, 2006;Rutten et al, 2009;Soyeurt et al, 2011). The accuracy of the RobustMilk predictions was evaluated using the root mean squared error of prediction (SEV), the coefficient of determination (validation R 2 ) and the ratio of the s.d.…”

Section: Validation Of Milk Fatty Acid Predictionmentioning

confidence: 99%

“…This method, however, is expensive and time consuming and therefore less suitable for extensive and regular recording. Another method of analyzing milk fat composition, MIR, is rapid and less expensive in case of extensive use (Wilson and Tapp, 1999;Soyeurt et al, 2006). MIR is routinely used in milk recording schemes to measure lactose, urea, total fat and protein percentages in bovine milk (Etzion et al, 2004;Bobe et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…MIR is routinely used in milk recording schemes to measure lactose, urea, total fat and protein percentages in bovine milk (Etzion et al, 2004;Bobe et al, 2007). MIR was used by Soyeurt et al (2006 and and Rutten et al (2009) to estimate calibration equations predicting the FA concentrations in milk (g/dl of milk) and milk fat (g/100 g of fat), and these equations were subsequently validated. In these studies, the predictions have low accuracy for FAs that are present in low concentrations, such as the trans and unsaturated 14, 16 and 18 FAs.…”

Section: Introductionmentioning

confidence: 99%

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Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

Eijndhoven

Soyeurt

Dehareng

et al. 2013

Animal

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The aim of this study was to investigate the accuracy to predict detailed fatty acid (FA) composition of bovine milk by mid-infrared spectrometry, for a cattle population that partly differed in terms of country, breed and methodology used to measure actual FA composition compared with the calibration data set. Calibration equations for predicting FA composition using mid-infrared spectrometry were developed in the European project RobustMilk and based on 1236 milk samples from multiple cattle breeds from Ireland, Scotland and the Walloon Region of Belgium. The validation data set contained 190 milk samples from cows in the Netherlands across four breeds: Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed (GWH) and Jersey (JER). The FA measurements were performed using gas-liquid partition chromatography (GC) as the gold standard. Some FAs and groups of FAs were not considered because of differences in definition, as the capillary column of the GC was not the same as used to develop the calibration equations. Differences in performance of the calibration equations between breeds were mainly found by evaluating the standard error of validation and the average prediction error. In general, for the GWH breed the smallest differences were found between predicted and reference GC values and least variation in prediction errors, whereas for JER the largest differences were found between predicted and reference GC values and most variation in prediction errors. For the individual FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0 and 16:0 and the groups' saturated FAs, short-chain FAs and medium-chain FAs, predictions assessed for all breeds together were highly accurate (validation R 2 . 0.80) with limited bias. For the individual FAs cis-14:1, cis-16:1 and 18:0, the calibration equations were moderately accurate (R 2 in the range of 0.60 to 0.80) and for the individual FA 17:0 predictions were less accurate (R 2 , 0.60) with considerable bias. FA concentrations in the validation data set of our study were generally higher than those in the calibration data. This difference in the range of FA concentrations, mainly due to breed differences in our study, can cause lower accuracy. In conclusion, the RobustMilk calibration equations can be used to predict most FAs in milk from the four breeds in the Netherlands with only a minor loss of accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Validation Of Milk Fatty Acid Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

Eijndhoven

Soyeurt

Dehareng

et al. 2013

Animal

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“…Therefore, research must be undertaken on developing phenomic tools for rapid phenotyping at a low cost. An example is the use of mid-infrared spectroscopy of milk in the estimation of milk fatty acid content (Soyeurt et al, 2006) which would normally require the resource intensive approach of gas chromatography. Considerable resources have been expended on comparative genomics, but there is also a justification for 'comparative phenomics' where phenotypic databases on different model organisms are available and comparable in a single database and the phenotypes of orthologous genes from different organisms can be directly compared.…”

Section: Challenges and Future Researchmentioning

confidence: 99%

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Berry

Meade

Mullen

et al. 2011

Animal

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Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists -and they will also be discussed.

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Chemometrics in Biospectroscopy

Köhler¹,

Afseth²,

Martens³

2001

Handbook of Vibrational Spectroscopy

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Vibrational spectroscopic techniques have been used for decades for predicting chemical composition of biological samples. For the establishment of prediction models, multivariate calibration techniques are often applied, since the use of single variables or combinations of few variables are not sufficient or robust enough. Multivariate techniques need to handle covariation among variables. We observe an especially high degree of covariation between variables in vibrational spectroscopy: Covariation is present (i) among neighbouring channels providing signals from the same spectral band, (ii) among spectral bands that are due to the same chemical bond, and (iii) finally among chemical bonds that arise from the same biomolecule. Consequently, the underlying dimensions in a given data set are rather few compared to the number of variables (channels) measured, also in cases where the number of samples is quite large. Multivariate methods based on latent variables that relate directly to underlying phenomena and mechanisms, are especially popular in vibrational spectroscopy. By using latent variables, the variations of objects can be studied with respect to these underlying phenomena and variation patterns can be studied in the light of chemical bonds and biomolecules. Another data modeling issue relates to the preprocessing of spectra: Vibrational spectra of biological materials are plagued by unwanted variability effects such as scatter effects in infrared spectroscopy or fluorescence effects in Raman spectroscopy. Calibration models often become simpler and even better when these effects are removed from the spectra before the calibration model is established. In addition, the interpretation of the results in terms of chemical bands is simpler and more reliable when the spectra are preprocessed. In this chapter, several issues related to data analysis of vibrational spectra of biological samples are discussed: (i) model‐based preprocessing techniques based on extended multiplicative signal correction (EMSC) are presented. Different variants of EMSC are discussed and a new extension of EMSC especially suited for the correction of fluorescence effects in Raman spectroscopy is introduced. (ii) Different aspects related to multivariate calibration and classification are discussed with a special focus on partial least squares regression (PLSR) and validation.

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Estimating Fatty Acid Content in Cow Milk Using Mid-Infrared Spectrometry

Cited by 235 publications

References 9 publications

Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Chemometrics in Biospectroscopy

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