Accuracy of Fourier transform infrared spectrometry in determination of casein in dairy cows' milk

Sørensen, Lambert K.; Lund, Mads; Juul, Bent

doi:10.1017/s0022029903006435

Cited by 22 publications

(17 citation statements)

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“…This result is in contrast with other reports that indicate a generally higher casein number for Jersey milk (Ng-Kwai-Hang et al 1986;NgKwaiHang and Kim 1996;Sorensen et al 2003). Even though the lactation stage is known to affect the casein number of cow's milk (Barbano et al 1991;Blake et al 1980), this was in the present analysis not found to be significant, perhaps because the relationship between casein number and lactation stage is complex and cannot be assumed to be the same for all breeds.…”

Section: Discussioncontrasting

confidence: 85%

Variations in coagulation properties of cheese milk from three Danish dairy breeds as determined by a new free oscillation rheometry-based method

Frederiksen

Hammershøj

Bakman

et al. 2011

Dairy Science & Technol.

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Section: Discussioncontrasting

confidence: 85%

Variations in coagulation properties of cheese milk from three Danish dairy breeds as determined by a new free oscillation rheometry-based method

Frederiksen

Hammershøj

Bakman

et al. 2011

Dairy Science & Technol.

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“…Similarly, MIR indicators of protein fractions such as casein and lactoglobulin (Barbano and Dellavalle, 1987;Sorensen et al, 2003) have been obtained. As it is possible to determine the total content of protein and some specific milk proteins, it could be possible to predict the LTF content of milk using MIR as LTF is a natural milk glycoprotein.…”

Section: Introductionmentioning

confidence: 99%

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Soyeurt

Bastin

Colinet

et al. 2012

Animal

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Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n 5 110), Ireland (n 5 1658) and Scotland (n 5 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS 3 LTF effects were significant (P , 0.001 and P 5 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r 5 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin -Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events -external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

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“…A espectroscopia no infravermelho já foi aplicada em leite para determinação de caseína 44 , proteína e lactose 45 , bem como para classificação 46 de leite e em casos de adulteração com gordura 47 , e com soro em leite em pó desnatado.…”

Section: A Espectroscopia Por Reflectância Difusa No Infravermelho Prunclassified

LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR

Ferrão

Borin²,

Maretto³

et al. 2007

Quím. Nova

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. Leastsquares support vector machines (LS-SVM) were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R 2 , RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.Keywords: support vector machines; multivariate regression; powdered milk INTRODUÇÃORecentemente, as redes neurais empregando perceptrons de múltiplas camadas e as redes neurais de função base radial vêm sendo amplamente utilizadas em diversos campos da ciência, como por ex.: na teoria de controle, no processamento de sinais e em problemas de modelagem linear e não linear 1,2 . Neste contexto, uma promissora ferramenta denominada máquina de vetor de suporte (SVM) 1-4 tem sido utilizada em problemas de classificação e de modelamento. Este algoritmo, baseado na aprendizagem de máquinas de vetores de suporte, foi recentemente introduzido e aplicado em problemas já conhecidos nas áreas da química 5-9 , farmacêutica 10 , microbiológica 11 e mais amplamente na área médica [12][13][14][15][16][17] .Entretanto, na área da quimiometria, à qual se referem os problemas de quantificação (regressão multivariada), as aplicações encontradas na literatura ainda são escassas como a exemplo das aplicações das máquinas de vetor de suporte com mínimos quadrados (LS-SVM) com o emprego de espectros Raman para monitorar reações de copolimerização e de espectros NIR na quantificação de misturas ternárias realizadas por Thissen et al. 18,19 na comparação do desempenho da LS-SVM com métodos clássicos de regressão publicado por Chauchard et al. 20 visando a previsão da acidez total em uvas, e ainda, do trabalho de Cogdill et al. 21 que emprega espectroscopia NIR para estimar propriedades da madeira.Neste sentido, o presente artigo vem apresentar à comunidade científica os principais fundamentos que tornam as máquinas de vetor de suporte excelentes alternativas aos já consagrados méto-dos de regressão multivariada, destacando-se sua capacidade de generalização e elevada robustez. Como aplicação desta nova ferramenta quimiométrica são apresentados e discutidos os resultados empregando espectros NIR de amostras de leite em pó puro e com adição de amido, soro de leite e/ou sacarose. MÁQUINAS DE VETORES DE SUPORTE (SVM)As máquinas de vetores de suporte (SVM -"Support Vector Machines") usadas para regressão são uma generalização do algoritmo "Generalized Portrait" desenvolvido, na Rússia, por Vapnik, Lerner e Chervonenkis na década de 60 22 . Uma abordagem similar, empregando programação linear ao invés de programação quadrática, foi desenvolvida na mesma época nos Estados Unidos 23 . A estratégia de aprendizado baseada em vetores de su...

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Accuracy of Fourier transform infrared spectrometry in determination of casein in dairy cows' milk

Cited by 22 publications

References 0 publications

Variations in coagulation properties of cheese milk from three Danish dairy breeds as determined by a new free oscillation rheometry-based method

Variations in coagulation properties of cheese milk from three Danish dairy breeds as determined by a new free oscillation rheometry-based method

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR

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