Towards combinatorial spectroscopy: The case of minor milk fatty acids determination

Stefanov, Ivan; Baeten, Vincent; Baets, Bernard De; Fievez, Veerle

doi:10.1016/j.talanta.2013.02.034

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…The variations in intensity between the Raman peaks of cow and buffalo milk show different concentration of the same molecular structures. Likewise, the shift in the Raman peak positions represents the variation in the molecular composition .…”

Section: Resultsmentioning

confidence: 99%

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Raman‐spectroscopy‐based differentiation between cow and buffalo milk

Ullah

Khan

Ali

et al. 2017

J Raman Spectroscopy

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In this study, Raman spectroscopy together with chemometric techniques has been demonstrated to differentiate between cow and buffalo milk. The Raman spectra of milk samples of bovine contain peaks which show significant variation in intensity as well as in Raman shift positions. The outcome of results indicates that Raman spectroscopy successfully differentiates between cow and buffalo milk based on the presence and absence of β-carotene, respectively. It also shows that buffalo milk contains comparatively high fat (unsaturated/saturated) content. Principal component analysis-based algorithm further highlighted the difference and shows clear separation between the two data sets on the basis of features obtained from their Raman spectra. Consequently, β-carotene could be a good biomarker for differentiating cow and buffalo milk. Present study will help to establish the use spectroscopic technique for the detection of adulterants in milk which is of significant concern worldwide.

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Section: Resultsmentioning

confidence: 99%

“…Among them, the Raman peak at shift position 1080 cm −1 corresponds specifically to free cholesterol . Some authors also reported that the Raman peaks at 950, 1170, 1303, 1320 and 1730 cm −1 arise due to the stretching mode of ν (C═C) cis‐ fatty acids (cholesterol) .…”

Section: Resultsmentioning

confidence: 99%

Raman‐spectroscopy‐based differentiation between cow and buffalo milk

Ullah

Khan

Ali

et al. 2017

J Raman Spectroscopy

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“…The FA show different prediction accuracy based on, for example, the amount of the individual FA in milk and the way of expressing that amount (on a milk or milkfat basis); for these reasons, these traits are considered to be difficult to predict and the level of accuracy is lower than when predicting major milk components (e.g., protein or fat). In part, this is because FA make up a smaller fraction of milk and many compounds with similar chemical composition are present (Stefanov et al, 2013; De Marchi et al, 2014). Calibration FTIR is even more difficult if an FA profile (i.e., each FA as a proportion of the sum of all FA) is to be predicted.…”

Section: Introductionmentioning

confidence: 99%

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Ferragina

Campos

Vázquez

et al. 2015

Journal of Dairy Science

102

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The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict “difficult-to-predict” dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm−1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R2 value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R2 (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R2 of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.

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“…Thus, sufficient evidence is available to conclude that maternal fatty acid nutrition serves as a source to supply DHA to the infant before and after birth, with short and long-term implications for neural function [9,25]. Raman peak at 1640 cm −1 corresponds to amino acids like tryptophan (Trp) which is an indispensable amino acid and is required for the biosynthesis of proteins, serotonin and niacin that helps in infant's normal growth [26][27][28]. Tryptophan and their metabolites regulate neurobehavioral effects such as appetite, sleeping-waking-rhythm and pain perception.…”

Section: Dimensionality Reduction and Support Vector Machine (Svm)mentioning

confidence: 99%

Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

Ullah

Khan

Javaid

et al. 2018

Biomed. Opt. Express

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This study presents differentiation in milk samples of mother's feeding male and female infants using Raman spectroscopy combined with a support vector machine (SVM). Major differences have been observed in the Raman spectra of both types of milk based on their chemical compositions. Overall, it has been found that milk samples of mother's having a female infant are richer in fatty acids, phospholipids, and tryptophan. In contrast, milk samples of mother's having a male infant contain more carotenoids and saccharides. Principal component analysis and SVM further highlighted the differences between the two groups on the basis of differentiating features obtained from their Raman spectra. The SVM model with two different kernels, i.e. polynomial kernel function (order-2) and Gaussian radial basis function (RBF sigma-2), are used for gender based milk differentiations. The performance of the proposed model in terms of accuracy, precision, sensitivity, and specificity using the polynomial kernel function of order-2 have been found to be 86%, 88%, 85% and 88%, respectively.

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Towards combinatorial spectroscopy: The case of minor milk fatty acids determination

Cited by 9 publications

References 31 publications

Raman‐spectroscopy‐based differentiation between cow and buffalo milk

Raman‐spectroscopy‐based differentiation between cow and buffalo milk

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

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