Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques

Iñón, Fernando A.; Garrigues, Salvador; Guárdia, Miguel de la

doi:10.1016/j.aca.2004.03.014

Cited by 91 publications

(39 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Iñó n, Garrigues, and de la Guardia (2004) evaluated the major components (carbohydrates, lipids, and proteins) of different types of milk by infrared spectroscopy Fourier transform (FTIR) with attenuated total reflection (ATR) accessory and multivariate calibration for quantitative analysis. Borin and co-authors (2006) proposed a method for quantifying whey, starch, and sucrose in milk powder using near infrared spectroscopy (NIR), multivariate calibration and artificial intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

Almeida

Oliveira

Stephani³

et al. 2012

Analytical Letters

View full text Add to dashboard Cite

In this work, FT-Raman spectroscopy is explored as a rapid technique for the assessment of the milk powder quality. Based on information provided by Raman spectra of samples adulterated with starch and whey, a quantitative method is developed to identify the fraud, using Partial Least Squares regression (PLS). In regression models using PLS the results are satisfactory, and such models can be used to identify and quantify samples presenting whey and starch in milk powder at concentrations of 2.32% and 1.64% (w/w), respectively. In the whey determination, the obtained values in the PLS model of the new samples are compared with those obtained by the spectrophotometric method of acid ninhydrin. This result shows that there is no significant difference with the 95% level of confidence between the values provided by the PLS regression method and the acid ninhydrin. The present work shows Raman spectroscopy as an analytical tool which can be used in quality control of milk powder, even in fraud processes, and the calculated figures of merit such as sensitivity, accuracy, limit of detection and limit of quantification clearly demonstrate this potential use. Although the multivariate models developed are not strictly quantitative, especially for low concentrations, they can be used as screening methods for routine analysis, as showed by this work. The authors wish to thank CNPq, FAPEMIG, CAPES, and FINEP (Brazilian agencies) for financial support. This work is a collaboration research project of members of the Rede Mineira de Química (RQ-MG) supported by FAPEMIG.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

Almeida

Oliveira

Stephani³

et al. 2012

Analytical Letters

View full text Add to dashboard Cite

show abstract

“…Spectral region between 1,803 and 2,800 cm −1 (262 wave numbers) has been reported to have no specific bands or useful chemical information (Andersen et al, 2002;Iñón et al, 2004;Dagnachew et al, 2013a). Region I without these spectral variables (between 1,803-2,800 cm −1 ) is referred to as region II (i.e., region II is a subset of region I).…”

Section: Selection Of Spectral Variablesmentioning

confidence: 99%

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Belay

Dagnachew

Kowalski

et al. 2017

Journal of Dairy Science

View full text Add to dashboard Cite

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

show abstract

“…These attributes make Raman spectroscopy especially well-suited to milk powder analysis. With regard to NIR, there have been a number of studies that analyse milk products for authenticity (Iñón et al 2004; Souza et al 2011; Botros et al 2013; Harnly et al 2014). Botros et al (2013) analysed 41 SMP and NFDM samples collected using diffuse reflectance NIR, and the milk powder variance was characterised using PCA and varimax rotation.…”

Section: Introductionmentioning

confidence: 99%

Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity

Karunathilaka

Farris

Mossoba

et al. 2016

Food Additives & Contaminants: Part A

View full text Add to dashboard Cite

There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.

show abstract

Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques

Cited by 91 publications

References 19 publications

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity

Contact Info

Product

Resources

About