Dataset Information

Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods.

ABSTRACT: Macronutrient contents in human milk are the common basis for estimating these nutrient requirements for both infants and lactating women. A mid-infrared human milk analyser (HMA, Miris, Sweden) was recently developed for determining macronutrient levels. The purpose of the study is to compare the accuracy and precision of HMA method with fresh milk samples in the field studies with chemical methods with frozen samples in the lab. Full breast milk was collected using electric pumps and fresh milk was analyzed in the field studies using HMA. All human milk samples were thawed and analyzed with chemical reference methods in the lab. The protein, fat and total solid levels were significantly correlated between the two methods and the correlation coefficient was 0.88, 0.93 and 0.78, respectively (p ?-1 vs 1.2 g 100 mL^-1 and 3. 7 g 100 mL^-1 vs 3.2 g 100 mL^-1 , respectively, p ?? 0.05). There was no statistically significant difference in the mean total solid concentration (12.2 g 100 mL^-1 vs 12.3 g 100 mL^-1 , p ?>? 0.05). Overall, HMA might be used to analyze macronutrients in fresh human milk with acceptable accuracy and precision after recalibrating fat and protein levels of field samples.

SUBMITTER: Zhu M

PROVIDER: S-EPMC6866206 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods.

Zhu Mei M Yang Zhenyu Z Ren Yiping Y Duan Yifan Y Gao Huiyu H Liu Biao B Ye Wenhui W Wang Jie J Yin Shian S

Maternal & child nutrition 20160223 1

Macronutrient contents in human milk are the common basis for estimating these nutrient requirements for both infants and lactating women. A mid-infrared human milk analyser (HMA, Miris, Sweden) was recently developed for determining macronutrient levels. The purpose of the study is to compare the accuracy and precision of HMA method with fresh milk samples in the field studies with chemical methods with frozen samples in the lab. Full breast milk was collected using electric pumps and fresh mil ...[more]

PMID: 26914990

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Project description:Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm-1 and 3017~2823 cm-1/1805~1734 cm-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

Project description:BackgroundFourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.ResultsSeparate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.ConclusionsThis study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Dataset Information

Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods.

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Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods.

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