Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy

Colostrum quality and volume are fundamental for calves because it is the primary supplier of antibodies and the first source of energy, carbohydrates, lipids, proteins, minerals, and vitamins for the newborn. Assessing the detailed composition (i.e., AA and mineral content) of bovine colostrum (BC) on-line and at a reasonable cost would help dairy stakeholders such as farmers or veterinarians for precision feeding purposes and industries producing preparations containing BC such as foodstuff, supplements, and medicaments. In the present study we evaluated mid- (MIRS) and near-infrared spectroscopy (NIRS) prediction ability for AA and mineral composition of individual BC. Second, we the investigated the major factors affecting the phenotypic variability of such traits also evaluating the correlations with the Ig concentration. Results demonstrated that MIRS and NIRS were able to provide sufficiently accurate predictions for all the AA. The coefficient of determination in external validation (R2V) fell, in fact, within the range of 0.70 to 0.86, with the exception of Ile, His, and Met. Only some minerals reached a sufficient accuracy (i.e., Ca, P, S, and Mg; R2V ≥ 0.66) using MIRS, and also S (R2V = 0.87) using NIRS. Phenotypically, both parity and calving season affected the variability of these BC composition traits. Heifers' colostrum was the one with the greatest concentration of Ca and P, the 2 most abundant minerals. These minerals were however very low in cows calving in summer compared with the rest of the year. The pattern of AA across parities and calving season was not linear, likely because their variability was scarcely (or not) affected by these effects. Finally, samples characterized by high IgG concentration were those presenting on average greater concentration of AA. Findings suggest that infrared spectroscopy has the potential to be used to predict certain AA and minerals, outlining the possibility of implementing on-site analyses for the evaluation of the broad-sense BC quality.

Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy

In the present study, the focus was to evaluate the potential of three spectroscopic techniques (Middle Infrared -MIR-, fluorescence, and multispectral imaging -MSI-) to check the level of adulteration in camel milk with goat, cow, and ewe milks. Camel milk was adulterated with goat, ewe, and cow milks, respectively, at 6 different levels viz. 0.5, 1, 2, 5, 10, and 15%. After preprocessing the data with standard normal variate (SNV), multiplicative scattering correction (MSC), and normalization (area under spectrum = 1), partial least squares regression (PLSR) and partial least squares discriminant analysis (PLSDA) were used to predict the adulteration level and their belonging group, respectively. The PLSR and PLSDA models, validated using external data, highlighted that fluorescence spectroscopy was the most accurate technique giving a Rp² ranging between 0.63 and 0.96 and an accuracy ranging between 67 and 83%. However, no technique has allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BACKGROUND

Fourier-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.

RESULTS

Separate 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.

CONCLUSIONS

This 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.

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α_S1-casein (CN), α_S2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R²) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R² values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R² remained comparable, however, the root mean squared errors increased substantially (4-10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.

Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage

The application of spectroscopic techniques can help in alleviating problems encountered during the processing of milk and dairy products. Indeed, traditional analytical methods (e.g., physicochemical measurements, sensory, chromatography) are relatively expensive, time-consuming, and require chemicals and sophisticated analytical equipment, and skilled operators. Hence, there is a need to develop faster and less costly methods for accurately monitoring changes in the quality of milk and other dairy products during processing and storage.Many nondestructive and noninvasive instrumental techniques are available for inline and online monitoring of food. These include fluorescence spectroscopy, mid-infrared (MIR), near-infrared (NIR), nuclear magnetic resonance (NMR), etc. These techniques are usually used in combination with chemometric tools a to explore the information present in spectral data.This review article will discuss the potential of the above-mentioned spectroscopic techniques for monitoring chemical modifications of dairy products and the prediction of their functional properties during processing. The advantages and disadvantages of each technique are also discussed in this review. Finally, some conclusions are drawn, and the future trends of these methods are presented.

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.

Predicting milk fatty acids and energy balance of dairy cows in Australia using milk mid-infrared spectroscopy

Mid-infrared spectroscopy (MIRS) is traditionally used for analysing milk fat, protein and lactose concentrations in dairy production, but there is growing interest in using it to predict difficult, or expensive-to-measure, phenotypes on a large scale. The resulting prediction equations can be applied to MIRS data from commercial herd-testing, to facilitate management and feeding decisions, or for genomic selection purposes. We investigated the ability of MIRS of milk samples to predict milk fatty acids (FAs) and energy balance (EB) of dairy cows in Australia. Data from 240 Holstein lactating cows that were part of two 32-day experiments, were used. Milk FAs were measured twice during the experimental period. Prediction models were developed using partial least-square regression with a 10-fold cross-validation. Measures of prediction accuracy included the coefficient of determination (R2cv) and root mean-square error. Milk FAs with a chain length of ≤16 were accurately predicted (0.89 ≤ R2cv ≤ 0.95), while prediction accuracy for FAs with a chain length of ≥17 was slightly lower (0.72 ≤ R2cv ≤ 0.82). The accuracy of the model prediction was moderate for EB, with the value of R2cv of 0.48. In conclusion, the ability of MIRS to predict milk FAs was high, while EB was moderately predicted. A larger dataset is needed to improve the accuracy and the robustness of the prediction models.

Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression

Simple Summary

Considered that all sheep milk in Italy is destined for cheese processing, traits describing rennet coagulation aptitude should be among the most important selection goals for dairy breeds. To reduce the costs and logistics related to the large-scale recording of these traits, mid-infrared (MIR) spectroscopy could be conveniently used to generate reliable predictions without any additional cost. The aims of this research were to predict the milk coagulation properties (MCP) and individual cheese yield (ILCY) in sheep by MIR spectrometry using partial least squares regression (PLS), and to compare different data pre-treatment procedures. The prediction results observed in the present study, although moderate, suggest the possibility of adding novel phenotypes (e.g., MCP and ILCY) in breeding schemes for dairy sheep breeds. Mid-infrared spectroscopy coupled with PLS regression could allow the prediction of phenotypes at the population level without additional costs.

Abstract

The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.

The use of mid-infrared spectra to map genes affecting milk composition

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL.

Prediction of blood β-hydroxybutyrate content and occurrence of hyperketonemia in early-lactation, pasture-grazed dairy cows using milk infrared spectra

The objective of this study was to evaluate the ability of milk infrared spectra to predict blood β-hydroxybutyrate (BHB) concentration for use as a management tool for cow metabolic health on pasture-grazed dairy farms and for large-scale phenotyping for genetic evaluation purposes. The study involved 542 cows (Holstein-Friesian and Holstein-Friesian × Jersey crossbreds), from 2 farms located in the Waikato and Taranaki regions of New Zealand that operated under a seasonal-calving, pasture-based dairy system. Milk infrared spectra were collected once a week during the first 5 wk of lactation. A blood "prick" sample was taken from the ventral labial vein of each cow 3 times a week for the first 5 wk of lactation. The content of BHB in blood was measured immediately using a handheld device. After outlier elimination, 1,910 spectra records and corresponding BHB measures were used for prediction model development. Partial least square regression and partial least squares discriminant analysis were used to develop prediction models for quantitative determination of blood BHB content and for identifying cows with hyperketonemia (HYK). Both quantitative and discriminant predictions were developed using the phenotypes and infrared spectra from two-thirds of the cows (randomly assigned to the calibration set) and tested using the remaining one-third (validation set). A moderate accuracy was obtained for prediction of blood BHB. The coefficient of determination (R²) of the prediction model in calibration was 0.56, with a root mean squared error of prediction of 0.28 mmol/L and a ratio of performance to deviation, calculated as the ratio of the standard deviation of the partial least squares model calibration set to the standard error of prediction, of 1.50. In the validation set, the R² was 0.50, with root mean squared error of prediction values of 0.32 mmol/L, which resulted in a ratio of performance to deviation of 1.39. When the reference test for HYK was defined as blood concentration of BHB ≥1.2 mmol/L, discriminant models indicated that milk infrared spectra correctly classified 76% of the HYK-positive cows and 82% of the HYK-negative cows. The quantitative models were not able to provide accurate estimates, but they could differentiate between high and low BHB concentrations. Furthermore, the discriminant models allowed the classification of cows with reasonable accuracy. This study indicates that the prediction of blood BHB content or occurrence of HYK from milk spectra is possible with moderate accuracy in pasture-grazed cows and could be used during routine milk testing. Applicability of infrared spectroscopy is not likely suited for obtaining accurate BHB measurements at an individual cow level, but discriminant models might be used in the future as herd-level management tools for classification of cows that are at risk of HYK, whereas quantitative models might provide large-scale phenotypes to be used as an indicator trait for breeding cows with improved metabolic health.

A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows

Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm^-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm^-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm^-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm^-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.

Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm(-1) was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale.

Short communication: Mid-infrared spectroscopy prediction of fine milk composition and technological properties in Italian Simmental

The objective of this study was to evaluate the ability of mid-infrared predictions of fine milk composition and technological traits to serve as a tool for large-scale phenotyping of the Italian Simmental population. Calibration equations accurately predicted the fatty acid profile of the milk, but we obtained moderate or poor accuracy for detailed protein composition, coagulation properties, curd yield and composition, lactoferrin, and concentration of major minerals. To evaluate the role of infrared predictions as indicator traits of fine milk composition in indirect selective breeding programs, the genetic parameters of the traits predicted using mid-infrared spectra need to be estimated.

Short communication: Selecting the most informative mid-infrared spectra wavenumbers to improve the accuracy of prediction models for detailed milk protein content

The objective of this study was to investigate the ability of mid-infrared spectroscopy (MIRS) to predict protein fraction contents of bovine milk samples by applying uninformative variable elimination (UVE) procedure to select the most informative wavenumber variables before partial least squares (PLS) analysis. Reference values (n=114) of protein fractions were measured using reversed-phase HPLC and spectra were acquired through MilkoScan FT6000 (Foss Electric A/S, Hillerød, Denmark). Prediction models were built using the full data set and tested with a leave-one-out cross-validation. Compared with MIRS models developed using standard PLS, the UVE procedure reduced the number of wavenumber variables to be analyzed through PLS regression and improved the accuracy of prediction by 6.0 to 66.7%. Good predictions were obtained for total protein, total casein (CN), and α-CN, which included αS1- and αS2-CN; moderately accurate predictions were observed for κ-CN and total whey protein; and unsatisfactory results were obtained for β-CN, α-lactalbumin, and β-lactoglobulin. Results indicated that UVE combined with PLS is a valid approach to enhance the accuracy of MIRS prediction models for milk protein fractions.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation.

Genetic and environmental information in goat milk Fourier transform infrared spectra

Fourier transform infrared (FTIR) spectroscopy is often used in prediction of major milk components in genetic evaluation of dairy animals. Until now genetic variability of goat milk FTIR spectra has only been known indirectly through their contribution to the major milk components. In this study, genetic and environmental components of goat milk FTIR spectra were examined directly. A data set containing 83,858 milk FTIR spectral observations belonging to 29,320 Norwegian dairy goats of 271 herds was used for the study. Principal components analysis was applied on both unprocessed and preprocessed spectral data, and new traits (latent traits) were defined because a multitrait analysis of all spectral variables for variance components could not be done. Eight and 7 latent variables, explaining approximately 99% of the total unprocessed and preprocessed spectral variation, respectively, were kept from the principal components analysis for genetic analysis. Genetic and environmental variance components were estimated for the latent traits using restricted maximum likelihood. Genetic-to-total phenotypic variance ratios (heritabilities) of the latent traits were between 0.011 and 0.285 for the unprocessed spectra and between 0.135 and 0.262 for the preprocessed spectra. The estimated variance components for the latent traits were back transformed to the spectral variables. Heritabilities of these spectral variables ranged from 0.018 to 0.408 and variance ratios of the permanent environmental effects of goats were between 0.002 and 0.184 of the phenotypic spectral variation. High-to-moderate heritabilities were observed in particular in spectral regions related to major milk components (fat, lactose, and protein): between 1,030 and 1,300 cm(-1), 1,500 and 1,600 cm(-1), 1,700 and 1,800 cm(-1), and 2,800 and 3,000 cm(-1). Our results confirmed that a substantial amount of genetic variation exists in goat milk FTIR spectra. Not all spectral variations are of genetic origin; some FTIR regions are highly influenced by herd test-day variation. The study also pointed out the possibility of using FTIR spectra as a monitoring tool in herd management.

Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk

Mid-infrared spectroscopy (MIR) is used to predict fatty acid (FA) composition of individual milk samples (n=267) of Brown Swiss cows. FAs were analyzed by gas chromatography as a reference method. Samples were scanned (4000 to 900 cm-1) by MIR, and predictive models were developed using modified partial least squares regressions with full cross-validation. The methods using a first derivative or multiplicative scatter corrected plus first derivative resulted, on average, in the best predictions. Coefficients of correlation between measured and predicted C8:0, C10:0, C12:0, C14:0, anteiso-C17:0, c9-C18:1, and medium- and long-chain FA, and saturated, monounsaturated and unsaturated FA ranged from 0.71 to 0.77, suggesting that prediction models can be implemented in milk recording schemes to routinely collect information on FA composition from the whole Brown Swiss population for breeding purposes.

Analysis of aggregation and dispersion states of small particles in concentrated suspension by using diffused photon density wave spectroscopy

Recently, inspection of chemical components like melamine plays an important role in food industry for the food safety. However, conventional analyzing methods require a lot of preparations and much of time. We propose a real-time method for investing the mean particle size and number density of concentrated suspensions without any preparations. There are several techniques of analyzing concentrated suspensions. Laser light scattering (LLS) and dynamic light scattering (DLS) are used to measure the particle size. Fourier transform infrared spectroscopy (FT-IR) is used to measure the number density. They have been successfully describing the physical and dynamics characteristics near single light scattering regions. On the other hand, they require samples to be highly diluted. This is a major disadvantage in studying real concentrated suspensions, or phenomena such as gelation. Our proposed method diffused photon density wave spectroscopy (DPDWS) is based on the multiple light scattering theory. Thus, DPDWS measures the particle size and number density of the concentrated suspensions without dilution in real time. Diffused photon density wave (DPDW) is a spherical energy wave generated from the intensity-modulated point light source in the concentrated suspensions. The absorption and scattering coefficients can be simultaneously obtained from the amplitude and phase of DPDW propagated through the concentrated suspensions. Furthermore, the particle size and number density of nanoparticles can be estimated from the obtained optical properties using the absorption and the scattering theory. In this study, we qualitatively estimated the gelation of milk from the measured particle size and number density by use of DPDWS.

Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy

This study investigated the potential application of mid-infrared spectroscopy (MIR 4,000-900 cm(-1)) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-infrared spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a(30), mm), TA (SH degrees/50 mL; SH degrees = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm(-1), 3,040 to 1,700 cm(-1), and 4,000 to 3,470 cm(-1). The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a(30), range 58 mm), 0.25 SH degrees/50 mL (TA, range 3.58 SH degrees/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R(2) = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R(2) = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

Potential and limitation of mid-infrared attenuated total reflectance spectroscopy for real time analysis of raw milk in milking lines

Real-time information about milk composition would be very useful for managing the milking process. Mid-infrared spectroscopy, which relies on fundamental modes of molecular vibrations, is routinely used for off-line analysis of milk and the purpose of the present study was to investigate the potential of attenuated total reflectance mid-infrared spectroscopy for real-time analysis of milk in milking lines. The study was conducted with 189 samples from over 70 cows that were collected during an 18 months period. Principal component analysis, wavelets and neural networks were used to develop various models for predicting protein and fat concentration. Although reasonable protein models were obtained for some seasonal sub-datasets (determination errors <~0·15% protein), the models lacked robustness and it was not possible to develop a model suitable for all the data. Determination of fat concentration proved even more problematic and the determination errors remained unacceptably large regardless of the sub-dataset analyzed or of the spectral intervals used. These poor results can be explained by the limited penetration depth of the mid-infrared radiation that causes the spectra to be very sensitive to the presence of fat globules or fat biofilms in the boundary layer that forms at the interface between the milk and the crystal that serves both as radiation waveguide and sensing element. Since manipulations such as homogenisation are not permissible for in-line analysis, these results show that the potential of mid-infrared attenuated total reflectance spectroscopy for in-line milk analysis is indeed quite limited.

The characterization and differentiation of higher plants by fourier transform infrared spectroscopy

Several techniques have been used to identify and classify plants. We proposed Fourier transform infrared (FT-IR) spectroscopy, together with hierarchical cluster analysis, as a rapid and noninvasive technique to differentiate plants based on their leaf fragments. We applied this technique to three different genera, namely, Ranunculus (Ranunculaceae), Acantholimon (Plumbaginaceae), and Astragalus (Leguminoseae). All of these genera are angiosperms and include a large number of species in Turkey. Ranunculus and Acantholimon have ornamental importance, while Astragalus is an important pharmaceutical genus. The FT-IR spectra revealed dramatic differences, which indicated the variations in lipid metabolism, carbohydrate composition, and protein conformation of the genera. Moreover, cell wall polysaccharides including diverse groups could be identified for each genus. Acantholimon was found to have the highest hydrogen capacity in its polysaccharide and proteins. A higher lignin content and a lower occurrence of decarboxylation and pectin esterification reactions were appointed for Ranunculus and Astragalus compared to Acantholimon. All these results suggested that FT-IR spectroscopy can be successfully applied to differentiate genera, as demonstrated here with Ranunculus, Astragalus, and Acantholimon. In addition, we used this technique to identify the same species from different geographical regions. In conclusion, the current FT-IR study presents a novel method for rapid and accurate molecular characterization and identification of plants based on the compositional and structural differences in their macromolecules.

Determination of Protein Concentration in Raw Milk by Mid-Infrared Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.