The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Non-destructive diagnosis of Inflammatory Bowel Disease by near-infrared spectroscopy and aquaphotomics

Inflammatory Bowel Disease includes Crohn's Disease and Ulcerative Colitis. Currently, diagnosing involves a series of current diagnostic methods that are invasive, time-consuming, and expensive. Near-infrared spectroscopy and aquaphotomics can detect changes in biofluids and thus have the potential to diagnose disease. This study aimed to investigate the diagnostic ability of near infrared spectroscopy and aquaphotomics for Inflammatory Bowel Disease and its types. This method used blood plasma and saliva samples absorbance spectrum and multivariate analysis with the Principal Component Analysis and, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Support Vector Machine in the range 1300-1600 nm and 12 water absorbance bands in this range, separately. In the near-infrared range, total accuracy of 100% led to the separation of the healthy group and Inflammatory Bowel Disease and then the separation of the healthy group and patients with Ulcerative Colitis and Crohn's Disease. The aquaphotomics approach was used to investigate the changes in the 12 water absorbance bands and their impact on the accuracy of the diagnostic method. Aquaphotomics also detected 100% of the mentioned samples. We achieved a fast, accurate, non-invasive method based on near-infrared spectroscopy and aquaphotomics to diagnose Inflammatory Bowel Disease and its types using blood plasma or saliva samples. The current study found that monitoring blood plasma or saliva using near-infrared spectra offers an opportunity to thoroughly investigate biofluids and changes in their water spectral patterns caused by complex physiological changes due to Inflammatory Bowel Disease and its types, and to visualize these changes using aquagram.

Standardization method, testing scenario, and accuracy of the infrared prediction model affect the standardization accuracy of milk mid-infrared spectra

The widespread use of milk mid-infrared (MIR) spectroscopy for phenotype prediction has urged the application of prediction models across regions and countries. Spectra standardization is the most effective way to reduce the variability in the spectral signal provided by different instruments and labs. This study aimed to develop different standardization models for MIR spectra collected by multiple instruments, across 2 provinces of China, and investigate whether the standardization method (piecewise direct standardization, PDS, and direct standardization, DS), testing scenario (standardization of spectra collected on the same day or after 7 mo), infrared prediction model accuracy (high or low), and instrument (6 instruments from 2 brands) affect the performance of the standardization model. The results showed that the determination coefficient (R2) between absorbance values at each wavenumber provided by the primary and the secondary instruments increased from less than 0.90 to nearly 1.00 after standardization. Both PDS and DS successfully reduced spectra variation among instruments, and performed significantly better than non-standardization (P < 0.05). However, DS was more prone to overfitting than PDS. Standardization accuracy was higher when tested using spectra collected on the same time compared with those collected 7 mo after (P < 0.05), but great improvement in model transferability was obtained for both scenarios compared with the non-standardized spectra. The less accurate infrared prediction model (for C8:0 and C10:0 content) benefited the most (P < 0.05) from spectra standardization compared with the more accurate model (for total fat and protein content). For spectra collected after 7 mo from standardization, after PDS the RMSE between predictions obtained by different machines decreased on average by 86 and 94% compared with the values before standardization, for C8:0 and C10:0 respectively. The secondary instrument had no significant effect on the R2 between predictions (P > 0.05). The variation in the spectral signal provided by different instruments was successfully reduced by standardization across 2 provinces in China. This study lays the foundations for developing a national MIR spectra database to provide consistent predictions across provinces to be used in dairy farm management and breeding programs in China. Besides, this provides opportunities for data exchange and cooperation at international levels.

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows

BACKGROUND

Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios.

RESULTS

The predictive ability of these approaches was measured by the coefficient of determination (R²). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R² for blood metabolites across the three CV scenarios, especially with the herd-out CV: R² values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators.

CONCLUSIONS

Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.

Predicting nitrogen use efficiency, nitrogen loss and dry matter intake of individual dairy cows in late lactation by including mid-infrared spectra of milk samples

BACKGROUND

Nitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment. Meanwhile, the increasing demand for dairy products will result in more pollution if N losses are not controlled. Therefore, a more efficient, and environmentally friendly production system is needed, in which nitrogen use efficiency (NUE) of dairy cows plays a key role. To genetically improve NUE, extensively recorded and cost-effective proxies are essential, which can be obtained by including mid-infrared (MIR) spectra of milk in prediction models for NUE. This study aimed to develop and validate the best prediction model of NUE, nitrogen loss (NL) and dry matter intake (DMI) for individual dairy cows in China.

RESULTS

A total of 86 lactating Chinese Holstein cows were used in this study. After data editing, 704 records were obtained for calibration and validation. Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait. Results showed that the coefficient of determination (R²) of the best model in within-herd validation was 0.66 for NUE, 0.58 for NL and 0.63 for DMI. For external validation, reasonable prediction results were only observed for NUE, with R² ranging from 0.58 to 0.63, while the R² of the other two traits was below 0.50. The infrared waves from 973.54 to 988.46 cm^-1 and daily milk yield were the most important variables for prediction.

CONCLUSION

The results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations. The model of NUE could be used for the datasets that are similar to the calibration dataset. The prediction models for NL and 3-day moving average of DMI (DMI_a) generated lower accuracies in within-herd validation. Results also indicated that information of MIR spectra variables increased the predictive ability of models. Additionally, pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external validation. These models will be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected.

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

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.

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk

Simple Summary

Mid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health.

Abstract

Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

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.

Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k₂₀), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCT_eq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CF_max), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CF_max (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCT_eq (0.53 and 0.41), t_max (0.38 and 0.48). Log MAC count was correlated with k₂₀ (+0.34), and log PMN-LYM count was correlated with CF_max (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for k-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CF_t) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CF_t parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CF_t parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm^-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R²_VAL), the root mean square error of validation (RMSE_VAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R²_VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSE_VAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.

Genetic and Non-Genetic Variation of Milk Total Antioxidant Activity Predicted from Mid-Infrared Spectra in Holstein Cows

Simple Summary

The total antioxidant activity (TAA) of food is important for human health and results from the contribution of different nutraceutical compounds. Direct determination of TAA in food is time-consuming and expensive. Infrared technologies allow the prediction of difficult-to-measure traits with certain accuracy in several organic matrices, including TAA of bovine milk. In order to understand the background of TAA and identify potential strategies to improve this feature in bovine milk, we explored its non-genetic sources of variation and estimated heritability and correlations with traits of economic interest in a large database of Holstein cows.

Abstract

Food antioxidants enhance products shelf life and stability during technological treatments through the maintenance of their physical and chemical properties. Moreover, they are endowed with several positive effects on human health, including cell membranes preservation, enzyme functionality, and DNA integrity. Milk has been described in relation to a wide array of fat soluble and water-soluble antioxidant compounds, in particular vitamin A, C, and E, lactoferrin and peptides derived from casein and whey proteins. The total antioxidant activity (TAA) of milk is a novel and scarcely explored trait, defined as the sum of antioxidant contributions of the aforementioned compounds. On this background, the aims of the present study were to investigate the variability of milk TAA on a large scale exploiting predictions obtained through mid-infrared (MIR) spectroscopy and to estimate genetic parameters of this trait in Holstein cows. Individual milk samples were collected between January 2011 and December 2018 during the routine milk recording procedure. Samples were analysed for gross composition through MIR spectroscopy and MIR spectra were stored. Milk TAA was then predicted (pTAA) from the stored milk MIR spectra (111,653 test-day records of 9519 cows in 344 herds) using the previously developed prediction model; considering the prediction accuracy, pTAA might be considered a proxy of the TAA determined through the reference method. Overall, pTAA averaged 7.16 mmoL/L of Trolox equivalents, showed a nadir around 40 days after calving and increased thereafter, following a linear trend up to the end of lactation. The lowest pTAA was observed in milk sampled from June to September. Milk pTAA was heritable (0.401 ± 0.015) and genetically associated to fat yield (0.366 ± 0.049), crude protein (CP) yield (0.238 ± 0.052), fat percentage (0.616 ± 0.022) and CP percentage (0.754 ± 0.015). The official selection index of Italian Holstein put the 49% of the emphasis on fat and protein yield and percentage; therefore, it derives that an indirect favourable selection for milk pTAA should be already in progress in Italian Holstein population.

Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples

Simple Summary

Information about fatty acid proportions in milk fat is important for many purposes, such as animal breeding, animal health control, as well as human nutrition. The routine methods for determining fatty acid proportions (e.g., mid-infrared spectroscopy) are rapid and relatively cheap, but there is a need to compare them with the reference analytical method (gas chromatography) to ensure their validity and suitability for various milk samples. The aim of this study is to compare the proportions of single fatty acids and their sums determined by utilizing both of these analytical methods and the resulting correlation coefficients. Our results show that the mid-infrared spectroscopy method is more appropriate (both for bulk and individual milk samples) for fatty acids present in high proportions of the total fat and for the sum of fatty acids (such as saturated and unsaturated) than for fatty acids with low proportions.

Abstract

Rapid analytical methods can contribute to the expansion of milk fatty acid determination for various important practical purposes. The reliability of data resulting from these routine methods plays a crucial role. Bulk and individual milk samples (60 and 345, respectively) were obtained from Czech Fleckvieh and Holstein dairy cows in the Czech Republic. The correlation between milk fatty acid (FA) proportions determined by the routine method (infrared spectroscopy in the mid-region in connection with Fourier transformation; FT-MIR) and the reference method (gas chromatography; GC) was evaluated. To validate the calibration of the FT-MIR method, a linear regression model was used. For bulk milk samples, the correlation coefficients between these methods were higher for the saturated (SFAs) and unsaturated FAs (UFAs) (r = 0.7169 and 0.9232; p < 0.001) than for the trans isomers of UFAs (TFAs) and polyunsaturated FAs (PUFAs) (r = 0.5706 and 0.6278; p < 0.001). Similar results were found for individual milk samples: r = 0.8592 and 0.8666 (p < 0.001) for SFAs and UFAs, 0.1690 (p < 0.01) for TFAs, and 0.3314 (p < 0.001) for PUFAs. The correlation coefficients for TFAs and PUFAs were statistically significant but too low for practical analytical application. The results indicate that the FT-MIR method can be used for routine determination mainly for SFAs and UFAs.