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

open4The 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.partially_openBonfatti, V; Degano, L; Menegoz, A; Carnier, PBonfatti, Valentina; Degano, Lorenzo; Menegoz, A; Carnier, Paol

Prediction of protein composition of individual cow milk using mid-infrared spectroscopy.

This study investigated the application of mid-infrared spectroscopy for the prediction of protein composition in individual milk samples (n=1,336) of Simmental cows. Protein fractions were quantified by RP-HPLC and MIR data were recorded over the spectral range from 4,000 to 900 cm-1. Models were developed by partial least squares regression using untreated spectra. The most successful predictions were for protein, casein, αS1-casein, whey protein, and β-lactoglobulin contents. The models could discriminate between high and low values of protein composition (R2=0.50 to 0.58). The root mean square errors of cross-validation were 3.11g/l for protein (range 39.91g/l), 2.76g/l for casein (range 35.16g/l), 1.07 g/l for αS1-casein (range 12.82g/l), 0.51 g/l for whey protein (range 4.97g/l), and 0.43 for β-lactoglobulin (range 4.37). Application of MIR spectroscopy is possible for the routinely assessment of protein, casein, αS1-casein, whey protein, and β-lactoglobulin and its implementation might be in future a tool for improving protein composition of bovine milk through breeding programs

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, \u3b2-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

Milk coagulation properties and methods of detection

ABSTRACT: One of the most crucial steps in cheesemaking is the coagulation process, and knowledge of the parameters involved in the clotting process plays an important technological role in the dairy industry. Milk of different ruminant species vary in terms of their coagulation capacities because they are influenced by the milk composition and mainly by the milk protein genetic variants. The milk coagulation capacity can be measured by means of mechanical and/or optical devices, such as Lactodynamographic Analysis and Near-Infrared and Mid-Infrared Spectroscopy

Social exclusion of older persons: a scoping review and conceptual framework

As a concept, social exclusion has considerable potential to explain and respond to disadvantage in later life. However, in the context of ageing populations, the construct remains ambiguous. A disjointed evidence-base, spread across disparate disciplines, compounds the challenge of developing a coherent understanding of exclusion in older age. This article addresses this research deficit by presenting the findings of a two-stage scoping review encompassing seven separate reviews of the international literature pertaining to old-age social exclusion. Stage one involved a review of conceptual frameworks on old-age exclusion, identifying conceptual understandings and key domains of later-life exclusion. Stage two involved scoping reviews on each domain (six in all). Stage one identified six conceptual frameworks on old-age exclusion and six common domains across these frameworks: neighbourhood and community; services, amenities and mobility; social relations; material and financial resources; socio-cultural aspects; and civic participation. International literature concentrated on the first four domains, but indicated a general lack of research knowledge and of theoretical development. Drawing on all seven scoping reviews and a knowledge synthesis, the article presents a new definition and conceptual framework relating to old-age exclusion