Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS + repeatability file (REP); (3) first derivative of spectral data + PLS; (4) first derivative + REP + PLS; (5) second derivative of spectral data + PLS; and (6) second derivative + REP + PLS. Methods were compared on the basis of the cross-validation coefficient of determination (R²cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R²v). The third and fourth methods had, on average, the highest R²cv, RPD, and R²v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R²cv greater than 0.95. With R²cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R²cv greater than 95% in milk payment systems and the usefulness of equations with R²cv greater than 75% for animal breeding purposes.     

Solid phase extraction with flame atomic absorption spectrometry for determination of traces of Ca,K, Mg and Na in quality control of white sugar

A fast and straightforward pre-concentration procedure based on solid phase extraction with a strongly acidic cation-exchanger Dowex 50 W × 8–400 was proposed to determine traces of Ca, K, Mg and Na in white sugar samples by means of flame atomic absorption spectrometry. For this purpose, 20% (m/v) white sugar solutions (100 ml) were driven through resin beds at 10 ml min⁻¹ to retain Ca, K, Mg and Na ions and to separate sucrose that passed through unretained. Thereafter, columns were rinsed with water and elements of interest were recovered prior to measurements using 5 ml of a 2 mol l⁻¹ HCl solution. Detection limits of 0.04, 0.05, 0.02 and 0.01 μg g⁻¹ for Ca, K, Mg and Na, respectively, and precision of measurements within 1–3% were achieved. The proposed method enabled to determine Ca, K, Mg and Na in samples of white sugar within corresponding ranges: 0.66–0.99 μg g⁻¹ (Ca), 2.9–12.2 μg g⁻¹ (K), 0.53–1.57 μg g⁻¹ (Mg) and 0.06–0.30 μg g⁻¹ (Na). Accuracy of this sample pre-treatment procedure and analysis method was assessed by performing spikes and recovery experiments. Recoveries of added Ca, K, Mg and Na were found to be within 97–102%, demonstrating good reliability of results.     

Impact of lowering dietary cation-anion difference in nonlactating dairy cows: a meta-analysis

A meta-analysis of previous studies was performed to clarify the response of prepartum dairy cows to lowering dietary cation-anion difference (DCAD) and to compare different equations that have been proposed to calculate DCAD. Twenty-two published studies containing 75 treatment groups met criteria for inclusion in the meta-analysis. Five different equations used to calculate DCAD were compared for their association with clinical milk fever and urinary pH. The DCAD equation (Na + K) − (Cl + 0.6 S) was the most highly associated with clinical milk fever (R² = 0.44) and urinary pH (R² = 0.85). Lowering DCAD reduced clinical milk fever but also reduced DM intake. Lowered DCAD was associated with reduced urinary pH, blood bicarbonate, and blood CO₂, suggesting a metabolic acidosis with respiratory compensation. Blood pH was very slightly lowered by lowered DCAD. Lowering DCAD increased ionized Ca in blood before and at calving. The model predicted that lowering DCAD from +300 to 0 mEq/kg reduced risk for clinical milk fever from 16.4 to 3.2%, reduced urinary pH from about 8.1 to 7.0, and reduced DM intake by 11.3%.     

Analytical capability of a medium power capacitively coupled plasma for the multielemental determination in multimineral/multivitamin preparations by atomic emission spectrometry

A method for multielemental (Ca, Cr, Cu, Fe, K, Mg, Mn, Na, P and Zn) determination in multimineral/multivitamins by atomic emission spectrometry in a medium power radiofrequency capacitively coupled plasma (275 W) and low Ar consumption (0.4 L min⁻¹) is proposed. Determinations were performed on commercially available tablets and a standard reference material after acidic high-pressure microwave assisted digestion and using the standard additions procedure. The detection limits (mg g⁻¹) were in the range 0.003 (Na)–1.5 (P) and were not depreciated by the non-spectral interference of mineral matrices of K, Ca, Mg and Na excepting Zn and P. Found concentrations corresponded generally to the labelled contents with recovery in the range of 90–107% and 1.0–13.0% repeatability. The proposed technique could be an advantageous alternative to the more expensive inductively coupled plasma atomic emission spectrometry in the quality control of multimineral/multivitamin preparations.     

Liver phosphorus content in Holstein-Friesian cows during the transition period

Hepatic lipidosis and hypophosphatemia are frequently observed in high-yielding periparturient dairy cows. Objectives of this study were to investigate the association of the liver P content with the degree of liver fat accumulation and serum P concentration and to characterize the change in liver P content throughout the transition period. In a cross-sectional study, liver biopsies obtained from 33 Holstein-Friesian cows 14 d postpartum (p.p.) were assayed for total lipid (TLip), triacylglycerol, DNA, P, Mg, K, Na, and Ca content. Serum samples obtained at the time of biopsy were analyzed for indices of liver function and injury and the serum P concentration was determined. From this cross-sectional study, 6 cows were selected for a longitudinal study and liver tissue obtained from the 6 cows on d −65, −30, −14, 1, 14, 28, and 49 relative to calving was assayed. The amounts of P, K, Mg, Na, and Ca were expressed as amount in dry weight (DW), wet weight (WW), nonfat wet weight (NFWW), and indexed to DNA. In the cross-sectional study, P_DW and P_WW decreased with increasing TLip, whereas P_NFWW and P_DNA were independent of TLip. Values for P_DNA varied widely, whereas P_NFWW varied within a narrow range. Stepwise regression analysis revealed the strongest associations between P_DW and the amount of tissue water (partial R² = 0.74) and the log to the base 10 of triacylglycerol (partial R² = 0.05). The P_WW was associated with the log to the base 10 of triacylglycerol (partial R² = 0.20), but no associations were found for P_NFWW. These findings indicate that decreased electrolyte content in dry and wet liver tissue with increased liver lipid content is predominantly due to the decrease in tissue water and therefore the distribution volume of electrolytes. In the longitudinal study, P_DW, P_WW, and P_NFWW were decreased on d 14 p.p. Similar directional decreases were found for K, Mg, and Na, but P was the only electrolyte that was significantly decreased in liver tissue at d 14 p.p. This finding indicates that the P content of liver tissue decreases in early lactation due to a reduction in hepatocellular cytosol volume as well as a decrease in cytosolic P concentration, with the latter having biological relevance. The clinical significance of decreased cytosolic P concentration in the hepatocytes of dairy cows in early lactation remains to be determined.     

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of mid-infrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of southeastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R²) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R² ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.     

X-ray Fluorescence Determination of Element Contents in Milk and Dairy Products

The concentrations of minerals (Na, Mg, P, S, Cl, K, and Ca) and trace elements (Mn, Fe, Ni, Cu, Zn, Rb, Sr, and Br) in different types of milk, dairy products, and infant formulas have been determined using wavelength-dispersive X-ray fluorescence analysis (WDXRF). Freeze-dried samples pressed as tablets of 4 g have been analyzed. Calibrations have been established using both plant and milk standard reference materials. The matrix correction method based on the power function of Compton scattered intensity was applied. The paper provides calibration data, detection limits for each element, and testing the accuracy of the proposed technique. The elemental compositions of the samples obtained by WDXRF were compared with the previously reported data from different countries.     

Effect of dietary ratio of Na:K on feed intake, milk production, and mineral metabolism in mid-lactation dairy cows

The objective of this study was to determine the effect of altering the dietary ratio of Na:K while keeping the dietary cation-anion difference (DCAD) constant, on dry matter (DM) intake, milk production, and mineral metabolism in lactating dairy cows. Fifteen mid-lactation Holstein cows averaging 160 d in milk were used in a replicated 3 × 3 Latin square design with treatments varying in the molar ratio of Na:K (0.21, 0.53, and 1.06). Diets contained A) 0.25% Na and 2.00% K, B) 0.50% Na and 1.60% K, or C) 0.75% Na and 1.20% K (on a DM basis), and all contained the same DCAD of 33 mEq (Na + K - Cl - S)/100 g of DM. There was a quadratic effect of the ratio of Na:K on DM intake (28.4, 27.5, and 28.3 kg/d for diets A, B, and C, respectively). The ratio of Na:K did not affect milk yield (average 39.2 kg/d), milk composition (average 3.60% fat; 3.01% protein; and 8.62% solids-not-fat), or coccygeal venous plasma concentrations of HCO₃⁻ (average 29.3 mEq/L), Na⁺ (average 136.7 mEq/L), K⁺ (average 4.53 mEq/L), Cl⁻ (average 97.5 mEq/L), Ca (average 10.06 mg/dL), and Mg (average 2.49 mg/dL), and urinary pH (average 8.38) and ratio of Cl⁻:creatinine (average 4.35). The ratios of urinary Na⁺:creatinine (1.80, 4.21, and 7.42), Ca:creatinine (0.035, 0.041, and 0.064), and Mg:creatinine (0.53, 0.60, and 0.77) increased linearly with increasing ratios of Na:K, whereas the ratio of urinary K⁺:creatinine decreased linearly as the ratio of Na:K increased (22.4, 15.9, and 10.3). Milk production and composition of mid-lactation cows was similar among dietary ratios of Na:K with the same DCAD of 33 mEq/100 g of DM.     

The mineral composition (Ca,P, Mg,K, Na) in cheeses (cow’s,ewe’s and goat’s) with different ripening times using near infrared spectroscopy with a fibre-optic probe

The results of this work show that it is possible to rapidly quantify calcium, phosphorus, magnesium, potassium and sodium in unknown cheeses elaborated with percentages (0–100%) of milk from different species (cow, ewe, goat) by direct application of the fibre-optic probe on the sample without previous destruction or treatment of the sample. Of the total number of samples, 170 were used to develop the calibration models using the Modified Partial Least Squares (MPLS) regression method and 57 samples were used for external validation. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained for calcium (0.74, 0.64), phosphorus (0.69, 0.29), potassium (0.86, 0.13), and sodium (0.92, 0.71) in g/kg respectively and magnesium (0.72, 30.9) in ppm, indicated that the models developed allow the determination of Ca, P, K, Na and Mg in unknown samples of cheeses of varying compositions up to 6 months of ripening.     

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows

Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (R_EX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (R_EX = 0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (R_EX = 0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.     

The use of mid-infrared spectrometry to estimate the ration composition of lactating dairy cows

The composition of cow milk is strongly affected by the feeding regimen. Because milk components are routinely determined using mid-infrared (MIR) spectrometry, MIR spectra could also be used to estimate an animal's ration composition. The objective of this study was to determine whether and how well amounts of dry matter intake and the proportions of concentrates, hay, grass silage, maize silage, and pasture in the total ration can be estimated using MIR spectra at an individual animal level. A total of 10,200 milk samples and sets of feed intake data were collected from 90 dairy cows at 2 experimental farms of the Agricultural Research and Education Centre in Raumberg-Gumpenstein, Austria. For each run of analysis, the data set was split into a calibration and a validation data set in a 40:60 ratio. Estimated ration compositions were calculated using a partial least squares regression and then compared with the respective observed ration compositions. In separate analyses, the factors milk yield and concentrate intake were included as additional predictors. To evaluate accuracy, the coefficient of determination (R²) and ratio to performance deviation were used. The highest R² values (for kg of dry matter intake/for % of ration) for the individual feedstuffs were as follows: pasture, 0.63/0.66; grass silage, 0.32/0.43; concentrate intake, 0.39/0.34; maize silage, 0.32/0.33; and hay, 0.15/0.16. Estimation of groups of feedstuffs (forages, energy-dense feedstuffs) mostly resulted in R² values >0.50. Including the parameters milk yield or concentrate intake improved R² values by up to 0.21, with an average improvement of 0.04. The results of this study indicate that not all ration components may be estimated equally accurately. Even if some estimates are good on average, there may be strong deviations between estimated and observed values in individual data sets, and therefore individual estimates should not be overemphasized. Further research including pooled samples (e.g., bulk milk, farm samples) or variations in ration composition is called for.     

Simultanbestimmungsmethoden für die Elemente Kupfer,Zink, Eisen und Mangan sowie Natrium,Kalium, Calcium und Magnesium mittels Flammen-Atomabsorptionsspektrometrie (F-AAS)

Simultaneous determination of the elements copper, zinc, iron and manganese as well as sodium, potassium, calcium and magnesium by flame atomic absorption spectrometry (F-AAS). The elements Na, K, Ca, Mg, Cu, Zn, Fe and Mn are very important for the examination of human food and foodstuff in the light of nutritional physiology. Two simultaneous determination methods for these elements with flame atomic absorption spectrometry (F-AAS) are presented. The sensitivity of the F-AAS is sufficient for these examinations. Using the calibration and the method of standard addition resp. and “Schinkellösung” (caesiumchlorid lanthanumchlorid buffer solution), the simultaneous determination of Na, K, Ca and Mg is possible. The detection limits of Na, K, Ca and Mg depend on the simultaneous determination of the elements, the used wavelengths and the kind of the examined samples (30 mg Na, 24 mg K, 1,9 mg Ca and 0,46 mg Mg per kg fresh matter). The detection limits for Cu, Zn, Fe and Mn with the simultaneous determination are 0,61 mg Cu, 0,14 mg Zn, 3,8 mg Fe and 0,44 mg Mn per kg sample. The new methods are tested with the standard reference materials CRM No 278 (Mussel Tissue), CMR No 185 (Bovine Liver) and CRM No 189 (Wholemeal Flour) and with selected samples.     

Phenotypic and genetic analysis of milk and serum element concentrations in dairy cows

Enhancing micronutrient (i.e., mineral and vitamin) concentrations within milk and serum from dairy cows is important for both the health of the cow and the nutritive value of the milk for human consumption. However, a good understanding of the genetics underlying the micronutrient content in dairy cattle is needed to facilitate such enhancements through feeding or breeding practices. In this study, milk (n = 950) and serum (n = 766) samples were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period and analyzed for concentrations of important elements. Additionally, a subset of 256 milk samples was analyzed for concentrations of vitamin B₁₂. Cows belonged to 2 genetic lines (average and highest genetic merit for milk fat plus protein yield) and were assigned to 1 of 2 diets based on either a by-product or homegrown ration. Univariate models accounting for repeated records were used to analyze element and vitamin B₁₂ data and investigate the effect of genotype and feeding system as well as derive estimates of variance components and genetic parameters. Bivariate models were used to study correlations both within and between milk and serum. Only concentrations of Hg in milk were seen to be affected by genotype, with higher concentrations in cows with high genetic merit. In contrast, element concentrations were influenced by feeding system such that cows fed the homegrown diet had increased milk concentrations of Ca, Cu, I, Mn, Mo, P, and K and increased serum concentrations of Cd, Cu, Fe, Mo, and V. Cows on the by-product diet had increased milk concentrations of Mg, Se, and Na and increased serum concentrations of P and Se. Heritability (h²) estimates were obtained for 6 milk and 4 serum elements, including Mg (h²_milk = 0.30), K (h²_serum = 0.18), Ca (h²_milk = 0.20; h²_serum = 0.12), Mn (h²_milk = 0.14), Cu (h²_serum = 0.22), Zn (h²_milk = 0.24), Se (h²_milk = 0.15; h²_serum = 0.10), and Mo (h²_milk = 0.19). Significant estimates of repeatability were observed in all milk and serum quantity elements (Na, Mg, P, K, and Ca) as well as 5 milk and 7 serum trace elements. Only K in milk and serum was found to have a significant positive genetic and phenotypic correlation (0.52 and 0.22, respectively). Significant phenotypic associations were noted between milk and serum Ca (0.17), Mo (0.19), and Na (−0.79). Additional multivariate analyses between measures within sample type (i.e., milk or serum) revealed significant positive associations, both phenotypic and genetic, between some of the elements. In milk, Se was genetically correlated with Ca (0.63), Mg (0.59), Mn (0.40), P (0.53), and Zn (0.52), whereas in serum, V showed strong genetic associations with Cd (0.71), Ca (0.53), Mn (0.63), Mo (0.57), P (0.42), K (0.45), and Hg (−0.44). These results provide evidence that element concentrations in milk and blood of dairy cows are significantly influenced by both diet and genetics and demonstrate the potential for genetic selection and dietary manipulation to alter nutrient concentration to improve both cow health and the healthfulness of milk for human consumption.     

Differentiation of milks and cheeses according to species based on the mineral content

Summary The mineral composition of 113 samples of ewes', cows', and goats' milk and 68 samples of different types of pure-milk cheeses made from the milks of these species was analysed. Stepwise discriminant analysis of the milk samples yielded the variables K/Mg, Na/Ca, Zn, Cu/Zn, and Cu/Na as the most useful in differentiating the samples, achieving correct classification in 98.2% of cases. The most useful variables for the cheese samples were Fe/K, Na/Ca, Zn/Cu, Na/Mg, and Zn, which yielded correct classification in 97.1% of cases. The three goat's milk cheeses were successfully distinguished using the variables K/Zn, Fe/Cu, and P.

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Unterscheidung zwischen verschiedenen Milch- und Käsesorten anhand ihrer Mineralstoff-Zusammensetzung

Zusammenfassung An 113 Milchproben und 68 mit Schafmilch, Kuhmilch und Ziegenmilch hergestellten Käseproben wurde die Mineralstoff-Zusammensetzung analysiert. Die Anwendung der schrittweisen Diskriminatoriusanalyse bei den Milchproben ergab die Auswahl der Veränderlichen K/Mg, Na/Ca, Zn, Cu/Zn und Cu/Na als beste Unterschiedlichkeitsparameter mit einer zu 98,2% einwandfreien Klassifizierung. Die bei Käse ausgewählten Veränderlichen waren Fe/K, Na/Ca, Zn/Cu, Na/Mg und Zn mit einer zu 97,1% einwandfreien Klassifizierung. Die Unterscheidung zwischen den 3 geprüften Ziegenmilch-Käsesorten wurde durch die Veränderlichen K/Zn, Fe/Cu und P ermöglicht.

     

Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

A group of milk components that has shown potential to be predicted with milk spectra is milk minerals. Milk minerals are important for human health and cow health. Having an inexpensive and fast way to measure milk mineral concentrations would open doors for research, herd management, and selective breeding. The first aim of this study was to predict milk minerals with infrared milk spectra. Additionally, milk minerals were predicted with infrared-predicted fat, protein, and lactose content. The second aim was to perform a genetic analysis on infrared-predicted milk minerals, to identify QTL, and estimate variance components. For training and validating a multibreed prediction model for individual milk minerals, 264 Danish Jersey cows and 254 Danish Holstein cows were used. Partial least square regression prediction models were built for Ca, Cu, Fe, K, Mg, Mn, Na, P, Se, and Zn based on 80% of the cows, selected randomly. Prediction models were externally validated with 8 herds based on the remaining 20% of the cows. The prediction models were applied on a population of approximately 1,400 Danish Holstein cows with 5,600 infrared spectral records and 1,700 Danish Jersey cows with 7,200 infrared spectral records. Cows from this population had 50k imputed genotypes. Prediction accuracy was good for P and Ca, with external R² ≥ 0.80 and a relative prediction error of 5.4% for P and 6.3% for Ca. Prediction was moderately good for Na with an external R² of 0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.     

Validation of quick measurement of mineral nutrients in milk powders: comparison of energy dispersive X-ray fluorescence with inductively coupled plasma-optical emission spectroscopy and potentiometry reference methods

Energy dispersive X-ray fluorescence method is a good candidate to be implemented close to production lines of fortified milk powders, in order to ensure their quality control. In this study an energy dispersive X-ray fluorescence (EDXRF) method was tested as a simple, fast (< 8 min/pellet) and simultaneous method for the quantification of a series of macroelements (Na, Mg, P, Cl, K and Ca) and micronutrients (Fe, Cu and Zn) from fortified milk powders. Calibrations were established using results from reference methods results (inductively coupled plasma-optical emission spectroscopy and potentiometry for chloride). Twenty-one calibration samples were selected to cover uniformly the expected concentration ranges for each element. The EDXRF results were validated by a strict and systematic comparison with data obtained from the reference methods on a set of thirty further and independent samples. This study confirmed that EDXRF is a fast and reliable method of choice for quality control analyses Na, Mg, P, Cl, K, Ca, Fe, Cu and Zn in milk powders allowing better process control and reaction in case of deviation. Although manganese is detected, its quantification was not possible due to unacceptable statistical performance characteristics.     

Variations of vitamin and mineral contents in raw goat milk of the indigenous Greek breed during lactation

Raw goat milk samples from the indigenous Greek breed in the area of Ioannina, northwestern Greece, were collected during one lactation and analyzed for vitamins A, E, B₁, B₂, and C and for minerals Ca, Mg, P, Na, K, Cu, Fe and Zn. Also, the major constituents of goat milk, namely fat, protein, lactose and solids-non-fat, were determined. The average composition (%) of milk was: fat 4.10, protein 3.36, lactose 4.48 and solids-non-fat 8.54. The mean concentration of the fat-soluble vitamins retinol (A) and α-tocopherol (E) were 0.013 and 0.121 mg/100 ml, respectively. The mean concentration of the water-soluble vitamins, thiamin (B₁), riboflavin (B₂) and ascorbic acid (C) were 0.260, 0.112 and 5.48 mg/100 ml, respectively. Seasonal variations were observed for all vitamins studied. Thiamin had significantly (P < 0.05) higher concentrations during summer than in winter and early spring. The observed variations of the studied vitamins might be attributed to the differences in the feeding of goats during lactation. The mean mineral contents (mg/100 g) of goat milk were Ca 132, P 97.7, Na 59.4, K 152, Mg 15.87, Cu 0.08, Fe 0.06, Zn 0.37 and Mn 6.53 μg/100 g. Seasonal variations were observed for the major minerals Ca, P, K, and the trace elements Cu and Zn.     

Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus,and titratable acidity of milk and their relationship with milk quality and coagulation properties

Individual milk samples from Holstein Friesian cows were collected and analysed by inductively coupled plasma optical emission spectrometry (ICP-OES) and titration for the determination of calcium (Ca), phosphorus (P) and titratable acidity (TA) contents, respectively. Prediction models were obtained using partial least squares (PLS) regression analyses using two statistical packages. The average Ca, P and TA were 1156 mg kg⁻¹, 934 mg kg⁻¹ and 3.42 °SH 50 mL⁻¹, respectively. Pearson's correlations between Ca and P and other milk traits were significant (P < 0.05) and ranged from 0.16 to 0.53 for chemical composition traits and from 0.17 to −0.35 for milk coagulation properties (MCP). Results from the two statistical packages were comparable. Prediction models using MIR spectroscopy were satisfactory for Ca, P and TA, with coefficients of correlation of cross-validation greater than 0.73. Moreover, the study highlighted favourable relationships of these traits with milk coagulation properties.     

Modified versus producer milk calibration: mid-infrared analyzer performance validation

Our objective was to determine the validation performance of mid-infrared (MIR) milk analyzers, using the traditional fixed-filter approach, when the instruments were calibrated with producer milk calibration samples vs. modified milk calibration samples. Ten MIR analyzers were calibrated using producer milk calibration sample sets, and 9 MIR milk analyzers were calibrated using modified milk sample sets. Three sets of 12 validation milk samples with all-laboratory mean chemistry reference values were tested during a 3-mo period. Calibration of MIR milk analyzers using modified milk increased the accuracy (i.e., better agreement with chemistry) and improved agreement between laboratories on validation milk samples compared with MIR analyzers calibrated with producer milk samples. Calibration of MIR analyzers using modified milk samples reduced overall mean Euclidian distance for all components for all 3 validation sets by at least 24% compared with MIR analyzers calibrated with producer milk sets. Calibration with modified milk sets reduced the average Euclidian distance from all-laboratory mean reference chemistry on validation samples by 40, 25, 36, and 27%, respectively for fat, anhydrous lactose, true protein, and total solids. Between-laboratory agreement was evaluated using reproducibility standard deviation (s_R). The number of single Grubbs statistical outliers in the validation data was much higher (53 vs. 7) for the instruments calibrated with producer milk than for instruments calibrated with modified milk sets. The s_R for instruments calibrated with producer milks (with statistical outliers removed) was similar to data collected in recent proficiency studies, whereas the s_R for instruments calibrated with modified milks was lower than those calibrated with producer milks by 46, 52, 61, and 55%, respectively for fat, anhydrous lactose, true protein, and total solids.     

Estimation of minerals,nitrate and nitrite contents of medicinal and aromatic plants used as spices,condiments and herbal tea

In this study, the minor and major mineral contents of 31 kinds of medicinal and aromatic plant collected from the south region of Turkey in 2004 year were established by inductively coupled plasma atomic emission spectrometry (ICP-AES). The samples were composed of Al, Ca, Fe, K, Mg, Na, P and Zn. The highest mineral concentration were measured between 57.70–2962.74 mg/kg Al, 1160.04–16452.88 mg/kg Ca, 44.83–1799.5 mg/kg Fe, 3570.73–27669.72 mg/kg K, 477.17–4313.59 mg/kg Mg, 1102.62–20912.33 mg/kg Na, 443.60–9367.80 mg/kg P and 7.18–48.36 mg/kg Zn. The highest values of Ca, K and P were established in Foeniculum vulgare (bitter fennel) (16452.88 mg/kg), Ocimum minumum (basil) (27669.72 mg/kg) and F. vulgare (bitter fennel) (9367.80 mg/kg), respectively. The heavy metal contents were determined too low in all samples.