A rapid and multi-element method for the analysis of major nutrients in grass (Lolium perenne) using energy-dispersive X-ray fluorescence spectroscopy

peer-reviewedElemental analysis of grass (Lolium perenne) is essential in agriculture to ensure grass quality and animal health. Energy-dispersive X-ray fluorescence (EDXRF) spectroscopy is a rapid, multi-element alternative to current methods using acid digestion and inductively coupled plasma optical emission spectrometry (ICP-OES). Percentage phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca), determined from grass samples using EDXRF, were within 0.035, 0.319, 0.025 and 0.061, respectively, of ICP-OES values. Concordance correlation coefficients computed using agreement statistics ranged from 0.4379 to 0.9669 (values close to one indicate excellent agreement); however, the level of agreement for each element depended on the calibrations used in EDXRF. Empirical calibrations gave excellent agreement for percentage P, K and Ca, but moderate agreement for percentage Mg due to a weaker correlation between standards and intensities. Standardless calibration using the fundamental parameters (FP) approach exhibited bias, with consistently lower values reported for percentage P and Mg, when compared with ICP-OES methods. The relationship between the methods was plotted as scatter plots with the line of equality included, and although correlation coefficients indicated strong relationships, these statistics masked the effects of consistent bias in the data for percentage P and Mg. These results highlight the importance of distinguishing agreement from correlation when using statistical methods to compare methods of analysis. Agreement estimates improved when a matching library of grass samples was added to the FP method. EDXRF is a comparable alternative to conventional methods for grass analysis when samples of similar matrix type are used as empirical standards or as a matching library

The Corrosion Protection of Copper and Copper Alloys using an Electrodeposited Conducting Polypyrrole Coating

Adherent polypyrrole films were electropolymerized from a near neutral sodium oxalate solution at pure Cu, CuZn and CuNi electrodes. The growth of these films was facilitated by the formation of a pseudo-passive oxalate layer. This layer was sufficiently protective to minimize dissolution of the substrate, but sufficiently conductive to enable the electropolymerization of pyrrole at the interface, and the generation of an adherent polypyrrole film. The rate of electropolymerization at the CuNi layer was reduced significantly by the formation of a nickel-rich oxide phase, however, the presence of Cu2+ increased the rate of polymer growth, enabling the formation of a thin polypyrrole layer during the early stages of polymerization. Likewise, the presence of zinc in the oxalate layer generated at the CuZn electrode reduced somewhat the rate of polymer formation. These films exhibited good corrosion protection properties in an acidified chloride solution

Systematic review of antimicrobial drug prescribing in hospitals.

Prudent antibiotic prescribing to hospital inpatients has the potential to reduce the incidences of antimicrobial resistance and healthcare-associated infection. We reviewed the literature from January 1980 to November 2003 to identify rigorous evaluations of interventions to improve hospital antibiotic prescribing. We identified 66 studies with interpretable data of which 16 reported 20 microbiological outcomes: Gram negative resistant bacteria (GNRB), 10 studies; Clostridium difficile associated diarrhoea (CDAD), 5 studies; vancomycin resistant enterococci (VRE), 3 studies and methicillin resistant Staphylococcus aureus (MRSA), 2 studies. Four studies provide good evidence that the intervention changed microbial outcomes with low risk of alternative explanations, eight studies provide less convincing evidence and four studies were negative. The strongest and most consistent evidence was for CDAD but we were able to analyse only the immediate impact of interventions because of nonstandardised durations of follow up. The ability to compare results of studies could be substantially improved by standardising methodology and reporting

Influence of protein standardisation media and heat treatment on viscosity and related physicochemical properties of skim milk concentrate

peer-reviewedThe effects of heat treatment and protein standardisation on the physical properties of skim milk concentrates were determined. Protein standardisation was carried out by the addition of lactose or milk permeate to skim milk. Unstandardised and standardised skim milk was subjected to heat treatment temperatures of 90 or 120 °C prior to evaporation whereafter the solids content was increased to 46% (w/w). Viscosity data showed non-standardised concentrates had the highest viscosity, followed by skim standardised with milk permeate followed by that standardised with lactose. Thermal treatment at 120 °C also resulted in a higher viscosity than that at 90 °C for all concentrates. Particle size data of evaporated skim milk showed a bimodal size distribution for skim milk standardised with liquid milk permeate, compared with monomodal distribution profiles for unstandardised skim milk and lactose standardised skim milk. Overall, this study showed that protein standardisation and standardisation media significantly affected concentrate properties

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

peer-reviewedRapid, 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 60 min 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 5,000 cm−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 60 min 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 60 min 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

Factors associated with milk processing characteristics predicted by mid-infrared spectroscopy in a large database of dairy cows

Despite milk processing characteristics being important quality traits, little is known about the factors underlying their variability, due primarily to the resources required to measure these characteristics in a sufficiently large population. Cow milk coagulation properties (rennet coagulation time, curd-firming time, curd firmness 30 and 60 min after rennet addition), heat coagulation time, casein micelle size, and pH were generated from available mid-infrared spectroscopy prediction models. The prediction models were applied to 136,807 spectra collected from 9,824 Irish dairy cows from research and commercial herds. Sources of variation were investigated using linear mixed models that included the fixed effects of calendar month of test; milking time in the day; linear regressions on the proportion of Friesian, Jersey, Montb\ue9liarde, Norwegian Red, and \u201cother\u201d breeds in the cow; coefficients of heterosis and of recombination loss; parity; stage of lactation; and the 2-way interaction parity 7 stage of lactation. Withinand across-parity cow effects, contemporary group, and a residual term were also included as random effects in the model. Supplementary analyses considered the inclusion of either test-day milk yield or milk protein concentration as fixed-effects covariates in the multiple regression models. Milk coagulation properties were most favorable (i.e., short rennet coagulation time and strong curd firmness) for cheese manufacturing in early lactation, concurrent with the lowest values of both pH and casein micelle size. Milk coagulation properties and pH deteriorated in mid lactation but improved toward the end of lactation. In direct contrast, heat coagulation time was more favorable in mid lactation and less suitable (i.e., shorter) for high temperature treatments in both early and late lactation. Relative to multiparous cows, primiparous cows, on average, yielded milk with shorter rennet coagulation time and longer heat coagulation time. Milk from the evening milking session had shorter rennet coagulation time and greater curd firmness, as well as lower heat coagulation time and lower pH compared with milk from the morning session. Jersey cows, on average, yielded milk more suitable for cheese production rather than for milk powder production. When protein concentration was included in the model, the improvement of milk coagulation properties toward the end of lactation was no longer apparent. Results from the present study may aid in decisionmaking for milk manufacturing, especially in countries characterized by a seasonal supply of fresh milk

Modelling the changes in viscosity during thermal treatment of milk protein concentrate using kinetic data

peer-reviewedThis work aimed to model the effect of heat treatment on viscosity of milk protein concentrate (MPC) using kinetic data. MPC obtained after ultrafiltration was subjected to different time-temperature heat treatment combinations. Heat treatment at high temperature and short time (i.e., 100 or 120 °C×30 s) led to a significant increase in viscosity in MPC systems. Second-order reaction kinetic models proved a better fit than zero- or first-order models when fitted for viscosity response to heat treatment. A distinct deviation in the slope of the Arrhenius plot at 77.9 °C correlated to a significant increase in the rate of viscosity development at temperatures above this, confirming the transition of protein denaturation from the unfolding to the aggregation stage. This study demonstrated that heat-induced viscosity of MPC as a result of protein denaturation/aggregation can be successfully modelled in response to thermal treatment, providing useful new information in predicting the effect of thermal treatment on viscosity of MPC

Effect of pH and heat treatment on viscosity and heat coagulation properties of milk protein concentrate

peer-reviewedThe effect of pH, adjusted using either hydrochloric acid (HCl), citric acid or sodium hydroxide, on calcium ion (Ca2+) activity, and consequent changes in viscosity and heat coagulation time (HCT) of milk protein concentrate (MPC) was investigated. Reducing the pH of MPC dispersions resulted in a reduction in their viscosity, which subsequently increased during heat treatment. The maximum heat stability of MPC was observed at pH 6.7. Reducing the pH of MPC from 6.7 to 6.2 resulted in a significant (P < 0.05) increase in Ca2+ activity, and reduction in HCT. Such changes were more extensive using HCl compared with citric acid. Increasing the pH greater than 6.7 also led to a reduction in HCT but a decrease in Ca2+ activity. These results demonstrate the importance of pH adjustment, and choice of acidulant, on Ca2+ activity, viscosity, and heat coagulation properties of MPC concentrates during processing

Prediction of individual milk proteins including free amino acids in bovine milk using mid-infrared spectroscopy and their correlations with milk processing characteristics

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n = 400 to 591 samples) and external validation on an independent data set (n = 143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total beta-lactoglobulin, and beta-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and beta-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a(30)). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K-20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level

Effectiveness of mid-infrared spectroscopy to predict the color of bovine milk and the relationship between milk color and traditional milk quality traits

The color of milk affects the subsequent color features of the resulting dairy products; milk color is also related to milk fat concentration. The objective of the present study was to quantify the ability of mid-infrared spectroscopy (MIRS) to predict color-related traits in milk samples and to estimate the correlations between these color-related characteristics and traditional milk quality traits. Mid-infrared spectral data were available on 601 milk samples from 529 cows, all of which had corresponding gold standard milk color measures determined using a Chroma Meter (Konica Minolta Sensing Europe, Nieuwegein, the Netherlands); milk color was expressed using the CIELAB uniform color space. Separate prediction equations were developed for each of the 3 color parameters (L* = lightness, a* = greenness, b* = yellowness) using partial least squares regression. Accuracy of prediction was determined using both cross validation on a calibration data set (n = 422 to 457 samples) and external validation on a data set of 144 to 152 samples. Moderate accuracy of prediction was achieved for the b* index (coefficient of correlation for external validation = 0.72), although poor predictive ability was obtained for both a* and L* indices (coefficient of correlation for external validation of 0.30 and 0.55, respectively). The linear regression coefficient of the gold standard values on the respective MIRS-predicted values of a*, L*, and b* was 0.81, 0.88, and 0.96, respectively; only the regression coefficient on L* was different from 1. The mean bias of prediction (i.e., the average difference between the MIRS-predicted values and gold standard values in external validation) was not different from zero for any of 3 parameters evaluated. A moderate correlation (0.56) existed between the MIRS-predicted L* and b* indices, both of which were weakly correlated with the a* index. Milk fat, protein, and casein were moderately correlated with both the gold standard and MIRS-predicted values for b*. Results from the present study indicate that MIRS data provides an efficient, low-cost screening method to determine the b* color of milk at a population level