Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems

International environmental agreements have led to the need to reduce methane emission by dairy cows. Reduction could be achieved through selective breeding. The aim of this study was to quantify the genetic variation of methane emission by Dutch Holstein Friesian cows measured using infrared sensors installed in automatic milking systems (AMS). Measurements of CH₄ and CO₂ on 1,508 Dutch Holstein Friesian cows located on 11 commercial dairy farms were available. Phenotypes per AMS visit were the mean of CH₄, mean of CO₂, mean of CH₄ divided by mean of CO₂, and their log₁₀-transformations. The repeatabilities of the log₁₀-transformated methane phenotypes were 0.27 for CH₄, 0.31 for CO₂, and 0.14 for the ratio. The log₁₀-transformated heritabilities of these phenotypes were 0.11 for CH₄, 0.12 for CO₂, and 0.03 for the ratio. These results indicate that measurements taken using infrared sensors in AMS are repeatable and heritable and, thus, could be used for selection for lower CH₄ emission. Furthermore, it is important to account for farm, AMS, day of measurement, time of day, and lactation stage when estimating genetic parameters for methane phenotypes. Selection based on log₁₀-transformated CH₄ instead of the ratio would be expected to give a greater reduction of CH₄ emission by dairy cows.     

Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography–based milk fatty acid profiles

The objective of the present study was to compare the prediction potential of milk Fourier-transform infrared spectroscopy (FTIR) for CH₄ emissions of dairy cows with that of gas chromatography (GC)–based milk fatty acids (MFA). Data from 9 experiments with lactating Holstein-Friesian cows, with a total of 30 dietary treatments and 218 observations, were used. Methane emissions were measured for 3 consecutive days in climate respiration chambers and expressed as production (g/d), yield (g/kg of dry matter intake; DMI), and intensity (g/kg of fat- and protein-corrected milk; FPCM). Dry matter intake was 16.3 ± 2.18 kg/d (mean ± standard deviation), FPCM yield was 25.9 ± 5.06 kg/d, CH₄ production was 366 ± 53.9 g/d, CH₄ yield was 22.5 ± 2.10 g/kg of DMI, and CH₄ intensity was 14.4 ± 2.58 g/kg of FPCM. Milk was sampled during the same days and analyzed by GC and by FTIR. Multivariate GC-determined MFA–based and FTIR-based CH₄ prediction models were developed, and subsequently, the final CH₄ prediction models were evaluated with root mean squared error of prediction and concordance correlation coefficient analysis. Further, we performed a random 10-fold cross validation to calculate the performance parameters of the models (e.g., the coefficient of determination of cross validation). The final GC-determined MFA–based CH₄ prediction models estimate CH₄ production, yield, and intensity with a root mean squared error of prediction of 35.7 g/d, 1.6 g/kg of DMI, and 1.6 g/kg of FPCM and with a concordance correlation coefficient of 0.72, 0.59, and 0.77, respectively. The final FTIR-based CH₄ prediction models estimate CH₄ production, yield, and intensity with a root mean squared error of prediction of 43.2 g/d, 1.9 g/kg of DMI, and 1.7 g/kg of FPCM and with a concordance correlation coefficient of 0.52, 0.40, and 0.72, respectively. The GC-determined MFA–based prediction models described a greater part of the observed variation in CH₄ emission than did the FTIR-based models. The cross validation results indicate that all CH₄ prediction models (both GC-determined MFA–based and FTIR-based models) are robust; the difference between the coefficient of determination and the coefficient of determination of cross validation ranged from 0.01 to 0.07. The results indicate that GC-determined MFA have a greater potential than FTIR spectra to estimate CH₄ production, yield, and intensity. Both techniques hold potential but may not yet be ready to predict CH₄ emission of dairy cows in practice. Additional CH₄ measurements are needed to improve the accuracy and robustness of GC-determined MFA and FTIR spectra for CH₄ prediction.     

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.     

Association between days open and milk spectral data in dairy cows

Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm^?1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5–90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm^?1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm^?1 [related to fat-A (C = O stretch)], wn 1,546 cm^?1 (related to protein), wn 1,465 cm^?1 (related to urea and fat), wn 1,399 to 1,245 cm^?1 (related to acetone), and wn 1,110 cm^?1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.     

Effect of KCl substitution on bacterial viability of Escherichia coli (ATCC 25922) and selected probiotics

Excessive intake of NaCl has been associated with the increased risk of several diseases, particularly hypertension. Strategies to reduce sodium intake include substitution of NaCl with other salts, such as KCl. In this study, the effects of NaCl reduction and its substitution with KCl on cell membranes of a cheese starter bacterium (Lactococcus lactis ssp. lactis), probiotic bacteria (Bifidobacterium longum, Lactobacillus acidophilus, and Lactobacillus casei), and a pathogenic bacterium (Escherichia coli) were investigated using Fourier-transform infrared (FTIR) spectroscopy. A critical NaCl concentration that inhibited the viability of E. coli without affecting the viability of probiotic bacteria significantly was determined. To find the critical NaCl concentration, de Man, Rogosa, and Sharpe (MRS) broth was supplemented with a range of NaCl concentrations [0 (control), 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0%], and the effect on cell viability and FTIR spectra was monitored for all bacteria. A NaCl concentration of 2.5% was found to be the critical level of NaCl to inhibit E. coli without significantly affecting the viability of most of the probiotic bacteria and the cheese starter bacterium. The FTIR spectral analysis also highlighted the changes that occurred mainly in the amide regions upon increasing the NaCl concentration from 2.5 to 3.0% in most of the bacteria. Escherichia coli and B. longum were more sensitive to substitution of NaCl with KCl, compared with Lb. acidophilus, Lb. casei, and Lc. lactis ssp. lactis. To evaluate the effect of substitution of NaCl with KCl, substitution was carried out at the critical total salt concentration (2.5%, wt/vol) at varying concentrations (0, 25, 50, 75, and 100% KCl). The findings suggest that 50% substitution of NaCl with KCl, at 2.5% total salt, could inhibit E. coli without affecting the probiotic bacteria.     

Measurement of rumen dry matter and neutral detergent fiber degradability of feeds by Fourier-transform infrared spectroscopy

This study explored the potential of partial least squares (PLS) and Fourier-transform infrared spectroscopy (FTIR) to predict rumen dry matter (DM) and neutral detergent fiber (NDF) degradation parameters of a wide range of feeds for ruminants, as an alternative to the in situ method. In total, 663 samples comprising 80 different feed types were analyzed. In situ DM and NDF degradabilities were determined as follows: effective degradability (ED), rumen soluble fraction (A), degradable but not soluble fraction (B), rate of degradation of the B fraction (C), and indigestible NDF (iNDF). Infrared spectra of dry samples were collected by attenuated total reflectance from 600 to 4000 cm⁻¹. Feeds were randomly classified into 2 subsets of samples with representation of all feed types; one subset was used to develop regression models using partial least squares, and the second subset was used to conduct an external validation of the models. This study indicated that universal models containing all feed types and specific models containing concentrate feeds could provide only a relatively poor estimation of in situ DM degradation parameters because of compositional heterogeneity. More research, such as a particle size distribution analysis, is required to determine whether this lack of accuracy was due to limitations of the FTIR approach, or simply due to methodological error associated with the in situ method. This latter hypothesis may explain the low accuracy observed in the prediction of degradation rates if there was physical leakage of fine particles from the mesh bags used during in situ studies. In contrast, much better predictions were obtained when models were developed for forage feeds alone. Models for forages led to accurate predictions of DM_A, DM_B, NDF_ED, and NDF concentration (R² = 0.91, 0.89, 0.85, and 0.79, standard error = 4.34, 5.97, 4.59, and 4.41% of DM, respectively), and could be used for screening of DM_ED, NDF_C, and iNDF. These models relied on certain regions of the FTIR spectrum (900–1150 and 1500–1700 cm⁻¹), which are mainly compatible with absorption of plant cell wall components, such as cellulose, pectin, lignin, cutin, and suberin, but also with nonstructural carbohydrates and certain active compounds. In conclusion, FTIR spectroscopy could be considered a low-cost alternative to in situ measurements in feed evaluation.     

Mechanistic and conformational studies on the interaction of food dye amaranth with human serum albumin by multispectroscopic methods

The mechanism of interaction between food dye amaranth and human serum albumin (HSA) in physiological buffer (pH 7.4) was investigated by fluorescence, UV–vis absorption, circular dichroism (CD), and Fourier transform infrared (FT-IR) spectroscopy. Results obtained from analysis of fluorescence spectra indicated that amaranth had a strong ability to quench the intrinsic fluorescence of HSA through a static quenching procedure. The negative value of enthalpy change and positive value of entropy change elucidated that the binding of amaranth to HSA was driven mainly by hydrophobic and hydrogen bonding interactions. The surface hydrophobicity of HSA increased after binding with amaranth. The binding distance between HSA and amaranth was estimated to be 3.03 nm and subdomain IIA (Sudlow site I) was the primary binding site for amaranth on HSA. The results of CD and FT-IR spectra showed that binding of amaranth to HSA induced conformational changes of HSA.     

Estimation of feed crude protein concentration and rumen degradability by Fourier-transform infrared spectroscopy

Currently, rapid methods are needed for feed analysis. This study examined the potential of Fourier-transform infrared (FTIR) spectroscopy to predict the nutritional value of a wide range of feeds for ruminants, as an alternative to the in situ technique. Moreover, we investigated whether universal equations could be developed that would allow the low-cost determination of crude protein (CP) concentrations and their kinetics of degradation into the rumen. Protein nutritional values of 663 samples comprising 80 different feed types were determined in terms of concentrations of CP, water-soluble CP (CP_WS), total-tract mobile bag CP digestibility (CP_TTD), and in situ CP degradability, including the rumen soluble fraction (CP_A), the degradable but not soluble fraction (CP_B), rate of CP_B degradation (CP_C), effective degradability (CP_ED), and potential degradability (CP_PD). Infrared spectra of dry samples were collected by attenuated total reflectance from 4000 to 600 cm⁻¹. Models were developed by partial least squares (PLS) regression in a randomly selected subset of samples, and the precision of the equations was confirmed by using an external validation set. Analysis by FTIR spectroscopy was sufficiently sensitive to allow the accurate prediction of sample CP concentration (R² = 0.92) and to classify feeds according to their CP_WS concentrations using universal models (R² = 0.78) that included all sample types. Moreover, substantial improvements in predictions were observed when samples were subdivided in groups. Models for forages led to accurate predictions of CP_WS and fractions CP_A and CP_B (R² > 0.83), whereas models for CP_TTD and CP_ED could be used for screening purposes (R² > 0.67). This study showed that models for protein-rich concentrates alone could also be used for screening according to the feed concentrations of CP_WS, CP_TTD, CP_ED, CP_A, and CP_B, but models for energy-rich concentrates gave relatively poor predictions. The general difficulty observed in predicting CP_C is because of a low correlation between FTIR spectra and the kinetics of CP degradation, which may be the result of large variation in the reference method (i.e., in situ degradation studies) and perhaps also because of the presence of compounds that can modify the CP degradation pattern in the rumen. In conclusion, FTIR spectroscopy should be considered as a low-cost alternative in the feed evaluation industry.     

Genomic selection of milk fatty acid composition in Sarda dairy sheep: Effect of different phenotypes and relationship matrices on heritability and breeding value accuracy

Fatty acid (FA) composition is one of the most important aspects of milk nutritional quality. However, the inclusion of this trait as a breeding goal for dairy species is hampered by the logistics and high costs of phenotype recording. Fourier-transform infrared spectroscopy (FTIR) is a valid and cheap alternative to laboratory gas chromatography (GC) for predicting milk FA composition. Moreover, as for other novel phenotypes, the efficiency of selection for these traits can be enhanced by using genomic data. The objective of this research was to compare traditional versus genomic selection approaches for estimating genetic parameters and breeding values of milk fatty acid composition in dairy sheep using either GC-measured or FTIR-predicted FA as phenotypes. Milk FA profiles were available for a total of 923 Sarda breed ewes. The youngest 100 had their own phenotype masked to mimic selection candidates. Pedigree relationship information and genotypes were available for 923 and 769 ewes, respectively. Three statistical approaches were used: the classical-pedigree-based BLUP, the genomic BLUP that considers the genomic relationship matrix G, and the single-step genomic BLUP (ssGBLUP) where pedigree and genomic relationship matrices are blended into a single H matrix. Heritability estimates using pedigree were lower than ssGBLUP, and very similar between GC and FTIR regarding the statistical approach used. For some FA, mostly associated with animal diet (i.e., C18:2n-6, C18:3n-3), random effect of combination of flock and test date explained a relevant quota of total variance, reducing the heritability estimates accordingly. Genomic approaches (genomic BLUP and ssGBLUP) outperformed the traditional pedigree method both for GC and FTIR FA. Prediction accuracies in the older cohort were larger than the young cohort. Genomic prediction accuracies (obtained using either G or H relationship matrix) in the young cohort of animals, where their own phenotypes were masked, were similar for GC and FTIR. Multiple-trait analysis slightly affected genomic breeding value accuracies. These results suggest that FTIR-predicted milk FA composition could represent a valid option for inclusion in breeding programs.     

Prediction and repeatability of milk coagulation properties and curd-firming modeling parameters of ovine milk using Fourier-transform infrared spectroscopy and Bayesian models

The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm^?1 were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results.     

高效液相色谱-串联质谱法对烤鸭皮色素的组成和结构分析

为探究烤鸭皮上色机理,对烤鸭皮色素进行提取,经超声提取后采用高效液相色谱-串联质谱法对色素分离,并结合紫外-可见吸收光谱、傅里叶变换红外光谱进行结构鉴定.结果 表明:随着烤制时间的延长,烤鸭皮色泽L*值逐渐下降,a*值和b*值上升.烤鸭皮色素在波长210 nm处有紫外-可见最大吸收.傅里叶变换红外光谱分析表明,色素中含...     

硫酸软骨素脱蛋白工艺优化

为了提高硫酸软骨素的品质,采用Sevag试剂对硫酸软骨素提取液进行脱蛋白处理,通过单因素试验和正交试验对脱蛋白工艺进行优化,得到最佳条件为:氯仿与异戊醇体积比2:1,Sevag试剂用量为硫酸软骨素提取液体积的1/2,处理时间25min,处理次数3。各因素对脱蛋白效果影响大小依次为氯仿异戊醇比例处理时间处理次数Sevag试剂用量。在该最佳条件下,硫酸软骨素保留率为87.98%,脱蛋白率52.15%。经190~350nm紫外扫描,未见280nm处蛋白质特征吸收峰;经红外光谱分析,硫酸软骨的构型未发生变化,说明该方法稳定可靠,适合于工业化硫酸软骨素的脱蛋白。     

A comparison and joint use of VIS-NIR and MIR spectroscopic methods for the determination of some chemical parameters in soft cheeses at external and central zones: a preliminary study

There is a strong tendency towards exploring rapid and low cost methods for determining chemical parameters and degree of the ripening of cheeses. The visible-near infrared (VIS-NIR), mid infrared (MIR) and combination of VIS-NIR and MIR spectroscopic methods for measurements of some selected parameters of soft cheeses were compared. Fifteen traditional and stabilised retail soft cheeses, differing in manufacturing process were studied. Fat, dry matter (DM), pH, total nitrogen (TN) and water soluble nitrogen (WSN) contents were determined by reference methods and scanned with VIS-NIR and MIR spectrophotometers in reflectance mode. Three separate prediction models were developed from the VIS-NIR, MIR and the joint VIS-NIR-MIR spectra using the partial least square (PLS) regression and leave one-out cross-validation technique. Results showed that fat, DM, TN and WSN were the best predicted with the VIS-NIR models providing the lowest values of the root mean square error of prediction (RMSEP) of 1.32, 0.70, 0.11 and 0.10, respectively. The combination of the VIS-NIR and MIR spectral improved slightly the prediction of only the pH. This suggests using the VIS-NIR for the determination of fat, DM, TN and WSN. The pH can also be predicted from the two techniques with approximate quantitative prediction, while a difference between low and high levels of WSN/TN ratio could be determined by the VIS-NIR, MIR or joint use of VIS-NIR-MIR.     

Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy

Fourier-transform infrared spectroscopy (FTIR), followed by multivariate treatment of the spectral data, was used to classify vegetable oils according to their botanical origin, and also to establish the composition of binary mixtures of extra virgin olive oil (EVOO) with other low cost edible oils. Oil samples corresponding to five different botanical origins (EVOO, sunflower, corn, soybean and hazelnut) were used. The wavelength scale of the FTIR spectra of the oils was divided in 26 regions. The normalized absorbance peak areas within these regions were used as predictors. Classification of the oil samples according to their botanical origin was achieved by linear discriminant analysis (LDA). An excellent resolution among all categories was achieved using an LDA model constructed with eight predictors. In addition, multiple linear regression models were used to predict the composition of binary mixtures of EVOO with sunflower, corn, soybean and hazelnut oils. For all the binary mixtures, models capable of detecting a low cost oil content in EVOO as low as 5% were obtained.     

Detection of fresh palm oil adulteration with recycled cooking oil using fatty acid composition and FTIR spectral analysis

There is a growing concern over the food safety issue related to increased incidence of cooking oil adulteration with recycled cooking oil (RCO). The objective of this study was to detect fresh palm olein (FPO) adulteration with RCO using fatty acid composition (FAC) and Fourier-transform infrared spectroscopy (FTIR) spectral analyses combined with chemometrics. RCO prepared in the laboratory was mixed with FPO in the proportion ranged from 1% to 50% (v/v) to obtain the adulterated oil samples (AO). FACs for FPO, RCO, and AO were determined using gas chromatography equipped with a flame ionization detector (GC-FID). The compositions of most fatty acids in RCO lied within the normal ranges of Codex standard, except for C8:0, C10:0, C11:0, C15:0, trans C18:1, and polyunsaturated fatty acids (PUFAs), C20:5. PUFAs showed a consistent decreasing trend with increasing magnitude of change with respect to increasing adulteration level and thus might be a good indicator for detecting FPO adulteration with RCO. The evaluation parameters (coefficient of determination, root mean standard error) of the FTIR-partial least square (PLS) model of palm oil adulteration with recycled oil are R² = 0.995 and 3.25, respectively. For FTIR spectral analysis, the distinct variations in spectral regions and aberrations in characteristic bands between FPO and RCO were observed. The optimized PLS calibration model developed from normal spectral of the combined region at 3602–3398, 3016–2642, and 1845–650 cm^?1 overpredict the adulteration level. On the other hand, the discriminant analysis classification model was able to classify the FPO and AO into two distinct groups. Improvement of the principles of combined techniques in authenticating AO from fresh oil is beneficial as a guideline to detect adulteration in cooking oil.