Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and visual color appearance in primiparous and multiparous dairy cows

Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and... Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and visual color appearance in primiparous and multiparous dairy cows J. J. Gross, E. C. Kessler, and R. M. Bruckmaier Veterinary Physiology, Vetsuisse Faculty University of Bern, CH-3012 Switzerland ABSTRACT: The control of colostrum quality is essen- multiparous cows. Milk fat content was greater in quar- tial for successful calf rearing. Instruments for on-farm ter and composite colostrum samples of primiparous colostrum quality determination are mostly utilized compared with multiparous dairy cows. No clear rela- for testing composite colostrum samples, but do not tionships between IgG content and SG, Brix, and the take potential variation between quarters into account. color space coordinates L*, a*, and b* were detected. In cases of low composite colostrum quality, feeding Interestingly, results indicate that despite a similar range of better quality colostrum from individual quarters of the variables investigated, correlations between those might be beneficial. The objective of the present study parameters can differ at quarter compared to composite was to identify relationships between colostrum color, level. Not only for SG and Brix determination, but also colostrum quality and composition. Besides labora- for the color space coordinates measured, correlation tory methods, a colostrometer and a Brix refractometer coefficients with IgG concentration of the respective were used to assess colostrum quality at quarter levels. samples were greater at a composite compared with the Quarter and composite colostrum samples from 17 individual quarter level. In conclusion, accuracy and primiparous and 11 multiparous Holstein cows were limitations of on-farm instruments estimating colos- analyzed for total IgG, fat, protein and lactose con- trum quality apply to both quarter colostrum samples tent; color was measured by a spectrophotometer. In and composite evaluations. Identification of quarters the present study, an IgG concentration below 50 g/L with superior colostrum quality would possibly be a as determined by ELISA was found in 14.3% of the way to improve the immunization of newborn calves. analyzed quarter samples. Concentration and mass of However, the potential on-farm methods validated in IgG in composite colostrum samples were greater in the present study to estimate quarter colostrum qual- multiparous compared with primiparous cows. Specific ity are not sufficiently sensitive to distinguish between gravity (SG) of colostrum of individual and compos- quarters. This is due to the variation of gross colostrum ite samples was lower in primiparous compared with composition between individual quarters of a cow. Key words: colostrum, dairy cow, immunoglobulin G, quarter sampling, refractometer © 2017 American Society of Animal Science. This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Transl. Anim. Sci. 2017.1:26–35 doi:10.2527/tas2016.0001 INTRODUCTION after parturition in dairy cows vary markedly be- tween herds and between animals (Kehoe et al., 2007; Since bovine calves are born agammaglobulin- Baumrucker et al., 2010), but also vary between quar- emic and therefore depend on passive immunization ters (Baumrucker et al., 2014; Samarütel et al., 2016). via colostrum-derived immunoglobulins (Ig), a timely The provision of a sufficient amount of colostrum is supply with good quality colostrum is essential for in many cases not limiting. Thus feeding single quar- calf health (McGuire et al., 1976; Tyler et al., 1999). ter derived colostrum differing in its quality could be However, yield and quality of colostrum obtained an alternative method to maximize Ig supply. Many studies investigated the suitability of colostrometers and refractometers to estimate colostrum quality (e.g., Corresponding author: josef.gross@vetsuisse.unibe.ch Chigerwe et al., 2008; Morrill et al., 2012; Quigley Received September 9, 2016. et al., 2013). However, predominantly composite co- Accepted October 3, 2016. Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 27 lostrum samples were analyzed, not taking individual quarter variation into account. It is currently not known if quarter differences in IgG content within a cow can be reflected by specific gravity (SG) and Brix determi- nation, and to which extent the outcome of measuring colostrum quality of only one quarter contributes to the overall composite colostrum obtained at first milking. The objective of the present study was to evaluate the accuracy and precision of 2 different devices (colos- trometer and optical Brix refractometer) and to identify relationships between visual appearance of colostrum, colostrum quality assessed by the 2 common on-farm in- struments and composition of colostrum in primiparous and multiparous dairy cows at quarter level. In addition, individual quarters were compared to the composite co- lostrum results obtained to illustrate the impact of single quarters on total colostrum as commonly fed to calves. MATERIALS AND METHODS Animals and Colostrum Sampling Colostrum sampling was conducted following the guidelines of the Swiss Law on Animal Protection and approved by the Veterinary Office of the Canton Fribourg, Switzerland. Cows were transferred to straw- bedded calving pens approximately 7 d before expected parturition. Dry cows were fed hay ad libitum plus 1 kg Figure 1. Specific gravity (Fig. 1A) and Brix-values (% Brix, Fig. of cereal-based concentrate and 0.5 kg of mineral en- 1B) of 3 fresh composite colostrum samples obtained from multiparous riched supplement (contents per kg of dry matter: crude cows affected by adding increased volume of distilled water. ash 170 g; crude fiber 112 g; crude protein 67 g; vitamin A 180,200 IE; vitamin D 14,400 IE; vitamin E 1170 IE; Ca 2 g; P 7 g) until calving. Calves were removed im- 250 mL of colostrum was transferred into the colostrom- mediately after birth to prevent suckling. eter measuring-cylinder provided by the manufacturer. From September to December 2012, colostrum After the colostrometer was lowered into the cylinder samples of 17 primiparous and 11 multiparous Holstein and allowed to float freely, SG was determined by read- dairy cows (parity: 3.5 ± 1.5; 66 ± 15 d dry) were ob- ing the scale (SG 1.023 to 1.077) above the submerged tained at 4 h 50 min ± 1 h 46 min, and 4 h 12 min ± portion of the instrument. 32 min after parturition (mean ± SD), respectively, by Additionally, all samples were measured with an op- machine-milking. All cows were milked empty and co- tical Brix refractometer (Manual Refractometer MHRB- lostrum of each quarter was collected into a separate 40 ATC, Mueller Optronic, Erfurt, Germany) with a container. The amount of colostrum produced per quar- scale ranging from 0 to 40% Brix. The refractometer was ter was recorded and representative samples (approxi- equipped with an automatic temperature compensation mately 50 mL) of each quarter as well as of all after mechanism to ensure accurate measurements without re- merging individual quarters (composite sample) were calibration after shifts in ambient working temperature. stored at -20°C until further analysis. According to the manufacturer, the accuracy of the in- strument was ± 0.2% Brix at 20°C. Composite colostrum of different quality derived On-farm Devices for Colostrum Quality from 3 multiparous cows was diluted with distilled water Determination to investigate the response of SG and Brix toward a re- Fresh quarter and composite colostrum samples were duced content of colostrum components. Figure 1 shows analyzed at 20°C using a colostrum densimeter (Kruuse a linear decrease of SG (Fig. 1A) and Brix (Fig. 1B) with A/S, Langeskov, Denmark). Temperature was measured the addition of 100 mL of water. When merging these 3 with a universal thermometer for liquids. Approximately different colostrum qualities in different ratios, the addi- Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 28 Gross et al. Table 1. Comparison of different mixing ratios of 3 fresh composite colostrum samples obtained from multiparous cows Volume, mL of Specific gravity Brix, % Total volume, 1 2 1 2 Colostrum #1 Colostrum #2 Colostrum #3 mL Expected Measured Deviation , % Expected Measured Deviation , % 125 125 0 250 1043.5 1044 0.05 17.4 17 -2.30 0 125 125 250 1041 1042 0.10 16.2 15.6 -3.70 125 0 125 250 1045.5 1046 0.05 17.8 17.4 -2.25 50 50 150 250 1043.2 1043 -0.02 16.9 16.6 -1.90 50 150 50 250 1041.6 1042 0.04 16.6 15.8 -4.82 150 50 50 250 1045.2 1046 0.08 17.9 17.2 -3.80 50 100 100 250 1042.4 1043 0.06 16.8 16 -4.53 100 50 100 250 1044.2 1045 0.08 17.4 17 -2.30 100 100 50 250 1043.4 1043 -0.04 17.2 16.6 -3.71 Arithmetic mean weighted by the volume of colostrum #1 (specific gravity = 1048, 19.0% Brix), colostrum #2 (specific gravity = 1039, 15.8% Brix), and colostrum #3 (specific gravity = 1043, 16.6% Brix). Deviation of measured values from expected values calculated as follows: (expected – measured)/expected × 100 [%]. tive contribution of single proportions was reflected by Württemberg e. V. (Ravensburg, Germany) as earlier the very similar results of SG and Brix determination be- described by Gross et al. (2014a,b). tween expected and measured values (Table 1). All measurements with the colostrometer (SG deter- Statistical Analysis mination) and refractometer were performed in duplicate. Statistical analysis was performed with SAS (Version 9.4, SAS Inst. Inc., Cary, NC). The Measurement of Colostrum Color UNIVARIATE procedure of SAS was used to check Colostrum color was assessed as described ear- for normal distribution of data. In cases of not being lier for dairy cows by Gross et al. (2014a). Color [CIE normally distributed, data were log-transformed. Data 1976 (L*, a*, b*) color space- CIELAB] was measured presented in text and tables are means ± SEM. In agree- in thawed and homogenized (gentle shaking in a water ment with earlier studies, for the present study a thresh- bath at 37°C for 20 min) samples in triplicate at 25°C old to distinguish between high- and low-quality colos- using a calibrated Microflash 200d spectrophotom - trum was set at 50 mg IgG/mL as determined by ELISA. eter (Datacolor International, Dietikon, Switzerland), Sensitivity, specificity, and the negative predictive val- with the coordinates L* representing relative lightness ue (NPV) of the Brix and SG measurements for testing (black to white), a* giving the relative value between IgG concentration were calculated using 2-way con- green and red, and b* indicating the relative position tingency tables according to Argüello et al. (2005) and between blue and yellow. Gross et al. (2014a). Considering IgG concentrations derived from the ELISA analysis as reference values, the Brix and SG results were classified as test values. Colostrum IgG, Fat, Protein and Lactose Analyses Sensitivity was calculated by the proportion of samples After thawing, colostral total IgG concentration identified by SG < 1045, and Brix < 22% reflecting an was determined with a modified ELISA (Bovine IgG IgG concentration < 50 mg/mL in all samples with an ELISA Quantitation Set, order no. E10–118; Bethyl IgG concentration < 50 mg/mL evaluated by ELISA. Laboratories Inc. Montgomery, TX) as described re- Specificity represents the probability of samples with cently by Lehmann et al. (2013). Samples were thawed an IgG concentration ≥ 50 mg/mL identified by the at room temperature and serially diluted in ELISA wash colostrometer and refractometer relative to all samples buffer (50 mM Tris, 0.14 M NaCl, 0.05% Tween 20, classified high-quality by ELISA. The calculation of adjusted to pH 8.0) to final dilutions of 1:400,000 and the NPV was in agreement with Pritchett et al. (1994) 1:800,000. Results were expressed as IgG concentration and Argüello et al. (2005), and depicts the portion of in mg/mL. Mass of IgG secreted by the individual quar- truly high samples (estimated via ELISA) in all samples ters was calculated by multiplying IgG concentrations identified as high-quality colostrum based on data from by the corresponding volume of colostrum produced. colostrometer and refractometer. Milk fat, protein and lactose contents in colos- Within individual quarter and composite samples, trum samples were determined using a FTS Infrared Pearson correlation coefficients between colostrum Milk Analyzer (Bentley Instruments Inc., Chaska, yield, IgG concentration, IgG mass, SG, Brix, fat, pro- MN) in the laboratory of the Milchprüfring Baden- tein, lactose content and color space coordinates (L*, a*, Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 29 b*) were calculated using the CORR procedure of SAS. Significant effects were considered at P < 0.05. RESULTS AND DISCUSSION The objective of the present study was to assess co- lostrum quality of primiparous and multiparous dairy cows via color measurement, specific gravity, and Brix refractometry, and to compare results at quarter and composite levels. Whereas previous studies exclusively focused on testing quality of composite colostrum sam- ples, this is the first attempt to investigate the contribu- tion of single quarters to the overall colostrum quality. Colostrum Quality and Composition Despite a considerable variation in individual quar- ters, composite colostrum yield was greater in multipa- rous compared with primiparous dairy cows (P < 0.05, Fig. 2A). Furthermore, no differences in colostrum yield between front and rear quarters were detected. In all cows, colostrum yield was not related to IgG concentration at quarter or composite levels (Tables 2–6). Time of milking relative to calving did not affect colostral IgG concentra- tion (P = 0.59). Concentration and total mass of IgG in composite colostrum samples were greater in multiparous compared with primiparous cows (P < 0.05), whereas no differences between quarters and number of parity were found (Fig. 2B-C). While the range of colostrum yield and IgG concentration in quarters was similar for primip- arous and multiparous cows, IgG mass had a lower range in quarters of primiparous compared to multiparous cows. Variations in colostrum yield, IgG concentration and IgG mass in quarter and composite colostrum samples were similar to findings reported in the literature (Kehoe et al., 2007; Baumrucker et al., 2010, 2014; Gross et al., 2014b). Figure 2. Colostrum yield (Fig. 2A), IgG concentration (Fig. 2B), Specific gravity of colostrum from individual quar - and IgG mass (Fig. 2C) in quarter and composite colostrum samples (FL ters tended to be lower (P < 0.10), and was lower in com- = front left, FR = front right, HL = hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box posite samples (P < 0.0001) from primiparous compared represents 25th to 75th percentile of observations and the line in the box with multiparous cows (Fig. 3A) as observed earlier by indicates the median, whiskers show fifth to 95th percentiles, and the line Morrill et al. (2015). In contrast, Brix-values from indi- in the box indicates the median. vidual quarter and composite colostrum samples did not differ between primiparous and multiparous cows (P > 0.05, Fig. 3B). Milk fat contents were greater in quarter lostrum compared with that of multiparous cows and this and composite colostrum samples of primiparous com- may be partly due to the greater fat content (Gross et al., pared with multiparous dairy cows (P < 0.05), whereas 2014a). As feeding conditions were the same for all cows, no differences in protein and lactose contents were found possible dietary effects may be excluded. (Fig. 4A-C). Milk fat and protein contents were not re- lated to each other (r = 0.12, P = 0.47). In primiparous Relationships between Colostrum Quality Estimation cows, both individual quarter samples and composite co- and Colostrum Composition lostrum samples had greater values for the CIE coordi- nates L* and b* (P < 0.05), but not for a*, compared with In the present study, an IgG concentration below 50 multiparous cows (Fig. 5A-C). The greater L* values in g/L as determined by ELISA was found in 20 of the 140 primiparous cows indicate the greater brightness of co- samples (14.3%) analyzed. Major limitations regarding Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 30 Gross et al. Table 2. Front left quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.15 (P = 0.05 (P = 0.61 (P < -0.12 (P = -0.01 (P = 0.05 (P = -0.30 (P = 0.02 (P = 0.65) 0.88) 0.05) 0.73) 0.97) 0.88) 0.36) 0.96) IgG concentration, mg/mL -0.05 (P = - 0.01 (P = 0.10 (P = -0.61 (P < -0.46 (P = -0.24 (P = 0.18 (P = -0.12 (P = 0.85) 0.99) 0.78) 0.05) 0.15) 0.48) 0.60) 0.72) Brix, % 0.63 (P < 0.39 (P = - 0.46 (P = -0.02 (P = 0.25 (P = -0.17 (P = 0.09 (P = 0.33 (P = 0.01) 0.12) 0.16) 0.96) 0.46) 0.62) 0.79) 0.32) SG -0.24 (P = 0.20 (P = 0.05 (P = - 0.30 (P = 0.45 (P = 0.32 (P = -0.59 (P = 0.30 (P = 0.36) 0.44) 0.84) 0.36) 0.16) 0.33) 0.05) 0.37) Fat, % 0.43 (P = -0.07 (P = 0.26 (P = -0.38 (P = - 0.90 (P < 0.27 (P = -0.31 (P = 0.29 (P = 0.12) 0.81) 0.36) 0.18) 0.001) 0.42) 0.35) 0.39) Protein, % 0.75 (P < 0.50 (P = 0.98 (P < -0.23 (P = 0.19 (P = - 0.00 (P = -0.15 (P = 0.29 (P = 0.01) 0.07) 0.0001) 0.42) 0.52) 0.99) 0.65) 0.39) L* -0.48 (P = -0.37 (P = -0.69 (P < 0.06 (P = 0.21 (P = -0.79 (P < - -0.91 (P < 0.49 (P = 0.05) 0.15) 0.01) 0.82) 0.48) 0.0001) 0.0001) 0.13) a* -0.04 (P = -0.08 (P = 0.40 (P = 0.48 (P = 0.19 (P = 0.44 (P = -0.15 (P = - -0.38 (P = 0.87) 0.76) 0.11) 0.05) 0.53) 0.11) 0.56) 0.25) b* 0.57 (P < 0.14 (P = 0.59 (P < -0.14 (P = 0.12 (P = 0.70 (P < -0.78 (P < -0.05 (P = - 0.05) 0.60) 0.05) 0.60) 0.68) 0.01) 0.001) 0.86) Primiparous cows Table 3. Front right quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration , kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.18 (P = -0.13 (P = 0.53 (P = -0.43 (P = 0.02 (P = 0.25 (P = -0.32 (P = 0.26 (P = 0.59) 0.70) 0.19) 0.19) 0.94) 0.45) 0.34) 0.44) IgG concentration, mg/mL -0.14 (P = - -0.02 (P = 0.14 (P = 0.25 (P = -0.05 (P = -0.16 (P = 0.19 (P = -0.21 (P = 0.59) 0.94) 0.68) 0.46) 0.88) 0.63) 0.58) 0.54) Brix, % 0.45 (P = 0.29 (P = - 0.03 (P = -0.17 (P = 0.96 (P < -0.49 (P = 0.51 (P = 0.18 (P = 0.07) 0.27) 0.93) 0.61) 0.0001) 0.12) 0.11) 0.59) SG 0.11 (P = 0.17 (P = 0.11 (P = - -0.32 (P = 0.28 (P = 0.33 (P = -0.48 (P = 0.21 (P = 0.68) 0.50) 0.67) 0.33) 0.40) 0.32) 0.13) 0.53) Fat, % 0.01 (P = -0.02 (P = -0.10 (P = -0.10 (P = - -0.29 (P = 0.33 (P = -0.18 (P = -0.04 (P = 0.97) 0.94) 0.74) 0.73) 0.38) 0.32) 0.60) 0.90) Protein, % 0.27 (P = 0.42 (P = 0.95 (P < -0.05 (P = -0.25 (P = - -0.33 (P = 0.31 (P = 0.28 (P = 0.35) 0.13) 0.0001) 0.87) 0.39) 0.31) 0.36) 0.40) L* -0.45 (P = -0.01 (P = -0.72 (P < 0.20 (P = 0.08 (P = -0.69 (P < - -0.97 (P < 0.54 (P = 0.07) 0.96) 0.01) 0.45) 0.80) 0.01) 0.0001) 0.09) a* 0.06 (P = 0.30 (P = 0.10 (P = -0.20 (P = 0.61 (P < 0.10 (P = -0.24 (P = - -0.43 (P = 0.83) 0.24) 0.70) 0.44) 0.05) 0.73) 0.35) 0.18) b* 0.41 (P = 0.16 (P = 0.49 (P < -0.26 (P = 0.23 (P = 0.51 (P = -0.68 (P < 0.79 (P < - 0.10) 0.54) 0.05) 0.31) 0.43) 0.06) 0.01) 0.001) Primiparous cows Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Multiparous cows Quarter colostrum quality assessment 31 Table 4. Hind left quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.59 (P = -0.16 (P = 0.47 (P = -0.05 (P = 0.00 (P = -0.02 (P = -0.25 (P = -0.09 (P = 0.06) 0.63) 0.15) 0.89) 0.99) 0.95) 0.45) 0.79) IgG concentration, mg/mL -0.40 (P = - 0.20 (P = 0.28 (P = -0.01 (P = 0.17 (P = -0.27 (P = 0.19 (P = 0.28 (P = 0.11) 0.57) 0.40) 0.98) 0.61) 0.42) 0.59) 0.40) Brix, % 0.10 (P = 0.15 (P = - 0.00 (P = -0.04 (P = 0.94 (P < -0.22 (P = 0.29 (P = 0.43 (P = 0.70) 0.57) 0.99) 0.90) 0.0001) 0.52) 0.38) 0.19) SG -0.03 (P = 0.00 (P = 0.34 (P = - -0.20 (P = 0.02 (P = 0.48 (P = -0.61 (P < 0.24 (P = 0.92) 0.99) 0.19) 0.56) 0.96) 0.13) 0.05) 0.47) Fat, % 0.46 (P = 0.04 (P = -0.19 (P = -0.33 (P = - -0.25 (P = 0.35 (P = 0.07 (P = 0.52 (P = 0.10) 0.88) 0.53) 0.25) 0.46) 0.29) 0.85) 0.10) Protein, % 0.08 (P = 0.12 (P = 0.95 (P < -0.04 (P = -0.30 (P = - -0.28 (P = 0.20 (P = 0.17 (P = 0.78) 0.68) 0.0001) 0.90) 0.30) 0.40) 0.55) 0.62) L* -0.26 (P = -0.21 (P = -0.82 (P < -0.07 (P = 0.10 (P = -0.84 (P < - -0.85 (P < 0.29 (P = 0.32) 0.42) 0.0001) 0.78) 0.72) 0.001) 0.001) 0.38) a* -0.14 (P = 0.33 (P = 0.33 (P = -0.30 (P = 0.37 (P = 0.21 (P = -0.52 (P < - 0.08 (P = 0.59) 0.19) 0.20) 0.24) 0.19) 0.47) 0.05) 0.81) b* -0.04 (P = 0.22 (P = 0.56 (P < -0.15 (P = 0.26 (P = 0.50 (P = -0.67 (P < 0.91 (P < - 0.88) 0.41) 0.05) 0.56) 0.38) 0.07) 0.01) 0.0001) Primiparous cows Table 5. Hind right quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield concentration (kg) (mg/mL) Brix (%) SG Fat (%) Protein (%) L* a* b* Milk yield (kg) - 0.27 (P = -0.45 (P = 0.05 (P = -0.08 (P = -0.43 (P = 0.04 (P = -0.21 (P = -0.07 (P = 0.42) 0.17) 0.88) 0.81) 0.19) 0.90) 0.53) 0.84) IgG concentration (mg/mL) -0.20 (P = - 0.02 (P = 0.05 (P = -0.12 (P = 0.05 (P = -0.50 (P = 0.46 (P = 0.12 (P = 0.45) 0.95) 0.88) 0.72) 0.88) 0.12) 0.15) 0.73) Brix (%) 0.10 (P = 0.24 (P = - 0.29 (P = 0.00 (P = 0.95 (P < -0.17 (P = 0.31 (P = 0.54 (P = 0.69) 0.35) 0.39) 0.99) 0.0001) 0.61) 0.35) 0.08) SG 0.05 (P = 0.06 (P = 0.22 (P = - -0.35 (P = 0.27 (P = 0.49 (P = -0.39 (P = 0.30 (P = 0.85) 0.82) 0.39) 0.29) 0.42) 0.13) 0.24) 0.38) Fat (%) -0.19 (P = -0.14 (P = -0.03 (P = -0.16 (P = - -0.24 (P = 0.07 (P = 0.02 (P = 0.10 (P = 0.50) 0.63) 0.93) 0.59) 0.48) 0.84) 0.95) 0.76) Protein (%) -0.04 (P = 0.43 (P = 0.94 (P < 0.05 (P = -0.17 (P = - -0.25 (P = 0.30 (P = 0.36 (P = 0.90) 0.13) 0.0001) 0.87) 0.57) 0.45) 0.36) 0.27) L* -0.06 (P = -0.07 (P = -0.55 (P < -0.13 (P = 0.15 (P = -0.54 (P < - -0.91 (P < 0.26 (P = 0.83) 0.80) 0.05) 0.62) 0.61) 0.05) 0.0001) 0.45) a* -0.14 (P = 0.01 (P = 0.27 (P = -0.19 (P = 0.42 (P = 0.24 (P = -0.36 (P = - -0.05 (P = 0.60) 0.96) 0.29) 0.46) 0.14) 0.41) 0.15) 0.88) b* 0.12 (P = 0.10 (P = 0.50 (P < -0.19 (P = 0.40 (P = 0.49 (P = -0.47 (P = 0.83 (P < - 0.66) 0.70) 0.05) 0.47) 0.16) 0.08) 0.06) 0.0001) Primiparous cows Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Multiparous cows 32 Gross et al. Table 6. Composite colostrum samples of primiparous and multiparous dairy cows: Pearson correlation coef- ficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - -0.42 (P = -0.24 (P = 0.41 (P = -0.34 (P = -0.19 (P = 0.09 (P = -0.29 (P = -0.17 (P = 0.20) 0.48) 0.21) 0.30) 0.57) 0.80) 0.39) 0.61) IgG concentration, mg/mL 0.01 (P = - 0.48 (P = 0.61 (P < 0.19 (P = 0.30 (P = -0.68 (P < 0.76 (P < -0.01 (P = 0.95) 0.13) 0.05) 0.57) 0.37) 0.05) 0.01) 0.99) Brix, % 0.32 (P = 0.18 (P = - 0.17 (P = -0.09 (P = 0.95 (P < -0.25 (P = 0.26 (P = 0.45 (P = 0.21) 0.48) 0.62) 0.80) 0.0001) 0.46) 0.45) 0.16) SG -0.03 (P = -0.05 (P = 0.42 (P = - -0.07 (P = 0.26 (P = 0.72 (P < -0.74 (P < 0.56 (P = 0.90) 0.84) 0.09) 0.83) 0.45) 0.05) 0.01) 0.08) Fat, % 0.20 (P = -0.30 (P = -0.01 (P = -0.36 (P = - -0.27 (P = 0.39 (P = -0.16 (P = 0.28 (P = 0.50) 0.29) 0.97) 0.20) 0.43) 0.23) 0.64) 0.40) Protein, % 0.35 (P = 0.43 (P = 0.82 (P < 0.35 (P = -0.36 (P = - -0.17 (P = 0.13 (P = 0.32 (P = 0.21) 0.13) 0.0001) 0.22) 0.20) 0.61) 0.76) 0.34) L* -0.23 (P = -0.24 (P = -0.72 (P < -0.24 (P = 0.39 (P = -0.68 (P < - -0.93 (P < 0.44 (P = 0.37) 0.35) 0.01) 0.35) 0.16) 0.01) 0.0001) 0.17) a* 0.14 (P = -0.23 (P = 0.35 (P = -0.11 (P = 0.52 (P = 0.12 (P = -0.39 (P = - -0.22 (P = 0.59) 0.38) 0.17) 0.67) 0.05) 0.67) 0.12) 0.51) b* 0.34 (P = -0.05 (P = 0.60 (P < -0.04 (P = 0.30 (P = 0.46 (P = -0.65 (P < 0.88 (P < - 0.19) 0.85) 0.05) 0.88) 0.30) 0.10) 0.01) 0.0001) Primiparous cows the suitability of SG determined by a colostrometer were Tables 2 through 6 show the relationships between pointed out by Morin et al. (2001). Parity, season of calv- the colostrum quality estimation by SG, Brix, and col- ing, colostrum temperature and colostral protein content, or measurement, and colostrum composition (content among other factors, were identified by several studies to of IgG, fat and protein) within quarter and for com- affect accuracy and reliability of IgG estimation via SG posite colostrum samples. In neither primiparous nor (Mechor et al., 1992; Morin et al. 2001; Bielmann et al., multiparous cows were clear relationships between 2010; Bartier et al., 2015). Therefore, the identification IgG contents and SG, Brix, and the color space coor- of sufficient quality colostrum for feeding calves requires dinates L*, a*, and b* detected. Except for the front more accurate and robust instruments, e.g., Brix refrac- left quarter in multiparous cows, the Brix-values had a tometers (Fleenor and Stott, 1980; Morin et al., 2001; high correlation coefficient with the protein content of Quigley et al., 2013). When using the recommended cut- the colostrum samples, but not with IgG concentration. off points of 1.045 for SG and 22% for Brix (Bielmann This is in agreement with earlier findings (Bielmann et al., 2010), 57.1% and 36.4%, respectively, of the pres- et al., 2010; Quigley et al., 2013; Bartier et al., 2015). ent samples would have been classified as poor qual- ity colostrum by using a colostrometer or refractometer Differences between Quarter and Composite alone. Due to the low number of observations per quarter Colostrum Samples of colostrum truly classified as poor or good by either colostrometer or refractometer compared to the ELISA Colostrometer, refractometer and color measure- results, sensitivity of both on-farm instruments was be- ments mirrored the variation in colostrum composition at tween 0 and 100%. No differences between primiparous both quarter and composite levels. Though earlier stud- and multiparous cows were detected for sensitivity cal- ies suggested that the colostrometer predicts the protein culations. Specificity of the SG determination per quarter content of colostrum better than true IgG content (Morin was lower in primiparous compared to multiparous cows et al., 2001), results from the present study do not show (21.4 to 40.0% vs. 50.0 to 70.0%). For the refractometer, any association between SG and colostrum constituents specificity was greater in quarter and composite colos- (Tables 2 through 6). Surprisingly, in quarter colostrum trum samples of primiparous cows compared with mul- samples SG did not correlate with IgG concentration tiparous cows (60.0 to 85.7% vs. 50.0 to 70.0%). The (Tables 2 through 5), but after merging the quarters into NPV was similar for both on-farm instruments in primip- a composite sample in multiparous cows, the correlation arous and multiparous cows (83.3 to 100%). was in an acceptable range (r = 0.61, P < 0.05, Table 6). Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Quarter colostrum quality assessment 33 Figure 3. Specific gravity (SG) assessed by colostrometer (Fig. 3A), and Brix-values evaluated by refractometer (%Brix; Fig. 3B) in quarters and composite colostrum samples (FL = front left, FR = front right, HL = hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box represents 25th to 75th percentile of ob- servations and the line in the box indicates the median, whiskers show fifth to 95th percentiles, and the line in the box indicates the median. This observation can be explained by the fact that indi- vidual quarters within a cow show a marked variation in both fat and protein contents. Both fat and protein con- tent of colostrum contribute to the overall SG and Brix- value (Mechor et al., 1992; Morin et al., 2001; Bielmann Figure 4. Colostrum fat (Fig. 4A), protein (Fig. 4B), and lactose contents et al., 2010). Quarters within a cow do not have the same (Fig. 4C) in quarter and composite colostrum samples (FL = front left, FR = ratio of colostrum components. Merging quarters to front right, HL = hind left, HR = hind right, Total = composite sample) of composite colostrum samples therefore seems to com- primiparous and multiparous dairy cows. The box represents 25th to 75th per- centile of observations and the line in the box indicates the median, whiskers pensate for the lack of single components of individual show fifth to 95th percentiles, and the line in the box indicates the median. quarters contributing to SG and Brix (Table 6). In ad- dition, the linear scale of the colostrometer and refrac- tometer used in our study followed the gradual dilution cell count (SCC) contributing to coagulation properties, of composite colostrum with water that concomitantly and indirectly to viscosity of milk. Consequently, the reduces all colostrum components in the same propor- measurement of SG by reading the submerged portion of tion (Fig. 1A,B). It is unclear if potentially further matrix a colostrometer is very likely to be affected by the overall effects of colostrum differ at quarter level compared to viscous properties of colostrum. Even though SCC was a composite colostrum sample that may affect its physi- not determined in colostrum samples from the current cal properties, e.g., density, viscosity etc.. Differences in study, the first milk obtained after parturition is known quality of mature milk at the quarter vs. composite level to have a high content of SCC (Wall et al., 2015). It can were previously reported (Forsbäck et al., 2009, 2011). therefore be speculated that the greater SCC in colostrum Forsbäck et al. (2011) suggested that this discrepancy contributes to its physicochemical properties. Directly may be due to the impact of individual quarter somatic after parturition, the blood-milk-barrier is also known to Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 34 Gross et al. dividual quarter differences contributing to coagulation properties of the composite milk samples, differences in the structure and size of casein micelles at the quarter level were shown to be related to SCC (Frederiksen et al., 2011). When merging individual quarter colostrum samples, it can be assumed that micelles are interact- ing and changing their structure at the composite level and thus affecting SG and Brix measurements. However, variations in protein and fat contents between quarters of an animal are most likely responsible for the differences in SG and Brix-values at quarter and composite levels. Practical Implementation of Quarter Colostrum Analysis and Collection in Dairy Farms Up to now, quarter milking was not a topic with re- spect to colostrum milking in dairy practice. It is acknowl- edged that quarters are different in terms of IgG concen- tration and volume of produced colostrum (Baumrucker et al., 2014; Gross et al., 2016; Samarütel et al., 2016). Present findings revealed differences in colostrum qual- ity between single quarters and composite colostrum samples. Technically, automatic milking systems and some devices in conventional milking parlors can sepa- rate milk (and colostrum) from single quarters. In com- bination with in-line devices abnormal milk, e.g., during cases of mastitis, can currently be separated (Brandt et al., 2010). If a quarter is characterized by a high colostrum quality, it would be useful to collect and feed only colos- trum from this quarter. Current findings using established and previously validated instruments (i.e., refractometer, colostrometer) confirmed their potential and limitations in addressing colostrum quality. Furthermore, the present results identified possible reasons for variation at quar - ter level (different protein and fat content etc.) that are masked by evaluating only composite samples. Moore et Figure 5. Colostrum color coordinates [CIE 1976 (L*, a*, b*) color al. (2005) also reported that colostrum of only one quarter space– CIELAB], L* (Fig. 5A), a* (Fig. 5B), and b* (Fig. 5C) in quarter is often tested although the composite colostrum is fed to and composite colostrum samples (FL = front left, FR = front right, HL = calves. Considering present results, however, single quar- hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box represents 25th to 75th percentile of ob- ters do not represent the overall colostrum quality. This servations and the line in the box indicates the median, whiskers show fifth might have detrimental effects if poor quality colostrum is to 95th percentiles, and the line in the box indicates the median. given to the newborn. On the other hand, the identification of superior colostrum quality and immediate separation of be permeable so that blood-derived proteins are present particular quarters at milking could reduce the impact of in colostrum (Wall et al., 2015). These may additionally low quality colostrum from other quarters and benefit calf affect the matrix structure of colostrum at the quarter and rearing despite the known problems in terms of variation composite levels. In addition, the same phenomenon was in colostrum composition at quarter and composite levels. observed when determining the Brix-values even though colostrum temperature and handling were kept constant Conclusions for all measurements. However, in the case of SG, Brix determination and also the measured color space coor- In conclusion, accuracy and limitations of on- dinates, correlation coefficients with IgG concentration farm instruments used to estimate colostrum quality of the respective samples were higher at the composite based on composite samples also apply for quarter compared with the individual quarter level. Besides in- colostrum samples. The variations in quarter colos- Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 35 Gross, J. J., E. C. Kessler, V. Bjerre-Harpoth, C. Dechow, C. R. trum quality and composition were reflected by the Baumrucker, and R. M. Bruckmaier. 2014b. Peripartal proges- outcome of refractometer and colostrometer measure- terone and prolactin have little effect on the rapid transport of immunoglobulin G into colostrum of dairy cows. J. Dairy Sci. ments. Within individual quarters, correlation coeffi- 97:2923–2931. doi:10.3168/jds.2013-7795 cients between colostral IgG concentration, SG, Brix- Gross, J. J., G. Schüpbach-Regula, and R. M. Bruckmaier. 2016. Rapid values, and colostrum color were poorer compared to communication: Colostrum immunoglobulin concentration in composite samples. On the other hand, feeding high mammary quarters is repeatable in consecutive lactations of dairy quality colostrum from individual quarters could be an cows. J. Anim. Sci. 94:1755–1760. doi:10.2527/jas.2016-0362 option if sufficient amount of colostrum is produced. Kehoe, S. I., B. M. Jayarao, and A. J. Heinrichs. 2007. A survey of bovine colostrum composition and colostrum management practices on Pennsylvania dairy farms. J. Dairy Sci. 90:4108– LITERATURE CITED 4116. doi:10.3168/jds.2007-0040 Argüello, A., N. Castro, and J. Capote. 2005. Short Communication: Lehmann, M., O. Wellnitz, and R. M. Bruckmaier. 2013. Concomitant Evaluation of a color method for testing immunoglobulin G con- lipopolysaccharide-induced transfer of blood-derived com- centration in goat colostrum. J. Dairy Sci. 88:1752-1754 ponents including immunoglobulins into milk. J. Dairy Sci. Bartier, A. L., M. C. Windeyer, and L. Doepel. 2015. Evaluation of 96:889–896. doi:10.3168/jds.2012-5410 on-farm tools for colostrum quality measurement. J. Dairy Sci. McGuire, T. C., N. E. Pfeiffer , J. M. Weikel, and R. C. Bartsch. 1976. 98:1878–1884. 10.3168/jds.2014-8415 Failure of colostral immunoglobulin transfer in calves dying Baumrucker, C. R., A. M. Burkett, A. L. Magliaro-Macrina, and C. D. from infectious disease. J. Am. Vet. Med. Assoc. 169:713–718. Dechow. 2010. Colostrogenesis: Mass transfer of IgG1 into co- Mechor, G. D., Y. T. Gröhn, L. R. McDowell, and R. J. Van Saun. lostrum. J. Dairy Sci. 93:3031–3038. doi:10.3168/jds.2009-2963 1992. Specific gravity of bovine colostrum immunoglobulins Baumrucker, C. R., A. Stark, O. Wellnitz, C. Dechow, and R. M. as affected by temperature and colostrum components. J. Dairy Bruckmaier. 2014. Short communication: Immunoglobulin vari- Sci. 75:3131–3135. doi:10.3168/jds.S0022-0302(92)78076-X ation in quarter-milked colostrum. J. Dairy Sci. 97:3700–3706. Moore, M., J. W. Tyler, M. Chigerwe, M. E. Dawes, and J. R. doi:10.3168/jds.2013-7107 Middleton. 2005. Effect of delayed colostrum collection on Bielmann, V., J. Gillan, N. R. Perkins, A. L. Skidmore, S. Godden, colostral IgG concentration in dairy cows. J. Am. Vet. Med. and K. E. Leslie. 2010. An evaluation of Brix refractometry in- Assoc. 226:1375–1377. doi:10.2460/javma.2005.226.1375 struments for measurement of colostrum quality in dairy cattle. Morin, D. E., P. D. Constable, F. P. Maunsell, and G. C. McCoy. 2001. J. Dairy Sci. 93:3713–3721. doi:10.3168/jds.2009-2943 Factors associated with colostral specific gravity in dairy cows. J. Brandt, M., A. Haeussermann, and E. Hartung. 2010. Invited review: Dairy Sci. 84:937–943. doi:10.3168/jds.S0022-0302(01)74551-1 Technical solutions for analysis of milk constituents and abnor- Morrill, K. M., K. E. Robertson, M. M. Spring, A. L. Robinson, and mal milk. J. Dairy Sci. 93:427–436. doi:10.3168/jds.2009-2565 H. D. Tyler. 2015. Validating a refractometer to evaluate im- Chigerwe, M., J. W. Tyler, J. R. Middleton, J. N. Spain, J. S. Dill, munoglobulin G concentration in Jersey colostrum and the and B. J. Steevens. 2008. Comparison of four methods to as- effect of multiple freeze-thaw cycles on evaluating colostrum sess colostral IgG concentration in dairy cows. J. Am. Vet. quality. J. Dairy Sci. 98:595–601. doi:10.3168/jds.2014-8730 Med. Assoc. 233:761–766. doi:10.2460/javma.233.5.761 Morrill, K. M., E. Conrad, J. Polo, A. Lago, J. Campbell, J. Quigley, Fleenor, W. A., and G. H. Stott. 1980. Hydrometer test for estimation and H. Tyler. 2012. Estimate of colostral immunoglobulin G con- of immunoglobulin concentration in bovine colostrum. J. Dairy centration using refractometry without or with caprylic acid frac- Sci. 63:973–977. doi:10.3168/jds.S0022-0302(80)83034-7 tionation. J. Dairy Sci. 95:3987–3996. doi:10.3168/jds.2011-5104 Forsbäck, L., H. Lindmark-Månsson, A. Andrén, M. Akerstedt, and Pritchett, L. C., C. C. Gay, D. D. Hancock, and T. E. Besser. 1994. K. Svennersten-Sjaunja. 2009. Udder quarter milk composi- Evaluation of the hydrometer for testing immunoglobulin G1 tion at different levels of somatic cell count in cow composite concentrations in Holstein colostrum. J. Dairy Sci. 77:1761– milk. Animal 3:710–717. doi:10.1017/S1751731109004042 1767. doi:10.3168/jds.S0022-0302(94)77117-4 Forsbäck, L., H. Lindmark-Månsson, K. Svennersten-Sjaunja, L. B. Quigley, J. D., A. Lago, C. Chapman, P. Erickson, and J. Polo. 2013. Larsen, and A. Andrén. 2011. Effect of storage and separation Evaluation of the Brix refractometer to estimate immunoglobu- of milk at udder quarter level on milk composition, proteolysis, lin G concentration in bovine colostrum. J. Dairy Sci. 96:1148– and coagulation properties in relation to somatic cell count. J. 1155. doi:10.3168/jds.2012-5823 Dairy Sci. 94:5341–5349. doi:10.3168/jds.2011-4371 Samarütel, J., C. R. Baumrucker, J. J. Gross, C. D. Dechow, and Frederiksen, P. D., K. K. Andersen, M. Hammershøj, H. D. Poulsen, R. M. Bruckmaier. 2016. Quarter variation and correlations of J. Sørensen, M. Bakman, K. B. Qvist, and L. B. Larsen. 2011. colostrum albumin, immunoglobulin G1 and G2 in dairy cows. Composition and effect of blending of noncoagulating, poorly J. Dairy Res. 83:209–218. doi:10.1017/S0022029916000091 coagulating, and well-coagulating bovine milk from indi- Tyler, J. W., D. D. Hancock, J. G. Thorne, C. C. Gay, and J. M. Gay. vidual Danish Holstein cows. J. Dairy Sci. 94:4787–4799. 1999. Partitioning the mortality risk associated with inadequate doi:10.3168/jds.2011-4343 passive transfer of colostral immunoglobulins in dairy calves. J. Gross, J. J., E. C. Kessler, and R. M. Bruckmaier. 2014a. Colour Vet. Intern. Med. 13:335–337. doi:10.1111/j.1939-1676.1999. measurement of colostrum for estimation of colostral IgG and tb02191.x colostrum composition in dairy cows. J. Dairy Res. 81:440– Wall, S. K., J. J. Gross, E. C. Kessler, K. Villez, and R. M. Bruckmaier. 444. doi:10.1017/S0022029914000466 2015. Blood-derived proteins in milk at start of lactation: Indicators of active or passive transfer. J. Dairy Sci. 98:7748– 7756. doi:10.3168/jds.2015-9440 Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Translational Animal Science Oxford University Press

Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and visual color appearance in primiparous and multiparous dairy cows

Download PDF
10 pages

Loading next page...
 
/lp/oxford-university-press/quarter-vs-composite-colostrum-composition-assessed-by-brix-yQ3zDFGw98

References (57)

eISSN
2573-2102
DOI
10.2527/tas2016.0001
Publisher site
See Article on Publisher Site

Abstract

Quarter vs. composite colostrum composition assessed by Brix refractometry, specific gravity and visual color appearance in primiparous and multiparous dairy cows J. J. Gross, E. C. Kessler, and R. M. Bruckmaier Veterinary Physiology, Vetsuisse Faculty University of Bern, CH-3012 Switzerland ABSTRACT: The control of colostrum quality is essen- multiparous cows. Milk fat content was greater in quar- tial for successful calf rearing. Instruments for on-farm ter and composite colostrum samples of primiparous colostrum quality determination are mostly utilized compared with multiparous dairy cows. No clear rela- for testing composite colostrum samples, but do not tionships between IgG content and SG, Brix, and the take potential variation between quarters into account. color space coordinates L*, a*, and b* were detected. In cases of low composite colostrum quality, feeding Interestingly, results indicate that despite a similar range of better quality colostrum from individual quarters of the variables investigated, correlations between those might be beneficial. The objective of the present study parameters can differ at quarter compared to composite was to identify relationships between colostrum color, level. Not only for SG and Brix determination, but also colostrum quality and composition. Besides labora- for the color space coordinates measured, correlation tory methods, a colostrometer and a Brix refractometer coefficients with IgG concentration of the respective were used to assess colostrum quality at quarter levels. samples were greater at a composite compared with the Quarter and composite colostrum samples from 17 individual quarter level. In conclusion, accuracy and primiparous and 11 multiparous Holstein cows were limitations of on-farm instruments estimating colos- analyzed for total IgG, fat, protein and lactose con- trum quality apply to both quarter colostrum samples tent; color was measured by a spectrophotometer. In and composite evaluations. Identification of quarters the present study, an IgG concentration below 50 g/L with superior colostrum quality would possibly be a as determined by ELISA was found in 14.3% of the way to improve the immunization of newborn calves. analyzed quarter samples. Concentration and mass of However, the potential on-farm methods validated in IgG in composite colostrum samples were greater in the present study to estimate quarter colostrum qual- multiparous compared with primiparous cows. Specific ity are not sufficiently sensitive to distinguish between gravity (SG) of colostrum of individual and compos- quarters. This is due to the variation of gross colostrum ite samples was lower in primiparous compared with composition between individual quarters of a cow. Key words: colostrum, dairy cow, immunoglobulin G, quarter sampling, refractometer © 2017 American Society of Animal Science. This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Transl. Anim. Sci. 2017.1:26–35 doi:10.2527/tas2016.0001 INTRODUCTION after parturition in dairy cows vary markedly be- tween herds and between animals (Kehoe et al., 2007; Since bovine calves are born agammaglobulin- Baumrucker et al., 2010), but also vary between quar- emic and therefore depend on passive immunization ters (Baumrucker et al., 2014; Samarütel et al., 2016). via colostrum-derived immunoglobulins (Ig), a timely The provision of a sufficient amount of colostrum is supply with good quality colostrum is essential for in many cases not limiting. Thus feeding single quar- calf health (McGuire et al., 1976; Tyler et al., 1999). ter derived colostrum differing in its quality could be However, yield and quality of colostrum obtained an alternative method to maximize Ig supply. Many studies investigated the suitability of colostrometers and refractometers to estimate colostrum quality (e.g., Corresponding author: josef.gross@vetsuisse.unibe.ch Chigerwe et al., 2008; Morrill et al., 2012; Quigley Received September 9, 2016. et al., 2013). However, predominantly composite co- Accepted October 3, 2016. Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 27 lostrum samples were analyzed, not taking individual quarter variation into account. It is currently not known if quarter differences in IgG content within a cow can be reflected by specific gravity (SG) and Brix determi- nation, and to which extent the outcome of measuring colostrum quality of only one quarter contributes to the overall composite colostrum obtained at first milking. The objective of the present study was to evaluate the accuracy and precision of 2 different devices (colos- trometer and optical Brix refractometer) and to identify relationships between visual appearance of colostrum, colostrum quality assessed by the 2 common on-farm in- struments and composition of colostrum in primiparous and multiparous dairy cows at quarter level. In addition, individual quarters were compared to the composite co- lostrum results obtained to illustrate the impact of single quarters on total colostrum as commonly fed to calves. MATERIALS AND METHODS Animals and Colostrum Sampling Colostrum sampling was conducted following the guidelines of the Swiss Law on Animal Protection and approved by the Veterinary Office of the Canton Fribourg, Switzerland. Cows were transferred to straw- bedded calving pens approximately 7 d before expected parturition. Dry cows were fed hay ad libitum plus 1 kg Figure 1. Specific gravity (Fig. 1A) and Brix-values (% Brix, Fig. of cereal-based concentrate and 0.5 kg of mineral en- 1B) of 3 fresh composite colostrum samples obtained from multiparous riched supplement (contents per kg of dry matter: crude cows affected by adding increased volume of distilled water. ash 170 g; crude fiber 112 g; crude protein 67 g; vitamin A 180,200 IE; vitamin D 14,400 IE; vitamin E 1170 IE; Ca 2 g; P 7 g) until calving. Calves were removed im- 250 mL of colostrum was transferred into the colostrom- mediately after birth to prevent suckling. eter measuring-cylinder provided by the manufacturer. From September to December 2012, colostrum After the colostrometer was lowered into the cylinder samples of 17 primiparous and 11 multiparous Holstein and allowed to float freely, SG was determined by read- dairy cows (parity: 3.5 ± 1.5; 66 ± 15 d dry) were ob- ing the scale (SG 1.023 to 1.077) above the submerged tained at 4 h 50 min ± 1 h 46 min, and 4 h 12 min ± portion of the instrument. 32 min after parturition (mean ± SD), respectively, by Additionally, all samples were measured with an op- machine-milking. All cows were milked empty and co- tical Brix refractometer (Manual Refractometer MHRB- lostrum of each quarter was collected into a separate 40 ATC, Mueller Optronic, Erfurt, Germany) with a container. The amount of colostrum produced per quar- scale ranging from 0 to 40% Brix. The refractometer was ter was recorded and representative samples (approxi- equipped with an automatic temperature compensation mately 50 mL) of each quarter as well as of all after mechanism to ensure accurate measurements without re- merging individual quarters (composite sample) were calibration after shifts in ambient working temperature. stored at -20°C until further analysis. According to the manufacturer, the accuracy of the in- strument was ± 0.2% Brix at 20°C. Composite colostrum of different quality derived On-farm Devices for Colostrum Quality from 3 multiparous cows was diluted with distilled water Determination to investigate the response of SG and Brix toward a re- Fresh quarter and composite colostrum samples were duced content of colostrum components. Figure 1 shows analyzed at 20°C using a colostrum densimeter (Kruuse a linear decrease of SG (Fig. 1A) and Brix (Fig. 1B) with A/S, Langeskov, Denmark). Temperature was measured the addition of 100 mL of water. When merging these 3 with a universal thermometer for liquids. Approximately different colostrum qualities in different ratios, the addi- Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 28 Gross et al. Table 1. Comparison of different mixing ratios of 3 fresh composite colostrum samples obtained from multiparous cows Volume, mL of Specific gravity Brix, % Total volume, 1 2 1 2 Colostrum #1 Colostrum #2 Colostrum #3 mL Expected Measured Deviation , % Expected Measured Deviation , % 125 125 0 250 1043.5 1044 0.05 17.4 17 -2.30 0 125 125 250 1041 1042 0.10 16.2 15.6 -3.70 125 0 125 250 1045.5 1046 0.05 17.8 17.4 -2.25 50 50 150 250 1043.2 1043 -0.02 16.9 16.6 -1.90 50 150 50 250 1041.6 1042 0.04 16.6 15.8 -4.82 150 50 50 250 1045.2 1046 0.08 17.9 17.2 -3.80 50 100 100 250 1042.4 1043 0.06 16.8 16 -4.53 100 50 100 250 1044.2 1045 0.08 17.4 17 -2.30 100 100 50 250 1043.4 1043 -0.04 17.2 16.6 -3.71 Arithmetic mean weighted by the volume of colostrum #1 (specific gravity = 1048, 19.0% Brix), colostrum #2 (specific gravity = 1039, 15.8% Brix), and colostrum #3 (specific gravity = 1043, 16.6% Brix). Deviation of measured values from expected values calculated as follows: (expected – measured)/expected × 100 [%]. tive contribution of single proportions was reflected by Württemberg e. V. (Ravensburg, Germany) as earlier the very similar results of SG and Brix determination be- described by Gross et al. (2014a,b). tween expected and measured values (Table 1). All measurements with the colostrometer (SG deter- Statistical Analysis mination) and refractometer were performed in duplicate. Statistical analysis was performed with SAS (Version 9.4, SAS Inst. Inc., Cary, NC). The Measurement of Colostrum Color UNIVARIATE procedure of SAS was used to check Colostrum color was assessed as described ear- for normal distribution of data. In cases of not being lier for dairy cows by Gross et al. (2014a). Color [CIE normally distributed, data were log-transformed. Data 1976 (L*, a*, b*) color space- CIELAB] was measured presented in text and tables are means ± SEM. In agree- in thawed and homogenized (gentle shaking in a water ment with earlier studies, for the present study a thresh- bath at 37°C for 20 min) samples in triplicate at 25°C old to distinguish between high- and low-quality colos- using a calibrated Microflash 200d spectrophotom - trum was set at 50 mg IgG/mL as determined by ELISA. eter (Datacolor International, Dietikon, Switzerland), Sensitivity, specificity, and the negative predictive val- with the coordinates L* representing relative lightness ue (NPV) of the Brix and SG measurements for testing (black to white), a* giving the relative value between IgG concentration were calculated using 2-way con- green and red, and b* indicating the relative position tingency tables according to Argüello et al. (2005) and between blue and yellow. Gross et al. (2014a). Considering IgG concentrations derived from the ELISA analysis as reference values, the Brix and SG results were classified as test values. Colostrum IgG, Fat, Protein and Lactose Analyses Sensitivity was calculated by the proportion of samples After thawing, colostral total IgG concentration identified by SG < 1045, and Brix < 22% reflecting an was determined with a modified ELISA (Bovine IgG IgG concentration < 50 mg/mL in all samples with an ELISA Quantitation Set, order no. E10–118; Bethyl IgG concentration < 50 mg/mL evaluated by ELISA. Laboratories Inc. Montgomery, TX) as described re- Specificity represents the probability of samples with cently by Lehmann et al. (2013). Samples were thawed an IgG concentration ≥ 50 mg/mL identified by the at room temperature and serially diluted in ELISA wash colostrometer and refractometer relative to all samples buffer (50 mM Tris, 0.14 M NaCl, 0.05% Tween 20, classified high-quality by ELISA. The calculation of adjusted to pH 8.0) to final dilutions of 1:400,000 and the NPV was in agreement with Pritchett et al. (1994) 1:800,000. Results were expressed as IgG concentration and Argüello et al. (2005), and depicts the portion of in mg/mL. Mass of IgG secreted by the individual quar- truly high samples (estimated via ELISA) in all samples ters was calculated by multiplying IgG concentrations identified as high-quality colostrum based on data from by the corresponding volume of colostrum produced. colostrometer and refractometer. Milk fat, protein and lactose contents in colos- Within individual quarter and composite samples, trum samples were determined using a FTS Infrared Pearson correlation coefficients between colostrum Milk Analyzer (Bentley Instruments Inc., Chaska, yield, IgG concentration, IgG mass, SG, Brix, fat, pro- MN) in the laboratory of the Milchprüfring Baden- tein, lactose content and color space coordinates (L*, a*, Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 29 b*) were calculated using the CORR procedure of SAS. Significant effects were considered at P < 0.05. RESULTS AND DISCUSSION The objective of the present study was to assess co- lostrum quality of primiparous and multiparous dairy cows via color measurement, specific gravity, and Brix refractometry, and to compare results at quarter and composite levels. Whereas previous studies exclusively focused on testing quality of composite colostrum sam- ples, this is the first attempt to investigate the contribu- tion of single quarters to the overall colostrum quality. Colostrum Quality and Composition Despite a considerable variation in individual quar- ters, composite colostrum yield was greater in multipa- rous compared with primiparous dairy cows (P < 0.05, Fig. 2A). Furthermore, no differences in colostrum yield between front and rear quarters were detected. In all cows, colostrum yield was not related to IgG concentration at quarter or composite levels (Tables 2–6). Time of milking relative to calving did not affect colostral IgG concentra- tion (P = 0.59). Concentration and total mass of IgG in composite colostrum samples were greater in multiparous compared with primiparous cows (P < 0.05), whereas no differences between quarters and number of parity were found (Fig. 2B-C). While the range of colostrum yield and IgG concentration in quarters was similar for primip- arous and multiparous cows, IgG mass had a lower range in quarters of primiparous compared to multiparous cows. Variations in colostrum yield, IgG concentration and IgG mass in quarter and composite colostrum samples were similar to findings reported in the literature (Kehoe et al., 2007; Baumrucker et al., 2010, 2014; Gross et al., 2014b). Figure 2. Colostrum yield (Fig. 2A), IgG concentration (Fig. 2B), Specific gravity of colostrum from individual quar - and IgG mass (Fig. 2C) in quarter and composite colostrum samples (FL ters tended to be lower (P < 0.10), and was lower in com- = front left, FR = front right, HL = hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box posite samples (P < 0.0001) from primiparous compared represents 25th to 75th percentile of observations and the line in the box with multiparous cows (Fig. 3A) as observed earlier by indicates the median, whiskers show fifth to 95th percentiles, and the line Morrill et al. (2015). In contrast, Brix-values from indi- in the box indicates the median. vidual quarter and composite colostrum samples did not differ between primiparous and multiparous cows (P > 0.05, Fig. 3B). Milk fat contents were greater in quarter lostrum compared with that of multiparous cows and this and composite colostrum samples of primiparous com- may be partly due to the greater fat content (Gross et al., pared with multiparous dairy cows (P < 0.05), whereas 2014a). As feeding conditions were the same for all cows, no differences in protein and lactose contents were found possible dietary effects may be excluded. (Fig. 4A-C). Milk fat and protein contents were not re- lated to each other (r = 0.12, P = 0.47). In primiparous Relationships between Colostrum Quality Estimation cows, both individual quarter samples and composite co- and Colostrum Composition lostrum samples had greater values for the CIE coordi- nates L* and b* (P < 0.05), but not for a*, compared with In the present study, an IgG concentration below 50 multiparous cows (Fig. 5A-C). The greater L* values in g/L as determined by ELISA was found in 20 of the 140 primiparous cows indicate the greater brightness of co- samples (14.3%) analyzed. Major limitations regarding Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 30 Gross et al. Table 2. Front left quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.15 (P = 0.05 (P = 0.61 (P < -0.12 (P = -0.01 (P = 0.05 (P = -0.30 (P = 0.02 (P = 0.65) 0.88) 0.05) 0.73) 0.97) 0.88) 0.36) 0.96) IgG concentration, mg/mL -0.05 (P = - 0.01 (P = 0.10 (P = -0.61 (P < -0.46 (P = -0.24 (P = 0.18 (P = -0.12 (P = 0.85) 0.99) 0.78) 0.05) 0.15) 0.48) 0.60) 0.72) Brix, % 0.63 (P < 0.39 (P = - 0.46 (P = -0.02 (P = 0.25 (P = -0.17 (P = 0.09 (P = 0.33 (P = 0.01) 0.12) 0.16) 0.96) 0.46) 0.62) 0.79) 0.32) SG -0.24 (P = 0.20 (P = 0.05 (P = - 0.30 (P = 0.45 (P = 0.32 (P = -0.59 (P = 0.30 (P = 0.36) 0.44) 0.84) 0.36) 0.16) 0.33) 0.05) 0.37) Fat, % 0.43 (P = -0.07 (P = 0.26 (P = -0.38 (P = - 0.90 (P < 0.27 (P = -0.31 (P = 0.29 (P = 0.12) 0.81) 0.36) 0.18) 0.001) 0.42) 0.35) 0.39) Protein, % 0.75 (P < 0.50 (P = 0.98 (P < -0.23 (P = 0.19 (P = - 0.00 (P = -0.15 (P = 0.29 (P = 0.01) 0.07) 0.0001) 0.42) 0.52) 0.99) 0.65) 0.39) L* -0.48 (P = -0.37 (P = -0.69 (P < 0.06 (P = 0.21 (P = -0.79 (P < - -0.91 (P < 0.49 (P = 0.05) 0.15) 0.01) 0.82) 0.48) 0.0001) 0.0001) 0.13) a* -0.04 (P = -0.08 (P = 0.40 (P = 0.48 (P = 0.19 (P = 0.44 (P = -0.15 (P = - -0.38 (P = 0.87) 0.76) 0.11) 0.05) 0.53) 0.11) 0.56) 0.25) b* 0.57 (P < 0.14 (P = 0.59 (P < -0.14 (P = 0.12 (P = 0.70 (P < -0.78 (P < -0.05 (P = - 0.05) 0.60) 0.05) 0.60) 0.68) 0.01) 0.001) 0.86) Primiparous cows Table 3. Front right quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration , kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.18 (P = -0.13 (P = 0.53 (P = -0.43 (P = 0.02 (P = 0.25 (P = -0.32 (P = 0.26 (P = 0.59) 0.70) 0.19) 0.19) 0.94) 0.45) 0.34) 0.44) IgG concentration, mg/mL -0.14 (P = - -0.02 (P = 0.14 (P = 0.25 (P = -0.05 (P = -0.16 (P = 0.19 (P = -0.21 (P = 0.59) 0.94) 0.68) 0.46) 0.88) 0.63) 0.58) 0.54) Brix, % 0.45 (P = 0.29 (P = - 0.03 (P = -0.17 (P = 0.96 (P < -0.49 (P = 0.51 (P = 0.18 (P = 0.07) 0.27) 0.93) 0.61) 0.0001) 0.12) 0.11) 0.59) SG 0.11 (P = 0.17 (P = 0.11 (P = - -0.32 (P = 0.28 (P = 0.33 (P = -0.48 (P = 0.21 (P = 0.68) 0.50) 0.67) 0.33) 0.40) 0.32) 0.13) 0.53) Fat, % 0.01 (P = -0.02 (P = -0.10 (P = -0.10 (P = - -0.29 (P = 0.33 (P = -0.18 (P = -0.04 (P = 0.97) 0.94) 0.74) 0.73) 0.38) 0.32) 0.60) 0.90) Protein, % 0.27 (P = 0.42 (P = 0.95 (P < -0.05 (P = -0.25 (P = - -0.33 (P = 0.31 (P = 0.28 (P = 0.35) 0.13) 0.0001) 0.87) 0.39) 0.31) 0.36) 0.40) L* -0.45 (P = -0.01 (P = -0.72 (P < 0.20 (P = 0.08 (P = -0.69 (P < - -0.97 (P < 0.54 (P = 0.07) 0.96) 0.01) 0.45) 0.80) 0.01) 0.0001) 0.09) a* 0.06 (P = 0.30 (P = 0.10 (P = -0.20 (P = 0.61 (P < 0.10 (P = -0.24 (P = - -0.43 (P = 0.83) 0.24) 0.70) 0.44) 0.05) 0.73) 0.35) 0.18) b* 0.41 (P = 0.16 (P = 0.49 (P < -0.26 (P = 0.23 (P = 0.51 (P = -0.68 (P < 0.79 (P < - 0.10) 0.54) 0.05) 0.31) 0.43) 0.06) 0.01) 0.001) Primiparous cows Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Multiparous cows Quarter colostrum quality assessment 31 Table 4. Hind left quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - 0.59 (P = -0.16 (P = 0.47 (P = -0.05 (P = 0.00 (P = -0.02 (P = -0.25 (P = -0.09 (P = 0.06) 0.63) 0.15) 0.89) 0.99) 0.95) 0.45) 0.79) IgG concentration, mg/mL -0.40 (P = - 0.20 (P = 0.28 (P = -0.01 (P = 0.17 (P = -0.27 (P = 0.19 (P = 0.28 (P = 0.11) 0.57) 0.40) 0.98) 0.61) 0.42) 0.59) 0.40) Brix, % 0.10 (P = 0.15 (P = - 0.00 (P = -0.04 (P = 0.94 (P < -0.22 (P = 0.29 (P = 0.43 (P = 0.70) 0.57) 0.99) 0.90) 0.0001) 0.52) 0.38) 0.19) SG -0.03 (P = 0.00 (P = 0.34 (P = - -0.20 (P = 0.02 (P = 0.48 (P = -0.61 (P < 0.24 (P = 0.92) 0.99) 0.19) 0.56) 0.96) 0.13) 0.05) 0.47) Fat, % 0.46 (P = 0.04 (P = -0.19 (P = -0.33 (P = - -0.25 (P = 0.35 (P = 0.07 (P = 0.52 (P = 0.10) 0.88) 0.53) 0.25) 0.46) 0.29) 0.85) 0.10) Protein, % 0.08 (P = 0.12 (P = 0.95 (P < -0.04 (P = -0.30 (P = - -0.28 (P = 0.20 (P = 0.17 (P = 0.78) 0.68) 0.0001) 0.90) 0.30) 0.40) 0.55) 0.62) L* -0.26 (P = -0.21 (P = -0.82 (P < -0.07 (P = 0.10 (P = -0.84 (P < - -0.85 (P < 0.29 (P = 0.32) 0.42) 0.0001) 0.78) 0.72) 0.001) 0.001) 0.38) a* -0.14 (P = 0.33 (P = 0.33 (P = -0.30 (P = 0.37 (P = 0.21 (P = -0.52 (P < - 0.08 (P = 0.59) 0.19) 0.20) 0.24) 0.19) 0.47) 0.05) 0.81) b* -0.04 (P = 0.22 (P = 0.56 (P < -0.15 (P = 0.26 (P = 0.50 (P = -0.67 (P < 0.91 (P < - 0.88) 0.41) 0.05) 0.56) 0.38) 0.07) 0.01) 0.0001) Primiparous cows Table 5. Hind right quarter of primiparous and multiparous dairy cows: Pearson correlation coefficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield concentration (kg) (mg/mL) Brix (%) SG Fat (%) Protein (%) L* a* b* Milk yield (kg) - 0.27 (P = -0.45 (P = 0.05 (P = -0.08 (P = -0.43 (P = 0.04 (P = -0.21 (P = -0.07 (P = 0.42) 0.17) 0.88) 0.81) 0.19) 0.90) 0.53) 0.84) IgG concentration (mg/mL) -0.20 (P = - 0.02 (P = 0.05 (P = -0.12 (P = 0.05 (P = -0.50 (P = 0.46 (P = 0.12 (P = 0.45) 0.95) 0.88) 0.72) 0.88) 0.12) 0.15) 0.73) Brix (%) 0.10 (P = 0.24 (P = - 0.29 (P = 0.00 (P = 0.95 (P < -0.17 (P = 0.31 (P = 0.54 (P = 0.69) 0.35) 0.39) 0.99) 0.0001) 0.61) 0.35) 0.08) SG 0.05 (P = 0.06 (P = 0.22 (P = - -0.35 (P = 0.27 (P = 0.49 (P = -0.39 (P = 0.30 (P = 0.85) 0.82) 0.39) 0.29) 0.42) 0.13) 0.24) 0.38) Fat (%) -0.19 (P = -0.14 (P = -0.03 (P = -0.16 (P = - -0.24 (P = 0.07 (P = 0.02 (P = 0.10 (P = 0.50) 0.63) 0.93) 0.59) 0.48) 0.84) 0.95) 0.76) Protein (%) -0.04 (P = 0.43 (P = 0.94 (P < 0.05 (P = -0.17 (P = - -0.25 (P = 0.30 (P = 0.36 (P = 0.90) 0.13) 0.0001) 0.87) 0.57) 0.45) 0.36) 0.27) L* -0.06 (P = -0.07 (P = -0.55 (P < -0.13 (P = 0.15 (P = -0.54 (P < - -0.91 (P < 0.26 (P = 0.83) 0.80) 0.05) 0.62) 0.61) 0.05) 0.0001) 0.45) a* -0.14 (P = 0.01 (P = 0.27 (P = -0.19 (P = 0.42 (P = 0.24 (P = -0.36 (P = - -0.05 (P = 0.60) 0.96) 0.29) 0.46) 0.14) 0.41) 0.15) 0.88) b* 0.12 (P = 0.10 (P = 0.50 (P < -0.19 (P = 0.40 (P = 0.49 (P = -0.47 (P = 0.83 (P < - 0.66) 0.70) 0.05) 0.47) 0.16) 0.08) 0.06) 0.0001) Primiparous cows Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Multiparous cows 32 Gross et al. Table 6. Composite colostrum samples of primiparous and multiparous dairy cows: Pearson correlation coef- ficients between colostrum constituents and quality parameters (SG = specific gravity, color coordinates L*, a*, and b*). Significant correlation coefficients ( P < 0.05) are highlighted in bold-type IgG Milk yield, concentration, kg mg/mL Brix, % SG Fat, % Protein, % L* a* b* Milk yield, kg - -0.42 (P = -0.24 (P = 0.41 (P = -0.34 (P = -0.19 (P = 0.09 (P = -0.29 (P = -0.17 (P = 0.20) 0.48) 0.21) 0.30) 0.57) 0.80) 0.39) 0.61) IgG concentration, mg/mL 0.01 (P = - 0.48 (P = 0.61 (P < 0.19 (P = 0.30 (P = -0.68 (P < 0.76 (P < -0.01 (P = 0.95) 0.13) 0.05) 0.57) 0.37) 0.05) 0.01) 0.99) Brix, % 0.32 (P = 0.18 (P = - 0.17 (P = -0.09 (P = 0.95 (P < -0.25 (P = 0.26 (P = 0.45 (P = 0.21) 0.48) 0.62) 0.80) 0.0001) 0.46) 0.45) 0.16) SG -0.03 (P = -0.05 (P = 0.42 (P = - -0.07 (P = 0.26 (P = 0.72 (P < -0.74 (P < 0.56 (P = 0.90) 0.84) 0.09) 0.83) 0.45) 0.05) 0.01) 0.08) Fat, % 0.20 (P = -0.30 (P = -0.01 (P = -0.36 (P = - -0.27 (P = 0.39 (P = -0.16 (P = 0.28 (P = 0.50) 0.29) 0.97) 0.20) 0.43) 0.23) 0.64) 0.40) Protein, % 0.35 (P = 0.43 (P = 0.82 (P < 0.35 (P = -0.36 (P = - -0.17 (P = 0.13 (P = 0.32 (P = 0.21) 0.13) 0.0001) 0.22) 0.20) 0.61) 0.76) 0.34) L* -0.23 (P = -0.24 (P = -0.72 (P < -0.24 (P = 0.39 (P = -0.68 (P < - -0.93 (P < 0.44 (P = 0.37) 0.35) 0.01) 0.35) 0.16) 0.01) 0.0001) 0.17) a* 0.14 (P = -0.23 (P = 0.35 (P = -0.11 (P = 0.52 (P = 0.12 (P = -0.39 (P = - -0.22 (P = 0.59) 0.38) 0.17) 0.67) 0.05) 0.67) 0.12) 0.51) b* 0.34 (P = -0.05 (P = 0.60 (P < -0.04 (P = 0.30 (P = 0.46 (P = -0.65 (P < 0.88 (P < - 0.19) 0.85) 0.05) 0.88) 0.30) 0.10) 0.01) 0.0001) Primiparous cows the suitability of SG determined by a colostrometer were Tables 2 through 6 show the relationships between pointed out by Morin et al. (2001). Parity, season of calv- the colostrum quality estimation by SG, Brix, and col- ing, colostrum temperature and colostral protein content, or measurement, and colostrum composition (content among other factors, were identified by several studies to of IgG, fat and protein) within quarter and for com- affect accuracy and reliability of IgG estimation via SG posite colostrum samples. In neither primiparous nor (Mechor et al., 1992; Morin et al. 2001; Bielmann et al., multiparous cows were clear relationships between 2010; Bartier et al., 2015). Therefore, the identification IgG contents and SG, Brix, and the color space coor- of sufficient quality colostrum for feeding calves requires dinates L*, a*, and b* detected. Except for the front more accurate and robust instruments, e.g., Brix refrac- left quarter in multiparous cows, the Brix-values had a tometers (Fleenor and Stott, 1980; Morin et al., 2001; high correlation coefficient with the protein content of Quigley et al., 2013). When using the recommended cut- the colostrum samples, but not with IgG concentration. off points of 1.045 for SG and 22% for Brix (Bielmann This is in agreement with earlier findings (Bielmann et al., 2010), 57.1% and 36.4%, respectively, of the pres- et al., 2010; Quigley et al., 2013; Bartier et al., 2015). ent samples would have been classified as poor qual- ity colostrum by using a colostrometer or refractometer Differences between Quarter and Composite alone. Due to the low number of observations per quarter Colostrum Samples of colostrum truly classified as poor or good by either colostrometer or refractometer compared to the ELISA Colostrometer, refractometer and color measure- results, sensitivity of both on-farm instruments was be- ments mirrored the variation in colostrum composition at tween 0 and 100%. No differences between primiparous both quarter and composite levels. Though earlier stud- and multiparous cows were detected for sensitivity cal- ies suggested that the colostrometer predicts the protein culations. Specificity of the SG determination per quarter content of colostrum better than true IgG content (Morin was lower in primiparous compared to multiparous cows et al., 2001), results from the present study do not show (21.4 to 40.0% vs. 50.0 to 70.0%). For the refractometer, any association between SG and colostrum constituents specificity was greater in quarter and composite colos- (Tables 2 through 6). Surprisingly, in quarter colostrum trum samples of primiparous cows compared with mul- samples SG did not correlate with IgG concentration tiparous cows (60.0 to 85.7% vs. 50.0 to 70.0%). The (Tables 2 through 5), but after merging the quarters into NPV was similar for both on-farm instruments in primip- a composite sample in multiparous cows, the correlation arous and multiparous cows (83.3 to 100%). was in an acceptable range (r = 0.61, P < 0.05, Table 6). Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Multiparous cows Quarter colostrum quality assessment 33 Figure 3. Specific gravity (SG) assessed by colostrometer (Fig. 3A), and Brix-values evaluated by refractometer (%Brix; Fig. 3B) in quarters and composite colostrum samples (FL = front left, FR = front right, HL = hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box represents 25th to 75th percentile of ob- servations and the line in the box indicates the median, whiskers show fifth to 95th percentiles, and the line in the box indicates the median. This observation can be explained by the fact that indi- vidual quarters within a cow show a marked variation in both fat and protein contents. Both fat and protein con- tent of colostrum contribute to the overall SG and Brix- value (Mechor et al., 1992; Morin et al., 2001; Bielmann Figure 4. Colostrum fat (Fig. 4A), protein (Fig. 4B), and lactose contents et al., 2010). Quarters within a cow do not have the same (Fig. 4C) in quarter and composite colostrum samples (FL = front left, FR = ratio of colostrum components. Merging quarters to front right, HL = hind left, HR = hind right, Total = composite sample) of composite colostrum samples therefore seems to com- primiparous and multiparous dairy cows. The box represents 25th to 75th per- centile of observations and the line in the box indicates the median, whiskers pensate for the lack of single components of individual show fifth to 95th percentiles, and the line in the box indicates the median. quarters contributing to SG and Brix (Table 6). In ad- dition, the linear scale of the colostrometer and refrac- tometer used in our study followed the gradual dilution cell count (SCC) contributing to coagulation properties, of composite colostrum with water that concomitantly and indirectly to viscosity of milk. Consequently, the reduces all colostrum components in the same propor- measurement of SG by reading the submerged portion of tion (Fig. 1A,B). It is unclear if potentially further matrix a colostrometer is very likely to be affected by the overall effects of colostrum differ at quarter level compared to viscous properties of colostrum. Even though SCC was a composite colostrum sample that may affect its physi- not determined in colostrum samples from the current cal properties, e.g., density, viscosity etc.. Differences in study, the first milk obtained after parturition is known quality of mature milk at the quarter vs. composite level to have a high content of SCC (Wall et al., 2015). It can were previously reported (Forsbäck et al., 2009, 2011). therefore be speculated that the greater SCC in colostrum Forsbäck et al. (2011) suggested that this discrepancy contributes to its physicochemical properties. Directly may be due to the impact of individual quarter somatic after parturition, the blood-milk-barrier is also known to Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 34 Gross et al. dividual quarter differences contributing to coagulation properties of the composite milk samples, differences in the structure and size of casein micelles at the quarter level were shown to be related to SCC (Frederiksen et al., 2011). When merging individual quarter colostrum samples, it can be assumed that micelles are interact- ing and changing their structure at the composite level and thus affecting SG and Brix measurements. However, variations in protein and fat contents between quarters of an animal are most likely responsible for the differences in SG and Brix-values at quarter and composite levels. Practical Implementation of Quarter Colostrum Analysis and Collection in Dairy Farms Up to now, quarter milking was not a topic with re- spect to colostrum milking in dairy practice. It is acknowl- edged that quarters are different in terms of IgG concen- tration and volume of produced colostrum (Baumrucker et al., 2014; Gross et al., 2016; Samarütel et al., 2016). Present findings revealed differences in colostrum qual- ity between single quarters and composite colostrum samples. Technically, automatic milking systems and some devices in conventional milking parlors can sepa- rate milk (and colostrum) from single quarters. In com- bination with in-line devices abnormal milk, e.g., during cases of mastitis, can currently be separated (Brandt et al., 2010). If a quarter is characterized by a high colostrum quality, it would be useful to collect and feed only colos- trum from this quarter. Current findings using established and previously validated instruments (i.e., refractometer, colostrometer) confirmed their potential and limitations in addressing colostrum quality. Furthermore, the present results identified possible reasons for variation at quar - ter level (different protein and fat content etc.) that are masked by evaluating only composite samples. Moore et Figure 5. Colostrum color coordinates [CIE 1976 (L*, a*, b*) color al. (2005) also reported that colostrum of only one quarter space– CIELAB], L* (Fig. 5A), a* (Fig. 5B), and b* (Fig. 5C) in quarter is often tested although the composite colostrum is fed to and composite colostrum samples (FL = front left, FR = front right, HL = calves. Considering present results, however, single quar- hind left, HR = hind right, Total = composite sample) of primiparous and multiparous dairy cows. The box represents 25th to 75th percentile of ob- ters do not represent the overall colostrum quality. This servations and the line in the box indicates the median, whiskers show fifth might have detrimental effects if poor quality colostrum is to 95th percentiles, and the line in the box indicates the median. given to the newborn. On the other hand, the identification of superior colostrum quality and immediate separation of be permeable so that blood-derived proteins are present particular quarters at milking could reduce the impact of in colostrum (Wall et al., 2015). These may additionally low quality colostrum from other quarters and benefit calf affect the matrix structure of colostrum at the quarter and rearing despite the known problems in terms of variation composite levels. In addition, the same phenomenon was in colostrum composition at quarter and composite levels. observed when determining the Brix-values even though colostrum temperature and handling were kept constant Conclusions for all measurements. However, in the case of SG, Brix determination and also the measured color space coor- In conclusion, accuracy and limitations of on- dinates, correlation coefficients with IgG concentration farm instruments used to estimate colostrum quality of the respective samples were higher at the composite based on composite samples also apply for quarter compared with the individual quarter level. Besides in- colostrum samples. The variations in quarter colos- Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018 Quarter colostrum quality assessment 35 Gross, J. J., E. C. Kessler, V. Bjerre-Harpoth, C. Dechow, C. R. trum quality and composition were reflected by the Baumrucker, and R. M. Bruckmaier. 2014b. Peripartal proges- outcome of refractometer and colostrometer measure- terone and prolactin have little effect on the rapid transport of immunoglobulin G into colostrum of dairy cows. J. Dairy Sci. ments. Within individual quarters, correlation coeffi- 97:2923–2931. doi:10.3168/jds.2013-7795 cients between colostral IgG concentration, SG, Brix- Gross, J. J., G. Schüpbach-Regula, and R. M. Bruckmaier. 2016. Rapid values, and colostrum color were poorer compared to communication: Colostrum immunoglobulin concentration in composite samples. On the other hand, feeding high mammary quarters is repeatable in consecutive lactations of dairy quality colostrum from individual quarters could be an cows. J. Anim. Sci. 94:1755–1760. doi:10.2527/jas.2016-0362 option if sufficient amount of colostrum is produced. Kehoe, S. I., B. M. Jayarao, and A. J. Heinrichs. 2007. A survey of bovine colostrum composition and colostrum management practices on Pennsylvania dairy farms. J. Dairy Sci. 90:4108– LITERATURE CITED 4116. doi:10.3168/jds.2007-0040 Argüello, A., N. Castro, and J. Capote. 2005. Short Communication: Lehmann, M., O. Wellnitz, and R. M. Bruckmaier. 2013. Concomitant Evaluation of a color method for testing immunoglobulin G con- lipopolysaccharide-induced transfer of blood-derived com- centration in goat colostrum. J. Dairy Sci. 88:1752-1754 ponents including immunoglobulins into milk. J. Dairy Sci. Bartier, A. L., M. C. Windeyer, and L. Doepel. 2015. Evaluation of 96:889–896. doi:10.3168/jds.2012-5410 on-farm tools for colostrum quality measurement. J. Dairy Sci. McGuire, T. C., N. E. Pfeiffer , J. M. Weikel, and R. C. Bartsch. 1976. 98:1878–1884. 10.3168/jds.2014-8415 Failure of colostral immunoglobulin transfer in calves dying Baumrucker, C. R., A. M. Burkett, A. L. Magliaro-Macrina, and C. D. from infectious disease. J. Am. Vet. Med. Assoc. 169:713–718. Dechow. 2010. Colostrogenesis: Mass transfer of IgG1 into co- Mechor, G. D., Y. T. Gröhn, L. R. McDowell, and R. J. Van Saun. lostrum. J. Dairy Sci. 93:3031–3038. doi:10.3168/jds.2009-2963 1992. Specific gravity of bovine colostrum immunoglobulins Baumrucker, C. R., A. Stark, O. Wellnitz, C. Dechow, and R. M. as affected by temperature and colostrum components. J. Dairy Bruckmaier. 2014. Short communication: Immunoglobulin vari- Sci. 75:3131–3135. doi:10.3168/jds.S0022-0302(92)78076-X ation in quarter-milked colostrum. J. Dairy Sci. 97:3700–3706. Moore, M., J. W. Tyler, M. Chigerwe, M. E. Dawes, and J. R. doi:10.3168/jds.2013-7107 Middleton. 2005. Effect of delayed colostrum collection on Bielmann, V., J. Gillan, N. R. Perkins, A. L. Skidmore, S. Godden, colostral IgG concentration in dairy cows. J. Am. Vet. Med. and K. E. Leslie. 2010. An evaluation of Brix refractometry in- Assoc. 226:1375–1377. doi:10.2460/javma.2005.226.1375 struments for measurement of colostrum quality in dairy cattle. Morin, D. E., P. D. Constable, F. P. Maunsell, and G. C. McCoy. 2001. J. Dairy Sci. 93:3713–3721. doi:10.3168/jds.2009-2943 Factors associated with colostral specific gravity in dairy cows. J. Brandt, M., A. Haeussermann, and E. Hartung. 2010. Invited review: Dairy Sci. 84:937–943. doi:10.3168/jds.S0022-0302(01)74551-1 Technical solutions for analysis of milk constituents and abnor- Morrill, K. M., K. E. Robertson, M. M. Spring, A. L. Robinson, and mal milk. J. Dairy Sci. 93:427–436. doi:10.3168/jds.2009-2565 H. D. Tyler. 2015. Validating a refractometer to evaluate im- Chigerwe, M., J. W. Tyler, J. R. Middleton, J. N. Spain, J. S. Dill, munoglobulin G concentration in Jersey colostrum and the and B. J. Steevens. 2008. Comparison of four methods to as- effect of multiple freeze-thaw cycles on evaluating colostrum sess colostral IgG concentration in dairy cows. J. Am. Vet. quality. J. Dairy Sci. 98:595–601. doi:10.3168/jds.2014-8730 Med. Assoc. 233:761–766. doi:10.2460/javma.233.5.761 Morrill, K. M., E. Conrad, J. Polo, A. Lago, J. Campbell, J. Quigley, Fleenor, W. A., and G. H. Stott. 1980. Hydrometer test for estimation and H. Tyler. 2012. Estimate of colostral immunoglobulin G con- of immunoglobulin concentration in bovine colostrum. J. Dairy centration using refractometry without or with caprylic acid frac- Sci. 63:973–977. doi:10.3168/jds.S0022-0302(80)83034-7 tionation. J. Dairy Sci. 95:3987–3996. doi:10.3168/jds.2011-5104 Forsbäck, L., H. Lindmark-Månsson, A. Andrén, M. Akerstedt, and Pritchett, L. C., C. C. Gay, D. D. Hancock, and T. E. Besser. 1994. K. Svennersten-Sjaunja. 2009. Udder quarter milk composi- Evaluation of the hydrometer for testing immunoglobulin G1 tion at different levels of somatic cell count in cow composite concentrations in Holstein colostrum. J. Dairy Sci. 77:1761– milk. Animal 3:710–717. doi:10.1017/S1751731109004042 1767. doi:10.3168/jds.S0022-0302(94)77117-4 Forsbäck, L., H. Lindmark-Månsson, K. Svennersten-Sjaunja, L. B. Quigley, J. D., A. Lago, C. Chapman, P. Erickson, and J. Polo. 2013. Larsen, and A. Andrén. 2011. Effect of storage and separation Evaluation of the Brix refractometer to estimate immunoglobu- of milk at udder quarter level on milk composition, proteolysis, lin G concentration in bovine colostrum. J. Dairy Sci. 96:1148– and coagulation properties in relation to somatic cell count. J. 1155. doi:10.3168/jds.2012-5823 Dairy Sci. 94:5341–5349. doi:10.3168/jds.2011-4371 Samarütel, J., C. R. Baumrucker, J. J. Gross, C. D. Dechow, and Frederiksen, P. D., K. K. Andersen, M. Hammershøj, H. D. Poulsen, R. M. Bruckmaier. 2016. Quarter variation and correlations of J. Sørensen, M. Bakman, K. B. Qvist, and L. B. Larsen. 2011. colostrum albumin, immunoglobulin G1 and G2 in dairy cows. Composition and effect of blending of noncoagulating, poorly J. Dairy Res. 83:209–218. doi:10.1017/S0022029916000091 coagulating, and well-coagulating bovine milk from indi- Tyler, J. W., D. D. Hancock, J. G. Thorne, C. C. Gay, and J. M. Gay. vidual Danish Holstein cows. J. Dairy Sci. 94:4787–4799. 1999. Partitioning the mortality risk associated with inadequate doi:10.3168/jds.2011-4343 passive transfer of colostral immunoglobulins in dairy calves. J. Gross, J. J., E. C. Kessler, and R. M. Bruckmaier. 2014a. Colour Vet. Intern. Med. 13:335–337. doi:10.1111/j.1939-1676.1999. measurement of colostrum for estimation of colostral IgG and tb02191.x colostrum composition in dairy cows. J. Dairy Res. 81:440– Wall, S. K., J. J. Gross, E. C. Kessler, K. Villez, and R. M. Bruckmaier. 444. doi:10.1017/S0022029914000466 2015. Blood-derived proteins in milk at start of lactation: Indicators of active or passive transfer. J. Dairy Sci. 98:7748– 7756. doi:10.3168/jds.2015-9440 Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/1/1/26/4636601 by Ed 'DeepDyve' Gillespie user on 10 April 2018

Journal

Translational Animal Science – Oxford University Press

Published: Feb 1, 2017

There are no references for this article.