Relationship of somatic cell count, physical, chemical and enzymatic properties to the bacterial standard plate count in dairy goat milk

Livestock Production Science 74 (2002) 63–77 www.elsevier.com / locate / livprodsci

Relationship of somatic cell count, physical, chemical and enzymatic properties to the bacterial standard plate count in dairy goat milk a b b, Chingwen Ying , Han-Tsung Wang , Jih-Tay Hsu * b

a Department of Microbiology, Soochow University, No. 70, Lin-His Road, Taipei 11102, Taiwan, ROC Department of Animal Science, National Taiwan University, No. 50, Lane 155, Kee-Lung Road, Sec. 3, Taipei 106, Taiwan, ROC

Received 7 September 1999; received in revised form 27 June 2001; accepted 27 June 2001

Abstract Weekly milk samples, collected from individual half mammary gland of three late lactation (experiment 1) and four early lactation (experiment 2) Alpine dairy goats in second lactation, were subjected to various physical and chemical measurements to examine the relationship of the logarithm of bacterial standard plate count (SPC) to electrical conductivity (EC), neutrophils / lymphocytes ratio, lactate dehydrogenase (LDH), tyrosine concentration, alkaline phosphatase (ALP), the logarithm of somatic cell count (SCC) and various milk constituents (fat, protein, lactose, K 1 , Na 1 , Cl 2 , P). Tank milk samples (experiment 3), obtained twice a month from 20 commercial farms for a period of 3 months, were also subjected to the same measurements and analysis. The measured SCC of the present study ranged from 0.137 to 8.043 3 10 6 / ml. The measured SPC ranged from 0 to 7.76 3 10 6 / ml. Both of the SCC and the SPC showed a wide distribution allowing a better opportunity to examine the correlation analysis of other measured parameters with SCC or SPC. The results showed that the logarithm of SCC did not have a significant correlation with the logarithm of SPC for late lactation and commercial tank milk samples. The best-fit analysis also showed a different pattern of regression equations for the logarithm of SPC and SCC, indicating that SCC and SPC were not equally interchangeable parameters as indicators of mammary gland infection for Alpine goats or commercial goats of mixed breeds. There was negative correlation between the logarithm of SPC and lactose content in the early lactation and the commercial farms’ tank milk samples, but not the late lactation milk samples. The logarithm of SCC was negatively correlated to the milk yield (P , 0.01), and positively to EC (P , 0.05) for both early and late lactation milk samples. Milk protein content consistently showed a significant (P , 0.01) positive correlation to the logarithm of SCC across all three experiments. The present study did showed a similar correlation between SCC and milk yield, EC or milk protein content of dairy goats’ milk as found in dairy cows’ milk. However, the present study showed that the possibility of employing commonly used physiological parameters for dairy cows was unsuitable in evaluating the mammary health status of dairy goats.  2002 Elsevier Science B.V. All rights reserved. Keywords: Goat milk; Standard plate count; Somatic cell count; Lactate dehydrogenase; Alkaline phosphatase; Sodium; Potassium; Chloride; Electrical conductivity; Tyrosine

*Corresponding author. Tel.: 1886-2-87-321-471; fax: 1886-2-27-324-070. E-mail address: [email protected] (J.-T. Hsu). 0301-6226 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved. PII: S0301-6226( 01 )00290-1

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1. Introduction Regarding the economic impact on milk production, somatic cell count (SCC) and bacterial standard plate count (SPC) have been shown to be negatively correlated (P , 0.001) with milk yield in dairy goats (Zeng and Escobar, 1995). There was also a negative correlation (P , 0.001) between SCC and cheese yield in dairy goat (Galina et al., 1996). The economic impact of the SCC and SPC can also be shown in the quality regulation of dairy goat milk (Wilson et al., 1995; Zeng and Escobar, 1995). SCC has been successfully used in dairy cattle for monitoring the occurrence of mastitis (Harmon, 1994). Dairy goat milk usually has a much higher SCC than dairy cattle milk due to the higher numbers of cytoplasmic particles resulting from the the apocrine secretion process in the goat mammary gland (Dulin et al., 1983). Park and Humphrey (1986) found no significant relationship between the goat milk SCC and its SPC. Smith and Sherman (1994) reached a similar conclusion that SCC was unsatisfactory for diagnosis of mastitis in dairy goat. The California Mastitis Test (CMT) is another common method for the detection of mastitis in dairy cattle. However, CMT failed to accurately detect subclinical mastitis in dairy goat (Upadhyaya and Rao, 1993; Boscos et al., 1996; Contreras et al., 1996). Maisi (1990) examined the potential use of N-acetyl-b-Dglucosaminidase; NAGase) in the diagnosis of dairy goat subclinical mastitis, but found the performance of NAGase no better than CMT. Droke et al. (1993) stated that the increase of neutrophils in goat milk can be used to indicate infection of the mammary gland. Based on the data of neutrophils and lymphocytes numbers in the goat milk (Dulin et al., 1983), the neutrophils / lymphocytes ratio of infected mammary gland was twice of that of uninfected gland. Park (1991) found significant correlation between electrical conductivity (EC) and SPC for pooled AM-PM milk and PM milk, but not for AM milk. Some enzymatic indicators of mammary gland infection have shown application potential in dairy cattle such as lactate dehydrogenase (LDH), protease and alkaline phosphatase (ALP) (Bogin et al., 1977; Kitchen, 1981). However, these enzymes have not been extensively tested in dairy goats. The objective of the present study is to examine the relationship of

the logarithm of SPC to EC, neutrophils / lymphocytes ratio, LDH, protease, ALP, the logarithm of SCC and various milk constituents (fat, protein, lactose, K 1 , Na 1 , Cl 2 , P) in order to identify a simple and fast measurement as an an indicator of SPC for dairy goats.

2. Materials and methods

2.1. Animals In experiment 1 (1998 / 10 / 12–1998 / 11 / 2), three Alpine lactating goats (average body weight (BW) 40 kg) in late lactation (average 187 days in milk) of their second lactation were used. The animals were housed in a sheltered concrete pen and fed a lactation diet twice daily composed of 30% alfalfa hay, 41% corn, 18% soybeans, 6% molasses, 3% soybean oil and 2% vitamin and mineral premix to meet the NRC (1981) nutrient recommendation for lactating goat (BW 5 50 kg, 4% FCM 5 2 kg, DMI 5 1.81 kg / day). Goats were milked twice daily at 8:00 and 17:00 h. In experiment 2 (1998 / 11 / 16–1999 / 3 / 8), four Alpine lactating goats (average BW 40 kg) in early lactation (average 7 days in milk) of their second lactation were used. The feeding and milking practice in experiment 2 were the same as in experiment 1 except that animals were housed in an elevated wood-slat floor pen. In experiment 3 (1999 / 3–1999 / 5), dairy goat herds (mix of various pure breed of Saanen or Alpine and various unidentified cross-breeds) of 20 commercial farms from various regions (Changfa, Nantou, Yunlin, Chaiyi, Kaohsiung) of Taiwan were used as survey subjects.

2.2. Sampling In experiment 1, the experimental period was 4 weeks. Weekly AM and PM milk samples (totaled 36 samples from two goats in weeks 1, 2 and 4 and three goats in week 3) of individual udder half of each goat were obtained by using separated container connected to the mobile milking unit (model Almatic, Alfa Laval Agri, Sweden). In experiment 2, the experimental period was 16 weeks. Weekly milk samples (totalled 240 samples with only three goats’ samples in weeks 13–16) were collected as in

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65

experiment 1. In experiment 3, milk tank samples were obtained twice a month from each commercial farm for a period of 3 months (totaled 120 samples).

ments were transferred to logarithm before testing the correlation with other measurements.

2.3. Physical and chemical measurements

3. Results and discussion

Using a hand held electrical conductivity meter (Suntex SC-120, Taiwan), the EC of individual milk sample was measured immediately after sampling from the container of the milking unit. Aliquots of 100 ml from each sample were subjected to the automatic measuring of milk fat, milk protein, and lactose contents by a Bentley 2000 (USA) and SCC by a Bentley SCC 300 (USA) unit calibrated with goat milk standards. The Bentley 2000 unit, using 2–15 nm mid-infrared wavebands conforming to the AOAC (1990) method 16.034, has been widely used by the North American Dairy Herd Improvement Association (DHIA). The Bentley SCC 300 used ethidium bromide to stain the DNA in somatic cells and laser-based flow cytometry for automatic counting, meeting the IDF standard 148:1991. Another 10 ml of each milk sample was used for standard plate count using Petrifilm AC (3M Co., USA) incubated at 358C for 48 h, and for selective culture counts of Staphylococcus (Medium No. 110), and Pseudomonas (DIFCO No.0927-17-1) according to FDA (1984), Bacillus according to Turnbull and Kramer (1991), Streptococcus according to MacFaddin (1985). Selective culture count was performed in experiment 1 only. The rest of the milk samples were used for staining count of neutrophils and lymphocytes (Paape et al., 1963), enzymatic activity measurement of protease (using tyrosine concentration as indicator of protease activity present in milk; Hull, 1947), LDH (LDH SFBC kit, Art. 0736570, Roche Co., USA), and ALP (ALP IFCC kit, Art. 0736333, Roche Co., USA), determination of Na 1 , K 1 , P (AOAC, 1990), and Cl 2 (Muldoon and Liska, 1971) concentrations.

The mean, standard deviation, and range of each measurement in three experiments are listed in Tables 1–3. Wierschem (1993) summarized the milk fat and milk protein contents of Alpine, LaMancha, Nubian, Saanen, and Toggenburg goats in the USA from 1979 to 1992. The statistics of milk fat and milk protein contents for the five breeds were 3.5860.079, 3.0460.027; 3.8160.047, 3.2460.107; 4.5160.048, 3.6660.039; 3.4760.063, 3.0360.017; and 3.3460.059, 2.9560.041, respectively. The milk fat contents of Alpine goats in experiments 1 and 2 were close to the reported data, but milk protein content were higher than expected. The milk fat and milk protein contents of mixed breeds of dairy goats in experiment 3 were even higher than experiments 1 and 2. Based on these milk composition data, it seems that animals in the present study were in nutritionally sound condition. The lactose contents of the present study (3.0–4.62%) were within the reported ranges such as 2.80–5.08% (Zeng and Escobar, 1995) and 1.50–6.76% (Zeng et al., 1997) and overlapped with the reported range as 3.59– 5.59% (Upadhyaya and Rao, 1993). The average lactose contents of the present study were somewhat low compared to 4.42% (Zeng and Escobar, 1995), 4.17% (Zeng et al., 1997), and 3.98–4.73% (Upadhyaya and Rao, 1993). Since the lactose measurements were done by the Bentley SCC 300 automatic instrument which was calibrated with goat milk standards, it is probably not a analytical problem. Some factors, such as lower feed intake (Osslon et al., 1997), higher SCC (Zeng and Escobar, 1995), and higher CMT reaction (Upadhyaya and Rao, 1993) were found to be related to a lower lactose content. The change of lactose content over the lactation days of the present study is shown in Fig. 1. There is a trend of decline of lactose over the lactation period as expected. The reported Na 1 , K 1 , Cl 2 , and P concentrations of goat milk are in the range 380–580, 1400–2420, 1040–2040, and 610–2700 ppm, respectively (Jenness, 1980; Jandal, 1996). Na 1 concentrations in the

2.4. Statistic analysis Data were tested for correlation significance by Pearson correlation coefficients, and the best fit regression equation identified by a stepwise method using the SAS system for Windows (SAS 6.11, TS040, SAS Institute). The SCC and SPC measure-

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Table 1 Mean and distribution of milk physical, chemical, enzymatic and bacterial measurements of dairy goats in early lactation Measurement Milk yield (ml / half mammary gland / time) Milk fat (%) Milk protein (%) Milk lactose (%) K 1 (ppm) Na 1 (ppm) Cl – (ppm) P (ppm) Somatic cell count (310 3 / ml) Neutrophils (%SCC) Lymphocytes (%SCC) Neutrophils / lymphocytes Electrical conductivity (ms / cm) Tyrosine (mg / ml) Alkaline phosphatase (U / l) Lactate dehydrogenase (U / l) Bacterial standard plate count (310 2 / ml) a

na

Mean

S.D.

Min.

Max.

240 238 238 238 240 240 229 240 190 237 237 237 232 240 232 233

346 3.82 3.91 3.94 1614 645 2025 1330 1989 79 22 3.62 5.18 47.9 128 425

165 1.02 0.42 0.13 452 270 496 331 1511 5 6 1.39 0.30 15.6 148 382

50 1.62 2.82 3.23 491 196 780 112 193 39 9 1.57 4.41 11.8 13 25

820 6.84 5.29 4.22 3175 1746 7163 2536 8043 92 39 10.5 6.15 114.4 1786 3545

212

166

230

2

1910

Observation number less than 240 were due to unexpected sample loss during analysis process.

Table 2 Mean and distribution of milk physical, chemical, enzymatic and bacterial measurements of dairy goats in late lactation Measurement

na

Mean

S.D.

Min.

Max.

Milk yield (ml / half mammary gland / time) Milk fat (%) Milk protein (%) Milk lactose (%) K 1 (ppm) Na 1 (ppm) Cl – (ppm) P (ppm) Somatic cell count (310 3 / ml) Neutrophils (%SCC) Lymphocytes (%SCC) Neutrophils / lymphocytes Electrical conductivity (ms / cm) Tyrosine (mg / ml) Alkaline phosphatase (U / l) Lactate dehydrogenase (U / l) Bacterial standard plate count (310 2 / ml) Pseudomonas (1 / ml) Staphylococcus (1 / ml) Streptococcus (1 / ml) Bacillus (1 / ml)

36

246

149

25

470

36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36

3.41 4.65 3.70 2157 1002 1144 831 2312 78 22 4.13 6.26 24.4 236 120 2544

0.69 0.83 0.43 518 565 469 208 1778 6 6 1.86 0.95 11.5 173 148 12887

2.38 3.26 3.00 999 350 536 225 137 64 11 1.75 4.57 6.8 51 4 1

5.68 6.38 4.62 3178 2130 3075 1125 6072 90 37 8.52 8.14 45.1 602 672 77600

36 36 35 36

389 10361 1569 21111

961 15686 6884 71582

0 0 0 10

5450 59120 40880 430000

a

Observation number less than 36 were due to unexpected sample loss during analysis process.

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Table 3 Mean and distribution of milk physical, chemical, enzymatic and bacterial measurements of 20 commercial dairy goat herds from north Taiwan region Measurement

na

Mean

S.D.

Min.

Max.

Milk fat (%) Milk protein (%) Milk lactose (%) K 1 (ppm) Na 1 (ppm) Cl – (ppm) P (ppm) Somatic cell count (310 3 / ml) Neutrophils (%SCC)b Lymphocytes (%SCC)b Neutrophils / lymphocytes b Electrical conductivity (ms / cm)b Tyrosine (mg / ml) Alkaline phosphatase (U / l) Lactate dehydrogenase (U / l) Bacterial standard plate count (310 2 / ml)

120 120 120 118 119 118 118 120 – – – – 119 118 118

4.06 3.96 3.83 1675 810 2023 1457 1589 – – – – 23.0 279 145

0.36 0.23 0.08 660 551 348 295 484 – – – – 9.5 98 58

3.35 3.54 3.64 433 216 1206 624 739 – – – – 10.8 66 60

5.34 4.58 4.06 3636 2332 3617 3086 2937 – – – – 47.0 657 450

120

27

101

0

960

a

Observation number less than 120 were due to unexpected sample loss during analysis process. Samples had been stored in refrigerator for more than 2 days before being sent to the lab, therefore, measurements (electrical conductivity, neutrophils and lymphocytes counting) requiring fresh samples could not be performed. b

present study were higher than in the reported data; however, the other three mineral concentrations were within the reported range. In dairy cattle, EC increased following mastitis infection (Kitchen, 1981), and the change of EC was due to the changes of Na 1 , K 1 , and Cl 2 concentrations in cows’ milk (Nielen et al., 1992). Changes of EC and concentrations of Na 1 and Cl 2 over the lactation days

of the present study are shown in Figs. 2–4, respectively. There were trends of increase of EC and Na 1 over the lactation period, but no clear trend was found for Cl 2 . The mean of SCC in the present study was higher than the recommended threshold of SCC for healthy goat (10 6 / ml; Boscos et al., 1996). The range of SCC in the present study varied around 10 6 / ml,

Fig. 1. Lactose content (%) of early and late lactation milk samples.

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C. Ying et al. / Livestock Production Science 74 (2002) 63 – 77

Fig. 2. Electrical conductivity of early and late lactation milk samples.

Fig. 3. Sodium content (%) of early and late lactation milk samples.

indicating healthy and infected goats were present which allowed a wide range of data for examining the correlation among the various parameters measured. The changes of the logarithm of SCC and the neutrophils / lymphocyte ratio over the lactation period are shown in Figs. 5 and 6, respectively. There was a trend for the neutrophils / lymphocyte ratio to increase over the lactation period. In agreement with the present study, Dulin et al. (1983) and Rota et al. (1993) reported that the number and ratio of neutrophils in milk changed with the progress of lactation.

The mean and range of bacterial standard plate counts (SPC) of Alpine and Nubian goats have been reported as 1.54460.533, 0.01–34.7 3 10 3 / ml (Park and Humphrey, 1986) and 3.8967.474, 0.07–46.0 3 10 4 / ml (Park, 1991), respectively. Zeng and Escobar (1995) reported the mean and range of SPC in Alpine goat as 3.9860.82 and 2.00–5.60 log 10 , respectively. The SPC of the present study overlapped with the reported range, and most were under 10 6 / ml. The distribution of the logarithm of SPC over the lactation period is shown in Fig. 7. As expected, there was no trend of change of SPC over

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69

Fig. 4. Chlorine content (%) of early and late lactation milk samples.

Fig. 5. The logarithm of somatic cell count of early and late lactation milk samples.

lactation days. Due to lack of laboratory personnel, selective culture counts of Staphylococcus, Bacillus, Streptococcus and Pseudomonas was only performed in experiment 1 (Table 2). It showed that Bacillus was the most predominant bacteria in the milk of Alpine goat. Staphylococcus and Streptococcus were the other two dominant bacteria. In the study of Egwu et al. (1994), Staphylococcus, followed by Bacillus, was found to be the most predominant bacteria in goat milk in Sahel, Nigeria. KalogridouVassiliadou (1991) had a similar finding that Staphylococcus, followed by Bacillus, was the most pre-

dominant bacteria in goat milk from northern Greece. Park (1991) also found that Staphylococcus was the most predominant bacteria in the milk of French-Alpine and Anglo-Nubian does. Sung et al. (1999) surveyed the bacterial profiles in four breeds of dairy goat in Taiwan, and found the most predominant bacteria was Staphylococcus followed by Coliform. Unlike dairy goat, Streptococcus was often found to be the predominant bacteria in dairy cattle (Erskine et al., 1988). The reason that Streptococcus was also found to be the predominant bacterium of goat milk in the present study could possibly be due

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C. Ying et al. / Livestock Production Science 74 (2002) 63 – 77

Fig. 6. The neutrophils / lymphocyte ratio of early and late lactation milk samples.

Fig. 7. The logarithm of standard plate count of early and late lactation milk samples.

to the fact that goats were housed rather close to the dairy cattle barn in our animal research farm. The correlation coefficient analysis results of three experiments are listed in Tables 4–6. The logarithm of SPC had a significant (P , 0.01) positive correlation with milk yield, EC, neutrophils / lymphocytes ratio, the logarithm of SCC, fat content and protein content, and a negative correlation with weeks of lactation, concentrations of tyrosine, K 1 , Na 1 , Cl 2 and P and activities of ALP and LDH in the early

lactation milk samples (Table 4). However, the logarithm of SPC had no significant (P . 0.05) correlation with any of the measured parameters in the late lactation milk samples except of weeks of lactation and the neutrophils / lymphocytes ratio (Table 5). It appears that none of the measured parameters have a consistent correlation with the logarithm of SPC throughout the whole lactation period. There was no significant (P . 0.05) correlation between the logarithm of SPC and the logarithm

Week Milk EC PMN/Ly log SPC log SCC Fat Protein Lactose Tyrosine ALP LDH K Na Cl P

Week

Milk

EC

PMN/Ly

log SPC

log SCC

Fat

Protein

Lactose

Tyrosine

ALP

LDH

K

Na

Cl

P

1

0.41** 1

0.30** 0.44** 1

0.11 0.34** 0.34** 1

20.26** 0.15* 0.32** 0.43** 1

20.19* 20.19** 0.22** 0.39** 0.42** 1

20.31** 20.18 20.20* 0.23** 0.44** 0.47** 1

20.27** 20.28** 20.22** 20.21** 0.22** 0.26** 0.39** 1

20.29** 20.29** 20.30** 20.23** 20.12 0.15* 0.30** 0.40** 1

20.32** 20.32** 20.32** 20.32** 20.29** 20.24** 0.04 0.25** 0.44** 1

20.31** 20.31** 20.32** 20.30** 20.33** 20.30** 20.26** 0.11 0.26** 0.43** 1

20.32** 20.31** 20.32** 20.32** 20.33** 20.34** 20.29** 20.19* 0.13* 0.38** 0.41** 1

20.26** 20.31** 20.33** 20.32** 20.35** 20.34** 20.34** 20.23** 20.21** 0.17** 0.34** 0.41** 1

20.24** 20.27** 20.32** 20.31** 20.33** 20.34** 20.33** 20.28** 20.24** 20.19** 0.29** 0.38** 0.48** 1

0.07 20.26** 20.26** 20.32** 20.33** 20.32** 20.33** 20.29** 20.30** 20.21** 20.14* 0.29** 0.41** 0.49** 1

0.35** 0.13* 20.20** 20.25** 20.32** 20.31** 20.33** 20.27** 20.31** 20.33** 20.26** 20.19** 0.21** 0.27** 0.45** 1

EC, electrical conductivity; PMN / Ly, neutrophils / lymphocyte; log SPC, log standard plate count; log SCC, log somatic cell count; ALP, alkaline phosphatase; LDH, lactate dehydrogenase. * P,0.05; ** P,0.01.

C. Ying et al. / Livestock Production Science 74 (2002) 63 – 77

Table 4 Correlation coeffficient analysis of measured parameters of dairy goat milk in the early lactation

71

72

Table 5 Correlation coeffficient analysis of measured parameters of dairy goat milk in the late lactation

Week 1 Milk EC PMN/Ly log SPC log SCC Fat Protein Lactose Tyrosine ALP LDH K Na Cl P Pseud. Staph. Strept. Bacil.

Milk

EC

PMN/Ly

log SPC

log SCC

Fat

Protein

0.21 1

20.51* 20.64** 20.63** 20.40** 1 0.47* 1

0.48* 20.05 20.23 20.49* 1

0.00 20.79** 0.33* 0.27 0.03 1

0.02 0.16 0.42* 20.79** 20.48* 0.52** 20.07 0.16 0.06 0.07 20.34* 0.69** 1 20.59 1

Lactose

Tyrosine

ALP

LDH

K

Na

Cl

P

Pseud.

Staph.

Strept.

Bacil.

0.24 20.02 20.14 20.05 0.09 0.13 0.01 0.16 1

20.36* 20.28 0.61** 0.36** 20.02 20.09 20.31 0.22 20.27 1

20.30 20.56** 0.34** 0.17 20.05 0.55** 20.20 0.48** 0.08 0.20 1

20.23 20.06 0.16 0.09 20.09 0.14 20.08 20.10 20.12 0.16 0.13 1

0.16 0.46 20.33 20.05 20.06 20.40* 0.14 20.47** 0.01 20.23 20.28 20.03 1

20.38 20.80** 0.71** 0.41* 20.15 0.69** 20.44** 0.66 20.24 0.373* 0.56** 0.13 20.48 1

0.35* 0.33* 20.30 20.39* 0.29 20.24 0.14 20.26 0.17 20.14 20.24 0.15 0.38* 20.39* 1

20.13 20.23 0.45* 0.21 20.09 0.01 20.20 0.26 0.05 0.41* 0.00 0.07 0.01 0.22 0.17 1

0.31 0.06 20.09 20.36* 0.16 20.21 0.15 0.01 0.08 20.19 20.11 20.17 0.14 20.12 0.15 20.01 1

0.42** 0.28 20.22 20.61** 0.34 20.24 0.14 20.19 20.37* 20.11 20.26 0.00 0.16 20.25 0.30 20.13 0.25 1

20.06 20.17 0.10 20.04 20.05 0.04 0.00 0.03 20.04 20.04 0.02 0.30 0.02 0.22 0.00 20.07 0.52** 0.11 1

0.55** 0.31 20.31 20.54** 0.30 20.27 0.08 20.07 20.09 20.15 20.18 20.16 0.31* 20.28 0.32 20.07 0.54** 0.68** 0.25 1

EC, electrical conductivity; PMN / Ly, neutrophils / lymphocyte; log SPC, log standard plate count; log SCC, log somatic cell count; ALP, alkaline phosphatase; LDH, lactate dehydrogenase; Pseud., Pseudomonas; Staph., Staphylococcus; Strept., Streptococcus; Bacil., Bacillus. * P,0.05; ** P,0.01.

C. Ying et al. / Livestock Production Science 74 (2002) 63 – 77

Week

log SPC log SCC Fat Protein Lactose Tyrosine LDH ALP K Na Cl P

log SPC

log SCC

Fat

Protein

Lactose

Tyrosine

LDH

ALP

K

Na

Cl

P

1

0.16 1

0.26* 0.11 1

0.18* 0.35** 0.14 1

20.33** 0.14 0.30** 0.18* 1

20.21* 20.33** 0.12 0.25** 0.15 1

20.50** 20.21* 20.31** 0.18* 0.30** 0.15 1

20.48** 20.49** 20.15 20.33** 0.14 0.29** 0.06 1

20.21* 20.50** 20.48** 20.29** 20.31** 0.14 0.29** 0.11 1

20.35** 20.21** 20.48** 20.48** 20.21** 20.32** 0.17 0.33** 0.11 1

0.10 20.33** 20.20* 20.49** 20.48** 20.22* 20.35** 0.17 0.32** 0.12 1

0.28* 0.16 20.31** 20.24** 20.49** 20.50** 20.22* 20.36** 0.18* 0.27* 0.12 1

log SPC, log standard plate count; log SCC, log somatic cell count; ALP, alkaline phosphatase; LDH, lactate dehydrogenase. * P,0.05; ** P,0.01.

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Table 6 Correlation coeffficient analysis of measured parameters of commercial dairy goat farms’ tank milk samples

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of SCC for the commercial farms’ milk samples (Table 6). In agreement with Park and Humphrey (1986), the present study also showed that SCC may not be a suitable indicator of mammary gland infection for Alpine goat or commercial goats of mixed breeds. Mastitis was expected to decrease the concentrations of lactose and total solids, but increase the SCC, pH, concentrations of tyrosine, Na 1 and Cl 2 , activities of ALP and LDH (Bogin et al., 1977; Kitchen, 1981; Fox and McSweeney, 1998). There was a negative correlation between the logarithm of SPC and lactose content in the early lactation (Table 4) and the commercial farms’ (Table 6) milk samples, but not the late lactation milk samples (Table 5). The lactose content was expected to be lowest during late lactation (Fox and McSweeney, 1998). It may be that mastitis can not lower the lactose content any further when the lactose content is at its lowest during late lactation. There were unexpected negative correlations between the logarithm of SPC and concentrations of tyrosine, Na 1 and Cl 2 or activities of ALP and LDH in the early lactation (Table 4) and the commercial farms’ (Table 6) milk samples. It is not clear whether bacteria in the mammary gland can uptake or degrade the tyrosine, Na 1 , Cl 2 , ALP and LDH present in the mastitis milk. As expected, the logarithm of SPC had positive correlation with neutrophils / lymphocytes ratio in the early lactation milk samples (Table 4). However, the logarithm of SPC had a negative correlation with the neutrophils / lymphocytes ratio in the late lactation milk samples (Table 5). Dulin et al. (1983) and Rota et al. (1993) reported that the number and ratio of neutrophils in milk changed with the progress of lactation which might have obscured the original mastitis effect on the neutrophils / lymphocytes ratio. The neutrophils / lymphocytes ratio distribution over sampling days did show a pattern of increase over lactation day in the present study (Fig. 6). For the early lactation milk samples, the correlation between the logarithm of SCC and other measured parameters was mostly similar to what is shown for the logarithm of SPC (Table 4). The only difference is the positive correlation between the logarithm of SCC and lactose content which was not shown to be significant for the late lactation (Table

5) and commercial farms’ milk samples (Table 6). There was a negative correlation between the logarithm of SCC and milk yield for both early and late lactation milk samples as expected (Tables 4 and 5). Also, there was a positive correlation between the logarithm of SCC and EC for both early and late lactation milk samples as expected (Tables 4 and 5). Milk protein content consistently showed a significant (P , 0.01) positive correlation to the logarithm of SCC across all three experiments. Zeng et al. (1997) and Sung et al. (1999) also found significant correlation between SCC and milk protein content in Alpine goat’s milk (P , 0.001). Tizard (1992) stated that the inflammatory response of acute mastitis can cause an influx of active phagocytes (especially neutrophils) and an exudation of serum protein. This may be related to the correlation that occurred between SCC and milk protein content. Milk K 1 content consistently showed a significant (P , 0.05) negative correlation to the logarithm of SCC across all three experiments. This may be due to the change of electrolyte exchange between blood and milk in the mammary gland with mastitic infections to balance the milk osmolarity (Fox and McSweeney, 1998). Based on the stepwise analysis, the best-fit regression equations for the logarithm of SPC were as follows: Early lactation: Log SPC 5 841.0742 2 0.50785*(week) 1 0.14808*(EC) 1 0.14095*(neutrophils / lymphocytes) 2 0.165875*(logSCC) 1 0.1582*(fat) 1 0.13377*(protein) 2 0.0943*(lactose) 2 0.36837*(tyrosine) 2 0.206658*(ALP) 2 0.2353*(Na) 2 0.1300*(Cl) 2 0.12828*(P) R 2 5 0.6262 Late lactation: Log SPC 5 0.8677 1 0.4928*(week) R 2 5 0.2991

C. Ying et al. / Livestock Production Science 74 (2002) 63 – 77

Commercial milk samples: Log SPC 5 1731.0298 2 0.35375*(log SCC)

Commercial milk samples: Log SCC 5 2362.3872 2 0.3743*(log SPC)

1 0.1374*(protein) 2 0.2098*(lactose)

2 0.3248*(fat) 2 0.2949*(tyrosine)

2 0.2156*(tyrosine) 2 0.3888*(LDH)

2 0.3722*(LDH) 2 0.4014*(ALP)

2 0.37018*(ALP) 2 0.20253*(K) 2 0.24388*(Na)

75

1 0.4334*(K) 2 0.3024*(Na) 2 0.3080*(Cl)

2

R 5 0.6087 The regression equations for log SPC of early and late lactation have no similarity. There are several similar components (log SCC, protein, tyrosine, ALP, Na) in the regression equations for logSPC of early and commercial milk samples. All these measurements seemed to be needed before we can find other bacterium-related parameter to make a better estimation of SPC. The best-fit regression equations for the logarithm of SCC were as follows: Early lactation: Log SCC 5 1194.3819 2 0.3949*(week) 2 0.45094 *(milk yield; ml / half mammary gland / time) 1 0.13482*(EC) 1 0.11237 *(neutrophils / lymphocytes)

R 2 5 0.6588 Except for Na and tyrosine, there is no other common component in the regression equations for the three groups of milk samples. It may indicate that breed of dairy goat or stage of lactation have a greater impact on the physiological response to the mastitis infection along with the change of SCC. Also, the equations of log SCC were not similar to those of log SPC. This may be another indication that SCC can not serve as an ideal index for the SPC in dairy goats. Two of four examined bacterium were fitted into the regression equation which had a very high R 2 value. The fact that not every bacterium examined could be fitted into the regression may be related to the effect of different bacterium in causing inflammation of the mammary gland (Smith and Sherman, 1994).

2 0.15818*(log SPC) 2 0.43785*(tyrosine) 2 0.2967*(ALP) 2 0.31991*(Na) 2 0.3797*(Cl) R 2 5 0.6035 Late lactation Log SCC 5 2.91978 1 0.10135*(week) 2 0.00192*(milk yield) 1 0.20105*(lactose) 2 11.73602*(tyrosine) 1 0.00064*(LDH) 1 0.00041*(Na)

4. Conclusion Although several parameters were found to be significantly correlated to the log SPC in the individual experiments, none of those stand out as having a consistent correlation with the log SPC across all three experiments. The correlation and the best-fit analysis both indicated that SCC is not a suitable indicator of SPC. The best-fit analysis results in a higher R 2 than the simple correlation analysis. Unfortunately, there is not a simple regression equation which can meet the application need for milk samples coming from different sources.

2 0.00034*(P) 2 0.08238*(Pesud.) 2 0.05253(Strept.) R 2 5 0.9230

Acknowledgements The present study was supported by a grant (NSC-

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88-2313-B-002-033) from the National Science Council, Taiwan, Republic of China. We gratefully acknowledge the assistance of Kuang-Chuan Ltd. Co., Taiwan on milk composition and somatic cell count automatic measurement.

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