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Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats
Small Ruminant Research 107 (2012) 157–163
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Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats G. Romero a,∗ , J.C.F. Pantoja b , E. Sendra a , C. Peris c , J.R. Díaz a a Departamento de Tecnología Agroalimentaria, Escuela Politécnica Superior de Orihuela, Universidad Miguel Hernández, Ctra. Beniel, Km. 3,2, 03312 Orihuela, Spain b Departamento de Higiene Veterinária e Saúde Pública, Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista, campus de Botucatu, Distrito de Rubião Júnior, s/n, 18.618-970 Botucatu, SP, Brazil c Instituto de Ciencia Animal, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
a r t i c l e
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Article history: Received 26 January 2012 Received in revised form 27 April 2012 Accepted 1 May 2012 Available online 1 June 2012 Keywords: Goat Milking fraction Electrical conductivity Mastitis Somatic cell count
a b s t r a c t The aim of the work was to study the effect of milking fraction on electrical conductivity of milk (EC) to improve its use in dairy goat mastitis detection using automatic EC measurements during milking. The experiment was carried out on a group of 84 MurcianoGranadina goats (28 primiparous and 56 multiparous). Goats were in the fourth month of lactation. A linear mixed model was used to analyse the relationship between EC or somatic cell count (SCC) of gland milk and parity, mammary gland health status, analysed fraction (first 100 mL = F-1; machine milk = F-2; and stripping milk = F-3) and their first order interactions. Additionally, the mastitis detection characteristics (sensitivity, specificity, positive predictive value and negative predictive value) of SCC and EC were studied at different thresholds. All factors considered were significant for EC and SCC. EC decreased significantly as milking progressed (from F-1 to F-3) in both healthy and infected glands. EC was not significantly different between healthy and infected glands in F-1 and F-2 fractions, but EC of healthy glands (5.01 mS/cm) was significantly lower than in infected glands (5.03 mS/cm) at F-3. Mastitis detection characteristics of EC did not differ amongst studied fractions. The small significant difference of EC between healthy and infected glands obtained in F-3 fraction did not yield better sensitivity results compared to F-1 and F-2. The best EC mastitis detection characteristics were obtained at 5.20 mS/cm threshold (sensitivity of 70% and specificity of 50%). The best SCC mastitis detection characteristics were obtained at 300,000 cells/mL threshold and F-3 fraction (sensitivity of 85% and specificity of 65%). It was concluded that mastitis detection characteristics of EC were similar in the three milking fractions analysed, being slightly better for SCC in F-3 fraction. As shown in previous studies, there are no factors other than the mammary gland health status that affect milk EC and should be considered in the algorithms for mastitis detection to improve the results. © 2012 Elsevier B.V. All rights reserved.
1. Introduction
∗ Corresponding author. Tel.: +34 966749704; fax: +34 966749677. E-mail address: [email protected] (G. Romero). 0921-4488/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.smallrumres.2012.05.001
The measurement of electrical conductivity in milk (EC) has been largely studied in dairy cows, having resulted in acceptable test sensitivity and specificity, especially when clinical mastitis is detected and on-line gland daily measurements are introduced in the algorithms used (Cavero
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et al., 2006, 2007, 2008; De Mol et al., 1999; Mele et al., 2001). Milk EC is measured automatically by gland at the same time as milk flows through short milk tubes thanks to conductimeters located inside them or in the claw (one EC sensor for every short milk tube). Recorded data are analysed daily and compared to previous data from the same gland or animal (depending on the algorithm employed), with the advantage of early mastitis detection. These algorithms resulted in better mastitis detection characteristics compared to using a common EC threshold, as they include the individual variations specific to each animal and the variations in EC related to other factors such as parity, month of lactation or farm, that were shown to significantly affect EC (Hamann and Zecconi, 1998). The analysed milking fraction affects EC and mastitis detection characteristics of EC measurements in dairy cows. In general, higher values of EC were reported in first squirts in these animals than in machine milk or stripping milk (Bruckmaier et al., 2004a; Hillerton and Walton, 1991; Lien and Wan, 2000). Some authors reported that the greatest differences in EC were observed between infected and healthy glands in first fraction (Bruckmaier et al., 2004b; Lien et al., 2005) and the best diagnostic features were observed when this fraction was considered. Holdaway et al. (1996) observed a different evolution of milk EC amongst milking fractions in infected glands than Bruckmaier et al. (2004a,b), Hillerton and Walton (1991) and Lien and Wan (2000): EC of infected glands from first squirts was lower than the remaining milk fractions studied. In contrast, Bansal et al. (2005) detected greater differences between healthy and infected glands in stripping milk, concluding that better results of mastitis detection were obtained in stripping milk, in line with a previous study (Fernando et al., 1985). In goats, published studies on the effect of mastitis on EC are scarce (Argüello, 2011). Most of goat’s milk is used for making products such as cheese and yogurt and premium quality milk is essential for getting high-quality products (Silanikove et al., 2010). Mastitis in its subclinical and clinical forms is the main factor that affects milk quality for product formation (Leitner et al., 2007, 2008). Therefore, early detection of mastitis should be an important tool in management milk hygiene in goat flocks. Ying et al. (2004), in a study carried out with different breeds of goats, reported contradictory results: EC increased with infection in Saanen but decreased in Alpine goats. Petzer et al. (2008) reported moderate correlation between EC and CMT, and significantly higher SCC results for red EC colour (highest EC level) compared to green or orange EC colour levels. Tangorra et al. (2010) reported a significant higher EC in infected glands compared to non infected ones at 0–60 days in milk period, although no significant differences were found in the other stages of lactation considered. They also reported a significant EC increasing throughout lactation. Díaz et al. (2011), in a study carried out in 3 different farms and analysing monthly gland milk samples, only observed a significant effect of mastitis if mastitis was unspecific (negative bacterial culture and SCC > 1,000,000 cells/mL); they related physiological factors other than mastitis to EC variation (farm, parity, month of lactation) and related EC variation to milk composition (coefficient of
determination of 0.91). In another study carried out with daily measurements (Díaz et al., 2012), they observed individual variations of EC and significant increases in EC after intramammary infection (IMI) was established, resulting in greater increases if major pathogens were causing the infection, and concluded that algorithms which take the observed individual and daily EC variations into account would increase the mastitis detection characteristics by EC measurements. Results on the relationship of milking fraction with milk EC in small ruminants are almost inexistent. Peris et al. (1991) reported higher EC in first squirts from sheep compared to machine or stripping milk, but no studies on EC of goat milk have been published. The purpose of this study was to improve the knowledge of EC in gland milk for on-line mastitis detection in dairy goats. Specific aims were to study: (1) the relationship between milking fraction (first milk, machine milk and stripping milk) and EC; and (2) mastitis detection characteristics of EC of different milking fractions. 2. Materials and methods 2.1. Animal management and sampling procedures The experiment was carried out at the Educational and Experimental Farm of the Escuela Politécnica Superior de Orihuela (Miguel Hernandez University, Spain). Farm-related features included intensive management of the Murciano-Granadina goats, permanent stabling, one parturition per year, artificial feeding of kids from birth and mechanical milking once a day (in the morning). Milking parameters were: rate of 90 pulsations per minute, vacuum level of 40 kPa and a 60% pulsation ratio. Milking routine of animals was: attachment of teatcups, machine milking, machine stripping, teatcup removal and iodine teat dipping. Animals were fed on a commercial mixture (unifeed system) for high production goats which was not changed during the course of the study (quantity and nutritional composition). 2.2. Experimental design and study variables Three consecutive weekly bacteriological and SCC analyses of milk were carried out to determine the gland health status of a group of 84 goats (28 primiparous and 56 multiparous) in the fourth month of lactation. On the second sampling day, 3 additional samples (100 mL) were obtained from each gland, corresponding to 3 studied milking fractions: first 100 mL by hand (F-1), machine milk without stripping (F-2) obtained using volumetric measurement devices; and stripping fraction (F-3) obtained by hand. In all three studied fractions (F-1, F-2, F-3), EC and SCC were performed for each mammary gland. EC (mS/cm) was a continuous variable recorded using a laboratory conductivity meter (GLP 32, Crison, Alella, Spain) equipped with a PT100 temperature probe and reading compensated at 25 ◦ C. SCC (×1000 cells/mL) was a continuous variable analysed in samples kept in azidiol using the fluoro-opto-electronic method (Fossomatic 5000, Foss Electric, Hillerød, Denmark) at the Inter-professional Dairy Laboratory of the Community of Valencia (LICOVAL, Valencia, Spain). 2.3. Mammary gland health status definition To determine the health status (HS) of mammary glands, both bacteriological analysis and SCC results were considered. Milk samples for bacteriological analysis (5 mL) were obtained aseptically from teats carefully cleaned with 70% ethanol after discarding the first three streams of foremilk and placed into sterile tubes. Tubes were kept at 4 ◦ C for a maximum of 12 h until testing, used for bacteriological analysis and afterwards kept frozen until the end of the experiment. Twenty L of each sample were plated on blood agar plates (5% washed sheep erythrocytes; Biomerieux, Lyon, France), incubated aerobically at 37 ◦ C, and examined at 24 h, 48 h and 72 h. Cultures with five or more identical colonies were considered positive for IMI. The definition of mammary gland HS was
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Table 1 Distribution of mammary glands by health status and parity of goats included in the study. Health status of glands
Parity Primiparous
Multiparous n
Total
n
%
%
Free Infected from goats unilaterally infected Infected from goats bilaterally infected
41 9 6
24.4 5.4 3.5
44 20 48
26.2 11.9 28.6
n 85 29 54
% 50.5 17.2 32.3
Total
56
33.3
112
66.7
168
100.0
n, number of observations. performed according to Díaz et al. (2011). A gland was defined as having bacterial mastitis (INF) when bacteriological analyses were positive for IMI. When the bacteriological analysis was negative and SCC was >1,000,000 cells/mL on two or more consecutive sampling days and due to non-physiological causes, it was considered unspecific (UNS). A physiological increase in SCC (for example due to oestrus; Christodoulopoulos et al., 2008) was defined when bacteriological analysis was negative and there was an increase of SCC in both glands in an isolated analysis which was followed by SCC < 1,000,000 cells/mL in a subsequent analysis, or end of lactation occurred. A gland was considered free from mastitis (FREE) with negative bacterial culture and if the increase of SCC values was due to physiological causes.
3. Results 3.1. Mastitis prevalence The prevalence of mastitis in the experiment was 49.5% for glands and 66.6% for goats (Table 1). No cases of UNS mastitis (caused by agents other than bacteria that would increase SCC) were observed and all cases presented INF mastitis (caused by bacteriological infection).
3.2. EC and SCC and associated factors 2.4. Data treatment and statistical analysis The association between HS or milking fraction and EC or SCC was studied. First, the distribution of variables was analysed using box-plots, histograms, and normal probability plots. Values of EC (mS/cm) and SCC (×1000 cells/mL) were transformed into base-ten logarithm to normalise their distributions. The association between the explanatory variables and EC or SCC was assessed using linear mixed models (Proc Mixed, SAS Institute Inc., V.9.1, 2002). Explanatory variables and first-order interaction terms that were associated (P-value <0.05) with EC or SCC were included in the final models after performing a stepwise variable selection procedure. The model considered was:
Yijklm = + ˛i + ˇj + k + ˛ˇij + ıl (εm ) + εm + eijklm
where is the mean, ˛i is the effect of the ith milking fraction (F-1, F-2, F3), ˇj is the effect of the jth health status of the gland (FREE or INF; no cases of UNS were detected), k is the effect of the kth parity of goat (primiparous or multiparous), ˛ˇij is the interaction between fraction and health status, ıl (εm ) is the random effect of the lth gland (left or right) nested to the mth goat, εm is the random effect of the goat and eijklm is the residual error. To account for the clustering of mammary glands within animals (Barkema et al., 1997), gland nested to the goat and the goat were considered random terms. An unstructured covariance structure was used to account for the repeated measurements. The model using this hierarchical structure provided the best fit for the data at every studied variable when compared to different models considering other covariance and hierarchical structures (as assessed using Bayesian and Akaike’s information criteria). The interactions between parity and HS and between parity and fraction were not significant and were not considered in the final model. Mastitis detection characteristics of EC and SCC were estimated at various thresholds for each fraction analysed. As no unspecific mastitis was observed, only intramammary infection was considered. Sensitivity was defined as the probability of a truly infected mammary gland being classified as test positive. Specificity was defined as the probability of a negative sample (FREE) being classified as such. Additionally, the positive predictive value (PPV) was calculated and defined as the probability of the gland being truly infected when the sample is classified as positive, as well as the negative predictive value (NPV), defined as the probability of the gland not being infected when the sample is classified as negative.
Average LEC was 0.725 (5.31 mS/cm) and average LSCC was 2.459 (288,000 cells/mL). Parity, fraction and the interaction of HS with fraction were significant in the final model with EC as an outcome variable (Table 2). For SCC, all considered variables (parity, HS, fraction and the interaction of HS with fraction) were significant. The most relevant factor was parity in both studied variables, EC and SCC. Mean of EC of multiparous goats (5.45 mS/cm) was significantly higher than in primiparous dams (5.07 mS/cm), similar to that observed for SCC (107 and 494 × 103 cells/mL, respectively) (Table 3). The interaction between parity and HS was not significant. EC was highest in F-1 (5.43 mS/cm; Fig. 1) and decreased significantly with as milking progressed in F-2 (5.28 mS/cm) and F-3 (5.07 mS/cm). Similar results were obtained in glands free from infection and infected ones in F-1 and F-2. The only significant differences between healthy and infected glands were obtained in F-3, with EC being higher in infected ones (5.01 vs 5.14 mS/cm). SCC increased significantly with the progress of milking and the highest values were observed for infected glands
Table 2 F value and significance level of electrical conductivity and somatic cell count in goat milk from glands. Variable
Parity Health status Fraction Fraction × health status
ECa
SCCb
F
S.L.
F
178.63 0.99 12.90 5.08
<0.0001 0.3207 0.0004 0.0067
79.39 29.62 44.24 5.78
S.L. <0.0001 <0.0001 <0.0001 0.0034
S.L., significance level. a Logarithm of electrical conductivity. Number of observations: 472. b Logarithm of somatic cell count. Number of observations: 470.
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Table 3 Electrical conductivity and somatic cell count of goat milk from glands by parity. Parity
ECa
SCCb c
Primiparous Multiparous Alle
Mean (antilog)
SE
n
Mean (antilog)d
SE
n
0.705a (5.07) 0.737b (5.45)
0.007 0.005
164 310
5.032a (107) 5.694b (494)
0.081 0.057
157 313
0.725 ± 0.044 (5.31)
2.459 ± 0.667 (288)
474
470
ab, means within a column with different letters differ (P < 0.001); n, number of observations; SE, standard error. a Logarithm of electrical conductivity. b Logarithm of somatic cell count. c Antilogarithm (mS/cm). d Antilogarithm (×1000 cells/mL). e Average ± standard deviation.
for all considered fractions (Fig. 2), approximately twice as high as for healthy glands in the three studied fractions. 3.3. Mastitis detection according to EC and SCC behaviour Sensitivity, specificity, PPV, and NPV values of EC (Figs. 3 and 4) were not different amongst milking fractions. SCC mastitis detection characteristics (Figs. 5 and 6) were very similar amongst milking fractions below 400,000 cells/mL, and were not different above that threshold: better test characteristics were observed for SCC when compared to EC. For SCC, 85% of sensitivity and specificity around 65% were observed at F-3 at a threshold of 300,000 cells/mL, whereas for EC best results were obtained at a threshold of 5.20 mS/cm, with 70% of sensitivity and 50% of specificity. At higher EC thresholds, the sensitivity was lower and the NPV decreased, due to the greater number of false negative results. PPV was better for SCC (always >60%) than EC; NPV decreased to 60% for SCC and 50% for EC.
Fig. 2. Effect of interaction of health status of glands (free or infected) and milking fraction on somatic cell count (SCC, antilog) of goat milk from glands. abcd, means within a health status with different scripts differ (P < 0.05); ␣, means within a fraction with different scripts differ (P < 0.05); F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
100 80 %
60 40 20 0 5.10 5.20 5.30 5.40 5.50 5.60 5.70 5.80 5.90 6.00 EC (mS/cm)
Sens.F-1 Spec F-1 Fig. 1. Effect of interaction of health status of glands (free or infected) and milking fraction on electrical conductivity (EC, antilogarithm) of goat milk from glands. abcd, means within a health status with different scripts differ (P < 0.001); ␣, means within a fraction with different scripts differ (P < 0.001); F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
Sens.F-2 Spec F-2
Sens.F-3 Spec F-3
Fig. 3. Sensitivity (Sens) and specificity (Spec) of electrical conductivity (EC) in mastitis detection, by proposed EC thresholds of goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
G. Romero et al. / Small Ruminant Research 107 (2012) 157–163
70
%
60 50 40 30 5.10 5.20 5.30 5.40 5.50 5.60 5.70 5.80 5.90 6.00
EC (mS/cm)
PPV.F-1 NPV. F-1
PPV.F-2 NPV. F-2
PPV.F-3 NPV. F-3
Fig. 4. Positive predictive value (PPV) and negative predictive value (NPV) of electrical conductivity (EC) in mastitis detection, by proposed EC thresholds of goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
100 90
%
80 70 60 50 40 30
200 300 400 500 600 700 800 900 1000 1100 1200 SCC (x 1,000 cells/mL) Sens.F-1 Sens.F-2 Sens.F-3 Spec F-1 Spec F-2 Spec F-3 Fig. 5. Sensitivity (Sens) and specificity (Spec) of somatic cell count (SCC) in mastitis detection, by proposed SCC thresholds in goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
90 80
%
70 60 50 40 30
200 300 400 500 600 700 800 900 1000 1100 1200
SCC (x 1,000 cells/mL) PPV.F-1 NPV. F-1
PPV.F-2 NPV. F-2
PPV.F-3 NPV. F-3
Fig. 6. Positive predictive value (PPV) and negative predictive value (NPV) of somatic cell count (SCC) in mastitis detection, by proposed SCC thresholds in goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
4. Discussion The mastitis prevalence observed, caused by intramammary infection, was greater than published by other authors in the same geographical area and breed (Sánchez
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et al., 1998). This may be because the goats included in this study were in an experimental farm where many of the studies carried out are related to factors affecting milk EC and mastitis. This situation caused a higher mastitis prevalence which was conducive to the aim of this study of including a sufficient number of mastitic glands. The observed decrease in EC amongst the different milking fractions agrees with results reported by different authors in cow milk (Bansal et al., 2005; Bruckmaier et al., 2004a,b; Hillerton and Walton, 1991; Lien and Wan, 2000; Lien et al., 2005) and with the experiment carried out in sheep milk by Peris et al. (1991), who obtained a negative correlation between EC and fat concentration, so EC varied with the milking fraction analysed because hand stripping milk had a higher fat content than milk obtained during machine milking or first squirts. Díaz et al. (2011) reported that high EC variation was explained if macro-composition and mineral content was considered (R2 = 0.91), but low variance was explained (R2 = 0.1285) if only macro-composition was considered, with fat as the constituent that explained most EC variance (partial R2 = 0.0643). These results agree with those obtained by Bansal et al. (2005) and Lien and Wan (2000), carried out in cows, who obtained a higher fat content in stripping milk than machine milk and a lower EC. The lack of a significant interaction between parity and fraction indicates that the higher EC values observed in multiparous compared to primiparous goats would also be obtained in the three studied fractions. These results agree with Díaz et al. (2011) who reported a significant effect of parity on EC, resulting in higher EC in multiparous goats compared to primiparous ones in foremilk fraction. SCC increased during milking, being significantly higher at F-3 compared to F-2 and F-1, and at F-2 significantly higher than F-1, in both healthy and infected glands. This result is similar to those published for different species for which an increase of SCC was observed with the progress of milking: Peris et al. (1991) in ewes reported a significantly higher SCC in stripping fraction than machine milk and foremilk, without significant differences between foremilk and machine milk. Bansal et al. (2005) in cows reported a significantly higher SCC in stripping fraction compared to first squirts, but not significant compared to machine milk fraction. A significant effect of HS on SCC was obtained, with significant higher values for infected glands than mastitis-free in all the studied fractions. The lack of a significant effect of HS on EC could be due to bacterial subclinical mastitis cases with only minor damage to mammary glands and low SCC were recorded (the highest SCC mean was obtained at F-3 of infected glands = 458,000 cells/mL). In a previous work carried out with monthly rather than daily analysis, Díaz et al. (2011) reported significant higher EC in the group of glands affected by unspecific mastitis (5.75 and 5.79 mS/cm at primiparous and multiparous, respectively) compared to healthy glands (5.15 and 5.66 mS/cm at primiparous and multiparous, respectively), but not in glands affected by bacterial mastitis (5.29 and 5.68 mS/cm at primiparous and multiparous, respectively) compared to healthy glands, and concluded that greater gland damage is necessary to obtain significant EC increases than for SCC increases
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(significant differences of SCC were obtained amongst the three different HS considered: 2,456,000 cells/mL in unspecific mastitis glands, 826,000 cells/mL in bacterial mastitis gland, and 315,000 cells/mL in healthy glands). In another study carried out in goats, Díaz et al. (2012) reported a significant increase in EC after establishment of IMI, showing a higher EC increase when glands were infected by major pathogens (Staphylococcus aureus and Gram Negative bacillus) compared to minor pathogens; EC increases were related to significant increases in chloride and sodium/potassium relationship, and a significant SCC increase. Tangorra et al. (2010) reported higher EC values in infected glands compared to healthy ones only at 0–60 days in milk period (11.74 vs 10.71 mS), but no significant differences were obtained in the other lactation stages considered. All the EC values reported were greater (>10 mS) than those obtained in the current study, probably due to the algorithm employed to calculate the EC value. The significant interaction between HS and fraction on EC was due to differences in EC found in F-3. The greatest differences between healthy and infected glands recorded in F-3 fraction agrees with two previous studies on cows (Bansal et al., 2005; Fernando et al., 1985); these authors obtained higher sensitivity when analysing stripping milk than first squirts, as stripping milk showed greater EC differences between infected and healthy glands, and concluded that diagnostic test characteristics for mastitis detection of a variable measured in milk would vary depending on the fraction analysed. In contrast, Bruckmaier et al. (2004b) reported a higher sensitivity of EC for subclinical mastitis detection of milk obtained during the first 60 s of milking. They explained that this fraction shows more differences in composition and thus in EC compared to the other studied fractions because the remaining milking milk is mixed with alveolar milk (as a response to milk ejection) which causes a reduction of its mastitis detection characteristics. In the current study on goats, mastitis detection characteristics (sensitivity, specificity, PPV and NPV) of EC did not differ amongst studied fractions at proposed thresholds due to the lack of differences obtained between HS levels at F-1 and F-2 fractions, and the small one obtained in F-3. The low sensitivity observed was in agreement with results of Díaz et al. (2011) for EC in goat gland milk; they explained that variables other than HS were related to EC (farm, parity and stage of lactation), and concluded that it is necessary to consider the intrinsic variation of the animal and avoid the use of the same threshold for the whole study population (as was done in cows), in order to improve mastitis detection feature and so avoid unnecessary mastitis treatments that are given when low specificity is obtained. The results of the on-line EC algorithms for mastitis detection using daily measurements in dairy cows provide better results for clinical than subclinical mastitis detection (Cavero et al., 2008; De Mol et al., 1999; Hovinen et al., 2006; Maatje et al., 1997; Mele et al., 2001; Zecconi et al., 2004) and many of the published studies are adjusted to obtain higher sensitivity compared to specificity, even when this option increases unnecessary costs (due to the greater proportion of false positive cases when specificity is lower).
Mastitis detection characteristics of SCC were greater than EC because SCC reported significant differences between healthy and infected glands in any studied fraction. Better SCC mastitis detection characteristics were obtained in F-3 because SCC differences between healthy and infected glands were slightly higher compared to F2 and F-1. This result agrees with Bansal et al. (2005), who reported greater SCC differences between healthy and infected cow glands in stripping milk, and obtained better mastitis detection results when analysing this fraction compared to first squirts, also in agreement with a previous study (Fernando et al., 1985). 5. Conclusions The lack of differences between fractions for goat mastitis detection characteristics using EC of gland milk indicates that all the fractions considered would obtain similar results, although the significantly higher EC of infected glands compared to healthy ones at F-3 suggests that better results for mastitis detection would be obtained if EC of F-3 was analysed during milking. In order to deepen the knowledge on improving and developing better mastitis detection systems in goats using EC, it would be necessary to test the daily EC measurements using algorithms, as done in dairy cows, including other significant factors (such as individual variation) and testing the best fraction to be considered. To carry out this kind of studies, specific conductimeters should be developed for incorporation in the short milk tubes of goat clusters, able to measure EC on-line (when the milk is flowing from the gland). Acknowledgement The study was supported by project AGL2006-06909 (Ministerio de Educación y Ciencia of Spain and FEDER). References Argüello, A., 2011. Trends in goat research: a review. J. Appl. Anim. Res. 39 (4), 429–434. Bansal, B.K., Hamann, J., Grabowski, N.T., Singh, K.B., 2005. Variation in the composition of selected milk fraction samples from healthy and mastitic quarters, and its significance for mastitis diagnosis. J. Dairy Res. 72, 144–152. Barkema, H.W., Schukken, Y.H., Lam, T.J.G.M., Gallican, D.T., Beiboer, M.L., Brand, A., 1997. Estimation of interdependence among quarters of the bovine udder with subclinical mastitis and implications for analysis. J. Dairy Sci. 80, 1592–1599. Bruckmaier, R.M., Ontsouka, C.E., Blum, J.W., 2004a. Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. 49, 283–290. Bruckmaier, R.M., Weiss, D., Wiedemann, M., Schmitz, S., Wendl, G., 2004b. Changes of physicochemical indicators during mastitis and the effects of milk ejection on their sensitivity. J. Dairy Res. 71, 316–321. Cavero, D., Tölle, K.H., Buxadé, C., Krieter, J., 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest. Sci. 105, 207–213. Cavero, D., Tölle, K.H., Rave, G., Buxadé, C., Krieter, J., 2007. Analyzing serial data for mastitis detection by means of local regression. Livest. Sci. 110, 101–110. Cavero, D., Tölle, K.H., Henze, C., Buxadé, C., Krieter, J., 2008. Mastitis detection in dairy cows by application of neural networks. Livest. Sci. 114, 280–286. Christodoulopoulos, G., Solomakos, N., Katsoulos, P.D., Minas, A., Kritas, S.K., 2008. Influence of oestrus on the heat stability and other characteristics of milk from dairy goats. J. Dairy Res. 75, 64–68.
G. Romero et al. / Small Ruminant Research 107 (2012) 157–163 De Mol, R.M., Keen, A., Kroeze, G.H., Achten, J.M.F.H., 1999. Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter. Comput. Electron. Agric. 22, 171–185. Díaz, J.R., Romero, G., Muelas, R., Sendra, E., Pantoja, J.C.F., Paredes, C., 2011. Analysis of the influence of variation factors on electrical conductivity of milk in Murciano-Granadina goats. J. Dairy Sci. 94, 3885–3894. Díaz, J.R., Romero, G., Muelas, R., Alejandro, M., Peris, C., 2012. Effect of mastitis on milk electrical conductivity in Murciano-Granadina goats. J. Dairy Sci. 95, 718–726. Fernando, R.S., Spahr, S., Jaster, E.H., 1985. Comparison of electrical conductivity of milk with other indirect methods for detection of subclinical mastitis. J. Dairy Sci. 68, 449–456. Hamann, J., Zecconi, A., 1998. Evaluation of the electrical conductivity of milk as a mastitis indicator. Bull. IDF 334, 5–22. Hillerton, J.E., Walton, A.W., 1991. Identification of subclinical mastitis with a hand-held electrical conductivity meter. Vet. Rec. 128, 513–515. Holdaway, R.J., Holmes, C.W., Steffert, I.J., 1996. A comparison of indirect methods for diagnosis of subclinical intramammary infection in lactating dairy cows. Aust. J. Dairy Technol. 51, 64–82. Hovinen, M., Aisla, A.M., Pyorala, S., 2006. Accuracy and reliability of mastitis detection with electrical conductivity and milk colour measurement in automatic milking. Acta Agric. Scand. A 56, 121–127. Leitner, G., Merin, U., Lavi, Y., Egber, A., Silanikove, N., 2007. Aetiology of intramammary infection and its effect on milk composition in goat flocks. J. Dairy Res. 74, 186–193. Leitner, G., Silanikove, N., Merin, U., 2008. Estimate of milk and curd yield loss of sheep and goats with intramammary infection and its relation to somatic cell count. Small Rumin. Res. 74, 221–225. Lien, C.C., Wan, Y.N., 2000. The nondestructive detection of dairy cow mastitis by electrical conductivity. ASAE paper. No. 006114. In: ASAE Annual-International-Meeting, Milwaukee, Wisconsin, USA.
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Lien, C.C., Wan, Y.N., Chen, H.N., 2005. Performance evaluation of an online EC measurement system for dairy cow mastitis inspection. Int. Agric. Eng. J. 14, 89–99. Maatje, K., De Mol, R.M., Rossing, W., 1997. Cow status monitoring (health and oestrus) using detection sensors. Comput. Electron. Agric. 16, 245–254. Mele, M., Secchiari, P., Serra, A., Ferruzzi, G., Paoletti, F., Biagioni, M., 2001. Application of the tracking signal method to the monitoring of udder health and oestrus in dairy cows. Livest. Prod. Sci. 72, 279–284. Peris, C., Molina, M.P., Fernandez, N., Rodriguez, M., Torres, A., 1991. Variation in somatic cell count, california mastitis test, and electrical conductivity among various fractions of ewe’s milk. J. Dairy Sci. 74, 1553–1560. Petzer, I.M., Donkin, E.F., Du Preez, E., Karzis, J., van der Schans, T.J., Watermeyer, J.C., van Reenen, R., 2008. Value of tests for evaluating udder health in dairy goats: somatic cell counts, California Milk Cell Test and electrical conductivity. Onderstepoort J. Vet. Res. 75, 279–287. Sánchez, A., Contreras, A., Corrales, J.C., 1998. Parity as a risk factor for caprine subclinical intramammary infection. Small Rumin. Res. 31, 197–201. SAS Institute Inc., 2002. SAS V.9.1. User’s Guide. SAS Institute Inc., Cary, NC. Silanikove, N., Leitner, G., Merin, U., Prosser, C.G., 2010. Recent advances in exploiting goat’s milk: quality, safety and production aspects. Small Rumin. Res. 89, 110–124. Tangorra, F.M., Zaninelli, M., Costa, A., Agazzi, A., Savoini, G., 2010. Milk electrical conductivity and mastitis status in dairy goats: results from a pilot study. Small Rumin. Res. 90, 109–113. Ying, C.H., Yang, C.H.-B., Hsu, J.-T., 2004. Relationship of somatic cell count, physical, chemical and enzymatic properties to the bacterial standard plate count in different breeds of dairy goats. Asian Australas. J. Anim. Sci. 17, 554–559. Zecconi, A., Piccinini, R., Giovannini, G., Casinari, G., Panzeri, R., 2004. Clinical mastitis detection by on-line measurements of milk yield, electrical conductivity and milking duration in commercial dairy farms. Milchwissenschaft 59, 240–244.
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Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats G. Romero a,∗ , J.C.F. Pantoja b , E. Sendra a , C. Peris c , J.R. Díaz a a Departamento de Tecnología Agroalimentaria, Escuela Politécnica Superior de Orihuela, Universidad Miguel Hernández, Ctra. Beniel, Km. 3,2, 03312 Orihuela, Spain b Departamento de Higiene Veterinária e Saúde Pública, Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista, campus de Botucatu, Distrito de Rubião Júnior, s/n, 18.618-970 Botucatu, SP, Brazil c Instituto de Ciencia Animal, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
a r t i c l e
i n f o
Article history: Received 26 January 2012 Received in revised form 27 April 2012 Accepted 1 May 2012 Available online 1 June 2012 Keywords: Goat Milking fraction Electrical conductivity Mastitis Somatic cell count
a b s t r a c t The aim of the work was to study the effect of milking fraction on electrical conductivity of milk (EC) to improve its use in dairy goat mastitis detection using automatic EC measurements during milking. The experiment was carried out on a group of 84 MurcianoGranadina goats (28 primiparous and 56 multiparous). Goats were in the fourth month of lactation. A linear mixed model was used to analyse the relationship between EC or somatic cell count (SCC) of gland milk and parity, mammary gland health status, analysed fraction (first 100 mL = F-1; machine milk = F-2; and stripping milk = F-3) and their first order interactions. Additionally, the mastitis detection characteristics (sensitivity, specificity, positive predictive value and negative predictive value) of SCC and EC were studied at different thresholds. All factors considered were significant for EC and SCC. EC decreased significantly as milking progressed (from F-1 to F-3) in both healthy and infected glands. EC was not significantly different between healthy and infected glands in F-1 and F-2 fractions, but EC of healthy glands (5.01 mS/cm) was significantly lower than in infected glands (5.03 mS/cm) at F-3. Mastitis detection characteristics of EC did not differ amongst studied fractions. The small significant difference of EC between healthy and infected glands obtained in F-3 fraction did not yield better sensitivity results compared to F-1 and F-2. The best EC mastitis detection characteristics were obtained at 5.20 mS/cm threshold (sensitivity of 70% and specificity of 50%). The best SCC mastitis detection characteristics were obtained at 300,000 cells/mL threshold and F-3 fraction (sensitivity of 85% and specificity of 65%). It was concluded that mastitis detection characteristics of EC were similar in the three milking fractions analysed, being slightly better for SCC in F-3 fraction. As shown in previous studies, there are no factors other than the mammary gland health status that affect milk EC and should be considered in the algorithms for mastitis detection to improve the results. © 2012 Elsevier B.V. All rights reserved.
1. Introduction
∗ Corresponding author. Tel.: +34 966749704; fax: +34 966749677. E-mail address: [email protected] (G. Romero). 0921-4488/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.smallrumres.2012.05.001
The measurement of electrical conductivity in milk (EC) has been largely studied in dairy cows, having resulted in acceptable test sensitivity and specificity, especially when clinical mastitis is detected and on-line gland daily measurements are introduced in the algorithms used (Cavero
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et al., 2006, 2007, 2008; De Mol et al., 1999; Mele et al., 2001). Milk EC is measured automatically by gland at the same time as milk flows through short milk tubes thanks to conductimeters located inside them or in the claw (one EC sensor for every short milk tube). Recorded data are analysed daily and compared to previous data from the same gland or animal (depending on the algorithm employed), with the advantage of early mastitis detection. These algorithms resulted in better mastitis detection characteristics compared to using a common EC threshold, as they include the individual variations specific to each animal and the variations in EC related to other factors such as parity, month of lactation or farm, that were shown to significantly affect EC (Hamann and Zecconi, 1998). The analysed milking fraction affects EC and mastitis detection characteristics of EC measurements in dairy cows. In general, higher values of EC were reported in first squirts in these animals than in machine milk or stripping milk (Bruckmaier et al., 2004a; Hillerton and Walton, 1991; Lien and Wan, 2000). Some authors reported that the greatest differences in EC were observed between infected and healthy glands in first fraction (Bruckmaier et al., 2004b; Lien et al., 2005) and the best diagnostic features were observed when this fraction was considered. Holdaway et al. (1996) observed a different evolution of milk EC amongst milking fractions in infected glands than Bruckmaier et al. (2004a,b), Hillerton and Walton (1991) and Lien and Wan (2000): EC of infected glands from first squirts was lower than the remaining milk fractions studied. In contrast, Bansal et al. (2005) detected greater differences between healthy and infected glands in stripping milk, concluding that better results of mastitis detection were obtained in stripping milk, in line with a previous study (Fernando et al., 1985). In goats, published studies on the effect of mastitis on EC are scarce (Argüello, 2011). Most of goat’s milk is used for making products such as cheese and yogurt and premium quality milk is essential for getting high-quality products (Silanikove et al., 2010). Mastitis in its subclinical and clinical forms is the main factor that affects milk quality for product formation (Leitner et al., 2007, 2008). Therefore, early detection of mastitis should be an important tool in management milk hygiene in goat flocks. Ying et al. (2004), in a study carried out with different breeds of goats, reported contradictory results: EC increased with infection in Saanen but decreased in Alpine goats. Petzer et al. (2008) reported moderate correlation between EC and CMT, and significantly higher SCC results for red EC colour (highest EC level) compared to green or orange EC colour levels. Tangorra et al. (2010) reported a significant higher EC in infected glands compared to non infected ones at 0–60 days in milk period, although no significant differences were found in the other stages of lactation considered. They also reported a significant EC increasing throughout lactation. Díaz et al. (2011), in a study carried out in 3 different farms and analysing monthly gland milk samples, only observed a significant effect of mastitis if mastitis was unspecific (negative bacterial culture and SCC > 1,000,000 cells/mL); they related physiological factors other than mastitis to EC variation (farm, parity, month of lactation) and related EC variation to milk composition (coefficient of
determination of 0.91). In another study carried out with daily measurements (Díaz et al., 2012), they observed individual variations of EC and significant increases in EC after intramammary infection (IMI) was established, resulting in greater increases if major pathogens were causing the infection, and concluded that algorithms which take the observed individual and daily EC variations into account would increase the mastitis detection characteristics by EC measurements. Results on the relationship of milking fraction with milk EC in small ruminants are almost inexistent. Peris et al. (1991) reported higher EC in first squirts from sheep compared to machine or stripping milk, but no studies on EC of goat milk have been published. The purpose of this study was to improve the knowledge of EC in gland milk for on-line mastitis detection in dairy goats. Specific aims were to study: (1) the relationship between milking fraction (first milk, machine milk and stripping milk) and EC; and (2) mastitis detection characteristics of EC of different milking fractions. 2. Materials and methods 2.1. Animal management and sampling procedures The experiment was carried out at the Educational and Experimental Farm of the Escuela Politécnica Superior de Orihuela (Miguel Hernandez University, Spain). Farm-related features included intensive management of the Murciano-Granadina goats, permanent stabling, one parturition per year, artificial feeding of kids from birth and mechanical milking once a day (in the morning). Milking parameters were: rate of 90 pulsations per minute, vacuum level of 40 kPa and a 60% pulsation ratio. Milking routine of animals was: attachment of teatcups, machine milking, machine stripping, teatcup removal and iodine teat dipping. Animals were fed on a commercial mixture (unifeed system) for high production goats which was not changed during the course of the study (quantity and nutritional composition). 2.2. Experimental design and study variables Three consecutive weekly bacteriological and SCC analyses of milk were carried out to determine the gland health status of a group of 84 goats (28 primiparous and 56 multiparous) in the fourth month of lactation. On the second sampling day, 3 additional samples (100 mL) were obtained from each gland, corresponding to 3 studied milking fractions: first 100 mL by hand (F-1), machine milk without stripping (F-2) obtained using volumetric measurement devices; and stripping fraction (F-3) obtained by hand. In all three studied fractions (F-1, F-2, F-3), EC and SCC were performed for each mammary gland. EC (mS/cm) was a continuous variable recorded using a laboratory conductivity meter (GLP 32, Crison, Alella, Spain) equipped with a PT100 temperature probe and reading compensated at 25 ◦ C. SCC (×1000 cells/mL) was a continuous variable analysed in samples kept in azidiol using the fluoro-opto-electronic method (Fossomatic 5000, Foss Electric, Hillerød, Denmark) at the Inter-professional Dairy Laboratory of the Community of Valencia (LICOVAL, Valencia, Spain). 2.3. Mammary gland health status definition To determine the health status (HS) of mammary glands, both bacteriological analysis and SCC results were considered. Milk samples for bacteriological analysis (5 mL) were obtained aseptically from teats carefully cleaned with 70% ethanol after discarding the first three streams of foremilk and placed into sterile tubes. Tubes were kept at 4 ◦ C for a maximum of 12 h until testing, used for bacteriological analysis and afterwards kept frozen until the end of the experiment. Twenty L of each sample were plated on blood agar plates (5% washed sheep erythrocytes; Biomerieux, Lyon, France), incubated aerobically at 37 ◦ C, and examined at 24 h, 48 h and 72 h. Cultures with five or more identical colonies were considered positive for IMI. The definition of mammary gland HS was
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Table 1 Distribution of mammary glands by health status and parity of goats included in the study. Health status of glands
Parity Primiparous
Multiparous n
Total
n
%
%
Free Infected from goats unilaterally infected Infected from goats bilaterally infected
41 9 6
24.4 5.4 3.5
44 20 48
26.2 11.9 28.6
n 85 29 54
% 50.5 17.2 32.3
Total
56
33.3
112
66.7
168
100.0
n, number of observations. performed according to Díaz et al. (2011). A gland was defined as having bacterial mastitis (INF) when bacteriological analyses were positive for IMI. When the bacteriological analysis was negative and SCC was >1,000,000 cells/mL on two or more consecutive sampling days and due to non-physiological causes, it was considered unspecific (UNS). A physiological increase in SCC (for example due to oestrus; Christodoulopoulos et al., 2008) was defined when bacteriological analysis was negative and there was an increase of SCC in both glands in an isolated analysis which was followed by SCC < 1,000,000 cells/mL in a subsequent analysis, or end of lactation occurred. A gland was considered free from mastitis (FREE) with negative bacterial culture and if the increase of SCC values was due to physiological causes.
3. Results 3.1. Mastitis prevalence The prevalence of mastitis in the experiment was 49.5% for glands and 66.6% for goats (Table 1). No cases of UNS mastitis (caused by agents other than bacteria that would increase SCC) were observed and all cases presented INF mastitis (caused by bacteriological infection).
3.2. EC and SCC and associated factors 2.4. Data treatment and statistical analysis The association between HS or milking fraction and EC or SCC was studied. First, the distribution of variables was analysed using box-plots, histograms, and normal probability plots. Values of EC (mS/cm) and SCC (×1000 cells/mL) were transformed into base-ten logarithm to normalise their distributions. The association between the explanatory variables and EC or SCC was assessed using linear mixed models (Proc Mixed, SAS Institute Inc., V.9.1, 2002). Explanatory variables and first-order interaction terms that were associated (P-value <0.05) with EC or SCC were included in the final models after performing a stepwise variable selection procedure. The model considered was:
Yijklm = + ˛i + ˇj + k + ˛ˇij + ıl (εm ) + εm + eijklm
where is the mean, ˛i is the effect of the ith milking fraction (F-1, F-2, F3), ˇj is the effect of the jth health status of the gland (FREE or INF; no cases of UNS were detected), k is the effect of the kth parity of goat (primiparous or multiparous), ˛ˇij is the interaction between fraction and health status, ıl (εm ) is the random effect of the lth gland (left or right) nested to the mth goat, εm is the random effect of the goat and eijklm is the residual error. To account for the clustering of mammary glands within animals (Barkema et al., 1997), gland nested to the goat and the goat were considered random terms. An unstructured covariance structure was used to account for the repeated measurements. The model using this hierarchical structure provided the best fit for the data at every studied variable when compared to different models considering other covariance and hierarchical structures (as assessed using Bayesian and Akaike’s information criteria). The interactions between parity and HS and between parity and fraction were not significant and were not considered in the final model. Mastitis detection characteristics of EC and SCC were estimated at various thresholds for each fraction analysed. As no unspecific mastitis was observed, only intramammary infection was considered. Sensitivity was defined as the probability of a truly infected mammary gland being classified as test positive. Specificity was defined as the probability of a negative sample (FREE) being classified as such. Additionally, the positive predictive value (PPV) was calculated and defined as the probability of the gland being truly infected when the sample is classified as positive, as well as the negative predictive value (NPV), defined as the probability of the gland not being infected when the sample is classified as negative.
Average LEC was 0.725 (5.31 mS/cm) and average LSCC was 2.459 (288,000 cells/mL). Parity, fraction and the interaction of HS with fraction were significant in the final model with EC as an outcome variable (Table 2). For SCC, all considered variables (parity, HS, fraction and the interaction of HS with fraction) were significant. The most relevant factor was parity in both studied variables, EC and SCC. Mean of EC of multiparous goats (5.45 mS/cm) was significantly higher than in primiparous dams (5.07 mS/cm), similar to that observed for SCC (107 and 494 × 103 cells/mL, respectively) (Table 3). The interaction between parity and HS was not significant. EC was highest in F-1 (5.43 mS/cm; Fig. 1) and decreased significantly with as milking progressed in F-2 (5.28 mS/cm) and F-3 (5.07 mS/cm). Similar results were obtained in glands free from infection and infected ones in F-1 and F-2. The only significant differences between healthy and infected glands were obtained in F-3, with EC being higher in infected ones (5.01 vs 5.14 mS/cm). SCC increased significantly with the progress of milking and the highest values were observed for infected glands
Table 2 F value and significance level of electrical conductivity and somatic cell count in goat milk from glands. Variable
Parity Health status Fraction Fraction × health status
ECa
SCCb
F
S.L.
F
178.63 0.99 12.90 5.08
<0.0001 0.3207 0.0004 0.0067
79.39 29.62 44.24 5.78
S.L. <0.0001 <0.0001 <0.0001 0.0034
S.L., significance level. a Logarithm of electrical conductivity. Number of observations: 472. b Logarithm of somatic cell count. Number of observations: 470.
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Table 3 Electrical conductivity and somatic cell count of goat milk from glands by parity. Parity
ECa
SCCb c
Primiparous Multiparous Alle
Mean (antilog)
SE
n
Mean (antilog)d
SE
n
0.705a (5.07) 0.737b (5.45)
0.007 0.005
164 310
5.032a (107) 5.694b (494)
0.081 0.057
157 313
0.725 ± 0.044 (5.31)
2.459 ± 0.667 (288)
474
470
ab, means within a column with different letters differ (P < 0.001); n, number of observations; SE, standard error. a Logarithm of electrical conductivity. b Logarithm of somatic cell count. c Antilogarithm (mS/cm). d Antilogarithm (×1000 cells/mL). e Average ± standard deviation.
for all considered fractions (Fig. 2), approximately twice as high as for healthy glands in the three studied fractions. 3.3. Mastitis detection according to EC and SCC behaviour Sensitivity, specificity, PPV, and NPV values of EC (Figs. 3 and 4) were not different amongst milking fractions. SCC mastitis detection characteristics (Figs. 5 and 6) were very similar amongst milking fractions below 400,000 cells/mL, and were not different above that threshold: better test characteristics were observed for SCC when compared to EC. For SCC, 85% of sensitivity and specificity around 65% were observed at F-3 at a threshold of 300,000 cells/mL, whereas for EC best results were obtained at a threshold of 5.20 mS/cm, with 70% of sensitivity and 50% of specificity. At higher EC thresholds, the sensitivity was lower and the NPV decreased, due to the greater number of false negative results. PPV was better for SCC (always >60%) than EC; NPV decreased to 60% for SCC and 50% for EC.
Fig. 2. Effect of interaction of health status of glands (free or infected) and milking fraction on somatic cell count (SCC, antilog) of goat milk from glands. abcd, means within a health status with different scripts differ (P < 0.05); ␣, means within a fraction with different scripts differ (P < 0.05); F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
100 80 %
60 40 20 0 5.10 5.20 5.30 5.40 5.50 5.60 5.70 5.80 5.90 6.00 EC (mS/cm)
Sens.F-1 Spec F-1 Fig. 1. Effect of interaction of health status of glands (free or infected) and milking fraction on electrical conductivity (EC, antilogarithm) of goat milk from glands. abcd, means within a health status with different scripts differ (P < 0.001); ␣, means within a fraction with different scripts differ (P < 0.001); F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
Sens.F-2 Spec F-2
Sens.F-3 Spec F-3
Fig. 3. Sensitivity (Sens) and specificity (Spec) of electrical conductivity (EC) in mastitis detection, by proposed EC thresholds of goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
G. Romero et al. / Small Ruminant Research 107 (2012) 157–163
70
%
60 50 40 30 5.10 5.20 5.30 5.40 5.50 5.60 5.70 5.80 5.90 6.00
EC (mS/cm)
PPV.F-1 NPV. F-1
PPV.F-2 NPV. F-2
PPV.F-3 NPV. F-3
Fig. 4. Positive predictive value (PPV) and negative predictive value (NPV) of electrical conductivity (EC) in mastitis detection, by proposed EC thresholds of goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
100 90
%
80 70 60 50 40 30
200 300 400 500 600 700 800 900 1000 1100 1200 SCC (x 1,000 cells/mL) Sens.F-1 Sens.F-2 Sens.F-3 Spec F-1 Spec F-2 Spec F-3 Fig. 5. Sensitivity (Sens) and specificity (Spec) of somatic cell count (SCC) in mastitis detection, by proposed SCC thresholds in goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
90 80
%
70 60 50 40 30
200 300 400 500 600 700 800 900 1000 1100 1200
SCC (x 1,000 cells/mL) PPV.F-1 NPV. F-1
PPV.F-2 NPV. F-2
PPV.F-3 NPV. F-3
Fig. 6. Positive predictive value (PPV) and negative predictive value (NPV) of somatic cell count (SCC) in mastitis detection, by proposed SCC thresholds in goat milk from glands and milking fraction. F-1, first 100 mL; F-2, machine milk; F-3, stripping milk.
4. Discussion The mastitis prevalence observed, caused by intramammary infection, was greater than published by other authors in the same geographical area and breed (Sánchez
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et al., 1998). This may be because the goats included in this study were in an experimental farm where many of the studies carried out are related to factors affecting milk EC and mastitis. This situation caused a higher mastitis prevalence which was conducive to the aim of this study of including a sufficient number of mastitic glands. The observed decrease in EC amongst the different milking fractions agrees with results reported by different authors in cow milk (Bansal et al., 2005; Bruckmaier et al., 2004a,b; Hillerton and Walton, 1991; Lien and Wan, 2000; Lien et al., 2005) and with the experiment carried out in sheep milk by Peris et al. (1991), who obtained a negative correlation between EC and fat concentration, so EC varied with the milking fraction analysed because hand stripping milk had a higher fat content than milk obtained during machine milking or first squirts. Díaz et al. (2011) reported that high EC variation was explained if macro-composition and mineral content was considered (R2 = 0.91), but low variance was explained (R2 = 0.1285) if only macro-composition was considered, with fat as the constituent that explained most EC variance (partial R2 = 0.0643). These results agree with those obtained by Bansal et al. (2005) and Lien and Wan (2000), carried out in cows, who obtained a higher fat content in stripping milk than machine milk and a lower EC. The lack of a significant interaction between parity and fraction indicates that the higher EC values observed in multiparous compared to primiparous goats would also be obtained in the three studied fractions. These results agree with Díaz et al. (2011) who reported a significant effect of parity on EC, resulting in higher EC in multiparous goats compared to primiparous ones in foremilk fraction. SCC increased during milking, being significantly higher at F-3 compared to F-2 and F-1, and at F-2 significantly higher than F-1, in both healthy and infected glands. This result is similar to those published for different species for which an increase of SCC was observed with the progress of milking: Peris et al. (1991) in ewes reported a significantly higher SCC in stripping fraction than machine milk and foremilk, without significant differences between foremilk and machine milk. Bansal et al. (2005) in cows reported a significantly higher SCC in stripping fraction compared to first squirts, but not significant compared to machine milk fraction. A significant effect of HS on SCC was obtained, with significant higher values for infected glands than mastitis-free in all the studied fractions. The lack of a significant effect of HS on EC could be due to bacterial subclinical mastitis cases with only minor damage to mammary glands and low SCC were recorded (the highest SCC mean was obtained at F-3 of infected glands = 458,000 cells/mL). In a previous work carried out with monthly rather than daily analysis, Díaz et al. (2011) reported significant higher EC in the group of glands affected by unspecific mastitis (5.75 and 5.79 mS/cm at primiparous and multiparous, respectively) compared to healthy glands (5.15 and 5.66 mS/cm at primiparous and multiparous, respectively), but not in glands affected by bacterial mastitis (5.29 and 5.68 mS/cm at primiparous and multiparous, respectively) compared to healthy glands, and concluded that greater gland damage is necessary to obtain significant EC increases than for SCC increases
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(significant differences of SCC were obtained amongst the three different HS considered: 2,456,000 cells/mL in unspecific mastitis glands, 826,000 cells/mL in bacterial mastitis gland, and 315,000 cells/mL in healthy glands). In another study carried out in goats, Díaz et al. (2012) reported a significant increase in EC after establishment of IMI, showing a higher EC increase when glands were infected by major pathogens (Staphylococcus aureus and Gram Negative bacillus) compared to minor pathogens; EC increases were related to significant increases in chloride and sodium/potassium relationship, and a significant SCC increase. Tangorra et al. (2010) reported higher EC values in infected glands compared to healthy ones only at 0–60 days in milk period (11.74 vs 10.71 mS), but no significant differences were obtained in the other lactation stages considered. All the EC values reported were greater (>10 mS) than those obtained in the current study, probably due to the algorithm employed to calculate the EC value. The significant interaction between HS and fraction on EC was due to differences in EC found in F-3. The greatest differences between healthy and infected glands recorded in F-3 fraction agrees with two previous studies on cows (Bansal et al., 2005; Fernando et al., 1985); these authors obtained higher sensitivity when analysing stripping milk than first squirts, as stripping milk showed greater EC differences between infected and healthy glands, and concluded that diagnostic test characteristics for mastitis detection of a variable measured in milk would vary depending on the fraction analysed. In contrast, Bruckmaier et al. (2004b) reported a higher sensitivity of EC for subclinical mastitis detection of milk obtained during the first 60 s of milking. They explained that this fraction shows more differences in composition and thus in EC compared to the other studied fractions because the remaining milking milk is mixed with alveolar milk (as a response to milk ejection) which causes a reduction of its mastitis detection characteristics. In the current study on goats, mastitis detection characteristics (sensitivity, specificity, PPV and NPV) of EC did not differ amongst studied fractions at proposed thresholds due to the lack of differences obtained between HS levels at F-1 and F-2 fractions, and the small one obtained in F-3. The low sensitivity observed was in agreement with results of Díaz et al. (2011) for EC in goat gland milk; they explained that variables other than HS were related to EC (farm, parity and stage of lactation), and concluded that it is necessary to consider the intrinsic variation of the animal and avoid the use of the same threshold for the whole study population (as was done in cows), in order to improve mastitis detection feature and so avoid unnecessary mastitis treatments that are given when low specificity is obtained. The results of the on-line EC algorithms for mastitis detection using daily measurements in dairy cows provide better results for clinical than subclinical mastitis detection (Cavero et al., 2008; De Mol et al., 1999; Hovinen et al., 2006; Maatje et al., 1997; Mele et al., 2001; Zecconi et al., 2004) and many of the published studies are adjusted to obtain higher sensitivity compared to specificity, even when this option increases unnecessary costs (due to the greater proportion of false positive cases when specificity is lower).
Mastitis detection characteristics of SCC were greater than EC because SCC reported significant differences between healthy and infected glands in any studied fraction. Better SCC mastitis detection characteristics were obtained in F-3 because SCC differences between healthy and infected glands were slightly higher compared to F2 and F-1. This result agrees with Bansal et al. (2005), who reported greater SCC differences between healthy and infected cow glands in stripping milk, and obtained better mastitis detection results when analysing this fraction compared to first squirts, also in agreement with a previous study (Fernando et al., 1985). 5. Conclusions The lack of differences between fractions for goat mastitis detection characteristics using EC of gland milk indicates that all the fractions considered would obtain similar results, although the significantly higher EC of infected glands compared to healthy ones at F-3 suggests that better results for mastitis detection would be obtained if EC of F-3 was analysed during milking. In order to deepen the knowledge on improving and developing better mastitis detection systems in goats using EC, it would be necessary to test the daily EC measurements using algorithms, as done in dairy cows, including other significant factors (such as individual variation) and testing the best fraction to be considered. To carry out this kind of studies, specific conductimeters should be developed for incorporation in the short milk tubes of goat clusters, able to measure EC on-line (when the milk is flowing from the gland). Acknowledgement The study was supported by project AGL2006-06909 (Ministerio de Educación y Ciencia of Spain and FEDER). References Argüello, A., 2011. Trends in goat research: a review. J. Appl. Anim. Res. 39 (4), 429–434. Bansal, B.K., Hamann, J., Grabowski, N.T., Singh, K.B., 2005. Variation in the composition of selected milk fraction samples from healthy and mastitic quarters, and its significance for mastitis diagnosis. J. Dairy Res. 72, 144–152. Barkema, H.W., Schukken, Y.H., Lam, T.J.G.M., Gallican, D.T., Beiboer, M.L., Brand, A., 1997. Estimation of interdependence among quarters of the bovine udder with subclinical mastitis and implications for analysis. J. Dairy Sci. 80, 1592–1599. Bruckmaier, R.M., Ontsouka, C.E., Blum, J.W., 2004a. Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. 49, 283–290. Bruckmaier, R.M., Weiss, D., Wiedemann, M., Schmitz, S., Wendl, G., 2004b. Changes of physicochemical indicators during mastitis and the effects of milk ejection on their sensitivity. J. Dairy Res. 71, 316–321. Cavero, D., Tölle, K.H., Buxadé, C., Krieter, J., 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest. Sci. 105, 207–213. Cavero, D., Tölle, K.H., Rave, G., Buxadé, C., Krieter, J., 2007. Analyzing serial data for mastitis detection by means of local regression. Livest. Sci. 110, 101–110. Cavero, D., Tölle, K.H., Henze, C., Buxadé, C., Krieter, J., 2008. Mastitis detection in dairy cows by application of neural networks. Livest. Sci. 114, 280–286. Christodoulopoulos, G., Solomakos, N., Katsoulos, P.D., Minas, A., Kritas, S.K., 2008. Influence of oestrus on the heat stability and other characteristics of milk from dairy goats. J. Dairy Res. 75, 64–68.
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