https://doi.org/10.52973/rcfcv-e34483
Received: 08/07/2024 Accepted: 28/10/2024 Published: 23/12/2024
1 of 8
Revista Científica, FCV-LUZ / Vol. XXXIV, rcfcv-e34483
ABSTRACT
This study was carried out by collecting tank milk samples every
month, from a total of 240 farms producing cold chain milk for
one year. Somatic cell count (SCC), total bacterial count (TBC),
some nutritional elements (lactose, protein, casein, fat) and some
physicochemical parameters (dry matter, fat–free dry matter, freezing
point, density, free fatty acids, Soxhlet Henkel acidity degree) and
citric acid were analyzed. According to the results; TBC were at
the highest level in March (M), April (A) and June (J) (835.07 x10
3
;
940.25 × 10
3
and 1007.30 × 10
3
cfu·mL
–1
), whereas between July (Ju)
and December (De), TBC (446.09 × 10
3
and 795.15 × 10
3
cfu·mL
–1
) were
signicantly lower (P<0.001). The highest SCC were found in M, A and
May (Ma), whereas the lowest SCC were found between September
(S) and De. Between S and De, when SCC decreased, (varied between
236.13 × 10
3
cells·mL
–1
and 284.43 × 10
3
cells·mL
–1
; P<0.05). Lactose
values were found to be signicantly higher in spring and summer
compared to other months. A signicant decrease was determined
in protein values in the summer months compared to November
(N) and De (P<0.05; P<0.01). It was also revealed that casein values
were higher in the summer months Ma–August (Au) compared to
the other lower months (P<0.01). For physico–chemical parameters,
it was determined that non–fat solids and freezing point (FP) values
decreased signicantly during the summer months (P<0.01; P<0.001).
The results obtained show that the parameters in question are
seasonally affected, but these parameters change depending on
the changes in both TBC and SCC values (high level of positive or
negative correlation).
Key words: Bulk tank milk; somatic cell count; raw milk; total
bacteria; milk quality
RESUMEN
Este estudio se llevó a cabo recogiendo muestras de leche en tanque
cada mes, de un total de 240 explotaciones que mejorar redacción
durante un año. Recuento de células somáticas (SCC), recuento
bacteriano total (TBC), algunos elementos nutricionales (lactosa,
proteínas, caseína, grasa) y algunos parámetros sicoquímicos
(materia seca, materia seca magra, punto de congelación, densidad,
ácidos grasos libres, Soxhelet Se analizó el grado de acidez Henkel) y
el ácido cítrico. De acuerdo a los resultados; Los TBC alcanzaron su
nivel más alto en marzo (M), abril (A) y junio (J) (835,07 x10
3
; 940,25 × 10
3
y 1007,30 × 10
3
ufc·mL
–1
), mientras que, entre julio (Ju) y diciembre
(De), los TBC (446,09 × 10
3
y 795,15 × 10
3
ufc·mL
–1
) fueron mas bajos nivel
signicativo (P<0,001). El RCS más alto se encontró en M, A y mayo
(Ma), mientras que el RCS más bajo se encontró entre septiembre (S)
y De. Entre S y De, cuando el SCC disminuyó (varió entre 236,13 × 10
3
células·mL
-1
y 284,43 × 10
3
células·mL
-1
; P<0,05). Se encontró que los
valores de lactosa eran signicativamente más altos en primavera
y verano en comparación con otros meses. Se determinó una
disminución signicativa en los valores de proteína en los meses de
verano respecto a noviembre (N) y De (P<0,05; P<0,01). También se
reveló que los valores de caseína fueron más altos en los meses de
verano de mayo a agosto (Au) en comparación con los otros meses
más bajos (P<0,01). Para los parámetros físico–químicos, se determinó
que los valores de sólidos no grasos (MS) y punto de congelación (FP)
disminuyeron signicativamente durante los meses de verano (P<0,01;
P<0,001). Los resultados obtenidos muestran que los parámetros en
cuestión se ven afectados estacionalmente, pero estos parámetros
cambian dependiendo de los cambios en los valores tanto de TBC
como de SCC (alto nivel de correlación positiva o negativa).
Palabras Clave: Leche cruda en tanque; recuento de células
somáticas; leche cruda; bacterias totales; calidad
de la leche
Changes and interactions of milk components over one year by months
Cambios e interacciones de los componentes de la leche durante periodos
mensuales a lo largo de un año
Tahire Darbaz
1
, Beyza Ulusoy
2,3
* , Isfendiyar Darbaz
2,4
, Feride Zabitler Tepik
4
, Canan Hecer
5
, Selim Aslan
4
1
Cyprus Health and Social Sciences University, Faculty of Veterinary Medicine, Department of Food Hygiene and Technology. Morphou, Cyprus.
2
Near East University, DESAM Institute. North Cyprus Mersin 10, Nicosia, Türkiye.
3
Near East University, Faculty of Veterinary Medicine, Department of Food Hygiene and Technology. Nicosia, Cyprus.
4
Near East University, Faculty of Veterinary Medicine, Department of Obstetrics and Gynecology. Nicosia, Cyprus.
5
Cyprus West University. Famagusta, Cyprus.
*Corresponding author: beyza.ulusoy@neu.edu.tr
TABLE I
Devices used in the analysis of milk samples and their relevant references
Device Name Performed Analyses Relevant References
BactoScan
TM
FC Total Bacterial Count ISO/IDF and FDA/NCIMS
Fossomatic
TM
FC 5000 Somatic Cell Count
AOAC ISO 13366–2
/ IDF 148–2:2006
MilkoScan
TM
FT–120
Lactose, Total Protein, Casein
Fat, Non–Fat Solids, Dry Matter,
Freezing Point, Acidity–SH, Density,
Free Fatty Acids, Citric Acid
AOAC and IDF
Milk components over one year by months / Darbaz et al. __________________________________________________________________________
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INTRODUCTION
Milk quality and safety carry great importance in terms of milk
technology, and also for public health [1]. There is a close relationship
between obtaining healthy and high–quality milk, herd health and farm
hygiene. For example, an infection due to mastitis pathogens causes
the activity of secretory cells to deteriorate, which leads to a decrease
in lactose, fat and protein synthesis [2]. In addition to reducing the
quality of the product, the permeability of cell membranes increases
in subclinical and clinical mastitis cases, and the nutritional properties
and chemical structure of milk change as substances from the blood
pass into the milk [3]. The transfer of relevant pathogens and possible
toxins into milk is also a negative situation in terms of food safety. To
ensure milk safety on a farm, rapid cooling and tank storage of milk
is important. The mandatory cooling and cold storage of raw milk
since the 1950s has had a signicant impact on the bacteriological
and chemical quality of raw milk [4].
In order to prevent the initial microbial load from increasing until
the milk is processed and to prevent the activity of some enzymes
that will affect the sensory properties of the milk, it must be cooled to
the appropriate storage temperature [5]. The food hygiene legislation
package came into force throughout the European Union in 2006. The
legislation affects all food chain, including caterers, primary producers
(such as farmers), manufacturers, distributors and retailers. For the
dairy industry, this package of legislation replaces the requirements of
the Dairy Hygiene Directive 92/46/EEC [6]. Samples taken from tank
milk and laboratory analysis results are indicators of raw milk quality
and reliability as well as parameters that give an idea about herd
health. In tank milk analyses, the primary determining factor is the
somatic cell count (SCC) and the total bacterial count of the milk [7].
SCC in raw milk is one of the indicators of animal udder health and
milk hygiene [8]. Somatic cells are among the cells that form the
body’s natural defense system, and the SCC count in milk taken from
a healthy cow’s udder is expected to be below 200,000 cells·mL
–1
.
Acceptable SCC in samples taken from tank milk should be below
400,000 cells·mL
–1
. There is a positive relationship between increases
in the number of somatic cells in milk and the degree of inammation
[6, 9]. It has been reported that in herds where udder health control
programs have been implemented for a long time, 80–90% success
is achieved in protecting against udder infections, and as a result of
these controls, quality and safe raw milk can be obtained [10]. If the
microorganism load and SCC in milk are above limits suggests the
existence of factors that may threaten human health [11], and is also
an indication that it will create quality problems in the processing of
dairy products and also cause milk production losses [12].
Since the quality of milk and dairy products depends on the composition
of raw milk and the content of nutrients, the factors responsible for
changes in the composition and physico–chemical properties of raw
milk are of great importance for milk processors [13]. The composition
covers the main nutritional elements of milk such as fat, protein, lactose
and total dry matter [14, 15]. The effects of seasonal change on milk yield
and composition have been investigated by many researchers [16, 17]. It
has also been reported that there is a positive correlation between the
increase in SCC and changes in milk composition [15].
With this study, it was aimed to reveal the relationship between
SCC and TBC depending on seasonal changes and the changes in
the nutritional elements and physico–chemical parameters in milk
according to months. It is also aimed to reveal how these parameters
affect each other.
MATERIAL AND METHODS
This study was carried out by collecting 2,880 milk tank samples from
a total of 240 farms producing cold chain milk throughout North Cyprus
between March and February. In order for the study to accurately reect
the situation in North Cyprus, all farms producing cold chain milk for
at least 1 year were included in the scope of the study. Samples were
collected regularly in the rst week of each month from 240 farms. Milk
samples were lled into sterile sample bottles with a sterile dipping
container from the mixed tank within 1 hour of morning milking, in
accordance with aseptic rules, and are kept under cold chain (+4°C)
without freezing, to be subjected to analysis.
Analyses applied to milk samples
All analyses applied to milk were carried out in the Turkish Cypriot
Dairy Industry Institution Quality Control Department Laboratory.
BactoScan
TM
FC, (Foss, Denmark) [18] for total bacterial count,
Fossomatic
TM
FC 5000 (Foss, Denmark) [19] for Somatic Cell Count
and MilkoScan
TM
FT–120 (Foss, Denmark), were used for the detection of
Lactose, Total Protein, Casein Fat, Non–Fat Solids, Dry Matter, Freezing
Point, Acidity–SH, Density, Free Fatty Acids, Citric Acid (TABLE I) [20].
Statistical analysis
SPSS Statistics 26.0, IBM, USA program was used for statistical
evaluation. Descriptive statistics (Mean ± Std. Dev.) were applied for
the mean value and standard error. The homogeneity distribution of
values was tested with Shapiro–Wilk. For non–homogeneous data,
the Kruskal Wallis test was used to test the difference between two
groups and the overall difference between all groups was determined,
and then the Mann–Whitney U test was used to test the difference
between each group. One–Way ANOVA (Tukey’s) was used for normally
distributed data, and One–Way ANOVA (Tamhane’s) was used for the
non–homogeneous distribution of values. T–test test was used for
homogeneously distributed data. In terms of statistical signicance,
results with P<0.05 and below were considered signicant.
RESULTS AND DISCUSSION
During the data collection of this study, every month, the samples
were collected from same farms. Visits were planned in the rst week
of every month and samples were gathered with same conditions every
time. According to the results (TABLE II and FIG. 1); TBC were at their
highest level in March (M), April (A) and June (J) (835.07 × 10
3
; 940.25 × 10
3
and 1007.30 × 10
3
cfu·mL
–1
), whereas TBC between July (Ju) and December
(De) (446.09 × 10
3
and 795.15 × 10
3
cfu·mL
–1
) has been shown to decrease
signicantly (P<0.001). The reason why it wasn’t obtained to be high in
Ma is the reection of weather changes in that period of seasons.
TABLE II
Analysis results of SCC, TBC and some nutrients in tank milk by months
1
n:240 SCC cell·mL
–1
TBC cfu·mL
–1
Lactose %
March 2019
553.94 × 10
3
± 417.45 × 10
3
b*;c***
(33–2121 × 10
3
)
835.07 × 10
3
± 1959.43 × 10
3
a*
(10–16933 × 10
3
)
4.50 ± 0.11 b***
(3.94–5.05)
April 2019
526.06 × 10
3
± 379.20 × 10
3
b***;c***
(26–2013 × 10
3
)
940.25 × 10
3
± 2247.24 × 10
3
a*
(7–17339 × 10
3
)
4.49 ± 0.11 a***; b***; c***
(3.78–4.73)
May 2019
463.88 × 10
3
± 329.94 × 10
3
b***;c***
(65–1628 × 10
3
)
636.67 × 10
3
± 1093.37 × 10
3
(8–7852 × 10
3
)
4.50 ± 0.10 b**; b***; c***; d***
(4.18–4.71)
June 2019
371.93 × 10
3
± 277.57 × 10
3
b***;c***;c*
(35–1564 × 10
3
)
1007.30 × 10
3
± 2102.96 × 10
3
a*
(7–13155 × 10
3
)
4.48 ± 0.11 b***
(4.05–5.25)
July 2019
291.27 × 10
3
± 235.76 x10
3
b**;b***;b*
(29–1802 × 10
3
)
522.27 × 10
3
± 1101.16 × 10
3
b*
(11–6792 × 10
3
)
4.42 ± 0.10 a***;a**;c***
(3.56–4.65)
August 2019
315.87 × 10
3
± 270.71 x10
3
b*;b***;d**
(34–1802 × 10
3
)
499.40 × 10
3
± 1013.08 × 10
3
(5–9522 × 10
3
)
4.41 ± 0.11 a***;a**;c***
(3.68–4.68)
September 2019
293.06 × 10
3
± 254.75 x10
3
b*;b***
(36–1662 × 10
3
)
475.95 × 10
3
± 1016.20 × 10
3
b*
(6–9913 × 10
3
)
4.40 ± 0.10 a***;a**;d***
(3.91–4.77)
October 2019
236.13 × 10
3
± 200.05 x10
3
b*;b***;b**)
(28–1574 × 10
3
)
446.09 × 10
3
± 947.12 × 10
3
b*
(6–9604 × 10
3
)
4.39 ± 0.12 a***;a**;d***
(3.93–5.07)
November 2019
278.30 × 10
3
± 277.25 × 10
3
b*;b***
(19–2201 × 10
3
)
791.32 × 10
3
± 2130.66 × 10
3
(6–17339 × 10
3
)
4.40 ± 0.16 a***;a**;d***
(3.46–6.11)
December 2019
284.43 × 10
3
± 283.63 × 10
3
b*
(16–2198 × 10
3
)
795.15 × 10
3
± 1808.80 × 10
3
(8–16906 × 10
3
)
4.43 ± 0.10 a***;a**;d***
(3.93–4.70)
January 2020
419.82 × 10
3
± 386.08 × 10
3
a*;a**;a***
(22–2737 × 10
3
)
813.10 × 10
3
± 1823.71 × 10
3
(10–15608 × 10
3
)
4.41 ± 0.10 a***;a**
(3.95–4.66)
February 2020
323.69 × 10
3
± 281.64 × 10
3
a*;a**;a***;c***
(25–1397 × 10
3
)
557.46 × 10
3
± 1227.65 × 10
3
(12–12647 × 10
3
)
4.43 ± 0.10 a***;a**
(4.03–4.69)
1
The
1
numbers in the table were given as Mean ± Std. Dev. (Min – Max). Values marked with dierent letters (a:b; a.c, a.d, b:c; c:d) are statistically dierent from each
other. Asterisks indicate *, **,***
P<0.05; P<0.01 and P<0.001 values, respectively. SCC: somatic cell count; TBC: total bacterial
TABLE II cont...
Analysis results of SCC, TBC and some nutrients in tank milk by months
1
n:240 Protein % Casein % Fat %
March 2019
3.32 ± 0.14 b*;c***
(2.72–3.92)
2.62 ± 0.13 a***;b***;c**
(1.96–3.28)
3.55 ± 0.44 b***;b**;d***
(2.03–4.47)
April 2019
3.30 ± 0.14 b***;c***
(2.72–3.61)
2.60 ± 0.14 a***; b***;c**
(1.89–2.93)
3.56 ± 0.37 b***;b**
(2.34–4.66)
May 2019
3.26 ± 0.14 b***
(2.89–3.75)
2.57 ± 0.13 b***;b**
(2.20–2.95)
3.46 ± 0.35 b***
2.18–4.36
June 2019
3.22 ± 0.14 b***
(2.76–3.66)
2.54 ± 0.13 b***;c**;c***
(2.05–3.07)
3.45 ± 0.33 b***
(2.43–4.89)
July 2019
3.21 ± 0.13 b***
(2.50–3.49)
2.51 ± 0.12 b***;c***
(1.84–2.78)
3.39 ± 0.29 b***;e***
(2.18–4.06)
August 2019
3.20 ± 0.14 b***
(2.71–3.60)
2.50 ± 0.13 b***;c***
(1.83–2.87)
3.42 ± 0.33 b***;e***
(1.95–4.30)
September 2019
3.31 ± 0.14 c***
(2.90–3.76)
2.60 ± 0.12 b***;b*;b**;c***
(2.17–3.02)
3.50 ± 0.35 b***
(1.75–4.64)
October 2019
3.39 ± 0.15 b***;c***
(3.02–3.74)
2.63 ± 0.13 a***;b***;d*;d***
(2.29–2.95)
3.61 ± 0.36 b***;c***
(2.06–4.36)
November 2019
3.44 ± 0.18 b***;c***
(2.74–4.29)
2.68 ± 0.16 b***;d***
(1.81–3.73)
3.79 ± 0.33 a****;b***;b**
(1.95–4.62)
December 2019
3.47 ± 0.15 b***;c***
(3.09–3.94)
2.7 ± 0.13 a***;b***;d***
(2.33–3.04)
3.87 ± 0.34 a***;b***;b**;d***
(2.28–4.84)
January 2020
3.36 ± 0.14 a*
(2.98–3.89)
2.64 ± 0.13 a***
(2.21–3.07)
3.80 ± 0.34 a***
(2.42–4.84)
February 2020
3.31 ± 0.13 b**;c***
(2.98–3.77)
2.61 ± 0.12 a***; b***
(2.20–2.93)
3.70 ± 0.33 a***;a**;c***
(2.17–5.12)
1
The
1
numbers in the table were given as Mean ± Std. Dev. (Min – Max). Values marked with dierent letters (a:b; a.c, a.d, b:c; c:d) are statistically
dierent from each other. Asterisks indicate *, **,***
P<0.05; P<0.01 and P<0.001 values, respectively
_____________________________________________________________________________Revista Cientifica, FCV-LUZ / Vol. XXXIV, rcfcv-e34483
3 of 8
January
February
March
April
May
June
July
August
September
October
November
December
0
200
400
600
800
1000
1200
SCC cell·mL
-1
( × 10
3
) TBC cfu·mL
-1
( × 10
3
)
January
February
March
April
May
June
July
August
September
October
November
December
2.5
3.0
3.5
4.0
4.5
Lactose (%)
Protein (%) Casein ( %) Fat (%)
FIGURE 1. Analysis results of SCC and TBC in tank milk by months
FIGURE 2. Analysis results of Lactose, Protein, Casein and Fat in tank milk by months
Milk components over one year by months / Darbaz et al. __________________________________________________________________________
4 of 8
In M and A the weather was more rainy but dry in Ma. It is thought
that in J warmer climate accelerated the TBC increase. On the other
hand, these months are the months when the animals are taken to
pasture feeding, the rumen content changes and TBC values are
higher due to the stress it creates. The highest SCC were found in M, A
and May (Ma), whereas the lowest SCC were found between September
(S) and De. Between S and De, when SCC decreased, values varied
between 236.13 × 10
3
cells·mL
–1
and 284.43 × 10
3
cells·mL
–1
(P<0.05).
While lactose values reached the highest average value of 4.48–4.50%
in M, A, Ma and J, it was revealed that signicantly different values
(between 4.39% and 4.42%) were obtained between these months
and other months. (P<0.01; P<0.001).
A high level of positive correlation (r=0,979) was found between both
TBC and SCC and lactose values (P<0.05; P<0.01). the acidity may not
be directly related with Protein values were found to be signicantly
lower (P<0.0001), with a range of 3.20% and 3.26% between Ma and
Au. On the other hand, between November (N) and De, protein values
(FIG. 2) increased to the highest levels with average values of 3.44%
and 3.47% (P<0.0001). The lowest casein values were found between
Ma and Au (Ma 2.57%; J 2.54%; Ju 2.51%; Au 2.50%), while the data
obtained in the other months were between 2.60% and 2.70% and
was found to be signicantly higher compared to the lowest months
(P<0.001; P<0.01). The highest casein values were obtained in N and
De with 2.68% and 2.70%. The highest mean fat values were in N
(3.79%), De (3.87%), January (Ja) (3.80%), February (F) (3.70%) and
decreased to 3.39% and 3.42 % in Ju and Au, respectively (P<0.001).
It was determined that starting from S, the values increased again
(P<0.001; P<0.0001) (TABLE II, FIG. 1).
While individual SCC being higher than 250,000 cells·mL
–1
suggests
the possibility of intra–mammary infection in the cow, if SCC measured
in tank milk is higher than 400,000 cells·mL
–1
, there are udder health
problems, so dairy cows should be checked and the necessary
precautions should be taken [9, 21]. According to the results of this
study, the mean values were signicantly higher (>400,000 cells·mL
–1
)
in M, A and Ma, while they were signicantly lower between J and D
(236,000–371,000 cells·mL
–1
; P<0.01; P<0.001). With these results,
SCC values were signicantly lower in the months when both the
environmental temperatures were warmer. According to Northern
Cyprus, Ministry of Public Works and Transportation, Meteorology
Department; the climatic conditions of Cyprus, the environmental
temperature did not fall below 10°C. Tank milk acceptable SCC levels
vary between countries from past to present. While maximum limit
values of <750,000 cells·mL
–1
in the USA, <500,000 cells·mL
–1
in Canada,
and <400,000 cells·mL
–1
in European Union laws are considered ideal
levels, in recent years these values have been further reduced to
below 200,000 cells·mL
–1
[21, 22]. When <400,000 cells·mL
–1
in EU
legislation is taken as the reference value, it has been observed that
there are farms where this value is exceeded even in the months
when the average SCC value is lowest.
Under similar conditions, in the north of Cyprus, Darbaz et al.
[23] reported that the average value of tank milk SCC was 521,583
cells·mL
–1
and concluded that SCC remained high. It should be taken
into consideration that increase in SCC is the indicator for poor quality
and changes the composition of milk and thus will have a negative
impact on dairy products technology [23, 24]. The important result
of this study is that high SCC values were obtained between M and
Ma, and in general, SCC values are signicantly below 400,000 mL/
cell in other months. Different studies also advocate the view that
SCC values may change depending on the season. As Riekerink et al.
[25] mentioned in their study bulk tank milk SCC in a herd is mainly
inuenced by the prevalence and incidence of subclinical and clinical
mastitis, which depends on variation of factors such as parity, stage
of lactation, type of housing, access to pasture, management, and
also temperature, humidity, and season.
In New Zealand, SCC achieved the highest levels from Ju to S
(around the calving period) and the lowest SCC occurred in S and
O, shortly after the calving period, and SCC then slowly increased
again towards the end of the season in A to Ma [26]. Morse et al.
[27] reported that intramammary infection was more common in
seasons where temperature and humidity prevailed. The dry and very
hot summer climate in Cyprus and the abundant rain in winter and
spring explain the high SCC during this period. In the mild and rainy
months of the year, the potential for microorganisms to multiply is
higher, which creates a hygiene problem in the farm environment. It
is thought that there is an increase in SCC average values in these
months due to the lack of necessary hygiene, especially in rainy and
unsuitable barn conditions. It is noteworthy that SCC values are low
TABLE III
Analysis results of some physico–chemical parameters by months
1
n:240 FF–DM (%) DM (%) FP SH Acidity (°SH)
March 2019
8.81 ± 0.23 (a;b;d)
(7.46–10.13)
12.32 ± 0.52 (b***;#)
(10.56–14.27)
0.54 ± 0.01 (a***)
(0.48–0.64)
6.89 ± 0.50 (b***)
(5.79–10.47)
April 2019
8.75 ± 0.25 (a;b;c;d;e)
(7.26–9.31)
12.29 ± 0.48 (b***:#;α;&)
(10.05–13.41)
0.54 ± 0.01 (a***)
(0.47–0.57)
6.97 ± 0.46 (b***)
(5.34–8.03)
May 2019
8.69 ± 0.23 (a;b;c;e)
(7.93–9.24)
12.15 ± 0.45 (b***;c***;α;&)
(10.41–13.49)
0.54 ± 0.01 (a**.a***)
(0.50–0.57)
6.90 ± 0.43 (b**)
(5.92–8.12)
June 2019
8.62 ± 0.25 (b;d;c)
(7.66–10.02)
12.09 ± 0.44 (b***;c***;α;&**)
(10.66–13.31)
0.54 ± 0.02 (a**;a***)
(0.49–0.66)
6.97 ± 0.51
(5.24–8.51)
July 2019
8.52 ± 0.22 (b;d;c)
(6.94–9.02)
11.95 ± 0.40 (b***;c***;α;μ;μ**)
(10.26–12.84)
0.53 ± 0.01 (b***)
(0.45–0.56)
6.70 ± 0.50 (b**;b***)
(4.80–8.20)
August 2019
8.48 ± 0.21 (b***;d;e)
(7.13–9.04)
11.95 ± 0.45 (b***;c***;α;μ)
(9.22–13.12)
0.53 ± 0.01 (b***)
(0.45–0.56)
6.65 ± 0.44 (b***)
(4.47–8.19)
September 2019
8.62 ± 0.20 (b;d;c)
(7.86–9.51)
12.14 ± 0.43 (b***;c***;#; α;μ)
(10.73–14.00)
0.54 ± 0.01
(0.49–0.60)
6.99 ± 0.43
(5.59–8.28)
October 2019
8.73 ± 0.23 (a;b;c;e)
(7.91–9.70)
12.33 ± 0.47(b**;b***)
(10.40–13.76)
0.53 ± 0.01(b**;b***)
(0.49–0.62)
7.28 ± 0.54 (b**)
(5.14–9.38)
November 2019
8.81 ± 0.32 (a*;b;d)
(6.94–11.77)
12.56 ± 0.52 (c**;#;α;μ)
(9.75–14.25)
0.54 ± 0.02
(0.42–0.74)
7.36 ± 0.63 (b***)
(5.15–10.23)
December 2019
8.90 ± 0.22 (b*;b***;d)
(8.00–9.41)
12.73 ± 0.43 (b***;c***;α;μ)
11.44–13.81)
0.54 ± 0.013
(0.49–0.57)
7.51 ± 0.73 (b***)
(5.91–15.75)
January 2020
8.76 ± 0.22 (a)
(7.68–9.44)
12.52 ± 0.44 (a)
(11.22–13.99)
0.54 ± 0.01 (a**;a***)
(0.51–0.58)
6.88 ± 0.44 (a**;a***)
(5.69–8.12)
February 2020
8.71 ± 0.20 (a;b;c)
(7.98–9.18)
12.38 ± 0.43 (b**)
(10.94–13.96)
0.54 ± 0.01 (a**;a***)
(0.51–0.57)
6.77 ± 0.45 (a**;a***)
(5.51–8.37)
1
The numbers in the table were given as Mean ± Std. Dev. (Min – Max). Values marked with dierent letters (a:b; a.c, a.d, b:c; c:d) are statistically dierent from each
other. Asterisks indicate *, **,***
P<0.05; P<0.01 and P<0.001 values, respectively. FF–DM: fat free dry matter, DM: dry matter, FP: freezing point, SH: soxhlet Henkel
_____________________________________________________________________________Revista Cientifica, FCV-LUZ / Vol. XXXIV, rcfcv-e34483
5 of 8
in Ju and Au. As Özlem & Kul [15] concluded in their study, the season
had signicant effect (P<0.01) on SCC and it was highest in summer
and the lowest in winter. Pavel and Gavan [28] and Maciuc et al. [29]
concluded the similar seasonal effect on SCC.
The researchers reported that higher mean results of SCC in the
summer, agreed on reasons such as high temperatures and humidity
which expose animals to a greater number of pathogens and increasing
occurrence of mastitis [30]. Considering the climate of the Island of
Cyprus, the months when temperatures and humidity increase are
generally spring months, and on the contrary, summer months are dry.
This situation explains the highest SCC value obtained in M, A and Ma.
Although Ju and Au were hot, the environment was dry and the areas
where the animals slept and roamed were better cleaned, which also
contributed to the result. Apparently, in different countries, depending
on different conditions, SCC values may exceed the threshold limit in
different months, but may also be lower in other months.
At current study; results of the analyses to determine the total
number of bacteria show the total number, not the bacterial prole.
Although bacterial identication provides a satisfactory idea to
determine the quality status and shelf life of raw milk, critical limits are
set up only depending on the total number by international standards.
In the European Union and the United States, the legal limit for raw
cow’s milk TBC is 100,000 cfu·mL
–1
, in Canada, 50,000 cfu·mL
–1
, and
in Brazil, 100,000 cfu·mL
–1
[31]. In this study, the results are given in
cfu·mL
–1
units (TABLE III). In this study, some samples’ results above
100,000 cfu·mL
–1
, which is specied as critical limit in the European
Union Directive (EC) No 853/2004, were observed [6]. TBC mean
values were also high, especially between M and J, when SCC values
reached the highest value. This relationship was revealed by the high
degree of positive correlation between TBC and SCC values 0.979;
P<0.005 (TABLE IV). Berry et al. [32] documented temporal trends
in SCC and TBC in Irish dairy herds during the years 1994 to 2004.
According to the conclusion of that study, SCC decreased during
the years 1994 to 2000, followed by an annual increase thereafter
of more than 2,000 cells·mL
-1
. As concluded by the researchers, the
reason of the decline in mean bulk tank SCC may be due in part to a
dilution effect of greater yields per cow.
On the other hand; increased awareness of farmers of cows with
elevated SCC, and the impact of EU policies were mentioned to be
other factors for the decrease. Across all years, bulk tank SCC was the
lowest in A and highest in N; TBC were the lowest in May and highest
in December. The signicant seasonal pattern observed in herd SCC
and TBC was an artifact of seasonal calving in Ireland. The climate
difference between Ireland and Cyprus can be shown as the reason
for the differences in seasonal distribution results between this study
and the current study. Apparently, TBC values also vary seasonally in
Cyprus. On the other hand, in parallel with results of current study, it
was revealed that there was a positive correlation between SCC and
TBC. Previous research has also found highly positive correlations
between TBC, SCC and milk yield [31]. Berry et al. [32] examined the
changes in SCC and TBC according to months and revealed that there
was a correlation between increases or decreases according to months.
Another important result obtained in this study was that changes
in all parameters generally affected other parameters. TABLEII,
FIG.2 and TABLE III, FIG. 3 presents some of the chemical and
physico–chemical results, respectively. For example, for casein, it
TABLE III cont...
Analysis results of some physico–chemical parameters by months
1
n:240 D (gr/cm
3
) FFA (%) CA (%)
March 2019
1031.63 ± 1.03 (b***;c***)
(1026.00–1036.00)
0.66 ± 0.22 (a**;a***)
(0.20–1.96)
0.14 ± 0.02 (a**;a***)
(0.09–0.018)
April 2019
1031.46 ±1.02 (b**;c***)
(1026.00–1034.00)
0.67 ± 0.20 (a**;a***)
(0.31–1.61)
0.13 ± 0.01 (a***)
(0.10–0.18)
May 2019
1031.40 ±0.92 (b*;b**)
(1028.00–1034.00)
0.75 ± 0.22
(0.25–1.54)
0.13 ± 0.01 (a**;a***;b**)
(0.09–0.18)
June 2019
1031.08 ± 1.01 (a*;a**;a***;d***)
(1027.00–1037.00)
0.77 ± 0.24 (b**;b***)
(0.11–2.09)
0.13 ± 0.01 (b*;b**)
(0.10–0.18)
July 2019
1030.72 ± 0.92 (b**;d***)
(1024.00–1033.00)
0.78 ± 0.22 (b**;b***)
(0.21–1.86)
0.12 ± 0.01 (a*;a***)
(0.10–0.17)
August 2019
1030.56 ± 0.92 (b***;d***)
(1026.00–1033.00)
0.80 ± 0.20 (b***)
(0.39–1.61)
0.12 ± 0.01 (a***)
(0.08–0.18)
September 2019
1030.84 ± 0.84 (b***;d***)
(1028.00–1033.00)
0.73 ± 0.18 (b**)
(0.28–1.31)
0.12 ± 0.01 (a***)
(0.07–0.18)
October 2019
1031.20 ± 1.00 (a**;b***;d***)
(1028.00–1037.00)
0.76 ± 0.28 (b***)
(0.32–3.38)
0.13 ± 0.01 (a**;a***;c**)
(0.10–0.19)
November 2019
1031.36 ± 1.29
(1024.00–1045.00)
0.77 ± 0.20 (a**;b***)
(0.18–1.36)
0.13 ± 0.01
(0.10–0.22)
December 2019
1031.60 ± 0.968 (b***)
(1026.00–1035.00)
0.78 ± 0.24 (b***)
(0.35–2.70)
0.13 ± 0.01
(0.11–0.18)
January 2020
1031.06 ± 0.97 (a**;a***)
(1026.00–1035.00)
0.69 ± 0.23 (a**;a***)
(0.16–1.82)
0.13 ± 0.01 (a***)
(0.10–0.18)
February 2020
1031.07 ± 0.87 (a**;a***)
(1028.00–1035.00)
0.69 ± 0.19 (a**.a***)
(0.16–1.40)
0.13±.01 (a***)
(0.10–0.17)
1
The numbers in the table were given as Mean ± Std. Dev. (Min – Max). Values marked with dierent letters (a:b; a.c, a.d, b:c;
c:d) are statistically dierent from each other. Asterisks indicate *, **,***
P<0.05; P<0.01 and P<0.001 values, respectively.
D: density, FFA: free fatty acid, CA: citric acid
January
February
March
April
May
June
July
August
September
October
November
December
6
7
8
9
10
11
12
13
14
FF-DM (%) DM (%)
SH Acidity (°SH)
FIGURE 3. Analysis results of some physico–chemical parameters (FF–DM, DM
and SH Acidity) by months
TABLE IV
Evaluation of the correlation between SHS and total bacterial count and the levels of other parameters
TBC Lactose Protein Casein Fat FF–DM DM FP SH D FFA CA
SCC
0.979**
P<0.005
(0.004)
-0.968** P<0.05
(0.007)
0.936**
P<0.01 NS NS -0.913* P<0.05 NS -0.910* P<0.05 NS P>0.05 NS NS
TBC -0.978**
P<0.01 0.924*P<0.05 -0.876 P<0.05 NS -0.973** P<0.005 NS -0.948* P<0.01 NS -0.879* P<0.05 0.922* P<0.05 NS
**: The correlation is signicant at the 0.01 level, *: The correlation is signicant at the 0.05 level, NS: Not signicant, SCC: somatic cell count, TBC: total bacterial count, FF–DM: fat free dry
matter, DM: dry matter, FP: freezing point, SH: Soxhelet Henkel, D: density, FFA: free fatty acid, CA: citric acid
Milk components over one year by months / Darbaz et al. __________________________________________________________________________
6 of 8
was found that there was a positive correlation between 0.702 and
0.975 with protein, fat, FF–DM, SH and DM parameters (P<0.01 and
P<0.001). Similarly, FF–DM was signicantly positively correlated with
protein, casein, fat and DM (r=0.819–0.933; P<0.001), while fat was
signicantly positively correlated with protein, casein, FF–DM, DM,
SH (r=0.661–0.910; P<0.05 and P<0.001). Likewise, there was a high
degree of positive correlation between DM, SH, D and CA and these
parameters (TABLE IV).
CONCLUSION
According to the results, the increase in TBC with the increase in
SCC values showed that there is a possibility of pathogens reaching
consumers through raw milk. These two parameters affect each other
and show that changes in other milk components also cause changes in
other milk components. With this study, we see that this situation also
varies depending on climatic conditions through the year. The increase
in SCC in the spring months, when the animals are new to pasture and
the season in Cyprus is mild and rainy, and even exceeds critical limits in
_____________________________________________________________________________Revista Cientifica, FCV-LUZ / Vol. XXXIV, rcfcv-e34483
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some months, requires more attention to pasture–farm management,
mastitis control programs and farm hygiene during these periods.
Because chemical parameters and physico–chemical parameters
also change with the changes in SCC and TBC, in which case negative
effects on dairy products technology and economic losses are expected
to occur. In order to prevent all these negative scenarios, GMP (good
manufacturing practice) and GAP (good agricultural practice) are also
gaining importance on the basis of farm management.
Conict of interest
The authors declare there are no conicts of interest.
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