DOI: https://doi.org/10.21203/rs.3.rs-1920222/v1
The study was conducted to determine the relationship among milk parameters. We included different milk constituents and lactation length as our milk parameters. The cross-sectional milk samples from 246 individual cattle were collected in a sterilized container and were analyzed by the ultra-sonic milk analyzer. The correlation coefficient (r) between SNF, Density, Protein, and Lactose is r ≥ 0.89. The temperature has the highest positive correlation with conductivity (r ≥ 0.46). Salt is negatively correlated with pH and added water r ≤ -0.31, whereas the Freezing point is positively influenced by every factor except pH & added water. Lactation length is positive and significant with the fat r ≥ 0.49 and negative with conductivity where r ≤- 0.14. Based on one of these parameters we can know about another correlated trait. It will be beneficial to understand the overall milk quality, and animal health status and also reduces the milk quality checking costs. All of these will be beneficial both to the farmer and public health.
Milk is high nutritional quality food. Milk can be considered a source of macro and micronutrients and contains many active compounds that have a significant role in health protection. Milk comprises water, proteins, fats, lactose, minerals, high nutritional food, and other dissolved components. These are the major milk parameters (Products, 1988). 87.7% of milk is water and other constituents are distributed in various forms. Milk composition and component yields can be altered by the breed, age, parity, feeding management, lactation, season, level of milk production, and disease (Manzanilla-Pech et al., 2016) (Alexander et al., 1960.) (Ménard et al., 2010).
The correlation is a method of measuring any two traits which p the nature and degree of association between two factors. These relations help us to select and to know the nature of other traits or factors (Ahmad et al., 2018). For example, milk protein and fat content are two valuable economical compositions of milk that determine the yield of the dairy product. An increase in the protein and fat content increases the dairy product yield like cheese, and butter but the yield is influenced by the milk salt present within (Bijl et al., 2013). Milk quality is not only affected by the fat and Solid-Non-Fat (SNF) but other factors that determine the quality of the milk, like the electrical conductivity of the milk and the pH, which are also related to the milk composition(Caprita et al., 2003).
The milk constituents' relationship helps determine other economic characteristics of farm animals. It is an important attribute as it will furnish criteria for making economic and market decisions of farm animals, choice and rearing of animals. Therefore, our study aims to determine the nature of the relationship within the milk constituents like fat, SNF, protein, lactose, density, conductivity, salt, added water, pH, temperature, and freezing point (FrP).
A cross-sectional study was performed. It can be divided into the following sections:
Whole milk samples were collected from the Dairy Cooperatives in a sterilized container (50ml). A total of 246 samples were taken. Out of them, only 223 samples were only included in the study. 23 samples were discarded because of sample contamination and missing information. Samples were marked with the permanent marker and were kept in the ice cool box for transport to the laboratory. We used an ultrasonic milk analyser (Lactoscan MCCW - K 3051 | Laboratory models, n.d.). The included parameters and their accuracy for a given milk analyser are given in Table 1 (Analyzer, 2019).\ Similarly, we also included information on the lactation stage via interviewing the farmers.
|
Parameters included |
Unit |
Standard Error (SE) |
|---|---|---|
|
Solids-non-fat (SNF) |
% |
± 0.15 |
|
Density |
kg/m3 |
± 0.3 |
|
Protein |
% |
± 0.15 |
|
Lactose |
% |
± 0.20 |
|
Added water |
% |
± 3 |
|
Milk sample temperature |
0C |
± 1 |
|
Freezing point (FrP) |
-0C |
± 0.001 |
|
Salts |
% |
±0.05 |
|
Fat |
% |
± 0.06 |
|
Electrical Conductivity |
mS/cm |
± 0.05 |
|
pH |
0 to 14 |
± 0.05% |
1.2 Lab Procedure (Analyzer, 2019)
Before starting to analyze the milk sample, cleaning of the milk analyzer was done. The milk analyser should be cleaned with the warm water and then with a 3% acidic solution and again with the warm water. After the cleaning of the analyzer, milk was placed in the tube where the machine takes 25 ml of milk sample and after 1 min it displays the milk parameters values. The milk sample was at room temperature for the analysis for the best result.
The relationship was expressed as the Pearson correlation coefficient. Data analysis and correlation plot visualization were done using the package ‘corrplot’ (Taiyun Wei, 2013).
The correlation coefficient (r) is positive and highest between the SNF, Density, Protein, and Lactose (r ≥ 0.89). However, their relationship to added water is significantly negative where r ≤ -0.7. Milk temperature has the highest significant positive correlation with the conductivity (r = 0.46) whereas the value of r is 3 times less than other parameters. The result shows that conductivity has a negative relationship with fat, added water, pH, and lactation length (LL). It is only being significant for pH and LL (r = -0.14). For the remaining parameters, the correlation coefficient is r ≥ 0.15.
In Table 2, we can see, that salt and Freezing Point (FrP) have more or less the same relationship with other parameters. They have a positive and significant correlation between them (r = 0.97). Similarly, the correlation coefficient between fat and Frp (r = 0.28) is twice the value of fat and salt.
In our findings, fat is positively correlated with the total solids and has a maximum correlation with the lactation length (LL) where r = 0.49. Regardless, it has a negative correlation with density (r = -0.16). While considering the pH, it has a negative relationship with every parameter except with the added water. Likewise added water has a negative correlation with other individual factors and only held a positive correlation with pH (r = 0.15).
|
Temp |
Fat |
SNF |
Den |
Pro |
Lac |
Adw |
Salt |
Cond |
pH |
Frp |
|
|---|---|---|---|---|---|---|---|---|---|---|---|
|
Temp |
|||||||||||
|
Fat |
0.07 |
||||||||||
|
SNF |
0.12 |
0.14* |
|||||||||
|
Den |
0.07 |
-0.16* |
0.89**** |
||||||||
|
Pro |
0.14* |
0.14* |
0.96**** |
0.93**** |
|||||||
|
Lac |
0.14* |
0.16* |
0.96**** |
0.92**** |
1.00**** |
||||||
|
AdW |
-0.05 |
-0.29**** |
-0.70**** |
-0.60**** |
-0.68**** |
-0.70**** |
|||||
|
Salt |
0.13* |
0.15* |
0.93**** |
0.91**** |
0.99**** |
0.99**** |
-0.64**** |
||||
|
Cond |
0.46**** |
-0.13 |
0.18** |
0.17* |
0.16* |
0.16* |
-0.09 |
0.18** |
|||
|
pH |
-0.02 |
-0.02 |
-0.29**** |
-0.26**** |
-0.31**** |
-0.31**** |
0.15* |
-0.31**** |
-0.14* |
||
|
FrP |
0.16* |
0.28**** |
0.95**** |
0.86**** |
0.98**** |
0.99**** |
-0.70**** |
0.97**** |
0.15* |
-0.32**** |
|
|
LL |
-0.03 |
0.49**** |
-0.01 |
-0.1 |
0.01 |
0.02 |
-0.12 |
0.01 |
-0.14* |
0.07 |
0.07 |
| Note = The p-value < 0.05 were considered to be significant and is marked by one or more *. | |||||||||||
| * Signifies P = < 0.05 *, P = < 0.01 **, P = < 0.001 ***, P = < 0.0001**** | |||||||||||
The temperature was significantly positively correlated with Protein, lactose, salt, Freezing point (FrP), and conductivity. Here, the conductivity had the strongest correlation with the temperature (p = < 0.0001). With the increase in temperature, the viscosity of milk decreases along with the dissociation of the salt. This led to an increase in dissolved calcium and phosphates (Henningsson et al., 2007). Henningsson et. al (Henningsson et al., 2005) had a similar finding and argued that the temperature increases the acidification of milk which causes the rise in conductivity. However, we found that the temperature had a negative correlation with the pH. In a study by Macej et al (Macej et al., 2002), there was also a negative correlation between temperature and pH, since as the temperature increases, the acidity of the milk also decreases.
Fat was found to be significantly positively correlated with the SNF, Protein, Lactose, and Salt. This is supported by the findings of Dehinenet et al (Dehinenet and Mekonnen, 2013), Sourabh et al (Sourabh et al., 2017), and Suryam et al (Suryam Dora et al., 2020). As the milk fat percentage increases, SNF, Protein, Lactose, and total solids of milk increase. It is obvious because SNF, Protein, and Lactose are part of total solids. We found the correlation of milk fat with the Freezing point (FrP) was 0.28, whereas Suryam et al (Suryam Dora et al., 2020) reported 0.235. Fat was found to be significantly negatively correlated with the Density and added water. Although a negative correlation exists between fat and pH, it was not a significant one. Fat decreases the conductivity of the milk whereas lactose doesn’t conduct current itself. However, with the fermentation of milk, lactose is converted into lactic acid in which the pH of milk has a great role and influences the quality of the milk (Mucchetti et al., 1994).
SNF was found to be significantly positively correlated with the Density, Protein, Lactose, Salt, Conductivity, and FrP. Sourabh et al (Sourabh et al., 2017) found SNF was positively correlated with density and protein. However, SNF was found to be significantly negatively correlated with added water as it dilutes the content in the milk and pH. Similarly, density was found to be significantly positively correlated with the Protein, Lactose, Salt, Conductivity, and FrP. Density had a weak correlation with the conductivity whereas there was a significantly negative correlation between the pH and the added water.
In a similar study by Science et al (Science and Science, 2020), protein had a strong significant and positive correlation with density which aligns with our results. However, they stated that there was a strong negative correlation with the temperature, whereas our study did not find a significant relationship since we performed our analysis with constant room temperature for all milk samples. Protein was found to be significantly positively correlated with Lactose, Salt, and Conductivity. Sourabh et al (Sourabh et al., 2017) were in agreement with our result. However, Suryam Dora et al (Suryam Dora et al., 2020) reported a non-significant positive correlation between protein and lactose.
We found lactose had a significantly positive correlation with Salt, Conductivity, and the FrP. Shipe (Shipe, 1959) had the same findings but the correlation between conductivity and lactose was reported weak by Mucchetti et al (Mucchetti et al., 1994). It was significantly negatively correlated with the added water and the pH.
We found that added water was negatively correlated with the Salt, and Conductivity, FrP. However, it was significant with the salt and FrP only. According to Shipe (Shipe, 1959), the freezing point is a more accurate index of the added water (Shipe, 1959). Water adulteration decreases the specific gravity of the milk and increases the freezing point (FrP) of the milk (Dehinenet and Mekonnen, 2013). It was found significantly positively correlated with pH.
Salt was found to be significantly positively correlated with Conductivity, FrP, and significantly negatively correlated with pH. Bijl et al (Bijl et al., 2013) had same findings. They reported protein was significantly positively and strongly correlated with the salt which agreed with our results.
Conductivity was found to be significantly positively correlated with the temperature, SNF, Density, Protein Lactose Salt, and FrP but had negatively correlated with the pH. Mucchetti et al (Mucchetti et al., 1994) found fat has a significant negative correlation with conductivity but we did not find a significant one.
Electrical conductivity can be used to predict the quality value of the milk. There is a significant correlation between electrical conductivity to Solid Non-fat (SNF), lactose, and the Freezing point (Yanthi et al., 2018). Electrical conductivity also significantly affects (p = < 0.05) the value of the density in the milk. It was found electrical conductivity increased significantly (p < 0.001) when the animal is clinically or sub-clinically affected by the mastitis(Norberg et al., 2004). More than that, electrical conductivity can be used as a tool for disease diagnosis in cattle. (Lukas et al., 2009) reported an 8.3% increase in conductivity of milk in animals suffering from different digestive disorders and a decrease in milk production. It was reported that the greatest daily effect estimated for milk fever increases 8.3% milk electrical conductivity. It suggested that the method of detection of disease through these parameters gives fewer false positive results. It was reported milk quality was reflected by the protein content, fat, SNF, Lactose, Density, pH, and the Freezing point deviation.
Our study reported a significant correlation between lactation stage and fat. Fat percentage increases during the first three months of the parturition and then it starts to decline and again in the late lactation length it starts to increase. This is illustrated in Fig. 1. Looper M (Looper, 1914), and U et al (U et al., 2002) agreed with our findings.
Through the research, highly significant positive correlations were concluded between fat, SNF, protein, lactose, and density. Similarly, milk’s electrical conductivity and salt had a significant correlation. In most places, only fat and SNF is measured to determine the quality of milk but this relation gives the idea of the relationship between other milk constituents. Furthermore, the electrical conductivity parameter can be used to understand the disease status of animals like Mastitis. This will not only help in the assessment of milk quality but would also be a sustainable and economical method for screening possible diseased animals. Similarly, the milk quality would determine the price of milk. The higher the milk quality, the higher will be the price. Thus, farmers would be motivated to the production of qualitative milk which subsequently improves the overall nutrition and public health.
Acknowledgment
The authors would like to thank Agriculture and Forestry University (AFU), National Cattle
Research Centre (NCRP), Veterinary Hospital and Livestock Services Expert Center
(VHLSEC), and dairy cooperatives, farmers of Chitwan, Nepal.
Statement of Animal Rights
Not applicable.
Author Contribution
PR conceptualized the research, collected and analysed the samples, and wrote the first draft. NA helped in data analysis, reviewing, and editing the manuscript. Both authors have read and approved the final manuscript.
Competing Interest
The authors declare no competing interest.
Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.