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.