Pages: 55
Words: 13793
Chapter 1 : Introduction
The milk and milk products have to undergo through the process by thermal methods which include the pasteurization to ensure that the products are free from pathogens. Following this, sterilization method is also used for ensuring the commercial sterility. The growth in population and rise in demand for milk has developed the requirement of greater shelf life. The fresh milk which has extended shelf life is referred to as the extended shelf life milk. Some of the food products including milk has a shorter shelf life due to which is one of the biggest challenges for the supply chain management(Raju et al., 2015). The short shelf life of the food products restricts the producers, wholesalers, retailers as well as the consumers. This is due to the fact that the shortshelf life food products can be sold within the restricted time periods. Due to this, the retailers have to keep limited inventory which sometime excess demand or supply of the food products. This can also result in damage of the food products in turn lead to severe loss within the supply chain(Parveen, Alabdulkarim, and Arzoo 2011).
There are numerous internal and external factors which result in the growth of the microorganisms during the storage of food products and particularly the dairy products. The longterm storage of the dairy products results in some kind of some kind of sensory changes in the products which could result in the spoilage of the product. This spoilage arises majorly due to the poor storage condition s in which this food is stored. The length of the shelf life of the milk products is depend upon the initial type of microorganismscurrent in the milk in the raw form. The changes in the microbial origin which eventually influences the shelf life of the milk products includes the postacidification the lactic acid which is produced by the bacteria is fermented lactose which results in an unacceptable taste. The damaged or poor quality resulting from spoilage by microorganisms results in wastage in terms of the quality, smell, texture, test and aroma of the products. This acidification process can be avoided through cold storage. The cold storage of the product minimizes and prevents the microbial growth which avoid the over acidification. Another affect resulting from microbial growth is the malty flavour and odour which damages the milk when the streptococcus lacticmaligns areconverted to amino acids resultingin formation of malty flavour(Raju et al., 2015). His mechanism can be prevented by coolingdown the milk below 5 degree. Further, spoilage of milk sometimes results in development of fruity flavour which is the result of the ethyl ere formation which is esterase's from the lactic acid. This defect can be prevented through adequate level of pasteurization. Apart fromthis, the shelf life also depends on the depends upon the production of the mill products, the state of packaging of the product, the hygienic and sanitary conditions under which the product is treated and so on(Kuchma and Barnes 2013).
Extended shelf life milk has increased the market share among the economies. According to some of the manufacturers, the shelf life has increased to 2145 days. On the other hand , as per some of the other manufacturers, the shelf life of milk has increased to 90 days. This milk is produced by two principal knowhows including the thermal processing which has developed conditions through pasteurizations. Another is the nonthermal processes including microfiltration. The second method is actuation which is combined with the final thermal pasteurization which were sued for meeting regulatory requirements(Perveen et al., 2011).
The heating system involves the direct and the indirect methods. The direct systems including the heating occurs when the direct contact within the steam and product within the indirect systems, the maount of heat which is transferred within the product. This product is extracted from the hot water through the steel barrier within the process of heat exchange. In the direct heating, the milk is heated indirectly which is in plate which is 7080 degree Celsius when heated at a high temperature through the direct contact which is through dry culinary steam. The heated milk which passes through the vacuum chamber and cools the milk on the same temperature(Ziyaina et al., 2018). The indirect heating is through the heating and cooling of the stagesthrough which considerable amount of heat can be recovered. The heat milk which is the direct systems which it is greater than 50%(Raju et al., 2015). In the current study, the researcher explores the numerous types of weather conditions which could affect the shelf life of milk before and after treatment. In the literature review , the researcher explained the different techniques which is used for extending the shelf life of milk and milk products. Additionally, the researcher explained the advantages of extended of shelf life of milk. Further , the rearcherexamined theway of monitoring the extended shelf life of milk.
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The aim of the current study is to explore the weather conditions which affect the shelf life of milk quality in derby.
The concept of extended shelf life has emerged to be significantly important in the dynamics of the diary market. The shelflife of the milk can be defined by the number of days during which the milk can be stored.Although, normally the shelf life of milk is between 5 to 7 days, it is affected by numerous kinds of factors including the quality of raw milk, the conditions under which pasteurization takes place, contamination from the food contacts and the environments and the type of distribution network. There are various types of treatments which can be used for the extended shelf life milk processing operations. In this context, the study of (Deeth, 2017; Rysstad & Kolstad, 2006) revealed ultraviolet treatment is an improvement over the microbiological quality for raw and pasteurized milk. The application of UV technology is the preferred treatment of raw material and allows the milk to be stored for the prolonged periods. The cold nature of treatment involved in the usage of the UV technology can improve the quality of milk and can be useful for reducing the loss from the world where there is lack of renewable energy and high costs farm refrigeration is prohibitive. This technology is particularly useful in those countries where there are warm climates. When the process of UV processing in conjunction with the pasteurization, the selflife of milk is increased by 30%. In addition to this, the application of the UV processing on the dairy products could include energy savings which could be due to the nonthermal type of technology. The sustainability and considerations for the environment are some of the important ways in which the consumers could purchase the produce in an environment friendly manner(Ziyaina et al., 2018).
The shelf life of milk can be extended through an ultraheat treatment by which the milk can be stored for a few months without cooling. The food products with an extended shelf life can be used to reduce the microbial count which is beyond normal pasteurization. This milk is packed under the extreme hygiene conditions. The study conducted by (Rysstad & Kolstad, 2006)highlight that the flavour and the aroma for extended shelf life is only reason due to which this milk is less preferred by the consumers within the market. The production of extended shelf life milk can be done using the microfiltration and pasteurization. This procedure and its variations contain microfiltration of isolated skim milk bringing about a saturate, which was included with or without ensuing HTST purification to the profoundly heated (115–130 °C, 4–6 s) a blend of microfiltration retentate and the necessary measure of cream.(Koutchma & Barnes, 2013) At long last, the recombined and fatbalanced milk is filled aseptically. Microfiltration is completed with aceramic film having a normal pore size of 1.4 am (Hoffmann et al., 1996). Readiness of ESL entire milk incorporates homogeneity and high warming of a mixture (15.0% fat) of isolated cream (31.5% fat) and part of microfiltration penetrate. The remaining penetrate was HTST purified and further to the profoundly warmed blend before the last entire milk (3.5% fat) was complete, cooled, and put away(Memes et al. 2014; Perveen, Alabdulkarim, and Arzoo 2011).
In this regard, the study of(Memiši et al., 2014)defined the preparation of the extended life milk using the process of microfiltration. The process of microfiltration is widely used for reducing the load of bacteria through mechanical separation. This process doesn't require the heat induced chemical alterations. The combination of microfiltration and pasteurization can be used effectively for the purpose of extended shelf life milk. As more and more consumers are becoming aware, the market is coming up with good quality of aseptic packaging which offers good quality of products in the market. The process of microfiltration is offered with minimal level of loss for nutritional value. The factors which affect the demand of the milk products in the market include the volume of past sales, product price, advertisements, seasonality, holidays, weather conditions, existence of the alternative products and discounts or promotions(Rusted and Kolstad 2006). In the similar context , the study of … revealed that the combination of microfiltration and pasteurized milk is used effectively for producing the ESL milk.
The extended shelf life quality pertains to certain set of benefits in comparison to the raw milk which has the maximum life of about 10 days. Apart from guarantying an extended life for milk as well high demanded milk products like yogurt; the thermal processing, use of UV technology and other forms of heat treatments extend the life of milk products like cheese and butter up to 90 days with the condition of keeping these products at a temperature of below 4 degree Celsius. In this context , the study conducted by(Memiši et al., 2014) highlighted that over the period the processing time of higher temperatures during thermal processes and other forms of methods wherein the milk is heated at high temperature is reduce de . With the reduction in the heating time and also the time required to cool down, the process generates a highquality product. Further, the packaging size which include the packaging conditions are autonomous of the  size. This allows for the filing up of the large containersregarding the service of food and sale of food manufacturers. Lastly, packaging and transportation of extended shelf life quality milk is relatively cheaper. The lactulose and the ferrozine content are low compared to the UHT milk. The cost of capital installations for the UHT is comparatively low. Lactulose and ferrozine content is low compared to UHT milk and less denaturation of αlactalbumin and βlactoglobulin in ESL compared to UHT milk(Balogun 2016).
The study conducted by (Barros et al., 2017) highlighted that if the shelf life of milk is extended then it would enable the processors and the marketers to develop a competitive position in the market. This is due to the fact that extended life quality milk will enable the producers to distribute high quality of milk over the wide geographic areas. Additionally , it would help the retailers to expand their business outlets in the market. To this , the study of (Chapman et al., 2001)revealed that the bacterial spoilage is the most limiting factor in extending the shelf life of the pasteurized milk. The mycobacterial spoilage resulting from the
The extended shelf life milk also offers better taste than the raw milk which is being sterilized for the hours at a high temperature which offers the consumers with the caramelised taste. Apart from this, this procedure reduces the number of trips that the consumers have to make in the market. Besides consumers , one major advantage of the ESL milk is that it can be used in the preparation of the flavoured milk , fermented products, creams and tea as well as coffee(Rysstad & Kolstad, 2006).
One of the most important variables affecting the shelf life of the milk products is the weather conditions and seasonality. This is due to the fact that the amount of moisture current in the air is always connected with the microbial growth. In this context, the study conducted by … highlighted that milk products, particularly cheese storage is affected by the level of moisture in which it is stored. Temperaturesubordinate stockpiling of most nourishments has three significant jobs to consider restoring/maturing of nourishments that contain included dynamic starter societies and chemicals, to forestall quality imperfections, and to control microbial development. Corrosiveness (explicitly decreased pH) causes the protein lattice in the curd to agreement and press out dampness. That procedure of compression is called syneresis. Cream cheddar has very helpless waterholding limit (WHC) and is profoundly vulnerable to syneresis(Balog 2016; Deeth 2017; Raju et al. 2015).
In the current chapter, the researcher explains the research methodology adopted within the the study. The research methodology describes the scientific procedure used by the researcher to answer the research questions. The aim of the current study is to examine the factors affecting the shelf life of milk in a pub in derby. The researcher applies various processes, techniques and methods to achieve this aim. In addition this, the researcher lays down the process of data collection and analysis. The data analysis procedure describes the sampling plan, sampling instruments and questionnaire design. This was followed by a description of ethical considerations followed by the researcher.
The set of rules and procedures which the researcher follows during the procedure is called research paradigm. There are three major research paradigms in guiding the research study : Positivism, interpretivism and realism. The positivism paradigm recurrents the social reality which is external to individuals indicating that there exists a reality which is independent of the researcher and needs to be discovered. Interpretivist paradigm is the observation of social reality that has already been discovered. It involves observing the viewpoint and interpreting the results in terms of the world around it(Dammak, 2015; Kivunja & Kuyini, 2017). Lastly, the realism research paradigm focuses upon the set of beliefswhich are consistent with reality in the environment. In this study , the researcher uses the positivism approach through which the researcher examines the reality as to explore how the weather conditions in clouding the cloudy cover, partly cloudy conditions and the clear sky affects the quality of milk in different weeks.
A research design incorporates theoretical framework which can be used to farme a research problem in order to ensure that the objectives of the study (Saunders et al., 2009). There are four types of research design including the exploratory, descriptive and explanatory design(Dudovskiy, 2016; Saunders & Tosey, 2012). In the currentstudy, as the rearcher explores the driftnetfactors which affects the selflife of milk in derby , the researcher used the descriptive research design in order to have a clear idea with context to the variables.
The researcher used the milk quantity data from the period of 1^{st} may, 2009 till 30^{th} April 2014. In addition to this, the rearcher also collected the weekly data for the milk quantity . Further , in order to examine the impact of weather conditions on the milk quantity , the researcher collected the data for cloud cover , partly cloudy and the clear conditions. The data for the weather conditions was collected through the weather forecast reports in derby. The researcher has used the purposive sampling which is a type of nonprobability sampling. The researcher specifically collected this data to examine the type of weather conditions which affect the shelf life of milk in derby. The researcher collected this data after considering all the ethical guidelines which necessarily need to be followed.
The researcher entered the collected data in MS excel and formatted into a proper manner. Following this, the researcher imported the adapt into IBM SPSS v21 for further analysis. Primarily , the researcher conducted the stationarity and normality test by plotting the values against in order to ensure that the data is normally distributed and doesn't depicts any trend movement within the data. Following this , the researcher conducted correlation analysis in order to determine the extent of relationship among the variables.
The mean and standard deviation of every quality boundary at each time point were determined. An observational nonlinear series was then used to suit the information from quality boundaries across time, independently according to the climate conditions. Primarily , the researcher used Auto regressive time series model to examine the parameters within the model. A novel methodology for estimating the impact of variability factors (both internal and external) on predicting the demand for shortshelf life products have been introduced. The factors recurrenting noise and time shift are not of high significance according to results from PCA and hence it can be omitted from the demand prediction.
The ARIMA model is applied where the researcher displays the movement or behaviour of the variables which describes the linear between the current value and the past values within the variables. The models are also referred to as the BoxJenkins model which would have pioneering regarding the forecasting techniques used in time series analysis. The time series modelling can be described in terms of the integrated and the stationarity component. The integratedcomponents will help the researcher in examining the level of stationarity and the second component would be rendering stationarity through differentiation(Malley & Molana, 2002).
The ARIMA components can further be classified in term of the AR and the MR process wherein the AR incorporates between the current and the past values. Further , the MA process measures the duration of influence of the random shocks. The number of autoregressive orders specify the previous values which are used to produce the current values. Thedifferentiation denoted by the letter d is used to estimate the level of differencing required in the stationary models. The order of differencing is corresponded to the degree of trend order differencing accounts which include the linear trends and the second order differencing trends. The moving average in the model are used to depict the extent of deviations from the series mean which are used to predict the current values(Imam, 2016; Solari & Gelder, 2011).
The seasonal autoregressive moving average also plays a very important role in identifying the value of the current series within the seasonal periods. The current periodicity is the integer which the models with the case .The autocorrelation function and the partial autocorrelation function are used for estimating using the lag values of p and q.
The principal component analysis is a commonly accepted multivariate analytic statistical techniques that can be applied to the quantitative data analysis. It is multivariate analysis which develops a way of extracting the structure from the variancecovariance or the correlation matrix. The extent of correlation within the dependent variables and the substitutes is called the factor or the attributes which are correlated. The analysis which examines the second and third group of characteristics which derives each factor and the residual variance.
This technique is used for reducing the attributes and factors which is based on the patterns and the correlation within the original variables. The resulting data can be applied to comparing the shelf life of the milk or milk products on the bases of seasonality ,cloudy, partly cloudy and clear weather conditions.
PCA distinguishes examples of relationship among subordinate factors and substitutes another variable, called a factor, for the gathering of unique characteristics that were connected. The investigation at that point recognizes a second and third gathering of characteristics and infers a factor for each, in view of the remaining fluctuation (that which is left after the change recurrented by the past factor has been evacuated). The characteristics will have a connection with the new measurements, called a factor stacking, and the items will have values on the new measurements, called factor grades . The factor loadings arevaluable in deciphering the measurements, and the factor grades show the relative situations among the items in a guide (Lawless and Heyman, 1998). Along these lines, PCA changes unique ward factors into new uncorrelated measurements to streamline the information structure, dispense with descriptor redundancies, and demonstrate possible inactive causal factors.
The researcher ensured that the data collected will be valid as the findings of the study can be used by the other researchers for their analysis. The reliability of the data is checked through the Cronbach alpha tests. The dataset is considered reliable when the value of the Cronbach alphas statistic is greater than 0.6. In the table shown below, the value of the Cronbach alphais 0.896 which means that the dataset used in the current study is reliable.
Reliability Statistics 

Cronbach's Alpha 
N of Items 
.896 
10 
The current section highlights the results for the data analysis conducted by the researcher. The researcher examined the results derived for the study. Perennial to the aim of the study, the data analysis chapter of the research study aids to assess the weather conditions which affect the shelf life of milk in derby. Primarily , the researcher conducted the stationarity test in order to examine the distribution of the variables in the model and the extent of variance current in the data. Following this , the researcher conducted the correlation analysis in order to examine the relationship among the variables . Subsequently , the researcher conducted ARIMA modelling to evaluate the number of lags which can be used to examine the weather conditions with lags and moving average which could affect the shelf life of the milk. Lastly , the researcher conducted the principal component analysis to exact the factors which the shelf life of milk. The table shown below describes the model
Model Description 

Model Name 
MOD_1 

Series or Sequence 
1 
Milk Quantity 
Transformation 
None 

NonSeasonal Differencing 
0 

Seasonal Differencing 
0 

Length of Seasonal Period 
No periodicity 

Horizontal Axis Lab 
Date 

Intervention Onsets 
None 

Reference Lines 
None 

Area Below the Curve 
Not filled 

Applying the model specifications from MOD_1 

Case Processing Summary 


Milk Quantity 

Series or Sequence Length 
1826 

Number of Missing Values in the Plot 
UserMissing 
0 
SystemMissing 
0 
In order to examine the stationarity of the model , the researcher conducted the test of stationarity using the graph. The baizegraph depicts the relationship of the date against the milk quality. The graph clearly indicates a linear trend. Additionally, all the values are positive. This means that the series is non stationary. In time series, a variable can't be taken for the furthered analysis if its sis non stationary. In order you frame stationarity in the mode, the variable needs to be differentiated at least once. Therefore, the researcher conducts the test of the variable with the first differentiation.
Model Description 

Model Name 
MOD_2 

Series or Sequence 
1 
Milk Quantity 
Transformation 
Natural logarithm 

NonSeasonal Differencing 
1 

Seasonal Differencing 
0 

Length of Seasonal Period 
No periodicity 

Horizontal Axis Labels 
Date 

Intervention Onsets 
None 

*Reference Lines 
None 

Area Below the Curve 
Not filled 

Applying the model specifications from MOD_2 

The accurate forecast of the produce of the small and medium enterprises among the wholesalers have led to the development of the numerus variability factors which impacts the nature and the quantity of the milk as well as products in the market. The variability factors which can affect which can affect the shelf life of milk could include the per litter price of the milk, the seasonality factors , number of public holidays and the advertisements or the promotion conducted for the milk. This helps in forecasting in the demand the shelf life of milk.
The preliminary correlation o the analysis is collected for the variability factors which against the actual demand to evaluate the dependencies in the actual demand. The principal component analysis would be widely of potential influencing factors to which the significant factors can be included in the forecasting model. The correlation analysis is conducted from the factors which are obtained through the principal component analysis against the quantity of variables.
Case Processing Summary 


Milk Quantity 

Series or Sequence Length 
1826 

Number of Missing Values in the Plot 
Negative or Zero Before Log Transform 
306^{a} 
UserMissing 
0 

SystemMissing 
0 

a. The minimum value is 35.000. 
In order to make the data normalized , the researcher first took the log transformation of the variable.. Log transformation makes the data for all the variables consistent for comparison over a period of time. Additionally , in order to make the time series stationary, the researcher did first differentiation of the variable which helped the researcher in arriving at accurate results. The results shown in the above graph clearly indicate that the series becomes stationary after the first differentiation . Hence , the milk quantity can be further taken into consideration in the analysis after the log transformation and the first differentiation of the variable. First, Principal Components Analysis (PCA) is a variable reduction technique which maximizes the amount of variance accounted for in the observed variables by a smaller group of variables called components.
The principal component analysis technique is applied by the researcher when the significance of the factors is used for transforming the variability of the factors into the noncorrelated PCA factors.
Correlations 


Milk Quantity 

W1 
Pearson Correlation 
.724^{**} 
Sig. (2tailed) 
.000 

N 
1782 

W2 
Pearson Correlation 
.695^{**} 
Sig. (2tailed) 
.000 

N 
1782 

W3 
Pearson Correlation 
.695^{**} 
Sig. (2tailed) 
.000 

N 
1782 

W4 
Pearson Correlation 
.690^{**} 
Sig. (2tailed) 
.000 

N 
1782 

W5 
Pearson Correlation 
.687^{**} 
Sig. (2tailed) 
.000 

N 
1782 

W6 
Pearson Correlation 
.668^{**} 
Sig. (2tailed) 
.000 

N 
1782 

Cloud Cover 
Pearson Correlation 
.168^{**} 
Sig. (2tailed) 
.000 

N 
1782 

Partly Cloudy 
Pearson Correlation 
.115^{**} 
Sig. (2tailed) 
.000 

N 
1782 

Clear 
Pearson Correlation 
.190^{**} 
Sig. (2tailed) 
.000 

N 
1782 
The above table shows the results of the Pearson correlation table. The values of the variable shown in the table clearly indicate the extent of relationship among the variables. The milk quantity is significantly correlated with all the variables as the conditioned threshold p value of less than 0.05. In addition to this , the table clearly indicates that the variables cloudy cover and the partly cloudy has a negative correlation with the milk quantity. This mans as the climatic condition's changes to cloudy cover and the partly cloudy , the shelf life of milk decreases over the period of time.
ACF
Notes 

Output Created 
31JUL2020 14:34:23 

Comments 


Input 
Active Dataset 
DataSet1 
Filter 
<none> 

Weight 
<none> 

Split File 
<none> 

N of Rows in Working Data File 
1826 

Date 
<none> 

Missing Value Handling 
Definition of Missing 
Userdefined missing values are treated as missing. 
Cases Used 
For a given time series variable, cases with missing values are not used in the analysis. Also, cases with negative or zero values are not used, if the log transform is requested. 

Syntax 
ACF VARIABLES=Milk Quantity /LN /DIFF=1 /MXAUTO 16 /SERROR=IND /PACF. 

Resources 
Processor Time 
00:00:00.58 
Elapsed Time 
00:00:00.56 

Use 
From 
First observation 
To 
Last observation 

Time Series Settings (TSET) 
Amount of Output 
PRINT = DEFAULT 
Saving New Variables 
NEWVAR = CURRENT 

Maximum Number of Lags in Autocorrelation or Partial Autocorrelation Plots 
MXAUTO = 16 

Maximum Number of Lags Per CrossCorrelation Plots 
MXCROSS = 7 

Maximum Number of New Variables Generated Per Procedure 
MXNEWVAR = 60 

Maximum Number of New Cases Per Procedure 
MXPREDICT = 1000 

Treatment of UserMissing Values 
MISSING = EXCLUDE 

Confidence Interval Percentage Value 
CIN = 95 

Tolerance for Entering Variables in Regression Equations 
TOLER = .0001 

Maximum Iterative Parameter Change 
CNVERGE = .001 

Method of Calculating Std. Errors for Autocorrelations 
ACFSE = IND 

Length of Seasonal Period 
Unspecified 

Variable Whose Values Label Observations in Plots 
Unspecified 

Equations Include 
CONSTANT 
Model Description 

Model Name 
MOD_3 

Series Name 
1 
Milk Quantity 
Transformation 
Natural logarithm 

NonSeasonal Differencing 
1 

Seasonal Differencing 
0 

Length of Seasonal Period 
No periodicity 

Maximum Number of Lags 
16 

Process Assumed for Calculating the Standard Errors of the Autocorrelations 
Independence(white noise)^{a} 

Display and Plot 
All lags 

Applying the model specifications from MOD_3 

a. Not applicable for calculating the standard errors of the partial autocorrelations. 

Case Processing Summary 


Milk Quantity 

Series Length 
1826 

Number of Missing Values 
Negative or Zero Before Log Transform 
306^{a} 
UserMissing 
0 

SystemMissing 
0 

Number of Valid Values 
1520 

Number of Values Lost Due to Differencing 
1 

Number of Computable First Lags After Differencing 
984 

a. The minimum value is 35.000 
Milk Quantity
Autocorrelations 

Series: Milk Quantity 

Lag 
Autocorrelation 
Std. Error 
BoxLung Statistic 

Value 
df 
Sib 

1 
.411 
.025 
268.657 
1 
.000 
2 
.027 
.022 
270.275 
2 
.000 
3 
.049 
.021 
275.655 
3 
.000 
4 
.045 
.021 
280.216 
4 
.000 
5 
.017 
.022 
280.866 
5 
.000 
6 
.020 
.025 
281.509 
6 
.000 
7 
.135 
.028 
305.106 
7 
.000 
8 
.054 
.025 
309.756 
8 
.000 
9 
.034 
.021 
312.236 
9 
.000 
10 
.061 
.021 
320.618 
10 
.000 
11 
.045 
.021 
325.229 
11 
.000 
12 
.028 
.021 
326.950 
12 
.000 
13 
.030 
.025 
328.383 
13 
.000 
14 
.085 
.028 
338.004 
14 
.000 
15 
.014 
.025 
338.331 
15 
.000 
16 
.037 
.021 
341.313 
16 
.000 
a. The underlying process assumed is independence (white noise). 

b. Based on the asymptotic chisquare approximation. 
Autocorrelation explains the extent of relationship within the factors due to different set of perception regarding the goven information. The knowldege or information regrading the autocorrelation and concept is related to the arrangement of time within the given timeset. This information could vary over a period of time and could be related to temperature and air(Connell, 1987).
The above table indicates the results of the autocorrelation of the variables within the given range .The autoregressive and moving average series are used to detriment the ACF and the PACF plots which are used to obtain the values of p and q within the ARIMA model. The ACF model is the autocorrelation which gives the values within its lagged values. It describes how the current value of the series. The components utilized in the time series includes the trends , seasonality, cyclic and residual values(Adhikari, 2013). The ACF graph incorporate all the components while determining the correlation which completes the autocorrelation plot.
The PACF graph on the other hand is the autocorrelation function in wherein lags like ACF are current within the correlation residuals with the next lag value of partial and plot was complete. The information used in the model can be incorporated with the following lag variable.
Occasional favorable to cesses show these examples at the occasional lags (the products of the occasional period). the analyst is qualified for treat nonsignificant qualities as 0. That is, you can disregard esteems that exist in the certainty stretches on the plots need to overlook them, nonetheless, especially on the off chance that they proceed with the example of the factually critical qualities. An intermittent autocorrelation will be factually noteworthy by chance alone. The analyst can disregard a measurably huge autocorrelation in the event that it is disengaged, ideally at a high slack, and on the off chance that it doesn't happen at an occasional lag.The above figures depict the results for the ACF graph. The upper limit and the lower confidence limit for the values and the Y line (0). There is a significant spike at the first lag number and the around the second lag. As the diagram doesn't depict any trend over the variables.
Partial Autocorrelations 

Series: Milk Quantity 

Lag 
Partial Autocorrelation 
Std. Error 
1 
.411 
.028 
2 
.170 
.028 
3 
.131 
.028 
4 
.149 
.028 
5 
.097 
.028 
6 
.090 
.028 
7 
.096 
.028 
8 
.056 
.028 
9 
.075 
.028 
10 
.006 
.028 
11 
.062 
.028 
12 
.034 
.028 
13 
.065 
.028 
14 
.014 
.028 
15 
.022 
.028 
16 
.063 
.028 
The above table shows the partial autocorrelation

The above graph shows the PACF values for the autocorrelated variables. The graph also highlights the upper and the lower limit for the variables. The results shown in the graph highlight the significant negative spike in the beginning of the of the milk quantity and the upper confidence and lower confidence limita re near the y(0) line.
Model Description 


Model Type 

Model ID 
Milk Quantity 
Model_1 
ARIMA(0,0,7) 
Model Summary
Model Fit 

Fit Statistic 
Mean 
SE 
Minimum 
Maximum 
Percentile 

5 
10 
25 
50 
75 
90 
95 


Stationary Rsquared 
.330 
. 
.330 
.330 
.330 
.330 
.330 
.330 
.330 
.330 
.330 

Rsquared 
.330 
. 
.330 
.330 
.330 
.330 
.330 
.330 
.330 
.330 
.330 

RMSE 
10.396 
. 
10.396 
10.396 
10.396 
10.396 
10.396 
10.396 
10.396 
10.396 
10.396 

MAPE 
68.362 
. 
68.362 
68.362 
68.362 
68.362 
68.362 
68.362 
68.362 
68.362 
68.362 

Maxie 
1527.241 
. 
1527.241 
1527.241 
1527.241 
1527.241 
1527.241 
1527.241 
1527.241 
1527.241 
1527.241 

MAE 
7.846 
. 
7.846 
7.846 
7.846 
7.846 
7.846 
7.846 
7.846 
7.846 
7.846 

Maxie 
68.961 
. 
68.961 
68.961 
68.961 
68.961 
68.961 
68.961 
68.961 
68.961 
68.961 

Normalized BIC 
4.699 
. 
4.699 
4.699 
4.699 
4.699 
4.699 
4.699 
4.699 
4.699 
4.699 

The above table shows the fit of the model. As the researcher collected the secondary data, ahigh R square is not expected. The stationarity part of the model is used for comparing the simple mean model. The positive values of the stationary R squared model indicate that the modelis the good fit in comparison to the baseline model. This measure is generally preferred in comparison to the usual R square the results shown in the table above table indicates that the R square and the stationary R square is 0.33 which means that the independent variables explain about 33% of the variation in the dependent variables. The value so the root means square error indicate that how much the dependent variable varies according to the model predicted level and the mean absolute percentage values indicate that how much the dependent variable series is used for the model predicted values. The value of RMSE is 10.396 and that of MAPE is 68.362.
Model Statistics 

Model 
Number of Predictors 
Model Fit statistics 
LungBox Q(18) 
Number of Outliers 

Stationary Rsquared 
Statistics 
DF 
Sig. 

MilkQuantityModel_1 
0 
.330 
699.385 
15 
.000 
0 
The above table shows the model statistics with the number of outliers to equivalent to zero and the significance level equal to 0.00 which is less than the threshold value of 0.05. The table also indicates the values of the outliers. The outliers which are the variables in the model which is equal to zero.
KMO and Barletta Test 

Kaiser Meeyer Olkin Measure of Sampling Adeqaucy 0.890 

Barlett's Test of Sphericity 


The KMO value indicates that the data can be taken into consideration which indicate that the results are good for the variable. However, the Bralett's test of sphericity with the associated with the p value of 0.00 which is less than the threshold value of 0.01 which means that the data can be taken into consideration for the principal component analysis. The value of the adequacy is 0.89 which is close to 1. This value gives a clear indication of the extent of variance determined by the in the variables which caused by the factors, in this test , the value of adequacy is high indicating the fact that the factor analysis will be useful for the data analysis procedure.
The arrangements within the headings of the principal components are extracted by the researcher and the results are shown through the factor analysi. The above graph shows the results of the milk quantity of lag one within the stationary. The compoennet analysis is derived through the footnotes which are extracted through the SPSS software. The components through the interpretation through the interpretation of the factors . These factors could be xetracted through the component analysis which are used for the reduction in the data.
The factor loadings indicate the extent of correlation between the observed and the specific factor wherein the higher values indicate a closer relationship. This is equivalent to the standardized regression coefficients which are used in multiple regression analysis.
Communalities 


Initial 
Extraction 
W1 
1.000 
.710 
W2 
1.000 
.715 
W3 
1.000 
.728 
W4 
1.000 
.712 
W5 
1.000 
.706 
W6 
1.000 
.679 
Cloud Cover 
1.000 
.888 
Partly Cloudy 
1.000 
.887 
Milk Quantity 
1.000 
.691 
Extraction Method: Principal Component Analysis. 

a. Only cases for which Clear = 0 are used in the analysis phase.

In order to examine the total influence of the single observed variable through the factorswhich are associated with it , the researcher used the squared of the factor loadings. The higher values of the variable indicate the stronger relationship.
The above table shows the communalities to examine the total influence of the observed variable from the factors which are related it. It is the sum of the squared factor loadings which are related to the factors related with the observed variables and the values which is the same of the R square within the multiple regression. The value so these variable ranges within zero to 1 indicating the variables can be fully defined through the factors with no uniqueness. The researcher derived the value of each variable by taking into consideration the sum of the factor loading for each of the variables. The values can eb interpreted in the similar way then the R squared values used within the multiple regression which the total percentage of variation attributed within the model. The results shown in the above model depict that the value of milk quality is derived through the communality explains the 0.691 extraction of the values
Total Variance Explained 

Component 
Initial Eigenvalues 
Extraction Sums of Squared Loadings 
Rotation Sums of Squared Loadings 

Total 
% of Variance 
Cumulative % 
Total 
% of Variance 
Cumulative % 
Total 
% of Variance 
Cumulative % 

1 
4.941 
54.899 
54.899 
4.941 
54.899 
54.899 
4.941 
54.898 
54.898 
2 
1.775 
19.720 
74.619 
1.775 
19.720 
74.619 
1.775 
19.721 
74.619 
3 
.403 
4.483 
79.102 






4 
.378 
4.205 
83.306 






5 
.356 
3.953 
87.259 






6 
.332 
3.684 
90.943 






7 
.316 
3.508 
94.451 






8 
.278 
3.087 
97.538 






9 
.222 
2.462 
100.000 






Extraction Method: Principal Component Analysis. 

a. Only cases for which Clear = 0 are used in the analysis phase. 
The above table depicts the results of the initial eigen values, extracted sum of loadings the rotation sum of squared loadings within the variables. The results shown in table indicate that out of 9 , he first two components have the eigen values of 4.941 and the 1.775 indicating that only two variables have the eigen value of greater than one and these explain about 74% ofthe total variability in the dataset. This means that the twofactor solution will be adequate for the further analysis within the variables.
Component MariaD^{B} 


Component 

1 
2 

W1 
.842 
.025 
W2 
.846 
.003 
W3 
.853 
.008 
W4 
.844 
.001 
W5 
.840 
.016 
W6 
.824 
.020 
Cloud Cover 
.034 
.942 
Partly Cloudy 
.022 
.942 
Milk Quantity 
.831 
.004 
Extraction Method: Principal Component Analysis. 

a. 2 components extracted. 

b. Only cases for which Clear = 0 are used in the analysis phase. 
The above table highlights the results of the component matrix which include the component loadings which the correlation within the variable and the component. The correlation with the possible values ranging within the values of 1 and 1. On the format subcommand which is used for blank 0.30 through which the software which in this case SPSS in which the correlations which are less than 0.3 which output the easier to read by eliminating the low correlations which are not meaningful in any way.
Rotated Component MariaD^{B} 


Component 

1 
2 

W1 
.842 
.021 
W2 
.846 
.001 
W3 
.853 
.011 
W4 
.844 
.003 
W5 
.840 
.012 
W6 
.824 
.023 
Cloud Cover 
.030 
.942 
Partly Cloudy 
.026 
.942 
Milk Quantity 
.831 
.007 
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 

a. Rotation converged in 3 iterations. 

b. Only cases for which Clear = 0 are used in the analysis phase. 
The above table depicts the eigen values including the percentage of variance used for explaining the extent of deviation. The middle part of the given table highlights the results of the eigne values and variation which is explained through the two factors including the initial solution. The first factor explains the first factor in amuch more important manner than the second.The right side of the table depicts the eigen values and the percentag of error.
The two rotated factors highlighted in the above table indicate the same level of variance as the two factors with the intial level fo solution. The extent of importance offered to each of the factors could however be different. The rotation of factors which could be used more or less quaickly within the factors. In the above table, the resercaher can note that the rotated factor are 2.895 and 2.881, compared to 4.164 and 1.612 in the initial solution . This gievs a clear idea of the number of extracted factors which have be taken into consideration. This will ensuer the variability within the distributed terms. The data used is not only in the form of the product within the factors which canbe xetracted when needed. The data can be the product of the two factors whioch can be used to extract an rotate the required variables.
The results generated in the form of the reporoduced correlation matrix would be based upon the number fo extracted compoents which are used by the researcher in the analysis. The values used in the reproduced matrix incorporates the differnces which arises between the original and the reproduced matrix incorporated in the system. This implies that the residual matrix is required in ordre to examine the difference which arises between the original as well as the reproduced matrx which is very close to zero. The reproduced matrix included in the matrix is similar to the original correlation matrix wherein the components were extarcetd through the few components which work similar context to the recurrenting the original data.
The new matric which si generated through the original one gives differnts et of relationhsios among the factors which are used to extract the varibles. The reults generated through this data can generate a new variance which is the copy of te original matrix. The components which are extracted through this analysis gives the new set of result for the data.
Component Transformation Matrix 

Component 
1 
2 
1 
1.000 
.004 
2 
.004 
1.000 
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 

a. Only cases for which Clear = 0 are used in the analysis phase. 
The above table shows the component transformation matrix including the transformation for the first two components. This is the rotation matrix which includes the extraction is 0.004 in within the cross section of first and the second component.
The current chapter highlights the conclusions derived therough the analysis in the studty. The first sectiron of the chapter explains the findings of the study, the second section includes the limitations and the third section reflcte the scorp of the future research in the study.
In the current study, the aim of the researcher was to examine the factors that shelf life of milk in derby. Primarily, the researcher used numerous secondary sources in the literature review to evaluate these factors. The previous studies gave precise information regarding the technological , seasonal and the weather conditions which affect the shelf life of milk in derby. Also ,some of the studies highlighted about the technological advancements used in this process of extending the shelf life of milk. Some of the authors used the UV technology and thermal process for extending the shelf life of milk by 30 days. Other studies focused upon the process of heat generation in order to extend the shelf life of milk. In this study , the researcher collected the quantitative data for the purpose of exploring the weather conditions which impact the extended life of the milk. For this purpose , the rearcher adopted the positivism approach which connects the milk quality with the weather conditions such as partly cloudy and clear climatic conditions. In addition the researcher sued the descriptive research analysis for describing the factor loadings or the extent of extraction within the variables which could affect the data. The results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The results derived through the study paved the for the implications that in ordre to increase the efficieny of the
Temperaturesubordinate stockpiling of most nourishments has three significant jobs to take into consideration relieving/aging of food sources that contain included dynamic starter societies and catalysts, to forestall quality deformities, and to control microbial development . Decrease in dampness substance of milk with increment away period was seen in all the examples the decrease was higher in tests which were put away at room temperature when contrasted with those refrigerated. Corrosiveness (explicitly decreased pH) causes the protein grid in the curd to agreement and crush out dampness. That procedure of compression is called syneresis. Cream milk has helpless waterholding limit (WHC) and is profoundly defenceless to syneresis.
Milk prepared which has a time series temperature during the zones among ESL line 1 and the B* = 1line and beneath the half line will have the best bacterial dependability, having B* values between 0.32furthermore, 1 and minimal cooked flavor. As a result, such milk could be named "monetarily sterile" ESL milk.An industrially sterile item is characterized as one in which no bacterial development happens under the typicalstates of capacity; for ESL milk, this is under refrigeration, ideally at ≤4 ?C. While this term is typically applied to UHT milk, it is likewise material to ESL milk handled to inactivate all spores of psychrotrophic microscopic organisms, and bundled aseptically. Business sterility suggests that not all bundles of each clump will be without microbes that could develop and cause waste. Brody proposed a target imperfection pace of ~1 in 10,000, equivalent to for UHT milk. Industrially sterile ESL milk with a normal long timeframe of realistic usability has an expanded danger of creating harshness. The local milk plasmin won't be inactivated under these warming conditions and, despite the fact that it has low action at low temperature, it isn't idle [108]. De Jong [11] indicated that ISIESL milk, an industrially sterile ESL milk that had plasmin action, didn't create harshness during capacity at 7 ?C for as long as 28 days. Be that as it may, harshness may create during longer times of capacity. Notwithstanding plasmin, leftover bacterial proteases from development of psychrotrophic microbes in the crude milk before preparing will be bound to cause proteolysis, and thus sharpness, during long capacity times (>30–40 days) than during shorter capacity times. For both plasmin and bacterial proteases, upkeep of lowslungclimatic conditions exposed chain, ideally at ≤4 ?C, is critical. Changes to higher temperatures will expand the danger of proteolysis and the improvement of sharpness. Further exploration is required to evaluate the danger of harshness advancement in "financially sterile" ESL milk, with long timeframe of realistic usability, from proteolysis by plasmin and bacterial proteases
The researcher collected the data for the period of only from the period of 1^{st} may, 2009 till 30^{th} April 2014. Apart from the fact that the data was collected for such a short period of time. In addition to this, another major limitation of the study is that the data was collected specifically for derby. Further , one major limitation of the study is that the researcher only targeted the weather condition for the analysis. However , there are many variables such as the number of public holidays, the price of milk, past sales and the advertisements, events and forecasting of the variables. Considering all these factors will help the rearcher in accurate forecasting of the variables.
ESL milk has been effectively showcased in different nations. Craven dale microfiltered milk in the UK is one genuine model, and more marked white milks with uncommon root or procedure will be propelled as ESL milks. It is likewise evident that new highcalibre and incredible tasting seasoned milks will be propelled as ESL milks. These sorts of items require longer time span of usability than conventional sanitized milk, yet might be portioned in the high finish of the chilled item bunch through painstakingly chose ESL innovation. The pattern of new dissemination channels and bundling configuration will likewise require longer timeframe of realistic usability than conventional sanitized milk, without essentially going all the approach to UHT preparing and bundling. More studies should focus upon such research in the near future.
The innovation of ESL handling and bundling will likewise develop. More delicate warmth medicines, betterstructured gear, and controls framework will take this item class further. The advancements of elective warmth medicines, for example, ohmic warming, radiofrequency or others have not been marketed to a huge degree. The equivalent is the circumstance for nonthermal strategies, for example, highpressure handling or electroporation. In spite of the fact that these strategies have not been a business accomplishment up to now for ESL milk items, we can expect components and mix of such techniques later on to deliver new items with various tactile or physical properties. In this context , the researcher must conduct the research in context of the radiofrequency of the technology sued in the model.
There will likewise be a further improvement of the filling frameworks. Improved and novel sanitization strategies for all machine surfaces and bundling material improved clean structure and delicate treatment of touchy items in the filling activity. The improvement of dynamic and savvy bundling may likewise assume a job later on for ESL milk items. As purchasers are searching for items with expanded newness and greater, the retail requires items with broadened time span of usability. So as to illuminate these two clashing requests, the business needs to detail and procedure items with these attributes, and the bundling frameworks must guarantee that the underlying quality is generally safeguarded through expanded stockpiling. This is the current and future test for ESL items.
Psychotropic microscopic organisms are the primary determinant of the timeframe of realistic usability of crude milk. A few compelling systems have been talked about for control of these life forms. The course picked will at last rely upon the accessibility of proper hardware at a given site. Interestingly, sanitization slaughters most decay living beings except for spore framing microscopic organisms. Control of such living beings is troublesome and no straightforward procedure has however rose, however microfiltration offers extensive guarantee.
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