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Shelf Life of Milk in Derby Assignment Sample

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Shelf Life of Milk in Derby Assignment Sample

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 short-shelf 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 long-term 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 post-acidification 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 micro-organisms 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 21-45 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 know-hows including the thermal processing which  has developed conditions through pasteurizations. Another is the non-thermal 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 70-80 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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Aim of the Study

The aim of the current study is to explore the weather conditions which affect the shelf life of milk quality in derby.

Chapter 2 : Literature Review

The concept of extended shelf life has emerged to be significantly important in the dynamics of the diary market. The shelf-life 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 pro-longed 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 self-life 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 non-thermal 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 ultra-heat 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 fat-balanced 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.

Advantages of Extended shelf -life quality 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 high-quality 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 α-lactalbumin and β-lactoglobulin 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).

Monitoring the shelf life of milk

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. Temperature-subordinate 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 water-holding limit (WHC) and is profoundly vulnerable to syneresis(Balog 2016; Deeth 2017; Raju et al. 2015).

Chapter 3 :Research Methodology

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.

Research Paradigm

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.

Research Design

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 self-life of milk in derby , the researcher used the descriptive research design in order to have  a clear idea with context to the  variables.

Data Collection Procedure

The researcher used the milk quantity data from the period of 1st may, 2009 till 30th 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 non-probability 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.

Data Analysis Procedure

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 non-linear 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 short-shelf 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.

Ø ARIMA  modelling

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 Box-Jenkins 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.

Ø Principal Component Analysis

The principal component analysis is a commonly accepted multivariate analytic statistical techniques that can be applied to the quantitative data analysis. It is multi-variate analysis which develops a way of extracting the structure from the variance-covariance 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.

Data Validity and Reliability

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


Chapter 4 : Data Analysis 

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

Non-Seasonal 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

User-Missing

0

System-Missing

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

Non-Seasonal 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

306a

User-Missing

0

System-Missing

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 non-correlated PCA factors.

 

Correlations

 

Milk Quantity

W1

Pearson Correlation

.724**

Sig. (2-tailed)

.000

N

1782

W2

Pearson Correlation

.695**

Sig. (2-tailed)

.000

N

1782

W3

Pearson Correlation

.695**

Sig. (2-tailed)

.000

N

1782

W4

Pearson Correlation

.690**

Sig. (2-tailed)

.000

N

1782

W5

Pearson Correlation

.687**

Sig. (2-tailed)

.000

N

1782

W6

Pearson Correlation

.668**

Sig. (2-tailed)

.000

N

1782

Cloud Cover

Pearson Correlation

-.168**

Sig. (2-tailed)

.000

N

1782

Partly Cloudy

Pearson Correlation

-.115**

Sig. (2-tailed)

.000

N

1782

Clear

Pearson Correlation

.190**

Sig. (2-tailed)

.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

31-JUL-2020 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

User-defined 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 Cross-Correlation Plots

MXCROSS = 7

Maximum Number of New Variables Generated Per Procedure

MXNEWVAR = 60

Maximum Number of New Cases Per Procedure

MXPREDICT = 1000

Treatment of User-Missing 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

Non-Seasonal 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

306a

User-Missing

0

System-Missing

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

Box-Lung 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 chi-square 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 auto-correlation 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 R-squared

.330

.

.330

.330

.330

.330

.330

.330

.330

.330

.330

 

R-squared

.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

Lung-Box Q(18)

Number of Outliers

Stationary R-squared

Statistics

DF

Sig.

MilkQuantity-Model_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

Approx  Chi-Square

Df

Sig

73.582

150

0.000

 

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.

Principal component analysis

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 two-factor solution will be adequate for the further analysis within the variables. 

Component MariaDB

 

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 MariaDB

 

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.

Chapter 5 : Conclusion

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.

Findings of 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

Temperature-subordinate 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 water-holding 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 ISI-ESL 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 low-slungclimatic 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

Limitations of the study

The researcher collected the data for the period of only from the period of 1st may, 2009 till 30th 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.

Scope for future research

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 high-calibre 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, better-structured 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, high-pressure 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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