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Titleoptimization of coagulation and syneresis processes in cheesemaking using a light backscatter
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Table of Contents
                            OPTIMIZATION OF COAGULATION AND SYNERESIS PROCESSES IN CHEESEMAKING USING A LIGHT BACKSCATTER SENSOR TECHNOLOGY
	Recommended Citation
Acknowledgments
List of Tables
List of Figures
List of Abbreviations
Chapter 1 : Introduction
Chapter 2 : Literature Review
	2.1.  Cheese manufacture
		2.1.1.  Milk pre-treatment
		2.1.2.  Coagulation
		2.1.3.  Syneresis
		2.1.4.  Pressing
	2.2.  Factors affecting coagulation and syneresis
		2.2.1.  Factors affecting coagulation
		2.2.2.  Factors affecting syneresis
	2.3.  Syneresis control and cheese quality parameters
	2.4.  Application of optical sensor technologies in cheesemaking automation
Chapter 3 : Materials and Methods
	3.1.  Experimental design
	3.2.  Milk preparation and compositional analysis
		3.2.1.  Skim milk powder analysis
		3.2.2.  Cream analysis
		3.2.3.  Milk reconstitution
	3.3.  Test procedure
		3.3.1.  The large field of view sensor
		3.3.2.  Milk coagulation
		3.3.3.  Cutting time selection and gel cutting procedure
		3.3.4.  Curd and whey sampling procedure
		3.3.5.  Compositional analysis of curd and whey
		3.3.6.  Pressure procedure for curd moisture and cheese yield
		3.3.7.  Curd and whey measurement at end of syneresis
	3.4.  Statistical analysis
		3.4.1.  Curd moisture
		3.4.2.  Fat
		3.4.3.  Curd yield
		3.4.4.  Cheese yield
Chapter 4 : Results and Discussion
	4.1.  The LFV light backscatter response
	4.2.  The effect of experimental factors on optical and chemical dependent variables.
		4.2.1.  The parameter tmax for LFV light backscatter
		4.2.2.  Curd moisture
		4.2.3.  Fat
		4.2.4.  Curd yield
		4.2.5.  Cheese yield
	4.3.  Prediction of curd moisture
		4.3.1.  Reflectance ratio equation
		4.3.2.  Curd moisture (dry basis) equation.
		4.3.3.  Curd moisture prediction equation
		4.3.4.  Prediction equation
Chapter 5 : Conclusions
APPENDICES
	Appendix A: Mat Lab Program
REFERENCES
Vita
                        
Document Text Contents
Page 1

University of Kentucky University of Kentucky

UKnowledge UKnowledge

University of Kentucky Master's Theses Graduate School

2011

OPTIMIZATION OF COAGULATION AND SYNERESIS PROCESSES OPTIMIZATION OF COAGULATION AND SYNERESIS PROCESSES

IN CHEESEMAKING USING A LIGHT BACKSCATTER SENSOR IN CHEESEMAKING USING A LIGHT BACKSCATTER SENSOR

TECHNOLOGY TECHNOLOGY

Tatiana Gravena Ferreira
University of Kentucky, [email protected]

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Recommended Citation Recommended Citation
Ferreira, Tatiana Gravena, "OPTIMIZATION OF COAGULATION AND SYNERESIS PROCESSES IN
CHEESEMAKING USING A LIGHT BACKSCATTER SENSOR TECHNOLOGY" (2011). University of Kentucky
Master's Theses. 125.
https://uknowledge.uky.edu/gradschool_theses/125

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Page 2

ABSTRACT OF THESIS




OPTIMIZATION OF COAGULATION AND SYNERESIS PROCESSES IN
CHEESEMAKING USING A LIGHT BACKSCATTER SENSOR TECHNOLOGY



Curd syneresis, a critical step in cheesemaking, directly influences the
quality of cheese. The syneresis process is empirically controlled in cheese
manufacturing plants. A sensor technology for this step would improve process
control and enhance cheese quality. A light backscatter sensor with a Large Field
of View (LFV) was tested using a central composite design over a broad range of
cheese process conditions including milk pH, calcium chloride addition level,
milk fat to protein ratio, temperature, and a cutting time factor ( ). The research
objectives were to determine if the LFV sensor could monitor coagulation and
syneresis steps and provide information for predicting pressed curd moisture.
Another objective was to optimize cheese yield and quality. The LFV sensor was
found to monitor coagulation and syneresis and provide light backscatter
information for predicting curd moisture content. A model for relating final curd
moisture content with light backscatter response was developed and tested.
Models for predicting whey fat losses, pressed curd moisture, and cheese yield
were successfully developed (R2>0.75) using the test factors as independent
variables. This was the first attempt to develop a technology for controlling
pressed curd moisture using a sensor to monitor the syneresis step.


KEYWORDS: sensor, syneresis, curd moisture control, cheese production

optimization, cheese quality.



Tatiana Gravena Ferreira

May 17, 2011

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46


Table 4.2 (continued)

Predictive Model

Source1 DF SS MS F Pr>F

pH 1 3.825052 3.825052 7.8530 0.0068

FP 1 92.65742 92.65742 190.23 <.0001

Model 2 96.48247 48.24124 99.042 <.0001

Error 62 30.19899 0.48708

(Lack of fit) 6 1.087732 0.181289 0.3487 0.9077

(Pure Error) 56 29.11126 0.519844

Total 64 126.6815
1T = temperature; β = cutting time factor; FP = fat/protein ratio; CC = calcium chloride addition

level; x denotes interaction of experimental factors.



The master model (Equation 4.1) includes all factors and their interactions.

The predictive model includes only the factors currently designated as significant

and significant interactions (αj≠0; P<0.05). When an interaction effect has been

selected but the corresponding lower-order main effects haven‘t, those were

added to the predictive model to preserve hierarchy.

Prediction profiler graphs were generated by displaying the predict

response as one variable is changed while the others are held constant at central

point (T = 32°C; β = 2.2; pH = 6.2; FP = 0.65; CC = 2mM) and for each significant

interaction a surface plot was generated by predicting response as two variables

are changed while the others are held constant at central point.



4.2.1. The parameter tmax for LFV light backscatter

The most significant time-based parameter determined from the light

backscatter profile is tmax as it is a measure of the enzymatic reaction rate

(Tabayehnejad, 2010). It is determined as the time between enzyme addition and

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47


the infection point of the sigmoidal section (coagulation step) of the light

backscatter ratio profile as shown in Figure 4.3.



Figure 4.3 - Light backscatter ratio profile (R) and its characteristic first

derivative (R‘) versus time for LFV sensor during coagulation phase (T = 32°C;

β = 2.2; pH = 6.2; FP = 0.65; CC = 2mM).

Table 4.3 shows the p-values from the analysis of variance for tmax. The

R-squared for master model and predictive model are also included in Table 4.3.

The tmax predictive model was highly significant in their fit (P < 0.001).











0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

0 10 20 30 40 50

L
ig

h
t

B
a
ck

sc
a
tt

e
r

R
a
ti

o


TIME (min)

tmax

Page 127

109


Weber, F. (1987): Curd drainage, pp. 22-36. In Eck, A. (Ed.): Cheesemaking: Science
and Technology. Lavoisier,NY.


van den Bijgaart , H.J.C.M. (1988): Syneresis of rennet-induced milk gels as

influenced by cheesemaking parameters. PhD Thesis Wageningen
Agricultural University, Wageningen, the Netherlands.


Zviedrans, Z.; Graham, E.R.B. (1981): An improved tracer method for measuring

the syneresis of rennet curd. The Australian Journal of Diary Technology, 117-
120.

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110


V ITA

TATIANA GRAVENA FERREIRA

BIRTH DATE: June 9, 1983

BIRTH PLACE: Taubaté, SP - Brazil



EDUCATION

Bachelor of Food Engineering March 2003 to December 2007

Federal University of Viçosa, Minas Gerais, Brazil



PROFESSIONAL EXPERIENCE

Graduate Student Research Assistant, January 2008 to present - Biosystems and

Agricultural Engineering, University of Kentucky, Lexington, KY

Engineering Intern, Sep 2005 - WOW, Caçapava, SP - Brazil

Undergraduate Research, June 2004 to June 2005



CERTIFICATIONS

Good Manufacturing Practice (GMP) and Hazard Analysis and Critical Control

Points (HACCP).

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