1Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
JOURNAL VITAE
School of Pharmaceutical and
Food Sciences
ISSN 0121-4004 | ISSNe 2145-2660
University of Antioquia
Medellin, Colombia
Filliations
1Institute Science and Technology
Al-Kamal, Jakarta, Indonesia
2Universitas Negeri Yogyakarta,
Yogyakarta, Indonesia
3SMA Negeri 3 Tarakan, kota Tarakan
4SMK Negeri 3 Madiun, Madiun City,
East Java, Indonesia
*Corresponding
Budianto
budianto_delta@yahoo.com
Received: 14 April 2022
Accepted: 14 October 2022
Published: 28 February 2023
Effect of the chemical composition of fluid
foods on the rate of fouling processing
during sterilization
Efecto de la composición química de los alimentos líquidos
sobre la tasa de procesamiento de la suciedad durante la
esterilización
Budianto 1
, Zefki Okta Feri 2, Anik Suparmi 3, Muh Jaenal Arifin 4
ABSTRACT
Background: This research was motivated by the determination of the sanitation schedule
in the heat exchanger area for some products (milk, avocado juice, and orange juice), as well
as the inconsistency of the results of previous studies related to the chemical composition
of the fouling layer. Objectives: a) to test the effect of raw material composition on the
chemical composition of the fouling layer. b) to test microbial growth’s effect on fouling’s
chemical composition (protein). Methods: mathematical derivation of the formation process
of Resistant Dirt Factor (Rd) in the form of an Equation; ANOVA was used to test the effect
of the dependent variable (protein) and predictor (microbial). Results: a) The composition of
the raw material strongly influences the chemical composition of the fouling layer; b) There
is a strong effect between microbial growth and protein content as a fouling composition
(p<0.05). Conclusion: A strong influence between microbial growth and the composition of
the fouling layer (protein) can close the research gap related to the inconsistency of previous
research results (fouling layer composition), so there is no prolonged debate.
Keywords: Fouling, Resistant Dirt Factor (Rd), Heat Exchanger, Heat transfer (Q), Dairy products
ORIGINAL RESEARCH
Published 28 February 2023
Doi: https://doi.org/10.17533/udea.vitae.v30n1a349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
JOURNAL VITAE
School of Pharmaceutical and
Food Sciences
ISSN 0121-4004 | ISSNe 2145-2660
University of Antioquia
Medellin, Colombia
Filliations
1Institute Science and Technology
Al-Kamal, Jakarta, Indonesia
2Universitas Negeri Yogyakarta,
Yogyakarta, Indonesia
3SMA Negeri 3 Tarakan, kota Tarakan
4SMK Negeri 3 Madiun, Madiun City,
East Java, Indonesia
*Corresponding
Budianto
budianto_delta@yahoo.com
Received: 14 April 2022
Accepted: 14 October 2022
Published: 28 February 2023
Effect of the chemical composition of fluid
foods on the rate of fouling processing
during sterilization
Efecto de la composición química de los alimentos líquidos
sobre la tasa de procesamiento de la suciedad durante la
esterilización
Budianto 1
, Zefki Okta Feri 2, Anik Suparmi 3, Muh Jaenal Arifin 4
ABSTRACT
Background: This research was motivated by the determination of the sanitation schedule
in the heat exchanger area for some products (milk, avocado juice, and orange juice), as well
as the inconsistency of the results of previous studies related to the chemical composition
of the fouling layer. Objectives: a) to test the effect of raw material composition on the
chemical composition of the fouling layer. b) to test microbial growth’s effect on fouling’s
chemical composition (protein). Methods: mathematical derivation of the formation process
of Resistant Dirt Factor (Rd) in the form of an Equation; ANOVA was used to test the effect
of the dependent variable (protein) and predictor (microbial). Results: a) The composition of
the raw material strongly influences the chemical composition of the fouling layer; b) There
is a strong effect between microbial growth and protein content as a fouling composition
(p<0.05). Conclusion: A strong influence between microbial growth and the composition of
the fouling layer (protein) can close the research gap related to the inconsistency of previous
research results (fouling layer composition), so there is no prolonged debate.
Keywords: Fouling, Resistant Dirt Factor (Rd), Heat Exchanger, Heat transfer (Q), Dairy products
ORIGINAL RESEARCH
Published 28 February 2023
Doi: https://doi.org/10.17533/udea.vitae.v30n1a349368
2Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
INTRODUCTION
Sterilization is an important process in beverage
products. This process has a significant role in
maintaining the quality of beverage products. The
sterilization area involving the heat exchanger must
be protected from dirt because it causes a decrease
in heat transfer during sterilization. It takes extra
effort to control dirt in the Heat Exchanger (HE)
area. The high cost of sterilization control is the
main focus of this research. Medium-scale beverage
companies in Indonesia still rely on the same process
and installation for their products. Therefore, the
sanitation schedule in the HE area becomes an
obstacle because of differences in composition that
affect the rate of dirt formation. This research is
empirical by comparing milk drinks, avocado juice,
and orange juice to the Resistant Dirt Factor (Rd),
the composition of impurities, and the number of
contaminant microorganisms. This study also finds
out the effect of microbial growth on protein content
as an impurity composition.
Previous studies have discussed Rd in the HE area
of processing beverage products, especially milk.
All the researchers agree that Rd is affected by
heat transfer (Q), overall heat transfer coefficient
when clean (Uc), and overall heat transfer coefficient
after the operation (Ud). Not yet a well-established
concept related to (i) which component settles
first; (ii) the composition of the impurity layer;
(iii) the emergence of microorganisms in the
fouling layer; (iv) how protein affects the number
of microorganisms in the fouling layer. The long
debate until now has caused research to only focus
on solutions to problems that occur.
Likewise, this study focuses on solutions by making
comparisons: (a) the rate of formation of Rd to
determine the sanitation time in the HE area, (b) the
composition of the impurity layer, and (c) the effect
of the number of microorganisms in the fouling layer
on protein. Efforts to fill the research gap can be
seen in b and c.
The components of the raw material strongly
influence the composition of the fouling layer. One
example is dairy drinks. In the sterilization process
in HE at a temperature >100 o C, different results
were found, namely: (i) protein content was greater
than mineral content and fat content was found
to be the least (protein > mineral > fat) [1–6] and
(ii) Mineral> protein> fat [7, 8]. In the sterilization
process in HE at a temperature of 100-140 o C also
found different results, namely: (i) protein > mineral
> fat [4, 9–11] and (ii) Mineral> protein> fat [9, 12, 13].
Research conducted by Skudder [9] gave different
results with the same treatment. To prove that Rd
is affected by the components of raw materials, this
study wants to prove it with samples of drinks from
different ingredients, namely milk, avocado juice,
and orange juice.
The fouling layer is formed from the components
of raw materials [10,11,14–17] and the activity of
microorganisms [17–20] due to the ineffectiveness
of sterilization in HE. These two topics still dominate
the research. There has been no research on the
effect of the number of microorganisms on the
protein content in the fouling layer. This study
wanted to see how decreasing protein levels affect
the number of microorganisms in the HE area.
This research is empirical because the data was
gathered from beverage companies with variants
of milk products, avocado juice, and orange juice.
Comparing the three different components can help
in understanding whether the composition of the
raw material affects: (a) the rate of formation of Rd,
RESUMEN
Antecedentes: Esta investigación fue motivada por la determinación del cronograma de sanitización en el área del intercambiador
de calor para diferentes productos (leche, jugo de aguacate y jugo de naranja), así como la inconsistencia de los resultados
de estudios previos relacionados con la composición química de la capa de suciedad. Objetivos: a) probar el efecto de la
composición de la materia prima sobre la composición química de la capa de suciedad. b) probar el efecto del crecimiento
microbiano en la composición química de la capa de suciedad (proteína). Método: etapas del proceso de formación del Factor
de Suciedad Resistente (Rd) en forma de una ecuación; Se usó ANOVA para probar el efecto de la variable dependiente (proteína)
y el predictor (microbiano). Resultados: a) La composición química de la capa de incrustación está fuertemente influenciada
por la composición de la materia prima; b) Existe un fuerte efecto entre el crecimiento microbiano sobre el contenido de
proteína como composición de ensuciamiento (p<0.05). Conclusión: Una fuerte influencia entre el crecimiento microbiano y la
composición de la capa de incrustación (proteína) puede cerrar la brecha de investigación relacionada con la inconsistencia de
los resultados de investigaciones anteriores (composición de la capa de incrustación) para que no haya un debate prolongado.
Palabra clave: Ensuciamiento, Factor de suciedad resistente (Rd), Intercambiador de calor, Transferencia de calor (Q),
Productos lácteos
INTRODUCTION
Sterilization is an important process in beverage
products. This process has a significant role in
maintaining the quality of beverage products. The
sterilization area involving the heat exchanger must
be protected from dirt because it causes a decrease
in heat transfer during sterilization. It takes extra
effort to control dirt in the Heat Exchanger (HE)
area. The high cost of sterilization control is the
main focus of this research. Medium-scale beverage
companies in Indonesia still rely on the same process
and installation for their products. Therefore, the
sanitation schedule in the HE area becomes an
obstacle because of differences in composition that
affect the rate of dirt formation. This research is
empirical by comparing milk drinks, avocado juice,
and orange juice to the Resistant Dirt Factor (Rd),
the composition of impurities, and the number of
contaminant microorganisms. This study also finds
out the effect of microbial growth on protein content
as an impurity composition.
Previous studies have discussed Rd in the HE area
of processing beverage products, especially milk.
All the researchers agree that Rd is affected by
heat transfer (Q), overall heat transfer coefficient
when clean (Uc), and overall heat transfer coefficient
after the operation (Ud). Not yet a well-established
concept related to (i) which component settles
first; (ii) the composition of the impurity layer;
(iii) the emergence of microorganisms in the
fouling layer; (iv) how protein affects the number
of microorganisms in the fouling layer. The long
debate until now has caused research to only focus
on solutions to problems that occur.
Likewise, this study focuses on solutions by making
comparisons: (a) the rate of formation of Rd to
determine the sanitation time in the HE area, (b) the
composition of the impurity layer, and (c) the effect
of the number of microorganisms in the fouling layer
on protein. Efforts to fill the research gap can be
seen in b and c.
The components of the raw material strongly
influence the composition of the fouling layer. One
example is dairy drinks. In the sterilization process
in HE at a temperature >100 o C, different results
were found, namely: (i) protein content was greater
than mineral content and fat content was found
to be the least (protein > mineral > fat) [1–6] and
(ii) Mineral> protein> fat [7, 8]. In the sterilization
process in HE at a temperature of 100-140 o C also
found different results, namely: (i) protein > mineral
> fat [4, 9–11] and (ii) Mineral> protein> fat [9, 12, 13].
Research conducted by Skudder [9] gave different
results with the same treatment. To prove that Rd
is affected by the components of raw materials, this
study wants to prove it with samples of drinks from
different ingredients, namely milk, avocado juice,
and orange juice.
The fouling layer is formed from the components
of raw materials [10,11,14–17] and the activity of
microorganisms [17–20] due to the ineffectiveness
of sterilization in HE. These two topics still dominate
the research. There has been no research on the
effect of the number of microorganisms on the
protein content in the fouling layer. This study
wanted to see how decreasing protein levels affect
the number of microorganisms in the HE area.
This research is empirical because the data was
gathered from beverage companies with variants
of milk products, avocado juice, and orange juice.
Comparing the three different components can help
in understanding whether the composition of the
raw material affects: (a) the rate of formation of Rd,
RESUMEN
Antecedentes: Esta investigación fue motivada por la determinación del cronograma de sanitización en el área del intercambiador
de calor para diferentes productos (leche, jugo de aguacate y jugo de naranja), así como la inconsistencia de los resultados
de estudios previos relacionados con la composición química de la capa de suciedad. Objetivos: a) probar el efecto de la
composición de la materia prima sobre la composición química de la capa de suciedad. b) probar el efecto del crecimiento
microbiano en la composición química de la capa de suciedad (proteína). Método: etapas del proceso de formación del Factor
de Suciedad Resistente (Rd) en forma de una ecuación; Se usó ANOVA para probar el efecto de la variable dependiente (proteína)
y el predictor (microbiano). Resultados: a) La composición química de la capa de incrustación está fuertemente influenciada
por la composición de la materia prima; b) Existe un fuerte efecto entre el crecimiento microbiano sobre el contenido de
proteína como composición de ensuciamiento (p<0.05). Conclusión: Una fuerte influencia entre el crecimiento microbiano y la
composición de la capa de incrustación (proteína) puede cerrar la brecha de investigación relacionada con la inconsistencia de
los resultados de investigaciones anteriores (composición de la capa de incrustación) para que no haya un debate prolongado.
Palabra clave: Ensuciamiento, Factor de suciedad resistente (Rd), Intercambiador de calor, Transferencia de calor (Q),
Productos lácteos
3Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
(b) the composition of the fouling layer, and (c)
the growth of microorganisms. There is not much
information regarding the Rd process for avocado
juice and orange juice, so that this study can provide
information regarding the two samples.
MATERIAL AND METHOD
The raw materials used in this study were pure
milk drinks, avocado juice, and orange juice. The
viscosity of the three samples was established at
the same level (1.75cP). The chemical composition
of the three samples was analyzed for fat, protein,
and mineral content based on standard BPOM RI
analysis procedures [21].
Table 1. Samples and chemical composition
No Sample Nutrition
Fat (w/w %) Protein (w/w%) Mineral (w/w%)
1 Milk 2.17 3.7 3.01
2 Avocado
Juice 2.08 0.98 8.08
3 Orange Juice 0.16 0.24 15.23
This research was conducted in a beverage
company, in Jakarta (Indonesia). The tools used for
processing are shown in table 2.
Table 2. The parameters of the tool and the Heat Exchanger are in the sterilization process
No Sensor Description Tool Design
1 TT-44 Sensor inlet temperature of the product Inner Pipe Anullus
2 TT-42 Temperature sensor exit product Pipe Inner Diameter 0.115 ft Pipe Inner Diameter 0,1725 ft
3 TT-08 Hot water inlet temperature sensor Pipe Thickness 0,0256 ft Pipe Thickness 0,0354 ft
4 TT-09 Temperature sensor comes out of hot water Flow Area 0,864 inch2 Flow Area 0,986 inch 2
5 TT-44 Steam inlet pressure on the PHE Fluid Type Milk, juice Fluid Type hot water
Heat exchanger double pipe (GLAQ421)
Type 80% efficiency 95% efficiency
Shell & Tube Rd: 0.070-0.078
Hr.Ft2.F/Btu
Rd: 0.027-0.035
Hr.Ft2.F/Btu
Working Procedures
a. The three samples received the same treatment,
namely the incoming feed around 8,000 L/h. The
flow chart is shown in figure 1.
b. Sample observations were carried out in 5
batches (Each processing batch is completed
in 110 minutes). Observations included the rate
of formation of Rd (see table 3), the number of
microorganisms, the composition of the fouling
layer in each batch, and the effect of protein
content on the growth of microorganisms in
the HE pipeline (can be removed to facilitate
analysis).
c. Microorganism growth analysis (triple analysis)
per 10 minutes was taken from samples that had
passed HE [17]. Quantitative microorganism tests
included Total Plate Count (TPC), yeast & mold.
The procedure for determining TPC, yeast, &
mold refers to BPOM RI [21].
d. Meanwhile, samples were taken in the HE pipeline
to test the effect of protein content on microbial
growth, which was intentionally removed for
microbial observation in the fouling layer after
the process was completed.
e. Quantitative analysis of microorganism growth
tests microbial growth’s effect on protein
content. A qualitative bacterial test aims to see
the type of bacteria in the fouling layer. In both
analyses, samples were taken in the HE pipeline
(HE pipe was intentionally removed) to observe
microbes in the fouling layer after the process
was completed. Qualitative microorganism test
refers to Setyaningsih et al. [27] and chemical
analysis using atomic absorption spectroscopy
(AAS) which refers to BPOM RI [21].
f. ANOVA test was used to see the effect of
microbial growth on protein content
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
(b) the composition of the fouling layer, and (c)
the growth of microorganisms. There is not much
information regarding the Rd process for avocado
juice and orange juice, so that this study can provide
information regarding the two samples.
MATERIAL AND METHOD
The raw materials used in this study were pure
milk drinks, avocado juice, and orange juice. The
viscosity of the three samples was established at
the same level (1.75cP). The chemical composition
of the three samples was analyzed for fat, protein,
and mineral content based on standard BPOM RI
analysis procedures [21].
Table 1. Samples and chemical composition
No Sample Nutrition
Fat (w/w %) Protein (w/w%) Mineral (w/w%)
1 Milk 2.17 3.7 3.01
2 Avocado
Juice 2.08 0.98 8.08
3 Orange Juice 0.16 0.24 15.23
This research was conducted in a beverage
company, in Jakarta (Indonesia). The tools used for
processing are shown in table 2.
Table 2. The parameters of the tool and the Heat Exchanger are in the sterilization process
No Sensor Description Tool Design
1 TT-44 Sensor inlet temperature of the product Inner Pipe Anullus
2 TT-42 Temperature sensor exit product Pipe Inner Diameter 0.115 ft Pipe Inner Diameter 0,1725 ft
3 TT-08 Hot water inlet temperature sensor Pipe Thickness 0,0256 ft Pipe Thickness 0,0354 ft
4 TT-09 Temperature sensor comes out of hot water Flow Area 0,864 inch2 Flow Area 0,986 inch 2
5 TT-44 Steam inlet pressure on the PHE Fluid Type Milk, juice Fluid Type hot water
Heat exchanger double pipe (GLAQ421)
Type 80% efficiency 95% efficiency
Shell & Tube Rd: 0.070-0.078
Hr.Ft2.F/Btu
Rd: 0.027-0.035
Hr.Ft2.F/Btu
Working Procedures
a. The three samples received the same treatment,
namely the incoming feed around 8,000 L/h. The
flow chart is shown in figure 1.
b. Sample observations were carried out in 5
batches (Each processing batch is completed
in 110 minutes). Observations included the rate
of formation of Rd (see table 3), the number of
microorganisms, the composition of the fouling
layer in each batch, and the effect of protein
content on the growth of microorganisms in
the HE pipeline (can be removed to facilitate
analysis).
c. Microorganism growth analysis (triple analysis)
per 10 minutes was taken from samples that had
passed HE [17]. Quantitative microorganism tests
included Total Plate Count (TPC), yeast & mold.
The procedure for determining TPC, yeast, &
mold refers to BPOM RI [21].
d. Meanwhile, samples were taken in the HE pipeline
to test the effect of protein content on microbial
growth, which was intentionally removed for
microbial observation in the fouling layer after
the process was completed.
e. Quantitative analysis of microorganism growth
tests microbial growth’s effect on protein
content. A qualitative bacterial test aims to see
the type of bacteria in the fouling layer. In both
analyses, samples were taken in the HE pipeline
(HE pipe was intentionally removed) to observe
microbes in the fouling layer after the process
was completed. Qualitative microorganism test
refers to Setyaningsih et al. [27] and chemical
analysis using atomic absorption spectroscopy
(AAS) which refers to BPOM RI [21].
f. ANOVA test was used to see the effect of
microbial growth on protein content
4Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
Figure 1. Production process flow chart. The sample goes through a homogenizer, expander, sterilizer (in the HE area), and packing.
Test of Resistant Dirt Factor (Rd) in sterilization process.
Table 3 is the stage of the Rd rate calculation process for the three research samples. The stages are
sequential from eq. 1 to eq. 13.
Table 3. Equations to predict the value of Resistant Dirt Factor (Rd) [25, 26]
Step Parameter Equation Equation
1 Heat Balance (Q) Q = W.Cp (T1 - T2) Eq.1
2 Log mean temperature different (LMTD) ( ) ( )
( ) ( )
1 2 2 1
( 1 2 / 2 1
T t T t
LMTD ln T t T t
− − −
= − − Eq.2
3 Temperature calories (Tc) Tc = T2 + Fc(T1 – T2) Eq.3
4 Stream Area (α) 144
ID x C x B
s x PT
α = Eq.4
5 Mass Flow Rate (G) w
G a
= Eg.5
6 Reynold (Re) DexG
Re μ
= Eq.6
7 Prandtl (Pr) cpx μ
Pr k
= Eq.7
8 Heat Transfer Coefficient ( h
ϕ ) ( )1/3ho k
JH x x Pr
De
ϕ = Eq.8
9 Temperature on the tube wall (tw) ( )
/ho s
tw tc x Tc tc
hio ho
t s
ϕ
ϕ ϕ
= −
+ Eq.9
Figure 1. Production process flow chart. The sample goes through a homogenizer, expander, sterilizer (in the HE area), and packing.
Test of Resistant Dirt Factor (Rd) in sterilization process.
Table 3 is the stage of the Rd rate calculation process for the three research samples. The stages are
sequential from eq. 1 to eq. 13.
Table 3. Equations to predict the value of Resistant Dirt Factor (Rd) [25, 26]
Step Parameter Equation Equation
1 Heat Balance (Q) Q = W.Cp (T1 - T2) Eq.1
2 Log mean temperature different (LMTD) ( ) ( )
( ) ( )
1 2 2 1
( 1 2 / 2 1
T t T t
LMTD ln T t T t
− − −
= − − Eq.2
3 Temperature calories (Tc) Tc = T2 + Fc(T1 – T2) Eq.3
4 Stream Area (α) 144
ID x C x B
s x PT
α = Eq.4
5 Mass Flow Rate (G) w
G a
= Eg.5
6 Reynold (Re) DexG
Re μ
= Eq.6
7 Prandtl (Pr) cpx μ
Pr k
= Eq.7
8 Heat Transfer Coefficient ( h
ϕ ) ( )1/3ho k
JH x x Pr
De
ϕ = Eq.8
9 Temperature on the tube wall (tw) ( )
/ho s
tw tc x Tc tc
hio ho
t s
ϕ
ϕ ϕ
= −
+ Eq.9
5Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
RESULTS
Table 4 shows that there is no significant difference
in the initial heat transfer rate (Qin), the shell heat
transfer coefficient (h0), the tube heat transfer
coefficient (h1), and U C . These data indicate that
the same treatment occurred for the three samples.
After processing for 110 minutes for the five batches,
different results were obtained for Log mean
temperature difference (LMTD). The Ud value is the
average batch value at the beginning of the process
(0 minutes) and the end of the process (110 minutes).
The largest Rd value occurred in milk samples, and
the smallest was in orange juice. The overall heat
transfer coefficient after the operation (Ud) for the
three samples can be seen in figure 2.
Step Parameter Equation Equation
10 Viscosity Ratio (φ) ( )0,14
/μ μw
ϕ = Eq.10
11 Overall heat transfer coefficient when clean (Uc) hioxho
Uc hio ho
= + Eq.11
12 Overall heat transfer coefficient after operation (Ud) Q
Ud Ax T
= ∆ Eq.12
13 Fouling factor (Rd) Uc Ud
Rd Uc xUd
−
= Eq.13
Table 4. Calculation results
Parameter Unit Orange Avocado Milk
( )1/3
0 k
h JH x x Pr
De
ϕ= Btu/(hr) (ft2 )(F) 3628.82 3633.98 3633.63
( )1/3
. kt
hi t JHt x x Prt
ID
ϕ= Btu/(hr) (ft2 )(F) 385.85 384.04 394.56
( ) ( )
( ) ( )
1 2 2 1
( 1 2 / 2 1ln T t T t
− − −
= − −
T t T t
LMTD o
F 2.11 5.23 10.8
Qin= W.Cp (T1 - T2) Btu/hr 190,858.01 190,798.34 183,871.61
hioxho
Uc hio ho
= + Btu/(hr) (ft2 )(F) 356.05 357.01 356.06
Q
Ud Ax T
= ∆ Btu/(hr) (ft2 )(F) 8.59 - 47.089 21.47 - 117.72 42.95- 250.45
Uc Ud
Rd Uc xUd
−
= Hr. Ft2 . F / Btu 0.0003 - 0.0041 0.0007 - 0.0102 0.0014 – 0.0205
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
RESULTS
Table 4 shows that there is no significant difference
in the initial heat transfer rate (Qin), the shell heat
transfer coefficient (h0), the tube heat transfer
coefficient (h1), and U C . These data indicate that
the same treatment occurred for the three samples.
After processing for 110 minutes for the five batches,
different results were obtained for Log mean
temperature difference (LMTD). The Ud value is the
average batch value at the beginning of the process
(0 minutes) and the end of the process (110 minutes).
The largest Rd value occurred in milk samples, and
the smallest was in orange juice. The overall heat
transfer coefficient after the operation (Ud) for the
three samples can be seen in figure 2.
Step Parameter Equation Equation
10 Viscosity Ratio (φ) ( )0,14
/μ μw
ϕ = Eq.10
11 Overall heat transfer coefficient when clean (Uc) hioxho
Uc hio ho
= + Eq.11
12 Overall heat transfer coefficient after operation (Ud) Q
Ud Ax T
= ∆ Eq.12
13 Fouling factor (Rd) Uc Ud
Rd Uc xUd
−
= Eq.13
Table 4. Calculation results
Parameter Unit Orange Avocado Milk
( )1/3
0 k
h JH x x Pr
De
ϕ= Btu/(hr) (ft2 )(F) 3628.82 3633.98 3633.63
( )1/3
. kt
hi t JHt x x Prt
ID
ϕ= Btu/(hr) (ft2 )(F) 385.85 384.04 394.56
( ) ( )
( ) ( )
1 2 2 1
( 1 2 / 2 1ln T t T t
− − −
= − −
T t T t
LMTD o
F 2.11 5.23 10.8
Qin= W.Cp (T1 - T2) Btu/hr 190,858.01 190,798.34 183,871.61
hioxho
Uc hio ho
= + Btu/(hr) (ft2 )(F) 356.05 357.01 356.06
Q
Ud Ax T
= ∆ Btu/(hr) (ft2 )(F) 8.59 - 47.089 21.47 - 117.72 42.95- 250.45
Uc Ud
Rd Uc xUd
−
= Hr. Ft2 . F / Btu 0.0003 - 0.0041 0.0007 - 0.0102 0.0014 – 0.0205
6Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
A
B
y = -1,6911x + 204,7
R² = 0,7764
0,0
20,0
40,0
60,0
80,0
100,0
120,0
140,0
160,0
180,0
200,0
220,0
240,0
260,0
280,0
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Milk
batch 1
batch 2
batch 3
batch 4
batch 5
y = -0,9242x + 109,2
R² = 0,8203
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Avocado
batch 1
batch 2
batch 3
batch 4
batch 5
C
y = -0,3522x + 42,161
R² = 0,8093
0,0
10,0
20,0
30,0
40,0
50,0
60,0
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Orange
batch 1
batch 2
batch 3
batch 4
batch 5
Figure 2. Overall heat transfer coefficient after operation (Ud)
Ud in milk products (Fig.2A), avocado (Fig.2B), and
orange (Fig.2C) at the beginning of the process at
1-10 minutes still shows a regular graph. In minute
15, there was an increase in Ud for milk and avocado.
The chart experienced a steady decline despite an
increase in minute 85. The decline continued until
minute 110.
In figure 2, the three samples experienced the same
condition, namely a regular decrease with the length
of the process. The highest Ud range is still shown
by milk, and the lowest is orange juice. Ud indicates
the magnitude of the heat transfer rate (BTU/hour)
per cross-sectional area (ft 2 ) and the temperature
difference /∆T (o F). The larger Ud value will reduce
Rd in the HE area.
A
B
y = -1,6911x + 204,7
R² = 0,7764
0,0
20,0
40,0
60,0
80,0
100,0
120,0
140,0
160,0
180,0
200,0
220,0
240,0
260,0
280,0
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Milk
batch 1
batch 2
batch 3
batch 4
batch 5
y = -0,9242x + 109,2
R² = 0,8203
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Avocado
batch 1
batch 2
batch 3
batch 4
batch 5
C
y = -0,3522x + 42,161
R² = 0,8093
0,0
10,0
20,0
30,0
40,0
50,0
60,0
0 10 20 30 40 50 60 70 80 90 100 110 120
Ud
Btu/ hr.ft 2 .F
Time (minute)
Orange
batch 1
batch 2
batch 3
batch 4
batch 5
Figure 2. Overall heat transfer coefficient after operation (Ud)
Ud in milk products (Fig.2A), avocado (Fig.2B), and
orange (Fig.2C) at the beginning of the process at
1-10 minutes still shows a regular graph. In minute
15, there was an increase in Ud for milk and avocado.
The chart experienced a steady decline despite an
increase in minute 85. The decline continued until
minute 110.
In figure 2, the three samples experienced the same
condition, namely a regular decrease with the length
of the process. The highest Ud range is still shown
by milk, and the lowest is orange juice. Ud indicates
the magnitude of the heat transfer rate (BTU/hour)
per cross-sectional area (ft 2 ) and the temperature
difference /∆T (o F). The larger Ud value will reduce
Rd in the HE area.
7Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
Referring to the Ud results, the following is the Rd
value of each sample per minute. Figure 3 shows the
same pattern; namely, there is a regular increase in
each batch for milk, avocado, and orange products.
The Rd of milk (Fig.3A), avocado (Fig.3B), and
Orange (Fig.3C) is inversely proportional to the value
of Ud in each sample. There was the same decrease
in minutes 80-90 for all three samples.
In figure 3, it can be seen that there is a significant
difference in the value of Rd for the three samples.
Dairy products have the highest rate of 1.7 x10 -4 hr.
Ft 2 . F / Btu in 1 minute. Avocado in the range 8.6
x10 -5 hr. Ft 2 . F / Btu, while orange has the lowest rate
of 3.4 x10 -5 hr. Ft 2 . F / Btu.
A
B
C
y = 0,000x - 0,000
R² = 0,933
0,0000
0,0050
0,0100
0,0150
0,0200
0,0250
0,0300
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr.Ft 2 .F/Btu
Time (minute)
Milk
batch 1
batch 2
batch 3
batch 4
batch 5
y = 8E-05x - 0,0001
R² = 0,9332
0,0000
0,0020
0,0040
0,0060
0,0080
0,0100
0,0120
0,0140
0,0160
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr. Ft 2 .F/Btu
Time (minute)
Avocado
batch 1
batch 2
batch 3
batch 4
batch 5
y = 3E-05x - 9E-05
R² = 0,9378
0,0000
0,0010
0,0020
0,0030
0,0040
0,0050
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr. Ft 2 . F / Btu
Time (Minute)
Orange
batch 1
batch 2
batch 3
batch 4
batch 5
Figure 3. Rd value per time for the three samples
Figure 4, shows that the Rd value is directly
proportional to the number of microorganisms in
the sample that comes out of the HE area. Growth
occurred at minutes 30-40 for milk products and
avocado juice, while growth occurred at minutes
20-30 for orange juice. Bacterial growth (TPC)
continued to increase while yeast & mold increased
until minute 80 and sloped at minutes 90-110 for
milk and avocado juice. In orange juice, bacterial
growth continued to increase until minute 110, and
yeast & mold growth increased so that the number
exceeded the number of bacteria (TPC).
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
Referring to the Ud results, the following is the Rd
value of each sample per minute. Figure 3 shows the
same pattern; namely, there is a regular increase in
each batch for milk, avocado, and orange products.
The Rd of milk (Fig.3A), avocado (Fig.3B), and
Orange (Fig.3C) is inversely proportional to the value
of Ud in each sample. There was the same decrease
in minutes 80-90 for all three samples.
In figure 3, it can be seen that there is a significant
difference in the value of Rd for the three samples.
Dairy products have the highest rate of 1.7 x10 -4 hr.
Ft 2 . F / Btu in 1 minute. Avocado in the range 8.6
x10 -5 hr. Ft 2 . F / Btu, while orange has the lowest rate
of 3.4 x10 -5 hr. Ft 2 . F / Btu.
A
B
C
y = 0,000x - 0,000
R² = 0,933
0,0000
0,0050
0,0100
0,0150
0,0200
0,0250
0,0300
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr.Ft 2 .F/Btu
Time (minute)
Milk
batch 1
batch 2
batch 3
batch 4
batch 5
y = 8E-05x - 0,0001
R² = 0,9332
0,0000
0,0020
0,0040
0,0060
0,0080
0,0100
0,0120
0,0140
0,0160
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr. Ft 2 .F/Btu
Time (minute)
Avocado
batch 1
batch 2
batch 3
batch 4
batch 5
y = 3E-05x - 9E-05
R² = 0,9378
0,0000
0,0010
0,0020
0,0030
0,0040
0,0050
0 10 20 30 40 50 60 70 80 90 100 110 120
Rd
Hr. Ft 2 . F / Btu
Time (Minute)
Orange
batch 1
batch 2
batch 3
batch 4
batch 5
Figure 3. Rd value per time for the three samples
Figure 4, shows that the Rd value is directly
proportional to the number of microorganisms in
the sample that comes out of the HE area. Growth
occurred at minutes 30-40 for milk products and
avocado juice, while growth occurred at minutes
20-30 for orange juice. Bacterial growth (TPC)
continued to increase while yeast & mold increased
until minute 80 and sloped at minutes 90-110 for
milk and avocado juice. In orange juice, bacterial
growth continued to increase until minute 110, and
yeast & mold growth increased so that the number
exceeded the number of bacteria (TPC).
8Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
A. Milk
B. Avocado
0,0000
0,0050
0,0100
0,0150
0,0200
0,0250
0
1
2
3
4
5
6
7
8
9
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
0,0000
0,0020
0,0040
0,0060
0,0080
0,0100
0,0120
0
1
2
3
4
5
6
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
C. Orange
0,0000
0,0005
0,0010
0,0015
0,0020
0,0025
0,0030
0,0035
0,0040
0,0045
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
Figure 4. Relationship of Rd and the growth of microorganisms
A qualitative analysis was also carried out for the
types of bacteria found in the fouling layer (Rd)
attached to the pipe (deliberately removed to
facilitate qualitative analysis of bacteria and chemical
composition) (figure 4).
Table 5 shows that the types of bacteria living in milk
are far more abundant when compared to avocado
juice and orange juice. Staphylococcus bacteria were
able to live in all three samples. All types of bacteria
can live in milk drinks, including Actinomycetes, which
are morphologically similar to yeast.
Table 5. Qualitative Test Results Bacteria attached to the
sterilization tube.
NO Microorganisms Milk Avocado Orange
1 Lactobacillus + +
2 Staphylococcus + + +
3 Enterobactericeae + +
4 Micrococcus +
5 Corynebacterium + +
6 Pseudomonas + +
7 Actinomycetes +
8 Bacillus + +
9 Streptococcus +
A. Milk
B. Avocado
0,0000
0,0050
0,0100
0,0150
0,0200
0,0250
0
1
2
3
4
5
6
7
8
9
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
0,0000
0,0020
0,0040
0,0060
0,0080
0,0100
0,0120
0
1
2
3
4
5
6
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
C. Orange
0,0000
0,0005
0,0010
0,0015
0,0020
0,0025
0,0030
0,0035
0,0040
0,0045
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
105 CFU/ mL
Time (minute)
TPC
Yeast & Mold
Rd
hr. Ft 2. F / Btu
Figure 4. Relationship of Rd and the growth of microorganisms
A qualitative analysis was also carried out for the
types of bacteria found in the fouling layer (Rd)
attached to the pipe (deliberately removed to
facilitate qualitative analysis of bacteria and chemical
composition) (figure 4).
Table 5 shows that the types of bacteria living in milk
are far more abundant when compared to avocado
juice and orange juice. Staphylococcus bacteria were
able to live in all three samples. All types of bacteria
can live in milk drinks, including Actinomycetes, which
are morphologically similar to yeast.
Table 5. Qualitative Test Results Bacteria attached to the
sterilization tube.
NO Microorganisms Milk Avocado Orange
1 Lactobacillus + +
2 Staphylococcus + + +
3 Enterobactericeae + +
4 Micrococcus +
5 Corynebacterium + +
6 Pseudomonas + +
7 Actinomycetes +
8 Bacillus + +
9 Streptococcus +
9Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
The chemical composition of the Rd layer showed
different results between milk and other samples
(Table 6). In dairy products, there are found proteins
(54.22%) and minerals 42.37%. The rest is fat and
other components. Meanwhile, in avocado and
orange juices, the mineral composition is more than
protein, while fat is still in the range of 2%. Especially
for minerals, dairy products are mostly calcium.
There are potassium, magnesium, and phosphorus
in avocado juice (potassium) and orange juice.
Table 6. Chemical composition of the Rd layer in the HE area
Milk
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.32 2.38 2.3440 .5671 1.268
Saturated fat 5 .03 .07 .0500 .0091 .0204
Protein 5 54.10 54.30 54.2200 .0911 .2038
Sodium 5 8.90 9.10 8.9800 .0099 .0221
Potassium 5 11.92 12.95 12.1720 .0229 .0511
Calsium 5 12.60 12.82 12.6980 .0331 .0741
Magnesium 5 2.11 2.20 2.1400 .0260 .0582
Phospor 5 3.45 3.75 3.5500 .1159 .2591
Chloride 5 1.60 1.80 1.7000 .1811 .4050
Etc, vitamin 5 1.10 1.40 1.2760 .1731 .3870
Avocado
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.23 2.30 2.2640 .1074 .2401
Saturated fat 5 .08 .18 .1380 .0051 .0114
Protein 5 44.70 44.98 44.8660 .1887 .4220
Sodium 5 11.84 11.96 11.9100 .0521 .1164
Potassium 5 11.90 12.20 12.0260 .0519 .1160
Calsium 5 8.90 9.00 8.9460 .0238 .0532
Magnesium 5 6.90 7.00 6.9680 .0242 .0542
Phospor 5 5.90 7.00 5.9460 .0287 .0641
Chloride 5 4.84 4.98 4.9360 .0382 .0855
Etc, vitamin 5 .94 1.10 1.0210 .0206 .0461
Orange
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.24 2.40 2.2840 .0734 .1642
Saturated fat 5 .38 .46 .4160 .0097 .0216
Protein 5 45.04 45.18 45.1160 .0681 .1522
Sodium 5 11.90 12.10 11.9800 .0809 .1810
Potassium 5 7.96 8.10 8.0260 .0520 .1162
Calsium 5 6.90 7.10 6.9540 .0242 .0541
Magnesium 5 8.20 8.40 8.3340 .0126 .0282
Phospor 5 8.88 9.01 8.9460 .0287 .0641
Chloride 5 3.90 3.96 3.9360 .0381 .0851
Etc, vitamin 5 1.98 2.08 2.0240 .0207 .0463
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
The chemical composition of the Rd layer showed
different results between milk and other samples
(Table 6). In dairy products, there are found proteins
(54.22%) and minerals 42.37%. The rest is fat and
other components. Meanwhile, in avocado and
orange juices, the mineral composition is more than
protein, while fat is still in the range of 2%. Especially
for minerals, dairy products are mostly calcium.
There are potassium, magnesium, and phosphorus
in avocado juice (potassium) and orange juice.
Table 6. Chemical composition of the Rd layer in the HE area
Milk
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.32 2.38 2.3440 .5671 1.268
Saturated fat 5 .03 .07 .0500 .0091 .0204
Protein 5 54.10 54.30 54.2200 .0911 .2038
Sodium 5 8.90 9.10 8.9800 .0099 .0221
Potassium 5 11.92 12.95 12.1720 .0229 .0511
Calsium 5 12.60 12.82 12.6980 .0331 .0741
Magnesium 5 2.11 2.20 2.1400 .0260 .0582
Phospor 5 3.45 3.75 3.5500 .1159 .2591
Chloride 5 1.60 1.80 1.7000 .1811 .4050
Etc, vitamin 5 1.10 1.40 1.2760 .1731 .3870
Avocado
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.23 2.30 2.2640 .1074 .2401
Saturated fat 5 .08 .18 .1380 .0051 .0114
Protein 5 44.70 44.98 44.8660 .1887 .4220
Sodium 5 11.84 11.96 11.9100 .0521 .1164
Potassium 5 11.90 12.20 12.0260 .0519 .1160
Calsium 5 8.90 9.00 8.9460 .0238 .0532
Magnesium 5 6.90 7.00 6.9680 .0242 .0542
Phospor 5 5.90 7.00 5.9460 .0287 .0641
Chloride 5 4.84 4.98 4.9360 .0382 .0855
Etc, vitamin 5 .94 1.10 1.0210 .0206 .0461
Orange
Chemical
Composition (% w/w)
Qty Minimum Maximum Mean Std. Deviation
batch Statistic Statistic Statistic Std. Error Statistic
Unsaturated fat 5 2.24 2.40 2.2840 .0734 .1642
Saturated fat 5 .38 .46 .4160 .0097 .0216
Protein 5 45.04 45.18 45.1160 .0681 .1522
Sodium 5 11.90 12.10 11.9800 .0809 .1810
Potassium 5 7.96 8.10 8.0260 .0520 .1162
Calsium 5 6.90 7.10 6.9540 .0242 .0541
Magnesium 5 8.20 8.40 8.3340 .0126 .0282
Phospor 5 8.88 9.01 8.9460 .0287 .0641
Chloride 5 3.90 3.96 3.9360 .0381 .0851
Etc, vitamin 5 1.98 2.08 2.0240 .0207 .0463
10Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
At the same sampling point, the protein content
and Total Plate Count (TPC) were analyzed in the
sterilization area pipe at the end of the process (the
pipe was removed for easy analysis). Test the effect
of the two variables (dependent and predictor) can
be seen in table 7.
Table 7. Anova test for effect of microbial growth on protein content.
ANOVAa
Model Sum of
Squares df Mean
Square F Sig.
1
Regression 1983.924 2 991.962 1517.8 .000b
Residual 4.576 7 .654
Total 1988.500 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999a .998 .997 .80854
a. Predictors: (Constant), Microbe, Time
A
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 1304.048 2 652.024 1940.948 .000b
Residual 2.352 7 .336
Total 1306.400 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999 a .998 .998 .57960
a. Predictors: (Constant), Microbe, Time
B
ANOVAa
Model Sum of
Squares df Mean
Square F Sig.
1
Regression 1974.211 2 987.106 1776.790 .000b
Residual 3.889 7 .556
Total 1978.100 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999a .998 .997 .74536
a. Predictors: (Constant), Microbe, Time
C
There was a significant effect between the number
of microbes on the protein content in the Rd layer
(p<0.05). In table 7A, in the fouling layer of milk
drinks, there was an increase in the number of
microbes (p = 0.000) to the decrease in protein
content. The variable number of microbes and time
can simultaneously affect the protein content of
99.8%. Table 7B (avocado sample) showed that the
number of microbes had an effect (99.8%) on protein
content (p<0.05), and Table 7C (orange) showed
that the number of microbes had an effect (99.8%)
on protein content.
DISCUSSION
Comparing milk drinks, avocado juice, and orange
juice to the formation of Rd in the HE area is an
effort to make a sanitation schedule in the HE area.
Figure 3 shows the formation of Rd in milk drinks is
faster than in avocado juice and orange juice. In a
matter of minutes, an Rd of 1.7 x10 -4 hr. Ft 2 . F / Btu is
formed. During the 110-minute process, the average
Rd value was 0.0205 hr. Ft 2 . F / Btu. The Rd value
makes the average bacterial growth 8 x 10 5 CFU/ml,
and the growth of yeast & molds reaches 2.2 x 10 5
CFU/ml. Meanwhile, for avocado juice, the formation
of Rd per minute is around 8.6 x10 -5 hr. Ft 2 . F / Btu
with bacterial contaminants at 110 minutes is 5.4 x
At the same sampling point, the protein content
and Total Plate Count (TPC) were analyzed in the
sterilization area pipe at the end of the process (the
pipe was removed for easy analysis). Test the effect
of the two variables (dependent and predictor) can
be seen in table 7.
Table 7. Anova test for effect of microbial growth on protein content.
ANOVAa
Model Sum of
Squares df Mean
Square F Sig.
1
Regression 1983.924 2 991.962 1517.8 .000b
Residual 4.576 7 .654
Total 1988.500 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999a .998 .997 .80854
a. Predictors: (Constant), Microbe, Time
A
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 1304.048 2 652.024 1940.948 .000b
Residual 2.352 7 .336
Total 1306.400 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999 a .998 .998 .57960
a. Predictors: (Constant), Microbe, Time
B
ANOVAa
Model Sum of
Squares df Mean
Square F Sig.
1
Regression 1974.211 2 987.106 1776.790 .000b
Residual 3.889 7 .556
Total 1978.100 9
a. Dependent Variable: Protein
b. Predictors: (Constant), Microbe, Time
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .999a .998 .997 .74536
a. Predictors: (Constant), Microbe, Time
C
There was a significant effect between the number
of microbes on the protein content in the Rd layer
(p<0.05). In table 7A, in the fouling layer of milk
drinks, there was an increase in the number of
microbes (p = 0.000) to the decrease in protein
content. The variable number of microbes and time
can simultaneously affect the protein content of
99.8%. Table 7B (avocado sample) showed that the
number of microbes had an effect (99.8%) on protein
content (p<0.05), and Table 7C (orange) showed
that the number of microbes had an effect (99.8%)
on protein content.
DISCUSSION
Comparing milk drinks, avocado juice, and orange
juice to the formation of Rd in the HE area is an
effort to make a sanitation schedule in the HE area.
Figure 3 shows the formation of Rd in milk drinks is
faster than in avocado juice and orange juice. In a
matter of minutes, an Rd of 1.7 x10 -4 hr. Ft 2 . F / Btu is
formed. During the 110-minute process, the average
Rd value was 0.0205 hr. Ft 2 . F / Btu. The Rd value
makes the average bacterial growth 8 x 10 5 CFU/ml,
and the growth of yeast & molds reaches 2.2 x 10 5
CFU/ml. Meanwhile, for avocado juice, the formation
of Rd per minute is around 8.6 x10 -5 hr. Ft 2 . F / Btu
with bacterial contaminants at 110 minutes is 5.4 x
11Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
10 5 CFU/ml, and yeast and mold numbers are 1.5
x 10 5 CFU/ml. The lowest Rd formation was orange
juice (3.4 x10 -5 hr. Ft 2 . F / Btu), but it caused almost
the same bacterial growth as yeast & mold, 1.3 x 10 5
CFU/ml. Referring to the data above, The schedule
for pipe cleaning in the sterilization area refers to the
company’s internal policy by choosing an efficiency
of 95% HE (see table 2, Rd: 0.027- 0.035 Hr.Ft 2 .F/
Btu). Based on the data above, the researcher
recommends a proper sanitation schedule for milk
drinks every 110 minutes (one batch) or every 2
hours. The sanitation schedule for avocado juice
is longer, at 4 hours. The longest sanitation time is
for orange juice, which can be done every 10 hours
of operation.
A comparison of raw material composition is used
to see the chemical composition of the fouling layer
(Rd) and to close the research gap related to the
inconsistency of research results so far. Table 6 shows
the chemical composition of the fouling layer (Rd)
for milk drinks, with the largest percentage being
protein, minerals, fats, and others. This condition is
very different from the results in avocado juice and
orange juice: minerals, protein, fats, and others.
This study’s results confirm previous researchers’
findings [1–6]. If we examined the initial components
of the sample, it shows that the milk component
had a greater protein content among other samples
(table 1), while avocado and orange juices had more
mineral content. This demonstrated that the raw
material components affect the components in the
fouling layer (Rd).
Fouling occurs due to the development of
microorganisms in the HE area, also known as
biofouling. Bott’s research [22] and Flint et al.
[14,23,24] emphasized two mechanisms for the
formation of biofouling which have become a
well-established concept until now, namely the
accumulation of microorganism growth in the fouling
layer and the attachment of microorganisms to the
outermost layer of fouling. Therefore, microbial
contamination is caused by the sterilization process
in the HE that is not optimal (innate microbes do not
die), and microbes that grow in the fouling layer are
carried away by the product flow. Figure 4 and Table
7 show the progress of biofilm formation, in which
this study focuses more on the effect of microbial
growth on protein content. This is motivated by the
well-established concept that “protein is a nutrient
for microbial growth”.
This study showed that microbial contamination
is caused by innate microbes and a non-optimal
sterilization process that renders a fouling layer along
the HE pipe and extends to the distribution pipe.
Fouling that sticks along the pipe is a potential for
the growth of microbes, so product contamination
cannot be avoided. Milk drinks dominate the type
of bacteria that grow because their components
are rich in chemicals for microbial growth. This is
inversely proportional to avocado and orange juice,
dominated by only a few types of bacteria.
Making a correlation between chemical composition
(protein) and the rate of microbial growth provides
an understanding that the growth of these microbes
largely determines the composition of the fouling
layer in the sterilization area. Determining the
chemical composition of the fouling layer should
be wiser by looking at microbial growth. Research
conducted without looking at microbial growth by
previous researchers [9,12,13] will prolong the debate
regarding the chemical composition of the fouling
layer (Rd).
CONCLUSION
Comparison of the composition of raw milk, avocado
juice, and orange juice helps in understanding:
1. The rate of formation of the fouling layer (Rd)
to assist in making a sanitation schedule in the
sterilization area. The results showed that the
sanitation schedule for milk (2 hours) was shorter
than that of avocado juice (4 hours) and orange
juice (10 hours);
2. The chemical composition of beverage raw
materials affecting the composition of the fouling
layer (Rd) that sticks along the distribution pipes;
3. A strong influence between microbial growths
on the composition of the fouling layer (protein)
that can close the research gap related to the
inconsistency of previous research results (fouling
layer composition) so that there is no prolonged
debate.
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
10 5 CFU/ml, and yeast and mold numbers are 1.5
x 10 5 CFU/ml. The lowest Rd formation was orange
juice (3.4 x10 -5 hr. Ft 2 . F / Btu), but it caused almost
the same bacterial growth as yeast & mold, 1.3 x 10 5
CFU/ml. Referring to the data above, The schedule
for pipe cleaning in the sterilization area refers to the
company’s internal policy by choosing an efficiency
of 95% HE (see table 2, Rd: 0.027- 0.035 Hr.Ft 2 .F/
Btu). Based on the data above, the researcher
recommends a proper sanitation schedule for milk
drinks every 110 minutes (one batch) or every 2
hours. The sanitation schedule for avocado juice
is longer, at 4 hours. The longest sanitation time is
for orange juice, which can be done every 10 hours
of operation.
A comparison of raw material composition is used
to see the chemical composition of the fouling layer
(Rd) and to close the research gap related to the
inconsistency of research results so far. Table 6 shows
the chemical composition of the fouling layer (Rd)
for milk drinks, with the largest percentage being
protein, minerals, fats, and others. This condition is
very different from the results in avocado juice and
orange juice: minerals, protein, fats, and others.
This study’s results confirm previous researchers’
findings [1–6]. If we examined the initial components
of the sample, it shows that the milk component
had a greater protein content among other samples
(table 1), while avocado and orange juices had more
mineral content. This demonstrated that the raw
material components affect the components in the
fouling layer (Rd).
Fouling occurs due to the development of
microorganisms in the HE area, also known as
biofouling. Bott’s research [22] and Flint et al.
[14,23,24] emphasized two mechanisms for the
formation of biofouling which have become a
well-established concept until now, namely the
accumulation of microorganism growth in the fouling
layer and the attachment of microorganisms to the
outermost layer of fouling. Therefore, microbial
contamination is caused by the sterilization process
in the HE that is not optimal (innate microbes do not
die), and microbes that grow in the fouling layer are
carried away by the product flow. Figure 4 and Table
7 show the progress of biofilm formation, in which
this study focuses more on the effect of microbial
growth on protein content. This is motivated by the
well-established concept that “protein is a nutrient
for microbial growth”.
This study showed that microbial contamination
is caused by innate microbes and a non-optimal
sterilization process that renders a fouling layer along
the HE pipe and extends to the distribution pipe.
Fouling that sticks along the pipe is a potential for
the growth of microbes, so product contamination
cannot be avoided. Milk drinks dominate the type
of bacteria that grow because their components
are rich in chemicals for microbial growth. This is
inversely proportional to avocado and orange juice,
dominated by only a few types of bacteria.
Making a correlation between chemical composition
(protein) and the rate of microbial growth provides
an understanding that the growth of these microbes
largely determines the composition of the fouling
layer in the sterilization area. Determining the
chemical composition of the fouling layer should
be wiser by looking at microbial growth. Research
conducted without looking at microbial growth by
previous researchers [9,12,13] will prolong the debate
regarding the chemical composition of the fouling
layer (Rd).
CONCLUSION
Comparison of the composition of raw milk, avocado
juice, and orange juice helps in understanding:
1. The rate of formation of the fouling layer (Rd)
to assist in making a sanitation schedule in the
sterilization area. The results showed that the
sanitation schedule for milk (2 hours) was shorter
than that of avocado juice (4 hours) and orange
juice (10 hours);
2. The chemical composition of beverage raw
materials affecting the composition of the fouling
layer (Rd) that sticks along the distribution pipes;
3. A strong influence between microbial growths
on the composition of the fouling layer (protein)
that can close the research gap related to the
inconsistency of previous research results (fouling
layer composition) so that there is no prolonged
debate.
12Journal Vitae | https://revistas.udea.edu.co/index.php/vitae Volume 30 | Number 01 | Article 349368Budianto, Zefki Okta Feri, Anik Suparmi, Muh Jaenal Arifin
NOMENCLATURE
Symbol Name, Units Symbol Name, Units
c Index for cold fluid σ proportional constant (BTU/hour ft 2 0 C)
s Index for shell part m flow rate of hot fluid flow (lb/hour)
t Index for tube section Cp specific heat coefficient (BTU/lb 0 F)
Q Heat transfer rate (BTU/hour) Fc caloric fraction
K Thermal conductivity (BTU/hour) ID inside diameter (ft
A Cross-sectional area of heat transfer (ft 2 ) OD outside diameter (in)
T Temperature (o
F) C distance between tubes (in)
x Heat flow path distance (ft) B distance between baffles (in)
h Heat transfer coefficient (BTU/hour ft 2 0C) P pitch (in)
e Emissivity (0 to 1) a flow area (ft 2 )
Nt number of tubes N number of passes
G fluid flow pressure (lb/ ft 2 ) De equivalent diameter (ft)
μ viscosity (lb/hr ft) φ Rasio of viscosity
hi0 the shell heat transfer coefficient Btu/(hr) (ft 2 )(F) h0 the tube heat transfer coefficient Btu/(hr) (ft 2 )(F)
Prs prandl number in shell Prt prandl number in tube
JHs Heat transfer factor in shell JHt Heat transfer factor in tube
W Mass flowrate of fluid (kmol/hr) LMTD Log mean temperature different ( 0 F)
G Mass Flow Rate Uc Overall heat transfer coefficient when clean (Btu/ hr.ft2.F)
Ud Overall heat transfer coefficient after operation (Btu/ hr.ft2.F) ( h
ϕ ) Heat Transfer Coefficient
tw Temperature on the tube wall ( 0 F) Rd Fouling factor (Hr. Ft2. F / Btu)
REFERENCES
[1] M. Abu Madi, R. A. Johns, and M. R. Heikal, “Performance
characteristics correlation for round tube and plate finned heat
exchangers: Equations relatives aux performances d’échangeurs
de chaleur constitués de tubes ronds et de plaques à ailettes,”
Int. J. Refrig., 1998; 21 (7): 507–517. DOI: https://doi.org/10.1016/
S0140-7007(98)00031-0.
[2] Y. Mahdi, A. Mouheb, and L. Oufer, “A dynamic model for milk
fouling in a plate heat exchanger,” Appl. Math. Model. 2009; 33
(2): 648–662. DOI: https://doi.org/10.1016/j.apm.2007.11.030
[3] K. R. Goode, K. Asteriadou, P. T. Robbins, and P. J. Fryer, “Fouling
and cleaning studies in the food and beverage industry classified
by cleaning type,” Compr. Rev. Food Sci. Food Saf. 2013; 12 (2):
121–143. DOI: https://doi.org/10.1111/1541-4337.12000
[4] C. Hagsten, F. Innings, C. Trägårdh, L. Hamberg, M. Paulsson,
and T. Nylander, “Removal of UHT dairy fouling — An efficient
cleaning process by optimizing the rate controlling alkaline
cleaning step,” Food Bioprod. Process. 2019; 113: 101–107. DOI:
https://doi.org/10.1016/j.fbp.2018.11.010
[5] C. Boxler, W. Augustin, and S. Scholl, “Composition of milk fouling
deposits in a plate heat exchanger under pulsed flow conditions,”
J. Food Eng.2014; 121 (1): 1–8. DOI: https://doi.org/10.1016/j.
jfoodeng.2013.08.003
[6] A. B. Kananeh, E. Scharnbeck, U. D. Kück, and N. Räbiger,
“Reduction of milk fouling inside gasketed plate heat exchanger
using nano-coatings,” Food Bioprod. Process.2010; 88 (4):
349–356. DOI: https://doi.org/10.1016/j.fbp.2010.09.010
[7] K. H. Teng et al., “Calcium carbonate fouling on double-pipe
heat exchanger with different heat exchanging surfaces,” Powder
Technol.2017;315: 216–226. DOI: https://doi.org/10.1016/j.
powtec.2017.03.057
[8] T. J. M. Jeurnink Walstra P De Kruif C G, “Mechanisms of fouling
in dairy processing,” Netherlands Milk Dairy J. 1996; 50: 407–426.
Available at: https://www.researchgate.net/profile/Cg-kees-De-
Kruif/publication/40202234_Mechanisms_of_Fouling_in_Dairy_
Processing/links/00b7d530d8dc137635000000/Mechanisms-of-
Fouling-in-Dairy-Processing.pdf.
[9] P. J. Skudder, B. E. Brooker, A. D. Bonsey, and N. R. Alvarez-
Guerrero, “Effect of pH on the formation of deposit from milk
on heated surfaces during ultra high temperature processing,”
J. Dairy Res.1986; 53(1): 75–87. DOI: https://doi.org/10.1017/
S0022029900024687
[10] H. C. Deeth, “The effect of UHT processing and storage on milk
proteins,” Milk Proteins From Expr. to Food. 2020: 385–421. DOI:
https://doi.org/10.1016/B978-0-12-815251-5.00010-4
[11] O. Fysun, H. Kern, B. Wilke, and H.-C. Langowski, “Formation
of dairy fouling deposits on food contact surfaces,” Int. J. Dairy
Technol. 2019; 72 (2): 257–265. DOI: https://doi.org/10.1016/
B978-0-12-815251-5.00010-4
NOMENCLATURE
Symbol Name, Units Symbol Name, Units
c Index for cold fluid σ proportional constant (BTU/hour ft 2 0 C)
s Index for shell part m flow rate of hot fluid flow (lb/hour)
t Index for tube section Cp specific heat coefficient (BTU/lb 0 F)
Q Heat transfer rate (BTU/hour) Fc caloric fraction
K Thermal conductivity (BTU/hour) ID inside diameter (ft
A Cross-sectional area of heat transfer (ft 2 ) OD outside diameter (in)
T Temperature (o
F) C distance between tubes (in)
x Heat flow path distance (ft) B distance between baffles (in)
h Heat transfer coefficient (BTU/hour ft 2 0C) P pitch (in)
e Emissivity (0 to 1) a flow area (ft 2 )
Nt number of tubes N number of passes
G fluid flow pressure (lb/ ft 2 ) De equivalent diameter (ft)
μ viscosity (lb/hr ft) φ Rasio of viscosity
hi0 the shell heat transfer coefficient Btu/(hr) (ft 2 )(F) h0 the tube heat transfer coefficient Btu/(hr) (ft 2 )(F)
Prs prandl number in shell Prt prandl number in tube
JHs Heat transfer factor in shell JHt Heat transfer factor in tube
W Mass flowrate of fluid (kmol/hr) LMTD Log mean temperature different ( 0 F)
G Mass Flow Rate Uc Overall heat transfer coefficient when clean (Btu/ hr.ft2.F)
Ud Overall heat transfer coefficient after operation (Btu/ hr.ft2.F) ( h
ϕ ) Heat Transfer Coefficient
tw Temperature on the tube wall ( 0 F) Rd Fouling factor (Hr. Ft2. F / Btu)
REFERENCES
[1] M. Abu Madi, R. A. Johns, and M. R. Heikal, “Performance
characteristics correlation for round tube and plate finned heat
exchangers: Equations relatives aux performances d’échangeurs
de chaleur constitués de tubes ronds et de plaques à ailettes,”
Int. J. Refrig., 1998; 21 (7): 507–517. DOI: https://doi.org/10.1016/
S0140-7007(98)00031-0.
[2] Y. Mahdi, A. Mouheb, and L. Oufer, “A dynamic model for milk
fouling in a plate heat exchanger,” Appl. Math. Model. 2009; 33
(2): 648–662. DOI: https://doi.org/10.1016/j.apm.2007.11.030
[3] K. R. Goode, K. Asteriadou, P. T. Robbins, and P. J. Fryer, “Fouling
and cleaning studies in the food and beverage industry classified
by cleaning type,” Compr. Rev. Food Sci. Food Saf. 2013; 12 (2):
121–143. DOI: https://doi.org/10.1111/1541-4337.12000
[4] C. Hagsten, F. Innings, C. Trägårdh, L. Hamberg, M. Paulsson,
and T. Nylander, “Removal of UHT dairy fouling — An efficient
cleaning process by optimizing the rate controlling alkaline
cleaning step,” Food Bioprod. Process. 2019; 113: 101–107. DOI:
https://doi.org/10.1016/j.fbp.2018.11.010
[5] C. Boxler, W. Augustin, and S. Scholl, “Composition of milk fouling
deposits in a plate heat exchanger under pulsed flow conditions,”
J. Food Eng.2014; 121 (1): 1–8. DOI: https://doi.org/10.1016/j.
jfoodeng.2013.08.003
[6] A. B. Kananeh, E. Scharnbeck, U. D. Kück, and N. Räbiger,
“Reduction of milk fouling inside gasketed plate heat exchanger
using nano-coatings,” Food Bioprod. Process.2010; 88 (4):
349–356. DOI: https://doi.org/10.1016/j.fbp.2010.09.010
[7] K. H. Teng et al., “Calcium carbonate fouling on double-pipe
heat exchanger with different heat exchanging surfaces,” Powder
Technol.2017;315: 216–226. DOI: https://doi.org/10.1016/j.
powtec.2017.03.057
[8] T. J. M. Jeurnink Walstra P De Kruif C G, “Mechanisms of fouling
in dairy processing,” Netherlands Milk Dairy J. 1996; 50: 407–426.
Available at: https://www.researchgate.net/profile/Cg-kees-De-
Kruif/publication/40202234_Mechanisms_of_Fouling_in_Dairy_
Processing/links/00b7d530d8dc137635000000/Mechanisms-of-
Fouling-in-Dairy-Processing.pdf.
[9] P. J. Skudder, B. E. Brooker, A. D. Bonsey, and N. R. Alvarez-
Guerrero, “Effect of pH on the formation of deposit from milk
on heated surfaces during ultra high temperature processing,”
J. Dairy Res.1986; 53(1): 75–87. DOI: https://doi.org/10.1017/
S0022029900024687
[10] H. C. Deeth, “The effect of UHT processing and storage on milk
proteins,” Milk Proteins From Expr. to Food. 2020: 385–421. DOI:
https://doi.org/10.1016/B978-0-12-815251-5.00010-4
[11] O. Fysun, H. Kern, B. Wilke, and H.-C. Langowski, “Formation
of dairy fouling deposits on food contact surfaces,” Int. J. Dairy
Technol. 2019; 72 (2): 257–265. DOI: https://doi.org/10.1016/
B978-0-12-815251-5.00010-4
13Journal Vitae | https://revistas.udea.edu.co/index.php/vitaeVolume 30 | Number 01 | Article 349368
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
[12] M. Lalande, F. Rene, and J. P. Tissier, “Fouling and its control in
heat exchangers in the dairy industry,” Biofouling. 1989; 1(3):
233–250. DOI: https://doi.org/10.1080/08927018909378111
[13] M. Lalande, J.-P. Tissier, and G. Corrieu, “Fouling of a plate heat
exchanger used in ultra-high-temperature sterilization of milk,”
J. Dairy Res. 1984; 51 (4): 557–568. DOI: https://doi.org/10.1017/
S0022029900032878
[14] S. Flint et al., “Bacterial fouling in dairy processing,” Int.
Dairy J. 1984; 101:104593. DOI: https://doi.org/10.1016/j.
idairyj.2019.104593
[15] K. Grijspeerdt, L. Mortier, J. De Block, and R. Van Renterghem,
“Applications of modelling to optimise ultra high temperature
milk heat exchangers with respect to fouling,” Food Control.
2004; 15 (2): 117–130. DOI: https://doi.org/10.1016/S0956-
7135(03)00023-9
[16] B. Malmgren et al., “Changes in proteins, physical stability and
structure in directly heated UHT milk during storage at different
temperatures,” Int. Dairy J. 2017; 71: 60–75. DOI: https://doi.
org/10.1016/j.idairyj.2017.03.002
[17] T. Mohammadi, S. S. Madaeni, and M. K. Moghadam, “Investigation
of membrane fouling,” Desalination. 2003; 153( 1–3): 155–160. DOI:
https://doi.org/10.1016/S0011-9164(02)01118-9
[18] M. Kazemimoghadam and T. Mohammadi, “Chemical cleaning
of ultrafiltration membranes in the milk industry,” Desalination.
2018; 204 (1–3): 213–218. DOI: https://doi.org/10.1016/j.
desal.2006.04.030
[19] T. Steinhauer, J. Schwing, S. Krauß, and U. Kulozik, “Enhancement
of ultrafiltration-performance and improvement of hygienic
quality during the production of whey concentrates,” Int. Dairy J.
2015; 45: 8–14. DOI: https://doi.org/10.1016/j.idairyj.2015.01.010
[20] L. P. Cappato et al., “Ohmic heating in dairy processing: Relevant
aspects for safety and quality,” Trends Food Sci. Technol.2017;
62: 104–112. DOI: https://doi.org/10.1016/j.tifs.2017.01.010
[21] Badan POM RI, “Peraturan Kepala Badan Pengawas Obat Dan
Makanan Republik Indonesia Tahun 2011 Tentang Metode Analisis
Kosmetika. Jakarta, 2011: 1–92p
[22] Bott T.R., “Aspects of Biofilm Formation and Destruction,”
Corros. Rev. 1993; 11 (1–2): 1–24. DOI: https://doi.org/10.1515/
CORRREV.1993.11.1-2.1
[23] S. Flint and N. Hartley, “A modified selective medium for the
detection of Pseudomonas species that cause spoilage of milk
and dairy products,” Int. Dairy J. 1996; 6(2): 223–230. DOI: https://
doi.org/10.1016/0958-6946(95)00007-0
[24] S. H. Flint, H. Van Den Elzen, J. D. Brooks, and P. J. Bremer,
“Removal and inactivation of thermo-resistant streptococci
colonising stainless steel,” Int. Dairy J. 1999; 9 (7) 429–436. DOI:
https://doi.org/10.1016/S0958-6946(99)00048-5
[25] ANGELETTI, SANDRO; MORESI, MAURO. Modelling of multiple-
effect falling-film evaporators. International Journal of Food
Science & Technology, 1983;18 (5): 539-563. 83. https://doi.
org/10.1111/j.1365-2621.1983.tb00296.x
[26] A. Kayode Coker, Heat Transfer: Fortran Programs for Chemical
Process Design,Analysis, and Simulation, Gulf Professional
Publishing,1995;590-720. DOI: https://doi.org/10.1016/B978-
088415280-4/50009-1.
[27] Setyaningsih, Ir Iiani, et al. Panduan Praktikum Mikrobiologi Hasil
Perairan. PT Penerbit IPB Press, Bogor, 2021: 20-90p
Effect of the chemical composition of fluid foods on the rate of fouling processing during sterilization
[12] M. Lalande, F. Rene, and J. P. Tissier, “Fouling and its control in
heat exchangers in the dairy industry,” Biofouling. 1989; 1(3):
233–250. DOI: https://doi.org/10.1080/08927018909378111
[13] M. Lalande, J.-P. Tissier, and G. Corrieu, “Fouling of a plate heat
exchanger used in ultra-high-temperature sterilization of milk,”
J. Dairy Res. 1984; 51 (4): 557–568. DOI: https://doi.org/10.1017/
S0022029900032878
[14] S. Flint et al., “Bacterial fouling in dairy processing,” Int.
Dairy J. 1984; 101:104593. DOI: https://doi.org/10.1016/j.
idairyj.2019.104593
[15] K. Grijspeerdt, L. Mortier, J. De Block, and R. Van Renterghem,
“Applications of modelling to optimise ultra high temperature
milk heat exchangers with respect to fouling,” Food Control.
2004; 15 (2): 117–130. DOI: https://doi.org/10.1016/S0956-
7135(03)00023-9
[16] B. Malmgren et al., “Changes in proteins, physical stability and
structure in directly heated UHT milk during storage at different
temperatures,” Int. Dairy J. 2017; 71: 60–75. DOI: https://doi.
org/10.1016/j.idairyj.2017.03.002
[17] T. Mohammadi, S. S. Madaeni, and M. K. Moghadam, “Investigation
of membrane fouling,” Desalination. 2003; 153( 1–3): 155–160. DOI:
https://doi.org/10.1016/S0011-9164(02)01118-9
[18] M. Kazemimoghadam and T. Mohammadi, “Chemical cleaning
of ultrafiltration membranes in the milk industry,” Desalination.
2018; 204 (1–3): 213–218. DOI: https://doi.org/10.1016/j.
desal.2006.04.030
[19] T. Steinhauer, J. Schwing, S. Krauß, and U. Kulozik, “Enhancement
of ultrafiltration-performance and improvement of hygienic
quality during the production of whey concentrates,” Int. Dairy J.
2015; 45: 8–14. DOI: https://doi.org/10.1016/j.idairyj.2015.01.010
[20] L. P. Cappato et al., “Ohmic heating in dairy processing: Relevant
aspects for safety and quality,” Trends Food Sci. Technol.2017;
62: 104–112. DOI: https://doi.org/10.1016/j.tifs.2017.01.010
[21] Badan POM RI, “Peraturan Kepala Badan Pengawas Obat Dan
Makanan Republik Indonesia Tahun 2011 Tentang Metode Analisis
Kosmetika. Jakarta, 2011: 1–92p
[22] Bott T.R., “Aspects of Biofilm Formation and Destruction,”
Corros. Rev. 1993; 11 (1–2): 1–24. DOI: https://doi.org/10.1515/
CORRREV.1993.11.1-2.1
[23] S. Flint and N. Hartley, “A modified selective medium for the
detection of Pseudomonas species that cause spoilage of milk
and dairy products,” Int. Dairy J. 1996; 6(2): 223–230. DOI: https://
doi.org/10.1016/0958-6946(95)00007-0
[24] S. H. Flint, H. Van Den Elzen, J. D. Brooks, and P. J. Bremer,
“Removal and inactivation of thermo-resistant streptococci
colonising stainless steel,” Int. Dairy J. 1999; 9 (7) 429–436. DOI:
https://doi.org/10.1016/S0958-6946(99)00048-5
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