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The culture conditions viz. additional carbon and n
itrogen content, inoculum size, age, temperature an
d pH of
Lactobacillus acidophilus
were optimized using response surface methodology (
RSM) and artificial neural network
(ANN). Kinetic growth models were fitted to cultiva
tions from a Box-Behnken Design (BBD) design experi
ments for
different variables. This concept of combining the
optimization and modeling presented different optim
al conditions
for
L. acidophilus
growth from their original optimization study. Thr
ough these statistical tools, the product yield
(cell mass) of
L. acidophilus
was increased. Regression coefficients (R
2
) of both the statistical tools predicted that
ANN was better than RSM and the regression equation
was solved with the help of genetic algorithms (GA
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normalized percentage mean squared error obtained f
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abstract: Ultrafiltered milk (UF1 and UF2), ultrafiltrate
retentate added milk (UF3 and UF4) and SMP added milk
(UF0) were used for dahi preparation in the present study.
Treatments were evaluated for rheological, textural and sensorial
characteristics. Significant increase (p < 0.01) in values
of firmness, stickiness, work of shear, work of adhesion and
sensory scores, but significant decrease (p < 0.01) in whey
syneresis values were observed with treatments UF1, UF2,
UF3 and UF4 as compared to UF0. Principal component analysis
(PCA) revealed that first four principal components (PC)
explained 87.39 % relationship between samples and attributes.
PC1 accounted for 48.34 % of data variance was characterized
by protein content, firmness, work of shear, body &
texture and opposed by total carbohydrates, stickiness, syneresis
and work of adhesion. Total carbohydrates content
(r = −0.982, P < 0.01), whey syneresis (r = −0.783,
P < 0.01), stickiness (r = −0.729, P < 0.01) and work of
adhesion (r = −0.684, P < 0.01) are negatively while body
and texture (r = +0.600, P < 0.01), firmness (r = +0.574,
P < 0.05) and work of shear (r = +0.538, P < 0.05) of dahi
are highly positively correlated with protein content.
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Ramayana: The Thread of Luminance Work Flow
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Rachna et al., 2014 . Effect of fat levels, strength and type of coagulants on quality of cow milk Chhana and Kheer Mohan. Research Paper; Green Farming Vol. 5 (5) : 938-941 ; September-October, 2014
Growth Characteristics Modeling of Lactobacillus acidophilus using RSM and ANN
The culture conditions viz. additional carbon and n
itrogen content, inoculum size, age, temperature an
d pH of
Lactobacillus acidophilus
were optimized using response surface methodology (
RSM) and artificial neural network
(ANN). Kinetic growth models were fitted to cultiva
tions from a Box-Behnken Design (BBD) design experi
ments for
different variables. This concept of combining the
optimization and modeling presented different optim
al conditions
for
L. acidophilus
growth from their original optimization study. Thr
ough these statistical tools, the product yield
(cell mass) of
L. acidophilus
was increased. Regression coefficients (R
2
) of both the statistical tools predicted that
ANN was better than RSM and the regression equation
was solved with the help of genetic algorithms (GA
). The
normalized percentage mean squared error obtained f
rom the ANN and RSM models were 0.06 and 0.2%,
respectively. The results demonstrated a higher pre
diction accuracy of ANN compared to RSM
Application of ultrafiltration technique for the quality improvement of dahi ...
abstract: Ultrafiltered milk (UF1 and UF2), ultrafiltrate
retentate added milk (UF3 and UF4) and SMP added milk
(UF0) were used for dahi preparation in the present study.
Treatments were evaluated for rheological, textural and sensorial
characteristics. Significant increase (p < 0.01) in values
of firmness, stickiness, work of shear, work of adhesion and
sensory scores, but significant decrease (p < 0.01) in whey
syneresis values were observed with treatments UF1, UF2,
UF3 and UF4 as compared to UF0. Principal component analysis
(PCA) revealed that first four principal components (PC)
explained 87.39 % relationship between samples and attributes.
PC1 accounted for 48.34 % of data variance was characterized
by protein content, firmness, work of shear, body &
texture and opposed by total carbohydrates, stickiness, syneresis
and work of adhesion. Total carbohydrates content
(r = −0.982, P < 0.01), whey syneresis (r = −0.783,
P < 0.01), stickiness (r = −0.729, P < 0.01) and work of
adhesion (r = −0.684, P < 0.01) are negatively while body
and texture (r = +0.600, P < 0.01), firmness (r = +0.574,
P < 0.05) and work of shear (r = +0.538, P < 0.05) of dahi
are highly positively correlated with protein content.
Keywords Dahi . Firmness .PCA .Syneresis .Ultrafiltration
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Viewers also liked (10)
Rachna et al., 2014 . effect of fat levels, strength and type of coagulants o...
Rachna et al., 2014 . effect of fat levels, strength and type of coagulants o...
Growth Characteristics Modeling of Lactobacillus acidophilus using RSM and ANN
Growth Characteristics Modeling of Lactobacillus acidophilus using RSM and ANN
Application of ultrafiltration technique for the quality improvement of dahi ...
Application of ultrafiltration technique for the quality improvement of dahi ...
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