Deep fat frying of yam slices: optimization of processing conditions using response surface methodology

The deep-fat frying of yam slices was investigated with the aim of optimizing the processing conditions. During frying, frying temperature, initial dry matter and frying time have a significant effect on moisture loss and oil uptake. Response surface methodology central composite rotatable design was used to study the effects of the independent variables on quality attributes of yam chips. Breaking force, oil content, moisture content and color parameters were determined. Statistical analysis with response surface regression showed that breaking force, oil and moisture contents and color parameters (L* and a*) were significantly (P < 0.05) correlated with frying temperature, initial dry matter and frying time. The optimum conditions were a frying temperature of 175–180C, using tubers of initial dry matter of 0.179–0.214 kg/kg with a frying time of 4–5 min. It was suggested that the regression equation can be used to estimate the dependent variables for fried yam chips except b* (yellowness) parameter.

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Bibliographic Details
Main Authors: Sobukola, O.P., Awonorin, S.O., Sanni, Lateef O., Bamiro, F.O
Format: Journal Article biblioteca
Language:English
Published: 2008
Subjects:frying temperature, tubers, yam chips, color parameters, oil and moisture contents,
Online Access:https://hdl.handle.net/10568/90770
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spelling dig-cgspace-10568-907702023-06-12T08:37:43Z Deep fat frying of yam slices: optimization of processing conditions using response surface methodology Sobukola, O.P. Awonorin, S.O. Sanni, Lateef O. Bamiro, F.O frying temperature tubers yam chips color parameters oil and moisture contents The deep-fat frying of yam slices was investigated with the aim of optimizing the processing conditions. During frying, frying temperature, initial dry matter and frying time have a significant effect on moisture loss and oil uptake. Response surface methodology central composite rotatable design was used to study the effects of the independent variables on quality attributes of yam chips. Breaking force, oil content, moisture content and color parameters were determined. Statistical analysis with response surface regression showed that breaking force, oil and moisture contents and color parameters (L* and a*) were significantly (P < 0.05) correlated with frying temperature, initial dry matter and frying time. The optimum conditions were a frying temperature of 175–180C, using tubers of initial dry matter of 0.179–0.214 kg/kg with a frying time of 4–5 min. It was suggested that the regression equation can be used to estimate the dependent variables for fried yam chips except b* (yellowness) parameter. 2008 2018-02-06T12:14:40Z 2018-02-06T12:14:40Z Journal Article Sobukola, O.P., Awonorin, S.O., Sanni, L.O. & Bamiro, F.O. (2008). Deep‐fat frying of yam slices: optimization of processing conditions using response surface methodology. Journal of Food Processing and Preservation, 32(3), 343-360. 0145-8892 https://hdl.handle.net/10568/90770 en Limited Access p. 343-360
institution CGIAR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cgspace
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CGIAR
language English
topic frying temperature
tubers
yam chips
color parameters
oil and moisture contents
frying temperature
tubers
yam chips
color parameters
oil and moisture contents
spellingShingle frying temperature
tubers
yam chips
color parameters
oil and moisture contents
frying temperature
tubers
yam chips
color parameters
oil and moisture contents
Sobukola, O.P.
Awonorin, S.O.
Sanni, Lateef O.
Bamiro, F.O
Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
description The deep-fat frying of yam slices was investigated with the aim of optimizing the processing conditions. During frying, frying temperature, initial dry matter and frying time have a significant effect on moisture loss and oil uptake. Response surface methodology central composite rotatable design was used to study the effects of the independent variables on quality attributes of yam chips. Breaking force, oil content, moisture content and color parameters were determined. Statistical analysis with response surface regression showed that breaking force, oil and moisture contents and color parameters (L* and a*) were significantly (P < 0.05) correlated with frying temperature, initial dry matter and frying time. The optimum conditions were a frying temperature of 175–180C, using tubers of initial dry matter of 0.179–0.214 kg/kg with a frying time of 4–5 min. It was suggested that the regression equation can be used to estimate the dependent variables for fried yam chips except b* (yellowness) parameter.
format Journal Article
topic_facet frying temperature
tubers
yam chips
color parameters
oil and moisture contents
author Sobukola, O.P.
Awonorin, S.O.
Sanni, Lateef O.
Bamiro, F.O
author_facet Sobukola, O.P.
Awonorin, S.O.
Sanni, Lateef O.
Bamiro, F.O
author_sort Sobukola, O.P.
title Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
title_short Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
title_full Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
title_fullStr Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
title_full_unstemmed Deep fat frying of yam slices: optimization of processing conditions using response surface methodology
title_sort deep fat frying of yam slices: optimization of processing conditions using response surface methodology
publishDate 2008
url https://hdl.handle.net/10568/90770
work_keys_str_mv AT sobukolaop deepfatfryingofyamslicesoptimizationofprocessingconditionsusingresponsesurfacemethodology
AT awonorinso deepfatfryingofyamslicesoptimizationofprocessingconditionsusingresponsesurfacemethodology
AT sannilateefo deepfatfryingofyamslicesoptimizationofprocessingconditionsusingresponsesurfacemethodology
AT bamirofo deepfatfryingofyamslicesoptimizationofprocessingconditionsusingresponsesurfacemethodology
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