Monitoring and Optimization of the Effects of the Blending Ratio of Corn, Sesame, and Perilla Oils on the Oxidation and Sensory Quality of Seasoned Laver Pyropia spp.

Suengmok, Cho;Jiyoung, Kim;Minseok, Yoon;Hyejin, Yang;Min Young, Um;Joodong, Park;Eun-jeong, Park;Hyunil, Yoo;Jeamin, Baek;Jinho, Jo

Fisheries and aquatic sciences.
2015.
Mar,
18(1):
27-33

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens (http://creativecommons.org/licenses/by-nc/3.0/)which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

- Received : December 03, 2014
- Accepted : December 12, 2014
- Published : March 30, 2015

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Seasoned laver
Pyropia
spp. is one of the most well-known Korean traditional seafoods, and is becoming more popular worldwide. Various mixed oils are used in the preparation of seasoned laver; however, there is no information available regarding the effects of the blending ratio of oils on the quality of seasoned laver. In this study, the effects of the blending ratio of corn, sesame, and perilla oils on the oxidation and sensory quality of seasoned laver were monitored and optimized using a response surface methodology. An increase in the proportion of corn and sesame oils resulted in an excellent oxidation induction time, whereas a high ratio of perilla oil reduced the thermal oxidative stability of the mixed oil. In the sensory test, the seasoned laver with the highest proportion of sesame oil was preferred. The optimal blending ratio (v/v) of corn, sesame, and perilla oils for both oxidation induction time (
Y
_{1}
) and sensory score (
Y
_{2}
) was 92.3, 6.0, and 1.7%. Under optimal conditions, the experimental values of
Y
_{1}
and
Y
_{2}
were 4.41 ± 0.3h and 5.58 ± 0.8 points, and were similar to the predicted values (4.34 h and 5.13 points). Our results for the monitoring and optimization of the blending ratio provide useful information for seasoned laver processing companies.
Pyropia
spp., to which various vegetable oils have been applied, at an ultra-high-temperature (UHT) of 300℃ for about 10 s (
Hwang, 2013
;
Jo et al., 1995
). It is one of the most well-known Korean traditional seafoods, and has become popular worldwide because of its special taste, compactness, texture, and health benefits (
Park et al., 2001
;
Cho et al., 2009
). The value of seasoned laver products exported in 2013 was USD 180 million (
Korea Customs Service, 2014
).
In the preparation of seasoned laver, vegetable oils are used as the major ingredient (usually 30–40%, w/w) (
Lee, 1999
;
Jo et al., 1995
). Our previous study, thermal oxidative stability of six vegetable oils (sesame, perilla, sunflower, rice bran, canola, and olive) was investigated (
Kim et al., 2015
). In commercial seasoned laver products, a mixture of corn, sesame, and perilla oils is most frequently used. In particular, because it is inexpensive, the corn oil content of seasoned laver oils generally exceeds 80% (w/w).
The blending ratio of seasoned laver oils is usually decided on the basis of economical and sensory considerations. However, the effects of the blending ratio on the oxidative stability of seasoned laver oils have not yet been studied. During the processing of seasoned laver, UHT treatment at 300℃ induces the thermal oxidation of the oils (
Jo et al., 1995
). In addition, due to the high oil content of the seasoned laver, the thermal oxidative stability of the oil is the most important factor in determining the product’s shelf life (
Wang et al., 2010
;
Hasenhuettl and Wan, 1992
).
In this study, the effects of the blending ratio of corn, sesame, and perilla oils on the oxidation and sensory quality of seasoned laver were monitored and optimized using a response surface methodology (RSM). In addition, the variations in price with different blending ratios of seasoned laver oils were also monitored. The thermal oxidative stability of the oils was evaluated by determination of the oxidation induction time using a Rancimat instrument.
Pyropia
spp.) was purchased from Korea Fishery Co., Ltd (Pyeongtaek, Korea). All reagents used were analytical grade.
X
_{1}
and
X
_{2}
) were defined using the following equations, and their range and levels are shown in
Table 2
.
Blending ratio of corn, sesame, and perilla oils at the center point
X _{1} = corn oil / (sesame oil + perilla oil); X _{2} = sesame oil / perilla oil.
The oxidation induction time (
Y
_{1}
, h), sensory score (
Y
_{2}
, point), and unit cost (
Y
_{3}
, won/L) were selected as the dependent variables. This CCRD matrix consisted of 2
^{2}
factorial points, 4 axial points (α = 1.414), and 3 replicates of the center point (
Table 3
).
X _{1} = corn oil / (sesame oil + perilla oil); X _{2} = sesame oil / perilla oil. Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
where
Y
is the dependent variable (induction time, sensory score, and unit cost), β
_{0}
is constant, β
_{i}
, β
_{ii}
, and β
_{ij}
are regression coefficients, and
X
_{i}
and
X
_{j}
are the levels of the independent variables. The adequacy of the model was predicted through regression analysis (
R
^{2}
) and an analysis of variance (ANOVA). Three dimensional response surface plots were produced using the Maple software (Ver. 7, Waterloo Maple Inc., Ontario, Canada), and represented a function of two independent variables.
Y
_{1}
), sensory score (
Y
_{2}
), and unit cost (
Y
_{3}
), respectively. The optimization targets of the dependent variables
Y
_{1}
,
Y
_{2}
, and
Y
_{3}
were set as the maximum, maximum, and minimum, respectively. Response optimization was calculated by the response optimizer of the MINITAB software. In addition, multiple response optimization was performed to search for the condition that simultaneously satisfied the oxidation induction time (
Y
_{1}
) and sensory score (
Y
_{2}
).
X
_{1}
,
X
_{2}
), quadratic (
X
_{1}
X
_{1}
,
X
_{2}
X
_{2}
), and interaction (
X
_{1}
X
_{2}
) terms were calculated for their significance using a
t
-test (
Table 4
). The constant coefficients of all dependent variables were highly significant (
P
< 0.01). The linear coefficients, except for the
X
_{2}
term of
Y
_{3}
(unit cost), were also significant (
P
< 0.05 and
P
< 0.01). The fitted response surface model equations are shown in
Table 5
. The determination coefficient (
R
^{2}
) value indicated that the model equations adequately described the experimental design (
Cho et al., 2005
). The
R
^{2}
values of
Y
_{1}
,
Y
_{2}
, and
Y
_{3}
were 0.848, 0.989, and 0.937, respectively, and were significant at the 95% probability level. This confirmed that the experimental design was adequate.
Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
X
_{1}
,
X
_{2}
) of
Y
_{1}
,
Y
_{2}
, and
Y
_{3}
were significant (
P
= 0.015,
P
= 0.001, and
P
= 0.002, respectively), whereas their interaction terms (
X
_{1}
X
_{2}
), except for
Y
_{2}
, were not significant at the 95% probability level (
P
> 0.05). The results of the lack-of-fit test, which indicates the fitness of the model (
Isa et al., 2011
), showed that the
P
-values of
Y
_{1}
(oxidation induction time) and
Y
_{2}
(sensory score) were not significant (0.103 and 0.374, respectively) at the 95% probability level. The
P
-value of
Y
_{3}
(unit cost) was not calculated because the pure error value of
Y
_{3}
mean square was zero (
Santos and Boaventura, 2008
).
Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
X
_{1}
and
X
_{2}
) on the dependent variables (
Y
_{1}
,
Y
_{2}
, and
Y
_{3}
). The oxidation induction time of the mixed oil increased with an increase in the proportion of corn and sesame oils. In concontrast, perilla oil accelerated the thermal oxidation of the mixed oil. Sesame oil contains natural antioxidants such as sesamin, sesamol, and sesamolin, and a high content of oleic and linoleic acids, which promote the stability of oxidation (
Bayder et al., 1999
;
Lee et al., 2008
). However, perilla oil contains a high content (59%) of oxidation-susceptible linolenic acid, which can induce rapid oxidation during UHT treatment (
Wang, 2010
;
Zhao, 2012
). These results indicate that the blending ratio of corn, sesame, and perilla oils significantly influences the thermal oxidation stability of the UHT processed seasoned laver. In Korea, sesame and perilla oils are most frequently used for seasoned laver preparation because of their excellent savory taste (
Yang et al., 2012
). In this sensory test, the seasoned laver with a high proportion of sesame oil (Nos. 3 and 5 in
Table 3
) was the most preferred product. When considering the overall results of this study, perilla oil was found to be ineffective in terms of oxidation stability, sensory properties, and the cost of the seasoned laver.
Three dimensional response surface plots for oxidation induction time (Y _{1}), sensory score (Y _{2}), and unit cost (Y _{3}).
Y
_{1}
), sensory score (
Y
_{2}
), and unit cost (
Y
_{3}
). In addition, multiple response optimization was performed to search for the condition that simultaneously satisfied the oxidation induction time (
Y
_{1}
) and sensory score (
Y
_{2}
). The optimal conditions of the independent variables
X
_{1}
and
X
_{2}
on each dependent variable were 1.414 and 0.044 for
Y
_{1}
, -1.414 and 1.232 for
Y
_{2}
, and 0.746 and 1.414 for
Y
_{3}
, respectively (
Table 7
). The multiple optimal conditions for the oxidation induction time (
Y
_{1}
) and sensory score (
Y
_{2}
) were
X
_{1}
: +1.414 and
X
_{2}
: -0.051 (
Table 8
). The blending ratios of corn, sesame, and perilla oils at each optimal condition are shown in
Table 9
. The optimal blending ratio of corn, sesame, and perilla oils for both oxidation induction time (
Y
_{1}
) and sensory score (
Y
_{2}
) were 92.3, 6.0, and 1.7%.
Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
Y _{1,} oxidation induction time (h); Y _{2}, sensory score (point). Value of Y _{3} (unit cost) at the multiple optimal conditions was 2,669 won/L.
Y _{1}, oxidation induction time (h); Y _{2}, sensory score (point); Y _{3}, unit cost (won/L).
Y
_{1}
) and sensory score (
Y
_{2}
) at the optimal condition were 4.34 h and 5.13 points, respectively (
Table 8
). To verify the accuracy of the predicted values of the dependent variables at the optimal condition (
Y
_{1}
and
Y
_{2}
), seasoned laver was prepared using a mixture of corn (92.3%, v/v), sesame (6.0%), and perilla (1.7%) oils. Under multiple optimal conditions, the experimental values of
Y
_{1}
and
Y
_{2}
were 4.41 ± 0.3 h and 5.58 ± 0.8 points, and were similar to the predicted values calculated in the RSM design.
In conclusion, our results show the effects of the blending ratio of corn, sesame, and perilla oils on the oxidation and sensory quality of seasoned laver. The price of the blended oil was also monitored. In addition, this study was designed and analyzed using an RSM. Therefore, the optimal conditions and values of the dependent variables in the RSM design matrix may be useful information for seasoned laver processing companies.

Introduction

Seasoned laver is a unique seaweed product that is prepared by roasting a sheet of dried laver
Materials and Methods

- Materials

Corn (CJ Cheiljedang Co., Ltd., Seoul, Korea), sesame (Chamgoeul Co. Ltd., Seoul, Korea), and perilla (Chamgoeul Co., Ltd.) oils were purchased from a local market and stored in a refrigerator until their use in the experiment. Sheet-type dried laver (
- Experimental design

A central composite rotatable design (CCRD;
Montgomery, 1996
) was used to monitor the effects of the blending ratio of corn, sesame, and perilla oils on the oxidation and sensory quality of the seasoned laver. The oil blending ratio (v/v) at the center point was: corn 86.3 %, sesame 10.7 %, and perilla 3.0%. The center point was based on the average blending ratio of the commercial seasoned laver in Korean market (
Table 1
). Two independent variables (
- X1= corn oil / (sesame oil + perilla oil) = 6.33
- X2= sesame oil / perilla oil = 3.60

Blending ratio of corn, sesame, and perilla oils at the center point

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Experimental range and values of the independent variables in the central composite rotatable design for the optimization of blending ratio of corn oil, sesame oil, and perilla oil

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Central composite rotatable design matrix and values of dependent variables for the optimization of blending ratio of corn oil, sesame oil, and perilla oil

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- Analysis of data

For the response surface regression procedure, the MINITAB software (Ver. 14, Minitab Inc., Harrisburg, PA, USA) was used to fit the following quadratic polynomial equation:
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- Optimization procedure

The blending ratio of corn, sesame, and perilla oils was individually optimized for the oxidation induction time (
- Preparation of seasoned laver

Seasoned laver was prepared using an automatic roasting machine (G-400, Woojeong machine Co., Ltd., Seoul, Korea). A sheet of dried laver (2.5 g) was roasted at 150℃ for 10 s. It was the roasted again at 300℃ for 10 s, after the application of salt (0.08 g) and the mixed oil (1.6 g) containing corn, sesame, and perilla oils.
- Oxidative stability using a Rancimat method

For the measurement of the oxidative stability of seasoned laver oil, the oil samples were isolated from the seasoned laver product using a press machine. The oxidative stability of the seasoned laver oils was evaluated by measuring the oxidation induction time using a Rancimat instrument (Metrohm CH series 743, MetrohmAG, Herisau, Switzerland). The oxidation process was monitored for a 3 g sample of oil using an air velocity of 20 L/h at 120℃. During the Rancimat test, volatile compounds, such as formic acid, were rapidly produced and carried by the stream of air to be collected in deionized water. The vapors were continually monitored by measuring the conductivity of the deionized water. The end point was determined in hours.
- Sensory evaluation

The sensory evaluation of the seasoned laver samples was performed with a panel consisting of 10 members (3 male, 7 female, aged 25-40 years). All 11 samples were evaluated simultaneously in a randomized order. The quality attributes selected for sensory evaluation were flavor, taste, and crispiness (
Liang et al., 2008
;
Gkatzionis et al., 2013
;
Sinija and Mishra, 2011
). Panel members were asked to take two or three samples to taste and provide a score for each. Between tastings each panel member rinsed their mouth with lukewarm water. The sensory factors assigned to each of the quality attributes were: dislike very much (1), dislike moderately (2), dislike slightly (3), neither like or dislike (4), like slightly (5), like moderately (6), and like very much (7).
Results and Discussion

- Diagnostic checking of the fitted models

It was necessary to fit a quadratic polynomial equation to describe the behavior of the dependent variables on the independent variables (
Bezerra et al., 2008
). The response surface regression procedure was used to fit the quadratic polynomial equation to the experimental data. The significance of all the coefficients of the constant, linear (
Estimated coefficients of the fitted quadratic polynomial equation for responses based ont-statistic

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Response surface model equations for the optimization of blending ratio of corn oil, sesame oil, and perilla oil

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- Analysis of variance

The statistical significance of the quadratic polynomial model equation was evaluated by an analysis of variance (ANOVA).
Table 6
shows the ANOVA results for the models that explain the response of the three dependent variables. The linear terms (
Analysis of variance for response of dependent variables

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- Effects of factors and response surface plots

RSM is a useful statistical technique for the evaluation of the relationships between independent (factor) and dependent (response) variables (
Dorta et al., 2013
).
Fig. 1
. shows the estimated response function and the effect of the independent variables (
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- Optimization of the oil blending ratio

The blending ratio of corn, sesame, and perilla oils was individually optimized for the oxidation induction time (
Optimal conditions for each dependent variable

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Multiple optimal conditions and verification of the predicted and experimental values

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Optimal blending ratios of corn oil, sesame oil, and perilla oil at the each optimal condition

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- Experimental verification of the predicted values at the optimal condition

The predicted values of the oxidation induction time (
Acknowledgements

This study was supported by grants from the National Fisheries Research & Development Institute, Seaweed Research Center

Baydar H
,
Marquard R
,
Turgut I
1999
Pure line selection for improved yield, oil content and different fatty acid composition of sesame, Sesamum indicum
Plant Breed
118
462 -
464
** DOI : 10.1046/j.1439-0523.1999.00414.x**

Bezerra MA
,
Santelli RE
,
Oliveira EP
,
Villar LS
,
Escaleira LA
2008
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
Talanta
76
965 -
977
** DOI : 10.1016/j.talanta.2008.05.019**

Cho S
,
Gu YS
,
Kim SB
2005
Extracting optimization and physical properties of yellow fin tuna (Thunnus albacares) skin gelatin compared to mammalian gelatins
Food Hydrocoll
19
221 -
229
** DOI : 10.1016/j.foodhyd.2004.05.005**

Cho S
,
Kim BM
,
Han KJ
,
Seo HY
,
Han YN
,
Yang EH
,
Kim DS
2009
Current status of the domestic processed laver market and manufacturers
Food Sci Ind
42
57 -
70

Dorta E
,
Lobo MG
,
Gonzalez M
2013
Optimization of factors affecting extraction of antioxidants from mango seed
Food Bioproc Technol
6
1067 -
1081
** DOI : 10.1007/s11947-011-0750-0**

Gkatzionis K
,
Hewson L
,
Hollowood T
,
Hort J
,
Dodd CER
,
Linforth RST
2013
Effect of Yarrowia lipolytica on blue cheese odour development: Flash profile sensory evaluation of microbiological models and cheeses
Int Dairy J
30
8 -
13
** DOI : 10.1016/j.idairyj.2012.11.010**

Hasenhuettl GL
,
Wan PJ
1992
Temperature effects on the determination of oxidative stability with the metrohm rancimat
JAOCS
69
525 -
527

Hwang ES
2013
Composition of amino acids, minerals, and heavy metals in differently cooked laver (Porphyra tenera)
J Korean Soc Food Sci Nutr
42
1270 -
1276
** DOI : 10.3746/jkfn.2013.42.8.1270**

Isa KM
,
Daud S
,
Hamidin N
,
Ismail K
,
Saad SA
,
Kasim FH
2011
Thermogravimetric analysis and the optimisation of bio-oil yield from fixed-bed pyrolysis of rice husk using response surface methodology (RSM)
Ind Crops Prod
33
481 -
487
** DOI : 10.1016/j.indcrop.2010.10.024**

Jo KS
,
Kim JH
,
Shin HS
1995
Effect of storage condition on the oxidative stability of lipid in roasted and roasted-seasoned laver (Porphyra tenera)
Korean J Food Sci Technol
27
902 -
908

2014
Statistical Database for Seasoned laver Export
Retrieved from on 2014

Kim J
,
Shin EC
,
Lim HJ
,
Yoon M
,
Yang H
,
Park J
,
Park EJ
,
Yoo H
,
Baek J
,
Cho S
2015
Thermal oxidative stability of various vegetable oils used for the preparation of the seasoned laver pyropia spp
fish aquat sci
18
xx -
xx

Lee J
,
Lee Y
,
Choe E
2008
Effect of sesamol, sesamin, and sesamolin extracted from roasted sesame oil on the thermal oxidation of methyl linolate
LWT - Food Sci Technol
41
1871 -
1875
** DOI : 10.1016/j.lwt.2007.11.019**

Lee SK
1999
Effect of packing on storage stability and chlorophyll contents of dried, roasted and roasted-seasoned laver during storage
J Fd Hyg Safety
14
164 -
139

Liang YR
,
Ye Q
,
Jin J
,
Liang H
,
Lu JL
,
Du YY
,
Dong JJ
2008
Chemical and instrumental assessment of green tea sensory preference
Int J Food Prof
11
258 -
272
** DOI : 10.1080/10942910701299430**

Montgomery DC
1996
Introduction to statistical quality control
3rd edn
Wiley
New York, US

Park BH
,
Choi HK
,
Cho HS
2001
A study on the oxidative stability and quality characteristics of kimbugak made of aqueous green tea
J Korean Soc Food Sci Nutr
30
557 -
564

Santos SC
,
Boaventura RA
2008
Adsorption modelling of textile dyes by sepiolite
Appl Clay Sci
42
137 -
145
** DOI : 10.1016/j.clay.2008.01.002**

Sinija VR
,
Mishra HN
2011
Fuzzy analysis of sensory data for quality evaluation and ranking of instant green tea powder and granules
Food Bioproc Technol
4
408 -
416
** DOI : 10.1007/s11947-008-0163-x**

Wang Y
,
Zhao M
,
Tang S
,
Song K
,
Han X
,
Ou S
2010
Evaluation of the oxidative stability of diacylglycerol-enriched soybean oil and palm olein under rancimat-accelerated oxidation conditions
J Am Oil Chem
87
483 -
491
** DOI : 10.1007/s11746-009-1521-1**

Yang JE
,
Kim HJ
,
Chung L
2012
Sensory characteristics and consumer acceptability of perilla porridges
Food Sci Biotechnol
21
785 -
797
** DOI : 10.1007/s10068-012-0102-5**

Zhao TT
,
Hong SI
,
Lee J
,
Lee JS
,
Kim IH
2012
Impact of roasting on the chemical composition and oxidative stability of perilla oil
J Food Sci
77
C1273 -
C127
** DOI : 10.1111/j.1750-3841.2012.02981.x**

Citing 'Monitoring and Optimization of the Effects of the Blending Ratio of Corn, Sesame, and Perilla Oils on the Oxidation and Sensory Quality of Seasoned Laver Pyropia spp.
'

@article{ E1HKAL_2015_v18n1_27}
,title={Monitoring and Optimization of the Effects of the Blending Ratio of Corn, Sesame, and Perilla Oils on the Oxidation and Sensory Quality of Seasoned Laver Pyropia spp.}
,volume={1}
, url={http://dx.doi.org/10.5657/FAS.2015.0027}, DOI={10.5657/FAS.2015.0027}
, number= {1}
, journal={Fisheries and aquatic sciences}
, publisher={The Korean Society of Fisheries and Aquatic Science}
, author={Cho, Suengmok
and
Kim, Jiyoung
and
Yoon, Minseok
and
Yang, Hyejin
and
Um, Min Young
and
Park, Joodong
and
Park, Eun-jeong
and
Yoo, Hyunil
and
Baek, Jeamin
and
Jo, Jinho}
, year={2015}
, month={Mar}