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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.
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.
Fisheries and aquatic sciences. 2015. Mar, 18(1): 27-33
Copyright © 2015, The Korean Society of Fisheries and Aquatic Science
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 12, 2014
  • Accepted : December 12, 2014
  • Published : March 31, 2015
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About the Authors
Suengmok Cho
Korea Food Research Institute, Sungnam 463-746, Korea
Jiyoung Kim
Korea Food Research Institute, Sungnam 463-746, Korea
Minseok Yoon
Korea Food Research Institute, Sungnam 463-746, Korea
Hyejin Yang
Korea Food Research Institute, Sungnam 463-746, Korea
Min Young Um
Korea Food Research Institute, Sungnam 463-746, Korea
Joodong Park
CJ Food R&D, CJ Cheiljedang, Seoul 152-051, Korea
Eun-jeong Park
Seaweed Research Center, National Fisheries Research and Development Institute, Mokpo 530-831, Korea
Hyunil Yoo
Seaweed Research Center, National Fisheries Research and Development Institute, Mokpo 530-831, Korea
Jeamin Baek
Seaweed Research Center, National Fisheries Research and Development Institute, Mokpo 530-831, Korea
Jinho Jo
Korea Food Research Institute, Sungnam 463-746, Korea
jhjo@kfri.re.kr
Abstract
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.
Keywords
Introduction
Seasoned laver is a unique seaweed product that is prepared by roasting a sheet of dried laver 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.
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 ( Pyropia spp.) was purchased from Korea Fishery Co., Ltd (Pyeongtaek, Korea). All reagents used were analytical grade.
- 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 ( X 1 and X 2 ) were defined using the following equations, and their range and levels are shown in Table 2 .
  • 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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Blending ratio of corn, sesame, and perilla oils at the center point
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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X1 = corn oil / (sesame oil + perilla oil); X2 = 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 ).
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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X1 = corn oil / (sesame oil + perilla oil); X2 = sesame oil / perilla oil. Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
- 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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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.
- Optimization procedure
The blending ratio of corn, sesame, and perilla oils was individually optimized for the oxidation induction time ( 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 ).
- 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 ( 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.
Estimated coefficients of the fitted quadratic polynomial equation for responses based ont-statistic
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Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
Response surface model equations for the optimization of blending ratio of corn oil, sesame oil, and perilla oil
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Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
- 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 ( 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 ).
Analysis of variance for response of dependent variables
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Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
- 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 ( 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.
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Three dimensional response surface plots for oxidation induction time (Y1), sensory score (Y2), and unit cost (Y3).
- Optimization of the oil blending ratio
The blending ratio of corn, sesame, and perilla oils was individually optimized for the oxidation induction time ( 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%.
Optimal conditions for each dependent variable
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Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
Multiple optimal conditions and verification of the predicted and experimental values
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Y1, oxidation induction time (h); Y2, sensory score (point). Value of Y3 (unit cost) at the multiple optimal conditions was 2,669 won/L.
Optimal blending ratios of corn oil, sesame oil, and perilla oil at the each optimal condition
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Y1, oxidation induction time (h); Y2, sensory score (point); Y3, unit cost (won/L).
- Experimental verification of the predicted values at the optimal condition
The predicted values of the oxidation induction time ( 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.
Acknowledgements
This study was supported by grants from the National Fisheries Research & Development Institute, Seaweed Research Center
References
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
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
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
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
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
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
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
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
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
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
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
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
Yang JE , Kim HJ , Chung L 2012 Sensory characteristics and consumer acceptability of perilla porridges Food Sci Biotechnol 21 785 - 797
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