Browse > Article

Analysis of Partial Least Square Regression on Textural Data from Back Extrusion Test for Commercial Instant Noodles  

Kim, Su kyoung (Department of Food Science and Technology, Dongguk University)
Lee, Seung Ju (Department of Food Science and Technology, Dongguk University)
Publication Information
Food Engineering Progress / v.14, no.1, 2010 , pp. 75-79 More about this Journal
Abstract
Partial least square regression (PLSR) was executed on curve data of force-deformation from back extrusion test and sensory data for commercial instant noodles. Sensory attributes considered were hardness (A), springiness (B), roughness (C), adhesiveness to teeth (D), and thickness (E). Eight and two kinds of fried and non-fried instant noodles respectively were used in the tests. Changes in weighted regression coefficients were characterized as three stages: compaction, yielding, and extrusion. Correlation coefficients appeared in the order of E>D>A>B>C, root mean square error of prediction D>C>E>B>A, and relative ability of prediction D>C>E>B>A. Overall, 'D' was the best in the correlation and prediction. 'A' with poor prediction ability but high correlation was considered good when determining the order of magnitude.
Keywords
partial least square regression; back extrusion test; texture; instant noodles; prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Meullent JFC, Sitakalin C, Marks BP. 1999. Prediction of rice texture by spectral stress strain analysis: A novel technique for treating instrumental extrusion data used for predicting sensory texture profiles. J. Texture Stud. 30: 435-450   DOI   ScienceOn
2 Song JM, Shin SN, Park HR, Yoo BS. 2001. Effect of potato starch content on physical properties of ramyon. J. Korean Soc. Food Sci. Nutr. 30: 450-454
3 Hung S, Morrison WR. 1988. Aspects of proteins in Chinese and British common (hexaploid) wheats related to quality of white and yellow Chinese noodles. J. Cereal Sci. 8: 177-187   DOI
4 Esbensen KH. 2001. Multivariate Data Analysis in Practice, 5th ed. CAMO Technologies, Corvallis, USA. pp. 115-219
5 Oh NH, Seib PA, Deyoe CW, Ward AB. 1985. Noodles II. The surface firmness of cooked noodles from soft and hard wheat flours. Cereal Chem. 62: 431-436
6 Epstein J, Morris CF, Huber KC. 2002. Instrumental texture of white salted noodles prepared from recombinant inbred lines of wheat differing in the three granule bound starch synthase (waxy) genes. J. Cereal Sci. 35: 51-63   DOI   ScienceOn
7 Oh NH, Seib PA, Deyoe CW, Ward AB. 1983. Noodles I. Measuring the textural characteristics of cooked noodles. Cereal Chem. 60: 433-438
8 Sitakalin C, Meullenet JF. 2000. Prediction of cooked rice texture using extrusion and compression tests in conjunction with spectral stress strain analysis. Cereal Chem. 77: 501-506   DOI   ScienceOn
9 Szczesniak AS. 1968. Correlation between objective and sensory texture measurements. Food Technol. 22: 981-985
10 Seib PA, Liang X, Guan F, Liang YT, Yang HC. 2000. Comparison of Asian noodles from some hard white and hard red wheat flours. Cereal Chem. 77: 816-822   DOI   ScienceOn
11 Lee CH, Gore PJ, Lee HD, Yoo BS, Hong SH. 1987. Utilization of Australian wheat for Korean style dried noodle making. J. Cereal Sci. 6: 283-297   DOI
12 Zhao LF, Seib PA. 2005. Alkaline-carbonate noodles from hard winter wheat flours varying in protein, swelling power and polyphenol oxidase. Cereal Chem. 82: 504-516   DOI   ScienceOn
13 Kim KO, Kim SS, Sung NK, Lee YC. 1993. Sensory Evaluation Methods and Applications. Shin Kwang Publishing Co., Seoul, Korea. pp. 127-193
14 eong JH. 1998. The effects of eggs on the quality properties of ramyon. Korean J. Food Nutr. 11: 420-425