시중 즉석 조리 면의 Back Extrusion 텍스처 데이터에 대한 Partial Least Square Regression 분석

김수경¹, 이승주¹^,^*

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

Su kyoung Kim¹, Seung Ju Lee¹^,^*

¹동국대학교 식품공학과

¹Department of Food Science and Technology, Dongguk University

^*Corresponding author: Seung Ju Lee, Department of Food Science and Technology, Dongguk University, 26, 3-ga, Pil-dong, Jung-gu, Seoul 100-715, Korea, Tel: +82-2-2260-3372; Fax: +82-2-2260-3372, E-mail: Lseungju@dongguk.edu

ⓒ Copyright 2010 Korean Society for Food Engineering. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Nov 18, 2009; Revised: Jan 30, 2010; Accepted: Feb 02, 2010

Published Online: Feb 28, 2010

Abatract

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