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KMID : 1024420100140010075
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Food Engineering Progress
2010 Volume.14 No. 1 p.75 ~ p.79
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Analysis of Partial Least Square Regression on Textural Data from Back Extrusion Test for Commercial Instant Noodles
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Kim Su-Kyoung
Lee Seong-Ju
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Abstract
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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.
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KEYWORD
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partial Least square regression, back extrusion test, texture, instant noodles, prediction
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