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Using Multivariate Techniques to Predict Wheat Flour Dough and Noodle Characteristics from Size‐Exclusion HPLC and RVA Data
Author(s) -
Ohm J.B.,
Ross A. S.,
Ong Y.L.,
Peterson C. J.
Publication year - 2006
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cc-83-0001
Subject(s) - chewiness , chemistry , wheat flour , high performance liquid chromatography , absorbance , multivariate statistics , chromatography , food science , mathematics , statistics
Flour proteins of hard and soft winter wheats grown in Oregon were characterized by size‐exclusion HPLC (SE‐HPLC). Flour pasting characteristics were assessed by a Rapid Visco Analyser (RVA). Principle component scores (PCS) were calculated from both RVA data and from absorbance area and % absorbance values from SE‐HPLC. The PCS and cross‐products, ratios, and squares were used to derive wheat classification and quality prediction models. A classification model calculated from PCS of SE‐HPLC data could reliably separate these hard and soft wheats. The prediction models for mixing and noodle characteristics showed better performance when calculated from PCS values of both SEHPLC and RVA data than from SE‐HPLC data only. The R 2 values of prediction models for mixograph absorption, peak time, and tolerance were 0.827, 0.813, and 0.851, respectively. Prediction models for noodle hardness, cohesiveness, chewiness, and resilience immediately after cooking had R 2 values of 0.928, 0.928, 0.896, and 0.855, respectively. These results suggest that multivariate methods could be used to develop reliable prediction models for dough mixing and noodle characteristics using just SE‐HPLC and RVA data.