- Texture, a critical property of Asian wheat noodles, is normally assessed by
sensory evaluation. However, sensory evaluation may be impractical for wheat breeders
and noodle researchers who need to evaluate a large number of samples and have limited
sample. Instrumental Texture Profile Analysis (TPA) has been widely employed to
evaluate Asian wheat noodle texture. Nevertheless, a standardized method for
performing TPA on these products has not been established. A series of studies were
conducted to develop a testing method to best relate TPA results to sensory texture
characteristics of Asian wheat noodles.
First, the optimum TPA testing conditions (crosshead speed and degree of
deformation) were determined for each noodle category (alkaline, instant fried, salted
flat, and salted round), and were defined as the conditions which best related their results
to the sensory data. Partial Least Squares (PLS2) was used to examine relationships
between sensory first-chew characteristics (hardness, cohesiveness, springiness,
denseness, starch between teeth, and toothpull) and TPA output (peak areas and heights).
Response Surface Methodology determined the optimum TPA conditions (crosshead speed and % deformation) as follow: 1 mm/s and 85 % for alkaline, 1 mm/s and 70% for
instant fried and salted round, and 5 mm/s and 65 % for salted flat noodles.
Second, the effects of two sample cooking factors: noodle weights (20, 50, 100
g) and noodle to water ratios (1:10, 1:20) and three holding factors: media (with, without
water), temperatures (25, 55 °C), and times (2, 15, 30 min), on the TPA results were
investigated. Cooking factors did not significantly affect the TPA results but higher
holding temperatures, the use of water as a holding media, and longer holding time
significantly decreased most TPA parameters' values.
Third, relationships between TPA and sensory first-chew parameters were
examined for each noodle category. Predictive models of each sensory first-chew
attribute were developed using linear and nonlinear (Fechner and Stevens) models, with
single and multiple parameters. Hardness could be satisfactorily predicted by a single
TPA parameter (area 1 or area 2), but other attributes required multiple parameters in the
models to be satisfactorily predicted. Different model types were selected for each
sensory attribute and noodle category. TPA peak area 1 and 2 were the best predictors
for first-chew characteristics of cooked Asian wheat noodles.