|Park, Seok Ho|
|Seabourn, Bradford - Brad|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/25/2005
Publication Date: 9/11/2005
Citation: Park, S., Seabourn, B.W., Xie, F., Chung, O.K. 2005. Prediction of alkaline noodle color and polyphenol oxidase activity using near-infrared reflectance (NIR) spectroscopy of wheat grain, meal and flour. Abstract No. 224 in: 2005 AACC Annual Meeting Program Book. p.140. Meeting Abstract. Interpretive Summary:
Technical Abstract: Noodle color is an important quality trait to wheat breeders as well as consumers. This study investigated the potential of NIR spectroscopy to predict noodle color and polyphenol oxidase (PPO) content directly from whole grain, meal, and flour. A total of 585 hard winter wheat samples (375 for calibration and 210 for validation) harvested in 2002 and 2003 were used. Reflectance measurements were collected over a wavelength range of 400-2498 nm. Alkaline noodle dough was made, and color was determined at 0 and 24 hr. PPO activity was also determined from the whole grain, meal and flour. Unscrambler (v8.0.5), a program for multivariate statistical analysis, was then used to process the spectral data and to develop NIR partial least squares (PLS) calibration models from the spectra and laboratory data. Calibration models were developed for predicting noodle color (L*, a*, and b* at 0 and 24 hr) and PPO content from grain, meal, and flour. Calibration model R-square values for PPO content were generally and unacceptably lower than those for noodle color. For noodle color, the highest R-square value was for L* at 24 hr from flour (0.84 and 0.68 for calibration and validation, respectively), with an RPD of 2.46. Other calibration models for noodle color at 0 and 24 hr from whole grain and meal also showed very comparable R-square values with an even higher RPD. The highest R-squares for a* and b* at 24 hr were 0.82 and 0.84 for calibration, and 0.78 and 0.70 for validation with RPD values of 3.23 and 2.98, respectively. The data suggest that there is a good potential for predicting noodle color using NIR spectra from such basic materials as grain, meal, and flour.