Flour particle size distribution in Chinese winter wheat and measurement by near infrared spectroscopy
Material type:
ArticlePublication details: 2005. Beijing, China : Institute of Crop Sciences,ISSN: - No (Revista en electrónico)
- 0496-3490
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | CIS-4676 (Browse shelf(Opens below)) | 1 | Available | 633964 |
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in Chinese, English abstract
Peer-review: No - Open Access: Yes|http://211.155.251.148:8080/zwxb/EN/column/column81.shtml
Flour particle size is an important quality parameter which has a significant effect on food processing. The objective of this study is to investigate the distribution of flour particle size in Chinese winter wheat cultivars and the rapid testing method by near infrared transmittance spectroscopy. Total of 256 wheat cultivars and advanced lines from four major wheat regions, i.e., North Winter Region, Yellow Huai Facultative Wheat Region, Middle and Low Yangtze Winter Region, and Southwestern Winter Wheat Region, were grown in Anyang in 2001 - 2002 season. They were used to measure flour particle size with laser diffraction particle size analyzer and near infrared transmittance spectroscopy (NITS), respectively. According to flour particle size by laser diffraction particle size analyzer, the Chinese winter wheat could be divided into three distinguished types, hard, soft and mixed wheats. It has been found that the hard wheat were dominant in North China , while a high percentage of soft wheat cultivars in South China. The percentage of hard, soft, and mixed wheat were 5914 %, 2811 % and 1215 %, respectively. A determination coefficient of prediction set of 0192 was observed between flour particle size determined by laser diffraction particle size analyzer and those predicted by NITS. Prediction residual error sum of square (PRESS) and cross validation were adopted to find the optimal number of principal components in developing calibration model. Model was optimized by deleting outlier samples twice and then RSQ of calibration set increased from 0182 to 0192 and SEC decreased from 12.75 to 8.54. This model could be used for selection of hardness in wheat breeding program, wheat quality classification and marketing. In addition , we found that the model developed with total samples was a little inferior to those developed with wheat samples from various wheat regions.
Global Wheat Program
Chinese
0603
INT2411
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