Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

J. S. Bao; Y. Z. Cai; H. Corke

doi:10.1111/j.1365-2621.2001.tb08215.x

Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

J. S. Bao, Y. Z. Cai, H. Corke^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

86 Scopus citations

Abstract

A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R²]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R²=0.92), and breakdown (BD) (SEP=10.2 RVU, R²=0.88); and gelatinization peak temperature (T_p) (SEP=1.33°C, R²=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (T_o), and textural properties of chewiness, hardness and gumminess, were modeled less well with R² between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.

Original language	English
Pages (from-to)	936-939
Number of pages	4
Journal	Journal of Food Science
Volume	66
Issue number	7
DOIs	https://doi.org/10.1111/j.1365-2621.2001.tb08215.x
State	Published - 2001
Externally published	Yes

Keywords

NIR
Near-infrared reflectance spectroscopy
Rice
Starch quality

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10.1111/j.1365-2621.2001.tb08215.x

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@article{098fa8afe2b94f29997e8769c0582ba4,

title = "Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy",

abstract = "A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.",

keywords = "NIR, Near-infrared reflectance spectroscopy, Rice, Starch quality",

author = "Bao, {J. S.} and Cai, {Y. Z.} and H. Corke",

year = "2001",

doi = "10.1111/j.1365-2621.2001.tb08215.x",

language = "英语",

volume = "66",

pages = "936--939",

journal = "Journal of Food Science",

issn = "0022-1147",

publisher = "Wiley-Blackwell",

number = "7",

}

TY - JOUR

T1 - Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

AU - Bao, J. S.

AU - Cai, Y. Z.

AU - Corke, H.

PY - 2001

Y1 - 2001

N2 - A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.

AB - A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.

KW - NIR

KW - Near-infrared reflectance spectroscopy

KW - Rice

KW - Starch quality

UR - http://www.scopus.com/inward/record.url?scp=0034775450&partnerID=8YFLogxK

U2 - 10.1111/j.1365-2621.2001.tb08215.x

DO - 10.1111/j.1365-2621.2001.tb08215.x

M3 - 文章

AN - SCOPUS:0034775450

SN - 0022-1147

VL - 66

SP - 936

EP - 939

JO - Journal of Food Science

JF - Journal of Food Science

IS - 7

ER -

Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

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Keywords

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