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Nondestructive Prediction of Total Phenolics, Flavonoid Contents, and Antioxidant Capacity of Rice Grain Using Near-Infrared Spectroscopy

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Institute of Statistics, Department of Mathematics, Zhejiang University, Yuquan Campus, Hangzhou 310027, Peopleʼs Republic of China; Key Laboratory for Nuclear-Agricultural Sciences of Zhejiang Province and Chinese Ministry of Agriculture, Institute of Nuclear Agricultural Sciences, Zhejiang University, Hua Jiachi Campus, Hangzhou 310029, Peopleʼs Republic of China; and Department of Applied Bioscience, Zhejiang University, Hua Jiachi Campus, Hangzhou 310029, Peopleʼs Republic of China
* To whom correspondence should be addressed. E-mail: [email protected]
†Department of Mathematics.
‡Institute of Nuclear Agricultural Sciences.
§Department of Applied Bioscience.
Cite this: J. Agric. Food Chem. 2008, 56, 18, 8268–8272
Publication Date (Web):August 27, 2008
https://doi.org/10.1021/jf801830z
Copyright © 2008 American Chemical Society
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    Abstract

    Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination (r2) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r2 of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods (r2 < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.

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