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Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance

Yong Hao1, Pei Geng1, Wenhui Wu1

  • 1School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.

Molecules (Basel, Switzerland)
|December 19, 2019
PubMed
Summary

A new method using near-infrared spectroscopy and chemometrics accurately identifies rice varieties and detects genetically modified (GM) rice. This fast, non-destructive technique offers a more efficient alternative to current food testing methods.

Keywords:
partial least squares discriminant analysis (PLS-DA)portable near-infrared reflectance spectroscopy (NIRDRS)rice varietiessupport vector machines (SVM)transgenic rice

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Genetically modified (GM) food safety and environmental impact are growing concerns.
  • Existing GM food detection methods are inefficient, costly, and time-consuming.
  • Development of rapid, convenient, and accurate detection techniques is crucial.

Purpose of the Study:

  • To develop an efficient and convenient method for identifying rice varieties and detecting transgenic rice.
  • To evaluate the performance of near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with chemometrics for GM rice detection.

Main Methods:

  • Near-infrared diffuse reflectance spectroscopy (NIRDRS) was employed.
  • Multivariate calibration methods including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machines (SVM) were utilized.
  • Spectral pretreatment techniques such as Norris-Williams smooth (NWS), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky-Golay 1st derivative (SG 1st-Der) were applied for data enhancement.

Main Results:

  • The optimal model for distinguishing rice varieties combined SG 1st-Der pretreatment with SVM, achieving 98.33% accuracy.
  • Models for discriminating transgenic (Bt63)/non-transgenic rice, including SNV-SVM, MSC-SVM, and SG 1st-Der-PLS-DA, all demonstrated 100% accuracy.
  • These results indicate high efficacy in identifying rice varieties and detecting GM traits.

Conclusions:

  • Portable NIR spectroscopy coupled with chemometrics provides a fast, non-destructive, and accurate approach for identifying rice varieties.
  • This method effectively distinguishes transgenic (Bt63) rice from non-transgenic rice.
  • The developed technique offers a significant advancement over conventional GM food testing methods.