Food Analytical Methods

, Volume 10, Issue 7, pp 2398–2403 | Cite as

Monitoring of Paddy Rice Varieties Based on the Combination of the Laser-Induced Fluorescence and Multivariate Analysis



Paddy rice is one of three major cereal crops in China, and the number of the paddy rice variety is increasing rapidly. The paddy rice variety is strongly related to crop yield and is also difficult to classify by using the naked eyes. A reliable approach is essential for accurately identifying different paddy rice varieties. Laser-induced fluorescence (LIF) technology has been widely utilized in many fields due to its particular advantages (rapid, non-intrusive, and sensitive). Thus, LIF combined with multivariate analysis that contained principal component analysis (PCA) and support vector machine (SVM) was proposed and was attempted to be utilized to identify different paddy rice varieties in this investigation. These fluorescence spectra displayed a high degree of multi-collinearity, and about 96.58% of the total variance contained in the laser-induced fluorescence spectra which were excited by a 532-nm excited wavelength can be explained by using the first three principle components. A SVM model with the help of threefold cross validation was used for paddy rice variety identification based on new variables calculated utilizing PCA. The numerical and experimental results displayed by using a confusion matrix and the classification accuracy can reach up to 91.36%. Thus, LIF technology combined with multivariate analysis can provide researchers with a faster and more effective tool for identifying different paddy rice varieties.


Laser-induced fluorescence Multivariate analysis Paddy rice variety Classification 



This work was supported by the National Natural Science Foundation of China (Grant No. 41601360), Fundamental Research Funds for the Central Universities (Grant No. 2042016kf0008), Natural Science Foundation of Hubei Province (Grant No. 2015CFA002), and Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No.15R01). The authors wish to thank the College of Plant Science & Technology of Huazhong Agricultural University for providing the experimental samples.

Compliance with Ethical Standards

Conflict of Interest

Jian Yang declares that he has no conflict of interest. Jia Sun declares that she has no conflict of interest. Lin Du declares that he has no conflict of interest. Biwu Chen declares that he has no conflict of interest. Zhenbing Zhang declares that he has no conflict of interest. Shuo Shi declares that he has no conflict of interest. Wei Gong declares that he has no conflict of interest.

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  2. 2.School of Physics and TechnologyWuhan UniversityWuhanChina
  3. 3.Collaborative Innovation Center of Geospatial TechnologyWuhanChina

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