Crops ›› 2018, Vol. 34 ›› Issue (2): 166-170.doi: 10.16035/j.issn.1001-7283.2018.02.029

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Identification of Rice Varieties Using NIR Spectroscopy and SIMCA, PLS-DA Methods

Qu Ge,Chen Zhengguang,Wang Xue   

  1. College of Electrical and Information Engineering, Heilongjiang Bayi Agricultural University,Daqing163319, Heilongjiang, China
  • Received:2017-10-20 Revised:2018-01-02 Online:2018-04-20 Published:2018-08-27

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Abstract:

Four varieties of rice, including Kengjing 5, Kenjing 6, Kenjing 9 and Hongyu 001-1, seeds were selected as research object in this paper. Two discrimination methods, such as Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), were employed to identify the four varieties of rice seeds based on the spectroscopy data of the rice seeds. The discrimination result of four kinds of rice by using SIMCA method is 100%. The correlation coefficients were more than 0.95 and 0.94 for calibration and reference value, validation and reference value, respectively, and the recognition rate of four kinds of rice in the validation set was 100% when using PLS-DA discriminant method to establish discriminant model. The results indicated that the rapid identification of rice varieties can be achieved by using near infrared spectroscopy combined with SIMCA and PLS-DA.

Key words: Near infrared spectroscopy, Rice varieties, PLS-DA, SIMCA, Variety identification

Fig.1

Near-infrared spectrograms of all rice samples"

Fig.2

Scores cluster plot using first three principal components for all rice samples"

Table 1

Recognition rate of SIMCA model at different number of principal components under 2 kind of pretreatment methods (α=0.05)"

预处理方法
Pretreatment
method		 水稻品种
Rice variety		 识别率Recognition rate (%)
2 3 4 5 6
垦粳5号Kenjing 5 95 99 99 100 99
MSC 垦粳6号Kenjing 6 71 97 97 98 100
垦粳9号Kenjing 9 92 97 99 99 99
鸿育001-1 Hongyu 001-1 97 98 100 98 100
垦粳5号Kenjing 5 94 99 99 100 99
SNV 垦粳6号Kenjing 6 65 97 97 98 100
垦粳9号Kenjing 9 92 97 99 99 99
鸿育001-1 Hongyu 001-1 97 97 100 98 100

Table 2

Calibration and validation results of PLS-DA discriminant model"

样本集Sample set 模型参数Model parameters 垦粳5号Kenjing 5 垦粳6号Kenjing 6 垦粳9号Kenjing 9 鸿育001-1 Hongyu 001-1
校正集Calibration set R2 0.95 0.90 0.93 0.95
相关性Correlation 0.98 0.95 0.96 0.98
偏差Bias 2.63×10-6 -2.27×10-6 3.26×10-6 -3.61×10-6
验证集Validation set R2 0.95 0.89 0.92 0.95
相关性Correlation 0.97 0.94 0.96 0.97
偏差Bias 7.39×10-5 2.9×10-4 -3.17×10-6 -4.66×10-5

Fig.3

Regression plot of reference and predicted category variables of four different varieties of rice samples by PLS-DA"

Fig.4

Discriminant results for rice samples in validation set by PLS-DA model"

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