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

Previous Articles     Next Articles

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

RichHTML

11

PDF (PC)

237

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"

[1] 梁剑, 刘斌美, 陶亮之 , 等. 基于水稻种子近红外特征光谱的品种鉴别方法研究. 光散射学报, 2013,25(4):423-428.
doi: 10.3969/j.issn.1004-5929.2013.04.014
[2] Nicolaï B M, Beullens K, Bobelyn E , et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:A review. Postharvest Biology & Technology, 2007,46(2):99-118.
doi: 10.1016/j.postharvbio.2007.06.024
[3] 王多加, 周向阳, 金同铭 , 等. 近红外光谱检测技术在农业和食品分析上的应用. 光谱学与光谱分析, 2004,24(4):447-450.
[4] He Y, Li X . Discrimination of varieties of Chinese bayberry using near infrared spectroscopy.//In International Conference on Photonics and Imaging in Biology and Medicine. 2006.
doi: 10.1117/12.710942
[5] Hu W, Guo X X, Wang X C , et al. Rapid discrimination of different grades of white croaker surimi by tri-step infrared spectroscopy combined with soft independent modeling of class analogy (SIMCA). Food Analytical Methods, 2016,9(4):831-839.
[6] Hirri A, Bassbasi M, Platikanov S , et al. FTIR spectroscopy and PLS-DA classification and prediction of four commercial grade virgin olive oils from morocco. Food Analytical Methods, 2016,9(4):1-8.
[7] 王逸之, 董文渊, 李永和 , 等. 基于近红外光谱结合PLS-DA法的野外竹种识别技术研究. 竹子研究汇刊, 2014,33(4):16-20.
[8] 杨忠, 任海青, 江泽慧 . PLS-DA法判别分析木材生物腐朽的研究. 光谱学与光谱分析, 2008,28(4):793-796.
doi: 10.3964/j.issn.1000-0593.2008.04.018
[9] 芦永军, 曲艳玲, 宋敏 . 多元散射校正算法用于近红外相关光谱的处理研究. //全国近红外光谱学术会议, 2006.
[10] 常敏, 褚鹏蛟, 徐可欣 . 近红外漫反射光谱无损检测乳粉蛋白质的研究. 光谱学与光谱分析, 2007,27(1):43-45.
doi: 10.3321/j.issn:1000-0593.2007.01.012
[11] 赵志伟 . 白檀果实油含量及组分近红外定标模型构建. 株洲:中南林业科技大学, 2015.
[12] 惠光艳, 孙来军, 王佳楠 , 等. 可见-近红外光谱的小麦硬度预测模型预处理方法的研究. 光谱学与光谱分析, 2016,36(7):2111-2116.
[13] 施丰成, 李东亮, 冯广林 , 等. 基于近红外光谱的PLS-DA算法判别烤烟烟叶产地. 烟草科技, 2013,48(4):56-59.
doi: 10.3969/j.issn.1002-0861.2013.04.014
[14] Mazivila S J, de Santana F B, Mitsutake H , et al. Discrimination of the type of biodiesel/diesel blend (B5) using mid-infrared spectroscopy and PLS-DA. Fuel, 2015,142:222-226.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Guangcai Zhao,Xuhong Chang,Demei Wang,Zhiqiang Tao,Yanjie Wang,Yushuang Yang,Yingjie Zhu. General Situation and Development of Wheat Production[J]. Crops, 2018, 34(4): 1 -7 .
[2] Baoquan Quan,Dongmei Bai,Yuexia Tian,Yunyun Xue. Effects of Different Leaf-Peg Ratio on Photosynthesis and Yield of Peanut[J]. Crops, 2018, 34(4): 102 -105 .
[3] Xuefang Huang,Mingjing Huang,Huatao Liu,Cong Zhao,Juanling Wang. Effects of Annual Precipitation and Population Density on Tiller-Earing and Yield of Zhangzagu 5 under Film Mulching and Hole Sowing[J]. Crops, 2018, 34(4): 106 -113 .
[4] Wenhui Huang, Hui Wang, Desheng Mei. Research Progress on Lodging Resistance of Crops[J]. Crops, 2018, 34(4): 13 -19 .
[5] Yun Zhao,Cailong Xu,Xu Yang,Suzhen Li,Jing Zhou,Jicun Li,Tianfu Han,Cunxiang Wu. Effects of Sowing Methods on Seedling Stand and Production Profit of Summer Soybean under Wheat-Soybean System[J]. Crops, 2018, 34(4): 114 -120 .
[6] Mei Lu,Min Sun,Aixia Ren,Miaomiao Lei,Lingzhu Xue,Zhiqiang Gao. Effects of Spraying Foliar Fertilizers on Dryland Wheat Growth and the Correlation with Yield Formation[J]. Crops, 2018, 34(4): 121 -125 .
[7] Xiaofei Wang,Haijun Xu,Mengqiao Guo,Yu Xiao,Xinyu Cheng,Shuxia Liu,Xiangjun Guan,Yaokun Wu,Weihua Zhao,Guojiang Wei. Effects of Sowing Date, Density and Fertilizer Utilization Rate on the Yield of Oilseed Perilla frutescens in Cold Area[J]. Crops, 2018, 34(4): 126 -130 .
[8] Pengjin Zhu,Xinhua Pang,Chun Liang,Qinliang Tan,Lin Yan,Quanguang Zhou,Kewei Ou. Effects of Cold Stress on Reactive Oxygen Metabolism and Antioxidant Enzyme Activities of Sugarcane Seedlings[J]. Crops, 2018, 34(4): 131 -137 .
[9] Jie Gao,Qingfeng Li,Qiu Peng,Xiaoyan Jiao,Jinsong Wang. Effects of Different Nutrient Combinations on Plant Production and Nitrogen, Phosphorus and Potassium Utilization Characteristics in Waxy Sorghum[J]. Crops, 2018, 34(4): 138 -142 .
[10] Na Shang,Zhongxu Yang,Qiuzhi Li,Huihui Yin,Shihong Wang,Haitao Li,Tong Li,Han Zhang. Response of Cotton with Vegetative Branches to Plant Density in the Western of Shandong Province[J]. Crops, 2018, 34(4): 143 -148 .