| 光谱学与光谱分析 |
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| Study on an Algorithm for Near Infrared Singular Sample Identification Based on Strong Influence Degree |
| WU Zhao-na1, DING Xiang-qian2, GONG Hui-li1*, DONG Mei3, WANG Mei-xun3 |
1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Center of Information Engineering, Ocean University of China, Qingdao 266071, China
3. Linyi Tobacco Co., Ltd. of Shangdong Province，Linyi 276000, China |
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Abstract Correcting sample selection and elimination of singular sample is very important for the quantitative and qualitative modeling of near infrared spectroscopy. However, methods for identification of singular sample available are generally based on data center estimates which require an experience decision threshold, this largely limit its recognition accuracy and practicability. Aiming at the low accuracy of the existing methods of singular sample recognition problem, this paper improves the existing metric - Leverage value and presents a new algorithm for near infrared singular sample identification based on strong influence degree. This metric reduces the dependence on the data center to a certain extent, so that the normal samples become more aggregation, and the distance between the singular samples and the normal samples is opened; at the same time, in order to avoid artificial setting threshold unreasonably according to experience, this paper introduces the concept of the jump degree in the field of statistics, and proposes an automatic threshold setting method to distinguish singular samples. In order to verify the validity of our algorithm, abnormal samples of 200 representative samples were eliminated in the calibration set with using Mahalanobis distance, Leverage- Spectral residual method and the algorithm presented in this paper respectively; then through partial least squares (PLS), the rest of the calibration samples were made quantitative modelings (took Nicotine as index), and the results of quantitative modelings were made a comparative analysis; besides, 60 representative testing samples were made a prediction through the modelings; at last, all the algorithms above were made a comparison with took Root Mean Square Error of Cross Validation (RMSECV), Correlation Coefficient (r) and Root Mean Square Error of Prediction (RMSEP) as evaluation Index. The experimental results demonstrate that the algorithm for near infrared singular sample identification based on strong influence degree significantly improves the accuracy of singular sample identification over existing methods. With lower RMSECV (0.104), RMSEP (0.112) and higher r (0.983), it also contribute to boost the stability and prediction ability of the model.
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Received: 2014-06-01
Accepted: 2014-09-05
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Corresponding Authors:
GONG Hui-li
E-mail: huiligong@163.com
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[1] Philip Williams, Karl Norris. Near Infrared Technology in the Agriculture and Food Industries. 2nd ed. Inc. St., American Association of Cereal Chemists, Minnesota USA: AACC, 2001.
[2] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立, 袁洪福, 陆婉珍). Modern Scientific Instruments(现代科学仪器), 2006, 16(3): 8.
[3] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. 77.
[4] YAN Yan-lu, ZHANG Lu-da, CHEN Bin, et al(严衍禄, 张录达, 陈 斌, 等). Modern Instruments(现代仪器), 2011，17(5): 5.
[5] Nieuwoudt H H, Prior B A, Pretorius I S, et al. Agricultural and Food Chemistry, 2004, 52(12): 3726.
[6] LENG Hong-qiong, GUO Ya-dong, LIU Wei, et al(冷红琼, 郭亚东, 刘 巍, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013，33(7): 1801.
[7] ZHU Shi-ping, WANG Yi-ming, ZHANG Xiao-chao, et al(祝诗平, 王一鸣, 张小超, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报)，2004，35(4): 115.
[8] CHEN Bin, ZOU Xian-yong, ZHU Wen-jing(陈 斌, 邹贤勇, 朱文静). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版)，2008，29(4): 277.
[9] YANG Hu, SHAO Hua(杨 虎, 邵 华). Chinese Journal of Engineering Mathematics(工程数学学报)，2009，26(1): 123.
[10] ZHANG De-ran(张德然). Statistical Research(统计研究), 2003, 5: 53. |
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