光谱学与光谱分析 |
|
|
|
|
|
Outlier Sample Discriminating Methods for Building Calibration Model in Melons Quality Detecting Using NIR Spectra |
TIAN Hai-qing, WANG Chun-guang*, ZHANG Hai-jun, YU Zhi-hong, LI Jian-kang |
College of Machinery and Electrical Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China |
|
|
Abstract Outlier samples strongly influence the precision of the calibration model in soluble solids content measurement of melons using NIR Spectra. According to the possible sources of outlier samples, three methods (predicted concentration residual test; Chauvenet test; leverage and studentized residual test) were used to discriminate these outliers respectively. Nine suspicious outliers were detected from calibration set which including 85 fruit samples. Considering the 9 suspicious outlier samples maybe contain some no-outlier samples, they were reclaimed to the model one by one to see whether they influence the model and prediction precision or not. In this way, 5 samples which were helpful to the model joined in calibration set again, and a new model was developed with the correlation coefficient (r) 0.889 and root mean square errors for calibration (RMSEC) 0.601°Brix. For 35 unknown samples, the root mean square errors prediction (RMSEP) was 0.854°Brix. The performance of this model was more better than that developed with non outlier was eliminated from calibration set(r=0.797, RMSEC=0.849°Brix, RMSEP=1.19°Brix), and more representative and stable with all 9 samples were eliminated from calibration set(r=0.892, RMSEC=0.605°Brix, RMSEP=0.862°Brix).
|
Received: 2011-12-02
Accepted: 2012-02-25
|
|
Corresponding Authors:
WANG Chun-guang
E-mail: wcgjdy@yahoo.com.cn
|
|
[1] Mitsuru T, Masahiro T, Naoki S, et al. Food Research International, 2009, 42: 137. [2] Kuroki S, Tohro M, Sakurai N. Journal of the Japanese Society for Horticultural Science, 2006, 75: 415. [3] Ito H, Morimoto S, Yamauchi R. Acta Hortticulturae, 2001, 566:483. [4] Dull G G, Leffler R G, Birth G S, et al. Transactions of the American Society of Agricultural Engineers, 1992, 35:735. [5] TIAN Hai-qing, WANG Chun-guang, WU Gui-fang(田海清, 王春光, 吴桂芳). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 130. [6] 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 for Agricultural Machinery(农业机械学报), 2004, 35(4): 115. [7] SHI Yong-gang, FENG Xin-hu, LI Zi-cun(史永刚,冯新沪,李子存). Chemometrics(化学计量学). Beijing: China Petrochemical Press(北京:中国石化出版社), 2003. 35. [8] Fuller M P, Ritter G L, Draper C S. Applied Spectroscopy, 1988, 42(2): 217. [9] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通, 等). Modern Near-Infrared Spectral Analysis Technology(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京: 中国石油化工出版社), 2000. [10] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Basis and Application of Near-Infrared Spectroscopy(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社),2005. [11] XU Hui-rong, CHEN Xiao-wei, YING Yi-bin(徐惠荣, 陈晓伟, 应义斌). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 126.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|