| 光谱学与光谱分析 |
|
|
|
|
|
| Application of Visible/Near-Infrared Spectroscopy to the Determination of Catalase and Peroxidase Content in Barley Leaves |
| ZHAO Yun1, ZHANG Chu2, LIU Fei2, KONG Wen-wen2, HE Yong2* |
1. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
|
|
Abstract Visible/near-infrared spectroscopy was applied to determine the content of catalase (CAT) and peroxidase (POD) in barley leaves under the herbicide stress of propyl 4-(2-(4, 6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate (ZJ0273). The spectral data of the barley leaves in the range of 500~900 nm were preprocessed by moving average with 11 points. Seven outlier samples for CAT and 8 outlier samples for POD were detected and removed by Monte Carlo-partial least squares (MCPLS). PLS, least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models were built for both CAT and POD. ELM model obtained best results for CAT, with correlation coefficient of calibration (Rc) of 0.916 and correlation coefficient of prediction (Rp) of 0.786. PLS model obtained best prediction results for POD, with Rc of 0.984 and Rp of 0.876. Successive projections algorithm (SPA) was applied to select 8 and 19 effective wavelengths for CAT and POD, respectively. PLS, LS-SVM and ELM models were built using the selected effective wavelengths of CAT and POD. ELM model performed best for CAT and POD prediction, with Rc of 0.928 and Rp of 0.790 for CAT and Rc of 0.965 and Rp of 0.941 for POD. The prediction results using the full spectral data and the effective wavelengths were quite close, and the prediction performance for POD was much better than the prediction performance for CAT, and the studies should be further explored to build more precise and more robust models for CAT and POD determination. The overall results indicated that it was feasible to use visible/near-infrared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ0273.
|
|
Received: 2014-04-14
Accepted: 2014-06-28
|
|
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] LIU Na, LI Pin-dao(刘 娜, 李频道). Shanghai Agricultural Science and Technology(上海农业科技), 2008, (2): 125.
[2] WU Yu-zhen, FENG Bao, DONG Pu(武玉臻, 冯 宝, 董 璞). Auhui Agricultural Science Bulletin(安徽农学通报), 2006, 12(4): 102.
[3] ZHANG Wen-fang, ZHANG Fan, JIN Zong-lai, et al(张文芳，张 帆，金宗来，等). Journal of Nuclear Agricultural Sciences(核农学报), 2008, 22(4): 488.
[4] Jin Z L, Zhang F, Ahmed Z I, et al. Pesticide Biochemistry and Physiology, 2010, 98(1): 1.
[5] Liu F, Nie P C, Huang M, et al. Science China Information Sciences, 2011, 54(3): 598.
[6] Liu F, Zhang F, Jin Z L, et al. Analytica Chemica Acta, 2008, 629(1-2): 56.
[7] Sankaran S, Mishra A, Maja J M, et al. Computers and Electronics in Agriculture, 2011, 77(2): 127.
[8] Kong W W, Zhao Y, Liu F, et al. Sensors (Basel), 2012, 12(8): 10871.
[9] XU Xin-gang, ZHAO Chun-jiang, WANG Ji-hua, et al(徐新刚, 赵春江, 王纪华，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2013, 32(4): 351, 365.
[10] Cakmak I, Strboe D, Marschner H. Journal of Experimental Botany, 1993, 44(1): 127.
[11] Zhou W J, Leul M. Plant Growth Regulation, 1998, 26(1): 41.
[12] Guo W L, Du Y P, Zhou Y C, et al. World Journal of Microbiology and Biotechnology, 2012, 28(3): 993.
[13] Bala M, Singh M. Industrial Crops and Products, 2013, 42: 357.
[14] Nie P C, Wu D, Yang Y, et al. Journal of Food Engineering, 2012, 109(1): 155.
[15] Huang G B, Zhu Q Y, Siew C K. Neurocomputing, 2006, 70(1-3): 489. |
| [1] |
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. |
| [2] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [3] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
| [4] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
| [5] |
HOU Bing-ru1, LIU Peng-hui1, ZHANG Yang1, HU Yao-hua1, 2, 3*. Prediction of the Degree of Late Blight Disease Based on Optical Fiber Spectral Information of Potato Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1426-1432. |
| [6] |
ZHANG Fu1, 2, 3, CUI Xia-hua1, DING Ke4*, ZHANG Ya-kun1, WANG Yong-xian1, PAN Xiao-qing5. Study on the Influence of Different Pretreatment Methods on Gender Determination of Multiposition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 434-439. |
| [7] |
ZHANG Fu1, 2, 3, CUI Xia-hua1, ZHANG Ya-kun1, WANG Yong-xian1. Relationship Between Visible/Near Infrared Spectral Data and Fertilization Information at Different Positions of Hatching Eggs[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3064-3068. |
| [8] |
LI Qing-xu1, WANG Qiao-hua1, 2*, MA Mei-hu3, XIAO Shi-jie1, SHI Hang1. Non-Destructive Detection of Male and Female Information of Early Duck Embryos Based on Visible/Near Infrared Spectroscopy and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1800-1805. |
| [9] |
ZHANG Xu1, ZHANG Tian-gang2, MU Wei-song1, FU Ze-tian2,3, ZHANG Xiao-shuan2,3*. Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 229-235. |
| [10] |
LIU Yan-de, ZHANG Yu, JIANG Xiao-gang, SUN Xu-dong, XU Hai, LIU Hao-chen. Detection on Firmness and Soluble Solid Content of Peach During Different Storage Days[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 243-249. |
| [11] |
KANG Wen-cui, LIN Hao*, ZUO Min*, WANG Zhuo, DUAN Ya-xian, CHEN Quan-sheng, LIN Jin-jin. Quantitative Determination of the Number of Moldy Wheat Colonies Based on the Nanoscaled Colorimetric Sensor-Visible/Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1569-1574. |
| [12] |
TANG Yun-feng1, 2, CHAI Qin-qin1, 2*, LIN Shuang-jie1, 2, HUANG Jie1, 2, LI Yu-rong1, 2, WANG Wu1, 2. Study on Detection System of Grape Seed Oil Adulteration Based on Visible/Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 202-208. |
| [13] |
JIANG Xue-song1, ZHANG Bin2, ZHAO Tian-xia2, XIONG Chao-ping2, SHEN Fei2*, HE Xue-ming2, LIU Qin2, ZHOU Hong-ping1*, LIU Xing-quan3. Screening of DON Contamination in Wheat Based on Visible/Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3904-3909. |
| [14] |
HUANG Yu-ping1, LIU Ying1, YANG Yu-tu1, ZHANG Zheng-wei2, CHEN Kun-jie2*. Assessment of Tomato Color by Spatially Resolved and Conventional Vis/NIR Spectroscopies[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3585-3591. |
| [15] |
SUN Hai-xia, ZHANG Shu-juan*, XUE Jian-xin, ZHAO Xu-ting, XING Shu-hai, CHEN Cai-hong, LI Cheng-ji. Model Transfer Method of Fresh Jujube Soluble Solids Detection Using Variables Optimization and Correction Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1041-1046. |
|
|
|
|
