|
|
|
|
|
|
Microscopic Raman Spectroscopy for Diagnosing Roots in Apple
Rootstock Under Heavy Metal Copper Stress |
LI Jun-meng1, ZHAI Xue-dong1, YANG Zi-han1, ZHAO Yan-ru1, 2, 3, YU Ke-qiang1, 2, 3* |
1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China
|
|
|
Abstract Heavy metal pollution will affect the normal growth of crops, and quickly detecting the content of heavy metals in crops has become a problem to be investigated. The traditional detection of heavy metals in plants depends on chemical methods. Although it can realize the high accurate detection of heavy metal content, its operation process is laborious, and it cannot meet the requirements of the high throughput detection, let alone the in-situ micro detection of plant tissues under heavy metal stress. Raman spectroscopy has the advantages of non-destructive detection of molecular vibration information of solid, liquid and gas species, high spectral resolution and insensitive to water. Therefore, it is feasible to monitor the content of heavy metals in crops by Raman spectroscopy. Apple rootstock is the basis of apple seedling grafting, which can ensure the health of the apple tree and apple quality and yield in the later stage. The root of apple rootstock is polluted by heavy metals directly, which hinders its healthy growth and affects the stress resistance of apple seedlings. Therefore, studying the interaction mechanism between heavy metals and apple rootstock root is necessary. In this study, five groups of apple rootstocks under the stress of CuSO4·5H2O solution with different concentrations were investigated. Firstly, the Raman scattering spectra of apple rootstocks under different copper ion (Cu2+) stress gradients were collected, and the adaptive iterative reweighting partial least squares (air-PLS) and S-G smoothing method were applied to preprocess the obtained raw Raman spectrum data for removing the fluorescence effect and correcting the baseline. Secondly, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) discriminant models were established to estimate the different heavy metal stress concentrations. Results showed that the accuracy of the SVM model and PLS-DA model could reach 100% and 96%, respectively, which is promising for predicting apple rootstocks’ heavy metal Cu stress situation; finally, the chemical imaging was mapped based on the characteristic Raman spectrum peaks at 1 096, 1 329, 1 605 and 2 937 cm-1. It was illustrated that the Raman signal intensity increased first and then decreased with the increase of stress concentration in the exact wavenumber. These findings demonstrated the potential of micro-Raman scattering for measuring apple rootstock heavy stress, which provides anovel method for detecting heavy metal stress of crops.
|
Received: 2021-07-13
Accepted: 2021-10-15
|
|
Corresponding Authors:
YU Ke-qiang
E-mail: yuke406336022@163.com
|
|
[1] ZOU Ri, SHEN Di, BO Xin-fu, et al(邹 日,沈 镝,柏新富,等). Chinese Vegetables(中国蔬菜),2011, (4): 1.
[2] Wan Huixue, Yang Fengying, Zhang Xiaolei, et al. Environmental Pollution, 2021, 287: 117610.
[3] Liu Cuicui, He Shaoyuan, Shen Kun, et al. Food Analytical Methods, 2015, 8(7): 1785.
[4] Aguilera A M, Aguilera-Morillo M C, Preda C. Chemometrics and Intelligent Laboratory Systems,2016, 154: 80.
[5] WANG Hai-yang, WU Zhi-jing, JING Li-xia, et al(王海阳,吴至境,江丽霞,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017, 37(5): 1418.
[6] Tomas M, Tinti A, Bofill R, et al. Journal of Inorganic Biochemistry,2016, 156: 55.
[7] Barimah A O, Guo Z, Agyekum A A,et al. Food Control,2021, 130: 108341.
[8] Chen Huazhou, Pan Tao, Chen Jiemei, et al. Chemometrics and Intelligent Laboratory Systems. 2011, 107(1): 139.
[9] Xu Xiangchun, Huo Xinming, Qian Xiang, et al. Analytica Chimica Acta,2021, 1157: 338386.
[10] Zontov Y V, Rodionova O Y, Kucheryavskiy S V, et al. Chemometrics and Intelligent Laboratory Systems,2020, 203: 104064.
[11] Vieira L S, Assis C, de Queiroz M E L R, et al. Food Chemistry,2021, 345: 128866.
[12] Gierlinger N, Keplinger T, Harrington M. Nature Protocols,2012, 7(9): 1694.
[13] JIN Ke-xia, WANG Kun, CUI He-shuai, et al(金克霞,王 坤,崔贺帅,等). Scientia Silvae Sinicae(林业科学),2018, 54(3): 144.
[14] Meyer M W, Lupoi J S, Smith E A. Analytica Chimica Acta,2011, 706(1): 164.
[15] Wang Quanying, Zhou Dongmei, Cang Long. Soil Biology and Biochemistry, 2009, 41(7): 1504.
[16] Wang Q Y, Liu J S, Hu B. Environmental & Experimental Botany,2016, 123: 125.
[17] Fu X, Dou C, Chen Y, et al. Journal of Hazardous Materials,2011,186(1): 103.
|
[1] |
SI Gan-shang1, 2, LIU Jia-xiang1, LI Zhen-gang1, 2, NING Zhi-qiang1, 2, FANG Yong-hua1, 2*, CHENG Zhen1, 2, SI Bei-bei1, 2, YANG Chang-ping1, 2. Raman Signal Enhancement for Liquid Detection Using a New Sample Cell[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 712-717. |
[2] |
LIU Feng-xiang, HE Shuai, ZHANG Li-hao, HUANG Xia, SONG Yi-zhi*. Application of Raman Spectroscopy in Detection of Pathogenic Microorganisms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3653-3658. |
[3] |
LIU Qing-sheng1, YANG De-wang2, GUO Jin-jia1*, YAN Ao-shuang1, ZHENG Rong-er1. Raman Spectroscopy for Gas Detection Using a Folded Near-Concentric Cavity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3390-3393. |
[4] |
LU Wei1, HAN Zhao1, JIAN Xing-liang1, Zhou Ji2, JIANG Dong3, DING Yan-feng3. Maize Root Phenotypic Detection Based on Thermal Imaging and Root Gap Repair Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2845-2850. |
[5] |
SUI Ya-nan1,2, ZHANG Lei-lei1,2, LU Shi-yang1,2, YANG De-hong1,2, ZHU Cheng1,2*. Research on the Shrimp Quality of Different Storage Conditions Based on Raman Spectroscopy and Prediction Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1607-1613. |
[6] |
XIA Zhen-qing, SI Lei-yong, JIN Yan, FU Ya-fang, WANG Qi, LU Hai-dong*. Effects of Root Zone Temperature Increase on Fourier Transform Infrared Spectroscopy Content of Main Metabolites and Chlorophyll in Maize Seedlings[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1283-1288. |
[7] |
LI Yang-yu1, MA Jian-guang2*, LI Da-cheng1, CUI Fang-xiao1, WANG An-jing1, WU Jun1. Research on Spatial Offset Raman Spectroscopy and Data Processing Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 71-74. |
[8] |
ZHANG Xu1, 2, WANG Shuang1*, LI Jie1, 2, QIN Jie3, WANG Kai-ge1, BAI Jin-tao1, 2, HE Qing-li2*. Study on a Non-Destructive Drug Testing Method Based on Spatially Offset Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1472-1476. |
[9] |
WANG Ning1, 2, WANG Chi1, BIAN Hai-yi2, WANG Jun3, WANG Peng2, BAI Peng-li3, YIN Huan-cai3, TIAN Yu-bing2, GAO Jing2*. The Identification Method of Blood by Applying Hilbert Transform to Extract Phase Information of Raman Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2412-2418. |
[10] |
HU Guang-xiao1, 2, 3, XIONG Wei1, 2, 3*, LUO Hai-yan1,3, SHI Hai-liang1, 3, LI Zhi-wei1, 3, SHEN Jing1, 2, 3, FANG Xue-jing1, 2, 3. The Research of Spatial Heterodyne Raman Spectroscopy with Standoff Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(12): 3951-3957. |
[11] |
CHEN Jing, LI Ying*, DU Zeng-feng, GU Yan-hong, GUO Jin-jia . Research on the Quantitative Analysis for In-Situ Detection of Acid Radical Ions Using Laser Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(09): 2548-2552. |
[12] |
DUAN Jing-bo, LIU Wen-qing, ZHANG Yu-jun*, ZHAO Nan-jing, WANG Zhi-gang, YIN Gao-fang, FANG Li, LIU Jing . Studies on Toxicity of Four Kinds of Heavy Metals in Water by Synchronous-Scan Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(05): 1262-1265. |
[13] |
QU Ying1, 2, 3, LIU Su-hong1, 2, 3*, LI Xiao-wen1, 2, 3 . A Novel Method for Extracting Leaf-Level Solar-Induced Fluorescence of Typical Crops under Cu Stress [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(05): 1282-1286. |
[14] |
XIONG Yi1, ZHAO Hong-xia1*, GAN Fu-xi1,2 . Influence of Cations on the Laser Raman Spectra of Silicate Glasses [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(04): 997-1001. |
[15] |
WANG Yi-bin1, 2, MIAO Jin-lai1, 2*, HE Bi-juan3, LIANG Qiang1, 2, LIU Fang-ming1, 2, ZHENG Zhou1, 2 . Laser Tweezers Raman Spectroscopy Analysis of Cold-Adapted Aromatic Hydrocarbons-Degradating Strains Isolated from Antarctic Sea [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(02): 418-421. |
|
|
|
|