Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra
Keywords:
lettuce, chlorophyll fluorescence spectra, hyperspectra, modeling, pesticide residueAbstract
Abstract: Fast identification of pesticide residue level in lettuce leaves plays a key role in the test of food safety. In order to identify the different concentrations pesticide residues of lettuce leaves in a fast and nondestructive way, the hyperspectra coupled with chlorophyll fluorescence spectra was used in this research. Transmission electron microscopy (TEM) was used to identify the microstructure changes of lettuce leaves under different concentrations of dimethoate residue. Besides, a method involving wavelet transform and MD-MCCV algorithm (WT-MD-MCCV) was developed for identifying the optimal wavelengths of the spectral data. The hyperspectra and chlorophyll fluorescence spectra data of 150 lettuce leaf samples at five different concentrations of pesticide residues were obtained using hyperspectral data acquisition device and Cary Eclipse Fluorescence Spectrophotometer. The combination of Savitzky-Golay (SG) algorithm and SNV algorithm (SG-SNV) preprocessing algorithms was used to preprocess the raw spectra. In addition, Principal Component Analysis (PCA), Successive Projections Algorithm (SPA) and wavelet transform coupled to MD-MCCV algorithm (WT-MD-MCCV) were applied to identify the optimal wavelengths of raw spectra including hyperspectra data, chlorophyll fluorescence spectra data and hyperspectra coupled with chlorophyll fluorescence spectra data. Support vector regression (SVR) was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths coupled with different spectral data. The results showed that with the increase of the concentration of dimethoate pesticide spraying, lettuce chloroplast number of osmiophilic particles increased and the starch granules decreased. Besides, the intercellular space of lettuce leaves increased gradually, with the increase of dimethoate concentration. Different concentrations of pesticide residues of lettuce in the near infrared and fluorescence spectrum have a certain difference. In addition, the related parameters of the three preferably prediction models were Rp2=0.956 and RMSEP=0.018, Rp2=0.937 and RMSEP=0.161, Rp2=0.987 and RMSEP =0.005, respectively, using WT-MD-MCCV algorithm combined with hyperspectra data, chlorophyll fluorescence spectra data and hyperspectra coupled to chlorophyll fluorescence spectra data. WT-MD-MCCV algorithm combined with hyperspectra and chlorophyll fluorescence spectra data performed best among the nine SVR models and the hyperspectra coupled with chlorophyll fluorescence spectra can be used to identify the pesticide residue level in lettuce leaves. Keywords: lettuce, chlorophyll fluorescence spectra, hyperspectra, modeling, pesticide residue DOI: 10.3965/j.ijabe.20160906.2519 Citation: Sun J, Zhou X, Mao H P, Wu X H, Zhang X D, Gao H Y. Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra. Int J Agric & Biol Eng, 2016; 9(6): 231-239.References
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[3] Cao X, Song H L, Yu C Y, Li X N. Simultaneous degradation of toxic refractory organic pesticide and bioelectricity generation using a soil microbial fuel cell. Bioresource Technology, 2015; 189: 87–93.
[4] Chadwick M, Gawthrop F, Michelmore R W, Wagstaff C, Methven L. Perception of bitterness, sweetness and liking of different genotypes of lettuce. Food Chemistry, 2016; 97: 66–74.
[5] He H J, Sun D W. Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products. Trends in Food Science & Technology, 2015; 46: 99–109.
[6] Teena M, Manickavasagan A, Ravikanth L, Jayas D S. Near infrared (NIR) hyperspectral imaging to classify fungal infected date fruits. Journal of Stored Products Research, 2014; 59: 306–313.
[7] Siripatrawan U, Makino Y. Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. International Journal of Food Microbiology, 2015; 199: 93–100.
[8] Serranti S, Gargiulo A, Bonifazi G. Hyperspectral imaging for process and quality control in recycling plants of polyolefin flakes. J. Near Infrared Spectrosc., 2012; 20: 573–581.
[9] Mo C, Kim G, Lim J, Kim M S, Cho H, Cho B K. Detection of lettuce discoloration using hyperspectral reflectance imaging. Sensors, 2015; 15: 29511–29534.
[10] Atherton J, Nichol C J, A. Porcar-Castell. Using spectral chlorophyll fluorescence and the photochemical reflectance index to predict physiological dynamics. Remote Sensing of Environment, 2016; 176: 17–30.
[11] Gameiro C, Utkin A B, Cartaxana P, Marques da S J, Matos A R. The use of laser induced chlorophyll fluorescence (LIF) as a fast and non-destructive method to investigate water deficit in Arabidopsis. Agricultural Water Management, 2016; 164: 127–136.
[12] Analia I, Gavin D, Alicia F C, Maria G L. Effect of arsenic on reflectance spectra and chlorophyll fluorescence of aquatic plants. Chemosphere, 2015; 119: 697–703.
[13] Jochem V, Christiaan V T, Federico M, Neus S, Juan P R, Gina M, et al. Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies: A SCOPE modeling study. Remote Sensing of Environment, 2016; 176: 139–151.
[14] Ragupathi C, Vijaya J J, Narayanan S, Jesudoss S K, John K L. Highly selective oxidation of benzyl alcohol to benzaldehyde with hydrogen peroxide by cobalt aluminate catalysis: A comparison of conventional and microwave methods. Ceramics International, 2015; 41(2): 2069–2080.
[15] GB/T 20769-2008. Determination of 450 pesticides and related chemicals residues in fruits and vegetables-LC- MS-MS method, China. (in Chinese)
[16] Staggs J E J. Savitzky–Golay smoothing and numerical differentiation of cone calorimeter mass data. Fire Safety Journal, 2005; 40: 493–505.
[17] Guo Y, Ni Y N, Kokot S. Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2016; 153: 79–86.
[18] Oh J Y, Nojun K. Generalized mean for robust principal component analysis. Pattern Recognition, 2016; 54: 116–127.
[19] Sun J, Jin X M, Mao H P, Wu X H. Identification of lettuce storage time based on spectral preprocessing technology and PCA+SVM. Journal of Pure and Applied Microbiology, 2013; 7: 747–752.
[20] Tomohiko M. Robustness analysis of preconditioned
successive projection algorithm for general form of separable NMF problem. Linear Algebra and its Applications, 2016; 497: 1–22.
[21] Liu K, Chen X J, Li L M, Chen H L, Ruan X K, Liu W B. A consensus successive projections algorithm –multiple linear regression method for analyzing near infrared spectra. Analytica Chimica Acta, 2015; 858: 16–23.
[22] Sun J, Zhou X, Wu X H, Zhang X D, Li Q L. Identification of moisture content in tobacco plant leaves using outlier sample eliminating algorithms and hyperspectral data. Biochemical and Biophysical Research Communications, 2016; 471: 226–232.
[23] Kang P, Kim D, Cho S. Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing. Expert Systems with Applications, 2016; 51: 85–106.
[24] Volkan U, Huseyin S. Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression. Applied Soft Computing, 2016; 43: 210–221.
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Published
2016-12-01
How to Cite
Jun, S., Xin, Z., Hanping, M., Xiaohong, W., Xiaodong, Z., & Hongyan, G. (2016). Identification of pesticide residue level in lettuce based on hyperspectra and chlorophyll fluorescence spectra. International Journal of Agricultural and Biological Engineering, 9(6), 231–239. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2519
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Safety, Health and Ergonomics
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