Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy
Keywords:
Chinese cabbage, mid-infrared spectroscopy, fuzzy uncorrelated discriminant vector, uncorrelated discriminant vector, lambda-cyhalothrin residuesAbstract
Excessive pesticide residues on Chinese cabbage will be harmful to people’s health. Therefore, an identification system was designed for qualitative analysis of lambda-cyhalothrin residues on Chinese cabbage leaves. In order to extract discriminant information from mid-infrared (MIR) spectra of Chinese cabbage effectively, fuzzy uncorrelated discriminant vector (FUDV) analysis was proposed by introducing the fuzzy set theory into uncorrelated discriminant vector (UDV) analysis. In this system, the Cary 630 FTIR spectrometer was used to scan four samples of Chinese cabbage with different concentrations of lambda-cyhalothrin. The MIR spectra were preprocessed by standard normal variable (SNV) and Savitzky-Golay smoothing (SG). Next, the high-dimensional MIR spectra were processed for dimension reduction by principal component analysis (PCA). Furthermore, UDV, FUDV, and some other discriminant analysis algorithms were used for feature extraction, respectively. Finally, the K-nearest neighbor (KNN) classifier was employed to classify the data. The experimental results showed that when FUDV was used as the feature extraction algorithm, the identification system reached the maximum classification accuracy of 100%. The results indicated that FUDV combined with MIR spectroscopy was an effective method to identify lambda-cyhalothrin residues on Chinese cabbage. Keywords: Chinese cabbage, mid-infrared spectroscopy, fuzzy uncorrelated discriminant vector, uncorrelated discriminant vector, lambda-cyhalothrin residues DOI: 10.25165/j.ijabe.20221503.6486 Citation: Wu X H, Zhang T F, Wu B, Zhou H X. Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy. Int J Agric & Biol Eng, 2022; 15(3): 217–224.References
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[2] Zhu Q Y, Yang Y, Zhong Y Y, Lao Z T, O’Neill P, Hong D, et al. Synthesis, insecticidal activity, resistance, photodegradation and toxicity of pyrethroids (A review). Chemosphere, 2020; 254: 126779. doi: 10.1016/j.chemosphere.202.126779.
[3] Hassan M M, Li H H, Ahmad W, Zareef M, Wang J J, Xie S C, et al. Au@Ag nanostructure based SERS substrate for simultaneous determination of pesticides residue in tea via solid phase extraction coupled multivariate calibration. LWT, 2019; 105: 290–297.
[4] Yang N, Wang P, Xue C Y, Sun J, Mao H P, Oppong P K. A portable detection method for organophosphorus and carbamates pesticide residues based on multilayer paper chip. Journal of Food Process Engineering, 2018; 41(8): e12867. doi: 10.1111/jfpe.12867.
[5] Ma P, Wang L Y, Xu L, Li J Y, Zhang X D, Chen H. Rapid quantitative determination of chlorpyrifos pesticide residues in tomatoes by surface-enhanced Raman spectroscopy. European Food Research and Technology, 2020; 246(1): 239–251.
[6] Zhou J W, Zou X, Song S H, Chen G H. Quantum dots applied to methodology on detection of pesticide and veterinary drug residues. Journal of Agricultural & Food Chemistry, 2018; 66(6): 1307–1319.
[7] Boydas M G, Ozbek I Y, Kara M. An efficient laser sensor system for apple impact bruise volume estimation. Postharvest Biology & Technology, 2014; 89: 49–55.
[8] Yao Y, Zhang P, Chen Q J, Liu W F, Zeng J, Xie J J, et al. Characterization of pesticide residual dynamics by in situ attenuated total reflection FTIR. Spectroscopy and Spectral Analysis, 2012; 32(12): 3217–3219. (in Chinese)
[9] Sun J, Ge X, Wu X H, Dai C X, Yang N. Identification of pesticide residues in lettuce leaves based on near infrared transmission spectroscopy. Journal of Food Process Engineering, 2018; 41(6): e12816. doi: 10.1111/jfpe.12816.
[10] Chen Q S, Cai J R, Wan X M, Zhao J W. Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy. LWT - Food Science and Technology, 2011; 44(10): 2053–2058.
[11] Armenta S, Quintas G, Garrigues S, Guardia M. Mid-infrared and Raman spectrometry for quality control of pesticide formulations. Trends in Analytical Chemistry, 2005; 24(8): 772–781.
[12] Jiang S Y, Sun J, Xin Z, Mao H P, Wu X H, Li Q L. Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging. Journal of Food Process Engineering, 2017; 40(4): e12510. doi: 10.1111/jfpe.12510.
[13] Sun J, Jin X M, Mao H P, Wu X H, Tang K, Zhang X D. Identification of lettuce leaf nitrogen level based on adaboost and hyperspectrum. Spectroscopy and Spectral Analysis, 2013; 33(12): 3372-3376.
[14] Yang T M, Zhou R, Jiang D, Fu H Y, Su R, Liu Y X, et al. Rapid detection of pesticide residues in Chinese herbal medicines by Fourier transform infrared spectroscopy coupled with partial least squares regression. Journal of Spectroscopy, 2016; 2016: 9492030. doi: 10.1155/2016/9492030.
[15] Etzion Y, Linker R, Cogan U, Shmulevich I. Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared/attenuated total reflectance spectroscopy. Journal of Dairy Science, 2004; 87(9): 2779–2788.
[16] Yang J B, Du C W, Shen Y Z, Zhou J M. Rapid determination of nitrate in Chinese cabbage using Fourier transforms mid-infrared spectroscopy. Chinese Journal of Analytical Chemistry, 2013; 41(8): 1264–1268.
[17] Su W H, Bakalis S, Sun D W. Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy. Journal of Food Measurement and Characterization, 2019; 13(2): 1218–1231.
[18] Fisher R A. The use of multiple measurements in taxonomic problems. Annals of Human Genetics, 2012; 7(7): 179–188.
[19] Jin Z, Yang J Y, Hu Z S, Lou Z. Face recognition based on the uncorrelated discriminant transformation. Pattern Recognition, 2001; 34(7): 1405–1416.
[20] Chen M S, Chen H X, Liu W. A new method for resolving the uncorrelated set of discriminant vector. Chinese Journal of Computers, 2004; 27: 913–917. (in Chinese)
[21] Zadeh L A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1978; 1(1): 3–28.
[22] Wu X H, Zhu J, Wu B, Huang D P, Sun J, Dai C X. Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley - Sammon transformation. Journal of Food Science and Technology-Mysore, 2020; 57(5): 1310–1319.
[23] Wu X H, Zhu J, Wu B, Zhao C, Sun J, Dai C X. Discrimination of Chinese liquors based on electronic nose and fuzzy discriminant principal component analysis. Foods, 2019; 8(1): 38. doi: 10.3390/foods8010038.
[24] Chen Z P, Jiang J H, Li Y, Liang Y Z, Yu R Q. Fuzzy linear discriminant analysis for chemical data sets. Chemometrics & Intelligent Laboratory Systems, 1999; 45(1): 295–302.
[25] Lin C F, Wang S D. Fuzzy support vector machines. IEEE Transactions
on Neural Networks, 2002; 13(2): 464–471.
[26] Ning Y W, Shi X Y, Yin J G, Xie D W. Application of fuzzy C-means clustering method in the analysis of severe medical images. Journal of Intelligent and Fuzzy Systems, 2020; 38: 1–11.
[27] Cadenas J M, Garrido M C, Martinez R, Munoz E, Bonissone P P. A fuzzy K-nearest neighbor classifier to deal with imperfect data. Soft Computing, 2018; 22: 3313–3330.
[28] Dong C W, Yang Y E, Zhang J Q, Zhu H K, Liu F. Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-spline lighting correction method. Journal of Integrative Agriculture, 2014; 13(10): 2229–2235.
[29] Wu X H, Wu B, Sun J, Yang N. Classification of apple varieties using
near infrared reflectance spectroscopy and fuzzy discriminant C-means clustering model. Journal of Food Process Engineering, 2017; 40(2): e12355. doi: 10.1111/jfpe.12355.
[30] Rozza A, Lombardi G, Casiraghi E, Campadelli P. Novel Fisher discriminant classifiers. Pattern Recognition, 2012; 45(10): 3725–3737.
[31] Foley D H, Sammon J W. An optimal set of discriminant vectors. IEEE Transactions on Computers, 1975; 24(3): 281–289.
[32] Bezdek J C. Pattern recognition with fuzzy objective function algorithms. New York: Plenum, 1981, 1–256.
[33] Barra V, Boire J Y. Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation. Journal of Magnetic Resonance Imaging Jmri, 2015; 11(3): 267–278.
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Published
2022-06-30
How to Cite
Wu, X., Zhang, T., Wu, B., & Zhou, H. (2022). Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy. International Journal of Agricultural and Biological Engineering, 15(3), 217–224. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6486
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Information Technology, Sensors and Control Systems
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