|
|
|
|
|
|
| Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering |
| TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1 |
1. Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China
2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
3. High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
4. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
|
|
|
|
|
Abstract Infrared spectroscopy is a technology used to identify the chemical composition of substances based on molecular vibration and quantum-jump theory. Due to the unique absorbance of different functional groups, the spectral data related to the absorbance and the wavelength (or wavenumber) can be obtained when the infrared beam irradiates the molecular. However, the spectral data from experiments always have high dimensions and overlap, making it difficult to process the data. Thus, this paper proposed an improved Gustafson-Kessel possibilistic c-means clustering (GKIPCM), introducing the Mahalanobis distance from GK clustering and the iterative equations of fuzzy membership values and cluster centers from improved possibilistic c-means clustering (IPCM). GKIPCM makes the data adapt to different mathematical distance measures and avoids identical cluster centers. Furthermore, GKIPCM has higher classification accuracy, which is less sensitive to parameters. In the experiments, four groups of washed Chinese cabbage were the objects of spectral analysis and different concentrations of lambda-cyhalothrin pesticide were sprayed on the Chinese cabbages. Spectral data of Chinese cabbages were collected with Agilent Cary 630 FTIR spectrometer. Firstly, multiplicative scatter correction (MSC) was applied to reduce the noise and eliminate data offset when pre-processing the data. Secondly, principal component analysis (PCA) was utilized to reduce dimensions due to the wide wavenumber range (4 300~590 cm-1) and the high data dimensions (971). After conducting PAC, the dimensionality of data was reduced to 23, and the total contribution of 23 principal components reached 99.60%. Nonetheless, the feature information was still mixed. So the linear discriminant analysis (LDA) was used to extract features of the spectral data, and the LDA algorithm reduced the dimensionality of the spectral data to 3. Finally, the fuzzy c-means clustering (FCM) was employed to obtain the optimal initial cluster centers. Then, the GKIPCM algorithm was applied to cluster four different groups of spectral data. Comparisons were made among the clustering results of GKIPCM, GK and IPCM. The running time and accuracy of GKIPCM were 0.218 8 seconds and 97.22%, and those of GK and IPCM were 0.093 8 seconds and 63.89%, 0.062 5 seconds and 91.67%, respectively. According to the results of the experiments, the GKIPCM algorithm finished the qualitative analysis of different concentrations of pesticide residues by analyzing the spectral data.
|
|
Received: 2021-04-17
Accepted: 2021-06-12
|
|
|
|
Corresponding Authors:
WU Xiao-hong
E-mail: wxh_www@163.com
|
|
[1] Azam S M R, Ma H l, Xu B G, et al. Trends in Food Science and Technology, 2020, 97: 417.
[2] Choudhary S, Raheja N, Kumar S, et al. Journal of Entomology and Zoology Studies, 2018, 6(3): 330.
[3] Alenyorege E A, Ma H l, Ayim I, et al. Journal of Food Safety and Food Quality, 2018, 69(3): 80.
[4] Jiang S Y, Sun J, Zhou X, et al. Journal of Food Process Engineering, 2017, 40(4): e12510.
[5] Zhou X, Sun J, Lu B, et al. Journal of Food Process Engineering, 2018, 41(8): e12903.
[6] Sun J, Zhou X, Mao H P, et al. Journal of Food Process Engineering, 2016, 40(4): e12509.
[7] Wu M M, Sun J, Lu B, et al. Journal of Food Process Engineering, 2019, 42(3): e13005.
[8] Sun J, Cong S, Mao H P, et al. Journal of Food Process Engineering, 2018, 41(2): e12654.
[9] Sinaga K P, Yang M S. IEEE Access, 2020, 8(1): 80716.
[10] Moradi Fard M, Thonet T, Gaussier E. Pattern Recognition Letters, 2020, 138: 185.
[11] Yuan Z, Chen H M, Li T R, et al. Information Sciences, 2021, 572: 67.
[12] Zhang L L, Luo M N, Liu J, et al. Pattern Recognition Letters, 2020, 130: 275.
[13] Yu H Y, Fan J L, Lan R. Applied Soft Computing Journal, 2019, 80: 845.
[14] Zhou H X, Fu H J, Wu X H, et al. Journal of Food Processing and Preservation, 2020, 44(10): e14795.
[15] Chaomurilige, Yu J, Yang M S. Information Sciences, 2017, 417: 435.
[16] Wu X H, Zhou J, Wu B, et al. Journal of Food Process Engineering, 2019, 42(8): e13298.
|
| [1] |
ZHU Gui-jun, WANG Gan-zhen, PENG Jun*, TIAN Zong-ping, HOU Zhi-hua. The Mineralogical and Spectroscopic Characteristics of Phosphohedyphane From Chenzhou of Hunan Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3017-3023. |
| [2] |
ZHENG Yu-xia1, 2, TUERSUN Paerhatijiang1, 2*, ABULAITI Remilai1, 2, CHENG Long1, 2, MA Deng-pan1, 2. Retrieval of Polydisperse Au-Ag Alloy Nanospheres by Spectral Extinction Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3039-3045. |
| [3] |
WU Bin1, SHEN Jia-qi2, WANG Xin2, WU Xiao-hong3, HOU Xiao-lei2. NIR Spectral Classification of Lettuce Using Principal Component
Analysis Sort and Fuzzy Linear Discriminant Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3079-3083. |
| [4] |
WANG Xu-yang1, SUN Tao1, ZHU Xin-ping1, TANG Guang-mu2, JIA Hong-tao1*, XU Wan-li2. Phosphorus Species of Biochar Modified by Phosphoric Acid and
Pyrophosphoric Acid Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3084-3090. |
| [5] |
HUANG Hua1, NAN Meng-di1, LI Zheng-hao1, CHEN Qiu-ying1, LI Ting-jie1, GUO Jun-xian2*. Multi-Model Fusion Based on Fractional Differential Preprocessing and PCA-SRDA for the Origin Traceability of Red Fuji Apples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3249-3255. |
| [6] |
WU Bing, YANG Ke-ming*, GAO Wei, LI Yan-ru, HAN Qian-qian, ZHANG Jian-hong. EC-PB Rules for Spectral Discrimination of Copper and Lead Pollution Elements in Corn Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3256-3262. |
| [7] |
HU Yu-xia1, CHEN Jie1, SHAO Hui1, YAN Pu1, XU Heng1, SUN Long1, XIAO Xiao1, XIU Lei3, FENG Chun2GAN Ting-ting2, ZHAO Nan-jing2*. Research Progress of Spectroscopy Detection Technologies for Waterborne Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2672-2678. |
| [8] |
CHEN Miao, HOU Ming-yu, CUI Shun-li, LI Zhen, MU Guo-jun, LIU Ying-ru, LI Xiu-kun, LIU Li-feng*. Construction of Near-Infrared Model of Peanut Sugar Content in
Different Seed Coat Colors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2896-2902. |
| [9] |
ZHANG Zhen-qing1, 2, 3, DONG Li-juan2*, HUANG Yu4, CHEN Xing-hai4, HUANG Wei5, SUN Yong6. Identification of True and Counterfeit Banknotes Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2903-2912. |
| [10] |
YANG Cheng-en1, WU Hai-wei1*, YANG Yu2, SU Ling2, YUAN Yue-ming1, LIU Hao1, ZHANG Ai-wu3, SONG Zi-yang3. A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2359-2365. |
| [11] |
JIANG Wan-li1, 2, SHI Jun-sheng1, 2*, JI Ming-jiang1, 2. Establishment of Visible and NIR Spectral Reflectance Database of Plant Leaves and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2366-2373. |
| [12] |
YAN Shu-fa, ZHU Yuan-chen, TAO Lei, ZHANG Yong-gang, HU Kai, REN Fu-chen. Spectral Oil Condition Monitoring Data Selection Method for Mechanical Transmission Based on Information Entropy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2637-2641. |
| [13] |
CHEN Wei-na1, GUO Zhong-zheng1, LI Kai-kai1, YANG Yu-zhu1, YANG Xu2*. Micro Confocal Raman Spectroscopy Combined With Chemometrical Method for Forensic Differentiation of Electrostatic Copy Paper[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2033-2038. |
| [14] |
XU Liang-ji1, 2, MENG Xue-ying2, WEI Ren2, ZHANG Kun2. Experimental Research on Coal-Rock Identification Method Based on
Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2135-2142. |
| [15] |
ZHANG Meng-jun1, LIU Li-li1*, YANG Xie-li2, GUO Jing-fang1, WANG Hao-yang1. Multispectral Analysis of Interaction Between Catechins and Egg Yolk Immunoglobulin and the Change of Bacteriostasis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2297-2303. |
|
|
|
|
