Detection and analysis of wheat storage year based on electronic tongue and DWT-IPSO-LSSVM algorithm

Tingting Guo, Zhengwei Yang, Nan Miao, Xin Zhang, Qingsheng Li, Jie Guo, Zhiqiang Wang

Abstract


Abstract: The electronic tongue system based on virtual instrument technology was used to qualitatively analyze the wheat, which achieves rapid and objective evaluation analysis of aged wheat with different storage years.  In view of the complex output signal of the electronic tongue and the large amount of data, the Discrete Wavelet Transform was used to extract the eigenvalues of the original data to reduce the data dimension size.  On this basis, the improved particle swarm optimization algorithm was used to optimize the parameters of Least squares support vector machine, and the analysis model of wheat storage age was established.  The experiments exhibited that the DWT-IPSO-LSSVM model had better classification performances than other pattern recognition models, such as DWT-GA-LSSVM, DWT-AF-LSSVM and DWT-PSO-LSSVM.  The results showed that the accuracy of the training set, the accuracy of the test set, overall classification accuracy and Kappa coefficient of the proposed combined model in this paper were 95%, 92%, 91% and 0.88 respectively.  This research indicated that the electronic tongue system combined with proposed model can be used to identify and discriminate the aged wheat with different storage years.

Keywords: electronic tongue, wheat storage year, discrete wavelet transform, improved particle swarm optimization, least squares support vector machine

DOI: 10.33440/j.ijpaa.20190202.46.

 

Citation: Guo T T, Yang Z W, Miao N, Zhang X, Li Q S, Guo J, Wang Z Q.  Detection and analysis of wheat storage year based on electronic tongue and DWT-IPSO-LSSVM algorithm.  Int J Precis Agric Aviat, 2019; 2(2): 19–24.

Full Text:

PDF

References


Tang W N, Liu J Y, Chen Z H, et al. Detection method of freshness adopted in grain purchasing. Gereal and feed industry, 2016, 12(10): 61–62. doi: 10.7633/j.issn.1003-6202. 2016.10.015. (in Chinese)

Zhang S P. Identification of wheat aging. Food science and technology, 2012(10): 135–138. (in Chinese)

Zhang H H, Wu X L, Qi M,Tang H L, Ye F K. Analysis on the present situation and New method of New maturity Identification of Wheat. Grain processing, 2016(3): 17–20. (in Chinese)

Yang H P, Song W, Yuan J, Wang S Y, Pan D J. Methods of Dedermining Wheat Aging Degree. Journal of the Chinese and oils Association, 2004, 19(6): 23–26. doi: 10.3321/ j.issn:1003-0174.2004.06.007. (in Chinese)

Xu M, Wang J, Zhu L Y. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food chemistry, 2019, 289: 482–489. doi: 10.1016/ j.foodchem.2019.03.080

Winquist F, Olsson J, Eriksson M. Multicomponent analysis of drinking water by a voltammetric electronic tongue. Analytica Chimica Acta, 2011, 683(2): 192–197. doi: 10.1016/j.aca.2010.10.027

Xavier Cetó, AndreuGonzález-Calabuig, NoraCrespo, Sandra Pérez and M. D. Valle. Electronic tongues to assess wine sensory descriptors. Talanta, 2017(162): 218–224. doi: 10.1016/j.talanta.2016.09.055.

Robertoh L, Rafael M, Miguel A, et al. Prediction of NaCl, nitrate and nitrite contents in minced meat by using a voltammetric electronic tongue and an impedimetric sensor. Food Chemistry, 2010, 122(3): 864–870. doi: 10.1016/j.foodchem.2010.02.04.

Lu L, Hu X Q, Tian S Y, Deng S P, Zhu Z W. Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue. Analytica chimica acta, 2016, 919: 11–19. doi: 10.1016/j.aca.2016.03.019

Liu S Y, Xu L Q, Li Z B, Li D L. Forecasting Model for pH Value of Aquaculture Water Quality Based on PCA-MCAFA-LSSVM. Transactions of the Chinese Society for Agricultural Machinery 2014, 45(5): 239–246. doi: 10.6041/j.issn.1000-1298.2014.05.037. (in Chinese)

Lu L, Hu X, Tian S, Deng S, Zhu Z. Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue. Analytica Chimica Acta, 2016, 919: 11–19. doi: 10.1016/j.aca.2016.03.019.

Wei Z B, Yang Y N, Wang J, Zhang W L, Ren Q F. The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis. Journal of food engineering, 2018, 217: 75–92. doi: 10.1016/j.jfoodeng.2017.08.005.

Tian X J, Wang J, Zhang X. Discrimination of preserved licorice apricot using electronic tongue. Mathematical & Computer Modelling, 2013, 58(3-4): 737–745. doi: 10.1016/j.mcm.2012.12.034.

Wei Z B, Wang J, Zhang X. Monitoring of quality and storage time of unsealed pasteurized milk by voltammetric electronic tongue. Electrochimica Acta, 2013, 88(2): 231–239. doi: 10.1016/ j.electacta.2012.10.042.

Liu M, Wang M J, Wang J, Li D. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical, 2013, 177: 970–980. doi: 10.1016/j.snb.2012.11.071.

Qiu S S, Wang J, Tang C, Du D D. Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.). Journal of food engineering, 2015, 166: 193–203. doi:10.1016/j.jfoodeng.2015.06.007

Ma Z L, Yin T J, Guo T T,Wang Z Q, Li C H, Guo Y M. The brand and purity of orange juice and liquor were tested by electronic tongue method. Science and Technology of Food Industry, 2018(8). (in Chinese)

Shi Q R, Guo T T, Yin T J, Wang Z Q, Li C H, Sun X, et al. Classification of Pericarpium Citri Reticulatae of Different Ages by Using a Voltammetric Electronic Tongue System. International Journal Of Electrochemical Science, 2018, 13(12): 11359–11374. doi: 10.20964/ 2018.12.45

He X, Wang H L, Lu J H, Jiang W. Analog circuit fault diagnosis method based on preferred wavelet packet and ELM. Chinese Journal of Scientific Instrument, 2013, 34(11): 2614–2619. doi: 10.3969/ j.issn.0254-3087.2013.11.030. (in Chinese)

Yu H H, Chen Y Y, Hassan S G, Li D L. Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO. Computers and Electronics in Agriculture, 2016, 122: 94–102. doi: 10.1016/j.compag.2016.01.019.

Shi Q R. Research on food and drug analysis based on electronic tongue and pattern recognition technology. Shandong University of Technology, 2018. (in Chinese)

Fernandez-Martinez J L, Garcia-Gonzalo E. Stochastic stability analysis of the linear continuous and discrete PSO models. IEEE Transactions on Evolutionary Computation, 2011, 15(3): 405–423. doi: 10.1109/ TEVC.2010.2053935.

Firouzib B, Sadeghi M S, Niknam T. A new hybrid algorithm based on PSO，SA，and K-means for cluster analysis. International Journal of Innovative Computing Information and Control, 2010, 6(7): 3177–3192．

Shi Q R, Ma Z L, Zhou Z, He X Q, Wang Z Q, Ma Y X. Research on Chinese patent medicine identification method based on electronic tongue technology and pattern recognition. Journal of Electronic Measurement and Instrumentation, 2017, 31(7): 1081–1089. doi: 10.13382/ j.jemi.2017.07.014. (in Chinese)


Refbacks

  • There are currently no refbacks.