{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:51Z","timestamp":1740120231619,"version":"3.37.3"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"name":"Guangdong AIB Excellent young scholar project","award":["xykt1701"],"award-info":[{"award-number":["xykt1701"]}]},{"name":"Guangdong province normal university characteristic innovation project","award":["2017GKTSCX034"],"award-info":[{"award-number":["2017GKTSCX034"]}]},{"name":"Guangzhou science research plan general project","award":["201804010342"],"award-info":[{"award-number":["201804010342"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,2]]},"abstract":"<jats:p> The colorimetric method is usually used to test the concentration of substances. However, this method has a big error since different people have different sensitivities to colors. In this paper, in order to solve the identification problem of the color and the concentration of the test paper, firstly, we found out that the concentration of substance is correlated with the color reading by using the Pearson\u2019s Chi-squared test method. And by the concentration coefficient of Pearson correlation analysis, the concentration of substance and color reading is highly correlated. Secondly, according to the RGB value of the paper image, the color moments of the image are calculated as the characteristics of the image, and the Levenberg\u2013Marquardt (LM) neural network is established to classify the concentration of the substance. The accuracy of the training set model is 94.5%, and the accuracy of the test set model is 87.5%. The model precision is high, and the model has stronger generalization ability. Therefore, according to the RGB value of the test paper image, it is effective to establish the LM neural network model to identify the substance concentration. <\/jats:p>","DOI":"10.1142\/s0218001420550046","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T08:44:37Z","timestamp":1554194677000},"page":"2055004","source":"Crossref","is-referenced-by-count":0,"title":["The Identification and Evaluation Model for Test Paper\u2019s Color and Substance Concentration"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7047-9422","authenticated-orcid":false,"given":"Jinlan","family":"Guan","sequence":"first","affiliation":[{"name":"Department of Basic Courses, Guangdong AIB Polytechnic College, Guangzhou 510507, P.\u00a0R.\u00a0China"}]},{"given":"Jiequan","family":"Ou","sequence":"additional","affiliation":[{"name":"E-Business Network Teaching Department, Guangzhou Light Industry Vocational School, Guangzhou 510650, P.\u00a0R.\u00a0China"}]},{"given":"Guanghua","family":"Liu","sequence":"additional","affiliation":[{"name":"Scientific Research and Industria Service Office, Guangdong AIB Polytechnic College, Guangzhou 510507, P.\u00a0R.\u00a0China"}]},{"given":"Minna","family":"Chen","sequence":"additional","affiliation":[{"name":"Basic Courses Department, Guangdong Polytechnic of Environmental Protection Engineering, Foshan 528216, P.\u00a0R.\u00a0China"}]},{"given":"Yuting","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Basic Courses, Guangdong AIB Polytechnic College, Guangzhou 510507, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"issue":"1","key":"S0218001420550046BIB001","first-page":"43","volume":"7","author":"Cui H.","year":"2018","journal-title":"Math. Model. Appl."},{"issue":"9","key":"S0218001420550046BIB002","first-page":"44","volume":"27","author":"Hu H.","year":"2011","journal-title":"Microcom. Appl."},{"key":"S0218001420550046BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/S0003-2670(02)00871-1"},{"key":"S0218001420550046BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/S0003-2670(02)00871-1"},{"key":"S0218001420550046BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2010.07.010"},{"key":"S0218001420550046BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2297381"},{"key":"S0218001420550046BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2422994"},{"key":"S0218001420550046BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2578642"},{"key":"S0218001420550046BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2740949"},{"key":"S0218001420550046BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867956"},{"issue":"2","key":"S0218001420550046BIB011","first-page":"24","volume":"51","author":"Li C.","year":"2012","journal-title":"Chin. Inorg. Chem."},{"issue":"7","key":"S0218001420550046BIB012","first-page":"66","volume":"3","author":"Li J.","year":"2018","journal-title":"Electron. Technol. Softw. Eng."},{"issue":"2","key":"S0218001420550046BIB013","first-page":"44","volume":"18","author":"Lin Q.","year":"2018","journal-title":"J. Heilongjiang Inst. Technol. (Comprehensive Edition)"},{"volume-title":"MATLAB Neural Network Application Design","year":"2000","author":"Xin W.","key":"S0218001420550046BIB014"},{"key":"S0218001420550046BIB016","unstructured":"Y. Qiu ,  Machine learning and R in action,  Beijing:  China Machine Press,  2016,  80\u201382."},{"issue":"2","key":"S0218001420550046BIB017","first-page":"9","author":"Shen J.","year":"2008","journal-title":"Opt. Instrum."},{"issue":"11","key":"S0218001420550046BIB018","first-page":"64","volume":"3","author":"Shen J.","year":"2013","journal-title":"Digit. Technol. Appl."},{"key":"S0218001420550046BIB019","first-page":"381","volume-title":"IS&T\/SPIE\u2019s Symp. Electronic Imaging: Science & Technology Society for Optics and Photonics","author":"Stricker M. A.","year":"1995"},{"issue":"56","key":"S0218001420550046BIB020","first-page":"7957","volume":"53","author":"Tmg C.","year":"2017","journal-title":"Catalytic Reactions in Ionic Liquids"},{"issue":"2","key":"S0218001420550046BIB021","first-page":"65","volume":"17","author":"Wang L.","year":"2018","journal-title":"J. Luohe Vocational Techn. Coll."},{"issue":"2","key":"S0218001420550046BIB022","first-page":"15","volume":"23","author":"Xiong Y.","year":"2018","journal-title":"J. Chongqing Electr. Power Coll."},{"key":"S0218001420550046BIB023","unstructured":"L. Zhang ,  MATLAB Data Analysis and Mining Practice  (Machine Press,  China Beijing,  2015), pp.  191\u2013202."},{"key":"S0218001420550046BIB024","unstructured":"J. Zheng ,  SPSS Statistical Analysis, from Entry to Mastery  (China Railway Publishing House,  Beijing,  2015), pp.  136\u2013142."}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001420550046","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T08:06:36Z","timestamp":1585037196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001420550046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":23,"journal-issue":{"issue":"02","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["10.1142\/S0218001420550046"],"URL":"https:\/\/doi.org\/10.1142\/s0218001420550046","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"type":"print","value":"0218-0014"},{"type":"electronic","value":"1793-6381"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}