{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:29:54Z","timestamp":1767652194737,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2015,9,25]],"date-time":"2015-09-25T00:00:00Z","timestamp":1443139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To develop an automatic tea-category identification system with a high recall rate, we proposed a computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures. We captured 300 tea images using a 3-CCD digital camera, and then extracted 64 color histogram features and 16 wavelet packet entropy (WPE) features to obtain color information and texture information, respectively. Principal component analysis was used to reduce features, which were fed into a fuzzy support vector machine (FSVM). Winner-take-all (WTA) was introduced to help the classifier deal with this 3-class problem. The 10 \u00d7 10-fold stratified cross-validation results show that the proposed FSVM + WTA method yields an overall recall rate of 97.77%, higher than 5 existing methods. In addition, the number of reduced features is only five, less than or equal to existing methods. The proposed method is effective for tea identification.<\/jats:p>","DOI":"10.3390\/e17106663","type":"journal-article","created":{"date-parts":[[2015,9,28]],"date-time":"2015-09-28T03:02:55Z","timestamp":1443409375000},"page":"6663-6682","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine"],"prefix":"10.3390","volume":"17","author":[{"given":"Shuihua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, 210023 Nanjing, China"},{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, 210042 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0009-4599","authenticated-orcid":false,"given":"Xiaojun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Mechanics, China University of Mining and Technology,  221008 Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, 210023 Nanjing, China"},{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, 210042 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preetha","family":"Phillips","sequence":"additional","affiliation":[{"name":"School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown,  25443 West Virginia, WV, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfei","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, 210042 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ti-Fei","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Psychology, Nanjing Normal University, 210008 Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1093\/jn\/130.10.2409","article-title":"Effects of tea consumption on nutrition and health","volume":"130","author":"Yang","year":"2000","journal-title":"J. Nutr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.jnutbio.2012.10.005","article-title":"Green tea catechin leads to global improvement among Alzheimer\u2019s disease-related phenotypes in NSE\/hAPP-C105 Tg mice","volume":"24","author":"Lim","year":"2013","journal-title":"J. Nutr. Biochem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1111\/ggi.12123","article-title":"Dose-response meta-analysis on coffee, tea and caffeine consumption with risk of Parkinson\u2019s disease","volume":"14","author":"Qi","year":"2014","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13793","DOI":"10.1002\/chem.201403188","article-title":"Natural compounds against neurodegenerative diseases: Molecular characterization of the interaction of catechins from Green Tea with A\u03b21-42, PrP106\u2013126, and Ataxin-3 Oligomers","volume":"20","author":"Sironi","year":"2014","journal-title":"Chem. Eur. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1039\/C4FO00209A","article-title":"Effects of black tea on body composition and metabolic outcomes related to cardiovascular disease risk: A randomized controlled trial","volume":"5","author":"Croft","year":"2014","journal-title":"Food Funct."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.foodchem.2014.07.005","article-title":"White tea (Camellia sinensis) inhibits proliferation of the colon cancer cell line, HT-29, activates caspases and protects DNA of normal cells against oxidative damage","volume":"169","author":"Hajiaghaalipour","year":"2015","journal-title":"Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"290","DOI":"10.2174\/18715206113136660339","article-title":"Green Tea catechins: Proposed mechanisms of action in breast cancer focusing on the interplay between survival and apoptosis","volume":"14","year":"2014","journal-title":"Anti-Cancer Agents Medicinal. Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.nut.2014.02.023","article-title":"Tea consumption and lung cancer risk: A meta-analysis of case-control and cohort studies","volume":"30","author":"Wang","year":"2014","journal-title":"Nutrition"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jfca.2013.03.005","article-title":"Determination of amino acids in white, green, black, oolong, pu-erh teas and tea products","volume":"31","author":"Horanni","year":"2013","journal-title":"J. Food Compos. Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1016\/S0039-9140(00)00619-6","article-title":"Pattern recognition procedures for differentiation of green, black and oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry","volume":"53","author":"Herrador","year":"2001","journal-title":"Talanta"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1016\/j.jpba.2006.02.053","article-title":"Qualitative identification of tea categories by near infrared spectroscopy and support vector machine","volume":"41","author":"Zhao","year":"2006","journal-title":"J. Pharm. Biomed Anal."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.saa.2006.03.038","article-title":"Feasibility study on identification of green, black and oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)","volume":"66","author":"Chen","year":"2007","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_13","first-page":"1382","article-title":"Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM","volume":"29","author":"Wu","year":"2009","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jpba.2013.05.046","article-title":"Classification of tea category using a portable electronic nose based on an odor imaging sensor array","volume":"84","author":"Chen","year":"2013","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1007\/s12161-013-9649-x","article-title":"Classification of green and black teas by PCA and SVM analysis of cyclic voltammetric signals from metallic oxide-modified electrode","volume":"7","author":"Liu","year":"2014","journal-title":"Food Anal. Method"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.jfoodeng.2006.02.022","article-title":"Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules","volume":"79","author":"Borah","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"623","DOI":"10.13031\/2013.24363","article-title":"Identification of tea varieties using computer vision","volume":"51","author":"Chen","year":"2008","journal-title":"Trans. ASABE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"639","DOI":"10.13031\/2013.32051","article-title":"Identification and grading of tea using computer vision","volume":"26","author":"Jian","year":"2010","journal-title":"Appl. Eng. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jfoodeng.2011.04.013","article-title":"Monitoring and grading of tea by computer vision \u2014A review","volume":"106","author":"Gill","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jfoodeng.2012.10.018","article-title":"Classification of tea grains based upon image texture feature analysis under different illumination conditions","volume":"115","author":"Laddi","year":"2013","journal-title":"J. Food Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jfoodeng.2014.07.001","article-title":"Fruit classification using computer vision and feedforward neural network","volume":"143","author":"Zhang","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cattani, C. (2012). Fractional calculus and Shannon wavelet. Math. Probl. Eng., 2012.","DOI":"10.1155\/2012\/502812"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/tee.22059","article-title":"Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging","volume":"10","author":"Zhang","year":"2015","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Atangana, A., and Goufo, E.F.D. (2014). Computational analysis of the model describing HIV infection of CD4(+)T cells. Biomed Res. Int., 2014.","DOI":"10.1155\/2014\/618404"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/JDT.2011.2164054","article-title":"Gap-type a-Si TFTs for front light sensing application","volume":"7","author":"Tai","year":"2011","journal-title":"J. Disp. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1007\/s00216-014-8015-1","article-title":"Using color histograms and SPA-LDA to classify bacteria","volume":"406","author":"Fernandes","year":"2014","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s11235-009-9208-3","article-title":"Harmonic wavelet approximation of random, fractal and high frequency signals","volume":"43","author":"Cattani","year":"2010","journal-title":"Telecommun. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.bspc.2015.05.014","article-title":"Detection of Alzheimer\u2019s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC","volume":"21","author":"Zhang","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fang, L.T., Wu, L.N., and Zhang, Y.D. (2015). A novel demodulation system based on continuous wavelet transform. Math. Probl. Eng., 2015.","DOI":"10.1155\/2015\/513849"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2528\/PIER10090105","article-title":"A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO","volume":"109","author":"Zhang","year":"2010","journal-title":"Prog. Electromagn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2298\/TSCI15S10S7A","article-title":"Numerical analysis of time fractional three dimensional difussion equation","volume":"19","author":"Abdon","year":"2015","journal-title":"Therm. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cattani, C., Pierro, G., and Altieri, G. (2012). Entropy and multifractality for the myeloma multiple TET 2 gene. Math. Probl. Eng., 2012.","DOI":"10.1155\/2012\/193761"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.3233\/BME-151426","article-title":"Pathological brain detection based on wavelet entropy and Hu moment invariants","volume":"26","author":"Zhang","year":"2015","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1007\/s11771-014-2422-5","article-title":"Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting","volume":"21","author":"Li","year":"2014","journal-title":"J. Cent. South Univ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.elecom.2014.08.005","article-title":"Application of wavelet entropy in analysis of electrochemical noise for corrosion type identification","volume":"48","author":"Moshrefi","year":"2014","journal-title":"Electrochem. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.neucom.2014.06.045","article-title":"Wavelet kernel entropy component analysis with application to industrial process monitoring","volume":"147","author":"Yang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.knosys.2014.03.015","article-title":"Binary PSO with mutation operator for feature selection using decision tree applied to spam detection","volume":"64","author":"Zhang","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1002\/ima.22132","article-title":"Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection","volume":"25","author":"Wang","year":"2015","journal-title":"Int. J. Imaging Syst. Techn."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.3390\/e17041795","article-title":"Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM)","volume":"17","author":"Zhang","year":"2015","journal-title":"Entropy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1166\/jmihi.2015.1542","article-title":"Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine","volume":"5","author":"Zhang","year":"2015","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2528\/PIER13121310","article-title":"Classification of alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree","volume":"144","author":"Zhang","year":"2014","journal-title":"Prog. Electromagn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y.D., Wang, S.H., Ji, G.L., and Dong, Z.C. (2013). An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci. World J., 2013.","DOI":"10.1155\/2013\/130134"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/72.991432","article-title":"Fuzzy support vector machines","volume":"13","author":"Lin","year":"2002","journal-title":"Neural Netw. IEEE Trans."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6737","DOI":"10.1016\/j.eswa.2010.02.067","article-title":"An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM","volume":"37","author":"Xian","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ins.2015.06.017","article-title":"Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging","volume":"322","author":"Zhang","year":"2015","journal-title":"Inform. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s11517-015-1264-0","article-title":"Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism","volume":"53","author":"Kumar","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1016\/j.patcog.2015.01.009","article-title":"Fuzzy support vector machines for multilabel classification","volume":"48","author":"Abe","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/S0167-8655(03)00090-4","article-title":"Rotation and scale invariant texture features using discrete wavelet packet transform","volume":"24","author":"Manthalkar","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ejrad.2007.01.004","article-title":"HVS scheme for DICOM image compression: Design and comparative performance evaluation","volume":"63","author":"Prabhakar","year":"2007","journal-title":"Eur. J. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4439","DOI":"10.3390\/e17064439","article-title":"Analysis of the Keller\u2013Segel model with a fractional derivative without singular kernel","volume":"17","author":"Atangana","year":"2015","journal-title":"Entropy"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1016\/j.physleta.2013.04.012","article-title":"Cantor-type cylindrical-coordinate method for differential equations with local fractional derivatives","volume":"377","author":"Yang","year":"2013","journal-title":"Phys. Lett. A"},{"key":"ref_52","first-page":"36","article-title":"Local fractional variational iteration method for diffusion and wave equations on cantor sets","volume":"59","author":"Yang","year":"2014","journal-title":"Romanian. J. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, Y.D., Dong, Z.C., Phillips, P., Wang, S.H., Ji, G.L., Yang, J.Q., and Yuan, T.-F. (2015). Detection of subjects and brain regions related to Alzheimer\u2019s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci., 9.","DOI":"10.3389\/fncom.2015.00066"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patrec.2015.04.016","article-title":"Effect of spider-web-plot in MR brain image classification","volume":"62","author":"Zhang","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2528\/PIER15040602","article-title":"Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization","volume":"152","author":"Zhang","year":"2015","journal-title":"Prog. Electromagn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5711","DOI":"10.3390\/e17085711","article-title":"Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization","volume":"17","author":"Wang","year":"2015","journal-title":"Entropy"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1007\/s12035-014-8783-9","article-title":"The effects of stress on glutamatergic transmission in the brain","volume":"51","author":"Yuan","year":"2015","journal-title":"Mol. Neurobiol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, Y.D., and Wang, S.H. (2015). Detection of Alzheimer\u2019s disease by displacement field and machine learning. PeerJ., 3.","DOI":"10.7717\/peerj.1251"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10851-007-0042-5","article-title":"Nonlocal Prior Bayesian Tomographic Reconstruction","volume":"30","author":"Chen","year":"2008","journal-title":"J. Math. Imag. Vis."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1364\/OE.18.001177","article-title":"Frequency-wavelet domain deconvolution for terahertz reflection imaging and spectroscopy","volume":"18","author":"Chen","year":"2010","journal-title":"Optic. Express"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1016\/j.ejrad.2010.07.003","article-title":"Improving Low-dose Abdominal CT Images by Weighted Intensity Averaging over Large-scale Neighborhoods","volume":"80","author":"Chen","year":"2011","journal-title":"Eur. J. Radiol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jcp.2014.12.043","article-title":"On the stability and convergence of the time-fractional variable order telegraph equation","volume":"293","author":"Atangana","year":"2015","journal-title":"J. Comput. Phys."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s00521-014-1586-0","article-title":"Convergence and stability analysis of a novel iteration method for fractional biological population equation","volume":"25","author":"Atangana","year":"2014","journal-title":"Neural Comput. Appl."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/10\/6663\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:49:16Z","timestamp":1760215756000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/17\/10\/6663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,25]]},"references-count":63,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2015,10]]}},"alternative-id":["e17106663"],"URL":"https:\/\/doi.org\/10.3390\/e17106663","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2015,9,25]]}}}