Classification of five Chinese tea categories with different fermentation degrees using visible and near-infrared hyperspectral imaging

ABSTRACT A total 206 samples of green, yellow, white, black, and Oolong teas were utilized to acquire hyperspectral imaging, and five tea categories were identified based on visible and near-infrared (NIR) hyperspectral imaging, combined with classification pattern recognition. The characteristic spectra were extracted from the region of interest (ROI), and the standard normal variate (SNV) method was preprocessed to reduce background noise. Four dominant wavelengths (589, 635, 670, and 783 nm) were selected by principal component analysis (PCA) as spectral features. Textural features were extracted by the Grey-level co-occurrence matrix (GLCM) from images at selected dominant wavelengths. Linear discriminant analysis (LDA), library support vector machine (Lib-SVM), and extreme learning machine (ELM) classification models were established based on full spectra, spectral features, textural features, and data fusion, respectively. Lib-SVM was the best model with the input data fusion, and the correct classification rate (CCR) achieved 98.39%. The results implied that visible and NIR hyperspectral imaging combined with Lib-SVM has the capability of rapidly and non-destructively classifying tea categories.

[1]  Jiewen Zhao,et al.  Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. , 2016, Food chemistry.

[2]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[3]  J. Cayuela,et al.  Rapid Determination of Olive Oil Chlorophylls and Carotenoids by Using Visible Spectroscopy , 2014 .

[4]  J. Sádecká,et al.  Classification of plum spirit drinks by synchronous fluorescence spectroscopy. , 2016, Food chemistry.

[5]  Quansheng Chen,et al.  Classification of tea category using a portable electronic nose based on an odor imaging sensor array. , 2013, Journal of pharmaceutical and biomedical analysis.

[6]  A. Jambrak,et al.  Comparison of Conventional and Ultrasound Assisted Extraction Techniques of Yellow Tea and Bioactive Composition of Obtained Extracts , 2012, Food and Bioprocess Technology.

[7]  Yibin Zhou,et al.  Chinese dark teas: Postfermentation, chemistry and biological activities , 2013 .

[8]  Jiewen Zhao,et al.  Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging , 2013 .

[9]  Li Gong,et al.  Column-chromatographic extraction and separation of polyphenols, caffeine and theanine from green tea , 2012 .

[10]  Hubiao Chen,et al.  Comparison of ten major constituents in seven types of processed tea using HPLC-DAD-MS followed by principal component and hierarchical cluster analysis , 2015 .

[11]  X. Wan,et al.  A novel colorimetric determination of free amino acids content in tea infusions with 2,4-dinitrofluorobenzene , 2009 .

[12]  A. G. González,et al.  Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. , 2001, Talanta.

[13]  Y. Zuo,et al.  Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector. , 2002, Talanta.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[16]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[17]  Wei Wang,et al.  Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit , 2016 .

[18]  Quansheng Chen,et al.  Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). , 2007, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[19]  J. M. Jurado,et al.  Differentiation of green, white, black, Oolong, and Pu-erh teas according to their free amino acids content. , 2007, Journal of agricultural and food chemistry.

[20]  Lu Wang,et al.  Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. , 2014, Food chemistry.

[21]  Da-Wen Sun,et al.  Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review , 2014 .

[22]  Renfu Lu,et al.  Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content , 2011 .

[23]  Fei Liu,et al.  Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique , 2014, Food and Bioprocess Technology.

[24]  Da-Wen Sun,et al.  Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review , 2015 .

[25]  P. Trumbo,et al.  U.S. Food and Drug Administration on modernization of the Nutrition and Supplements Facts labels , 2009 .

[26]  Quansheng Chen,et al.  Recent developments of green analytical techniques in analysis of tea's quality and nutrition , 2015 .

[27]  Hanping Mao,et al.  Classification of Black Beans Using Visible and Near Infrared Hyperspectral Imaging , 2016 .

[28]  Fei Liu,et al.  Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets , 2013, Food and Bioprocess Technology.

[29]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[30]  Yudong Zhang,et al.  Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine , 2015, Entropy.