Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality.

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.

[1]  Yong He,et al.  Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis , 2020, Foods.

[2]  Quansheng Chen,et al.  Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea , 2018, Journal of Food Science and Technology.

[3]  Xiaolin Tang,et al.  Applicability of multi-functional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea. , 2020, Journal of the science of food and agriculture.

[4]  Jingming Ning,et al.  Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging. , 2018, Journal of the science of food and agriculture.

[5]  Huanhuan Li,et al.  Measurement of total free amino acids content in black tea using electronic tongue technology coupled with chemometrics , 2020 .

[6]  Quansheng Chen,et al.  Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication , 2017 .

[7]  Wenyi Tan,et al.  Detecting and classifying minor bruised potato based on hyperspectral imaging , 2018, Chemometrics and Intelligent Laboratory Systems.

[8]  N. A. Khovanova,et al.  Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation , 2017, Biomed. Signal Process. Control..

[9]  I. Vermaak,et al.  Non-destructive quality assessment of herbal tea blends using hyperspectral imaging , 2018 .

[10]  Alison Nordon,et al.  Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling , 2019, Journal of Food Engineering.

[11]  Jun Sun,et al.  Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging , 2019, Comput. Electron. Agric..

[12]  Di Wu,et al.  Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging , 2018 .

[13]  Hui Jiang,et al.  Quantitative analysis of yeast fermentation process using Raman spectroscopy: Comparison of CARS and VCPA for variable selection. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[14]  M. Meloun,et al.  Fast gradient HPLC/MS separation of phenolics in green tea to monitor their degradation. , 2017, Food chemistry.

[15]  Jingming Ning,et al.  Classification of five Chinese tea categories with different fermentation degrees using visible and near-infrared hyperspectral imaging , 2016 .

[16]  Jingming Ning,et al.  Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging. , 2018, Journal of the science of food and agriculture.

[17]  Yong He,et al.  Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits , 2019, Foods.

[18]  Linna Zhang,et al.  Performance of calibration model with different ratio of sample size to the number of wavelength: Application to hemoglobin determination by NIR spectroscopy. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[19]  Wei Zhang,et al.  Prediction of soluble solid content of Agaricus bisporus during ultrasound-assisted osmotic dehydration based on hyperspectral imaging , 2020 .

[20]  Jingming Ning,et al.  Back Propagation-Artificial Neural Network Model for Prediction of the Quality of Tea Shoots through Selection of Relevant Near Infrared Spectral Data via Synergy Interval Partial Least Squares , 2013 .

[21]  Quansheng Chen,et al.  Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. , 2018, Journal of the science of food and agriculture.

[22]  Gaozhen Liang,et al.  Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods , 2018, Scientific Reports.

[23]  Jingming Ning,et al.  Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses. , 2019, Journal of the science of food and agriculture.

[24]  Qin Ouyang,et al.  Rapid sensing of total theaflavins content in black tea using a portable electronic tongue system coupled to efficient variables selection algorithms , 2019, Journal of Food Composition and Analysis.

[25]  Bin Hu,et al.  Rapid Sensing of Key Quality Components in Black Tea Fermentation Using Electrical Characteristics Coupled to Variables Selection Algorithms , 2020, Scientific Reports.

[26]  Feng Yu,et al.  Dynamic change in amino acids, catechins, alkaloids, and gallic acid in six types of tea processed from the same batch of fresh tea (Camellia sinensis L.) leaves , 2019, Journal of Food Composition and Analysis.

[27]  Chu Zhang,et al.  Shape induced reflectance correction for non-destructive determination and visualization of soluble solids content in winter jujubes using hyperspectral imaging in two different spectral ranges , 2020 .

[28]  Jingming Ning,et al.  Stepwise Identification of Six Tea (Camellia sinensis (L.)) Categories Based on Catechins, Caffeine, and Theanine Contents Combined with Fisher Discriminant Analysis , 2016, Food Analytical Methods.

[29]  S. Pérez-Burillo,et al.  Effect of brewing time and temperature on antioxidant capacity and phenols of white tea: Relationship with sensory properties. , 2018, Food chemistry.

[30]  Douglas Fernandes Barbin,et al.  Hyperspectral imaging as a powerful tool for identification of papaya seeds in black pepper , 2019, Food Control.

[31]  Jingming Ning,et al.  Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[32]  Yiyong Chen,et al.  Enzymatic Reaction-Related Protein Degradation and Proteinaceous Amino Acid Metabolism during the Black Tea (Camellia sinensis) Manufacturing Process , 2020, Foods.

[33]  Liang Zhang,et al.  Tea aroma formation from six model manufacturing processes. , 2019, Food chemistry.

[34]  Bosoon Park,et al.  A novel hyperspectral microscope imaging technology for rapid evaluation of particle size distribution in matcha , 2020 .

[35]  Frans van den Berg,et al.  Review of the most common pre-processing techniques for near-infrared spectra , 2009 .

[36]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[37]  Koushik Nagasubramanian,et al.  Plant disease identification using explainable 3D deep learning on hyperspectral images , 2019, Plant Methods.

[38]  Tao Xia,et al.  Evaluation of astringent taste of green tea through mass spectrometry-based targeted metabolic profiling of polyphenols. , 2020, Food chemistry.

[39]  X. Wan,et al.  Characterization of the orchid-like aroma contributors in selected premium tea leaves. , 2020, Food research international.

[40]  Eric Ziemons,et al.  Comparison of hyperspectral imaging techniques for the elucidation of falsified medicines composition. , 2019, Talanta.

[41]  A. Siedliska,et al.  Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging , 2018 .

[42]  Jingming Ning,et al.  Quantitative analysis and geographical traceability of black tea using Fourier transform near-infrared spectroscopy (FT-NIRS) , 2013 .

[43]  Janos C. Keresztes,et al.  Measuring colour of vine tomatoes using hyperspectral imaging , 2017 .

[44]  Xinjie Yu,et al.  Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm , 2018, Food Analytical Methods.

[45]  Zheng-Zhu Zhang,et al.  Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics. , 2020, Journal of the science of food and agriculture.

[46]  Xudong Sun,et al.  Enhanced cross-category models for predicting the total polyphenols, caffeine and free amino acids contents in Chinese tea using NIR spectroscopy , 2018, LWT.

[47]  Moon S. Kim,et al.  Detection of fish bones in fillets by Raman hyperspectral imaging technology , 2020 .

[48]  Chunwang Dong,et al.  Application of machine learning algorithms in quality assurance of fermentation process of black tea-- based on electrical properties , 2019, Journal of Food Engineering.

[49]  Quansheng Chen,et al.  Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms , 2018 .

[50]  W. Schwab,et al.  Aroma compositions of large-leaf yellow tea and potential effect of theanine on volatile formation in tea. , 2019, Food chemistry.