Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems.

BACKGROUND The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

[1]  Ramón Martínez-Máñez,et al.  A novel colorimetric sensor array for monitoring fresh pork sausages spoilage , 2014 .

[2]  Ping Yang,et al.  Discrimination of Chinese teas according to major amino acid composition by a colorimetric IDA sensor , 2017 .

[3]  Jiewen Zhao,et al.  Classification of vinegar with different marked ages using olfactory sensors and gustatory sensors , 2014 .

[4]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[5]  Nobuyuki Hayashi,et al.  Objective evaluation methods for the bitter and astringent taste intensities of black and oolong teas by a taste sensor , 2013 .

[6]  Qian Du,et al.  Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis , 2007, IEEE Geoscience and Remote Sensing Letters.

[7]  Giorgia Foca,et al.  Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors , 2016, Analytical and Bioanalytical Chemistry.

[8]  Liangpei Zhang,et al.  A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation , 2014, Remote. Sens..

[9]  Zou Xiaobo,et al.  Genetic Algorithm Interval Partial Least Squares Regression Combined Successive Projections Algorithm for Variable Selection in Near-Infrared Quantitative Analysis of Pigment in Cucumber Leaves , 2010 .

[10]  Muhammad Tahir,et al.  Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification , 2016, Inf. Sci..

[11]  Jun-Hu Cheng,et al.  Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis , 2015 .

[12]  Seung-Chul Yoon,et al.  Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging. , 2018, Meat science.

[13]  Jieping Ye,et al.  A two-stage linear discriminant analysis via QR-decomposition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Digvir S. Jayas,et al.  Hyperspectral imaging to classify and monitor quality of agricultural materials , 2015 .

[15]  Ning Wang,et al.  Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .

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

[17]  A. Visconti,et al.  Screening of deoxynivalenol contamination in durum wheat by MOS-based electronic nose and identification of the relevant pattern of volatile compounds , 2014 .

[18]  Chu Zhang,et al.  Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems , 2018, Sensors.

[19]  Gülsen Taskin Kaya,et al.  Support Vector Selection and Adaptation for Remote Sensing Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Huiyan Jiang,et al.  Data De-noising Based on PCA-KNN Algorithm in Billet Surface Temperature Measurement , 2013 .

[21]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

[22]  Jian Yang,et al.  Why can LDA be performed in PCA transformed space? , 2003, Pattern Recognit..

[23]  Jun Wang,et al.  Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals , 2009 .

[24]  Jiewen Zhao,et al.  Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array. , 2013, Food chemistry.

[25]  Xu Wang,et al.  Rational Design of an α-Ketoamide-Based Near-Infrared Fluorescent Probe Specific for Hydrogen Peroxide in Living Systems. , 2016, Analytical chemistry.

[26]  Jianhua Wang,et al.  A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds , 2018, Sensors.

[27]  Chi-Kuei Wang,et al.  Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification , 2016 .

[28]  Der-Chiang Li,et al.  A class possibility based kernel to increase classification accuracy for small data sets using support vector machines , 2010, Expert Syst. Appl..

[29]  E. Hines,et al.  Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules , 2007 .