Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

[1]  Marcelo Blanco,et al.  NIR spectroscopy: a rapid-response analytical tool , 2002 .

[2]  Nicolas H. Younan,et al.  Wavelet domain statistical hyperspectral soil texture classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[4]  SeichiI Okamura,et al.  Microwave Technology for Moisture Measurement , 2000 .

[5]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[6]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[7]  E. Chuvieco,et al.  Foliage moisture content estimation from one‐dimensional and two‐dimensional spectroradiometry for fire danger assessment , 2006 .

[8]  Ponnadurai Ramasami,et al.  Chemical and near-infrared determination of moisture, fat and protein in tuna fishes , 2007 .

[9]  Yong He,et al.  Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages , 2010 .

[10]  M. A. Pagani,et al.  Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra , 2005 .

[11]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[12]  Pengcheng Nie,et al.  Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea , 2011, Expert Syst. Appl..

[13]  Yong He,et al.  A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy , 2012, Sensors.

[14]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide version 1.7 , 2003 .

[15]  Yufeng Ge,et al.  A new perspective to near-infrared reflectance spectroscopy : A wavelet approach , 2007 .

[16]  D. Gillona,et al.  Estimation of foliage moisture content using near infrared reflectance spectroscopy , 2004 .

[17]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide , 2010 .

[18]  Yufeng Ge,et al.  Wavelet incorporated spectral analysis for soil property determination , 2006 .

[19]  H. Mishra,et al.  FTNIR Spectroscopic Method for Determination of Moisture Content in Green Tea Granules , 2011 .

[20]  W. Cai,et al.  Multiblock partial least squares regression based on wavelet transform for quantitative analysis of near infrared spectra , 2010 .

[21]  Alastair Robertson,et al.  Near-infrared reflectance prediction of quality, theaflavin content and moisture content of black tea , 1988 .

[22]  Somsubhra Chakraborty,et al.  Characterizing surface soil water with field portable diffuse reflectance spectroscopy. , 2010 .

[23]  Kevin McDonnell,et al.  Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy. , 2011, Bioresource technology.

[24]  Xueguang Shao,et al.  Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples. , 2011, The Analyst.

[25]  H. Ramon,et al.  Near infrared reflectance spectroscopy as a tool for the in-line determination of the moisture concentration in extruded semolina pasta , 2007 .

[26]  Fátima N. S. de Medeiros,et al.  Wavelet Analysis for Wind Fields Estimation , 2010, Sensors.

[27]  P. O. Hulth,et al.  Optical properties of deep glacial ice at the South Pole , 2006 .

[28]  Annia García Pereira,et al.  Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques , 2006 .

[29]  Susan L. Ustin,et al.  Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Ju , 2005 .

[30]  H. Büning-Pfaue.  Analysis of water in food by near infrared spectroscopy , 2003 .

[31]  Huaqing Wang,et al.  Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR , 2010, Sensors.