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 mea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/12/7/9847 |
| _version_ | 1818015502926086144 |
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| author | Chuanqi Xie Yong He Zhengjun Qiu Yanchao Zhang Xiaoli Li |
| author_facet | Chuanqi Xie Yong He Zhengjun Qiu Yanchao Zhang Xiaoli Li |
| author_sort | Chuanqi Xie |
| collection | DOAJ |
| description | 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 (<em>r</em> = 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. |
| first_indexed | 2024-04-14T06:58:54Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:58:54Z |
| publishDate | 2012-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-4de91c6740304566bc42940f54d3fffd2022-12-22T02:06:49ZengMDPI AGSensors1424-82202012-07-011279847986110.3390/s120709847Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate AnalysisChuanqi XieYong HeZhengjun QiuYanchao ZhangXiaoli LiEffects 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 (<em>r</em> = 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.http://www.mdpi.com/1424-8220/12/7/9847diffuse reflectance spectroscopymoisture contentteawavelet transformwavelength selection |
| spellingShingle | Chuanqi Xie Yong He Zhengjun Qiu Yanchao Zhang Xiaoli Li Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis Sensors diffuse reflectance spectroscopy moisture content tea wavelet transform wavelength selection |
| title | Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis |
| title_full | Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis |
| title_fullStr | Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis |
| title_full_unstemmed | Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis |
| title_short | Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis |
| title_sort | characterizing the moisture content of tea with diffuse reflectance spectroscopy using wavelet transform and multivariate analysis |
| topic | diffuse reflectance spectroscopy moisture content tea wavelet transform wavelength selection |
| url | http://www.mdpi.com/1424-8220/12/7/9847 |
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