[show abstract][hide abstract] ABSTRACT: 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.
[show abstract][hide abstract] ABSTRACT: This article presented an intelligent method for recognition of different types of Chinese famous tea based on multi-spectral imaging technique. Two kinds of feature extraction methods including gray level co-occurrence matrix and wavelet transform (WT) were adopted for mining characteristic of multi-spectral image. Then multi-class least square support vector machine models were adopted for classification of multi-spectral image, which has little been used in this domain. Meanwhile the receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of multi-spectral imaging classifier. To explore the structure of the wavelet textural features (WTFs), principal component analysis (PCA) was performed based on all the WTFs, and the most important features were detected through loading weight analysis of PCA. In experiments, the potential of WTFs was confirmed for extraction of characteristic from multi-spectral image with high recognition accuracy of 96.82%. And 18 WTFs were detected as the most important features for recognition by PCA. Furthermore, it can be found that the 18 features were the textural features of “contrast” of wavelet sub-space images. This finding may give great help for later research about multi-spectral image classification. The experimental results indicate that the proposed method is effective for recognition of multi-spectral image of different types of Chinese famous tea, the WT is an effective method for mining knowledge from mass multi-spectral imaging information, and PCA can be used to clear the structure of the WTFs.