Entity

Time filter

Source Type


Chen Q.,Jiangsu University | Guo Z.,Jiangsu University | Guo Z.,National Engineering Research Center for Information Technologies in Agriculture | Zhao J.,Jiangsu University | Ouyang Q.,Jiangsu University
Journal of Pharmaceutical and Biomedical Analysis | Year: 2012

To rapidly and efficiently measure antioxidant activity (AA) in green tea, near infrared (NIR) spectroscopy was employed with the help of a regression tool in this work. Three different linear and nonlinear regressions tools (i.e. partial least squares (PLS), back propagation artificial neural network (BP-ANN), and support vector machine regression (SVMR)), were systemically studied and compared in developing the model. The model was optimized by a leave-one-out cross-validation, and its performance was tested according to root mean square error of prediction (RMSEP) and correlation coefficient (R p) in the prediction set. Experimental results showed that the performance of SVMR model was superior to the others, and the optimum results of the SVMR model were achieved as follow: RMSEP=0.02161 and R p=0.9691 in the prediction set. The overall results sufficiently demonstrate that the spectroscopy coupled with the SVMR regression tool has the potential to measure AA in green tea. © 2011 Elsevier B.V. Source


Guo Z.,National Engineering Research Center for Information Technologies in Agriculture | Chen Q.,Jiangsu University | Chen L.,National Engineering Research Center for Information Technologies in Agriculture | Huang W.,National Engineering Research Center for Information Technologies in Agriculture | And 2 more authors.
Applied Spectroscopy | Year: 2011

Epigallocatechin-3-gallate (EGCG) is credited with the majority of the health benefits associated with green tea consumption. It has a high economic and medicinal value. The feasibility of using different variable selection approaches in Fourier transform near-infrared (FT-NIR) spectroscopy for a rapid and conclusive quantitative determination of EGCG in green tea was investigated. Graphically oriented multivariate calibration modeling procedures such as interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and genetic algorithm optimization combined with siPLS (siPLS-GA) were applied to select the most efficient spectral variables that provided the lowest prediction error. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and coefficient of determination (R 2) for the prediction set. Experimental results showed that the siPLS-GA model obtained the best results in comparison to other models. The optimal models were achieved with R 2p = 0.97 and RMSEP = 0.32. The model can be obtained with only 36 variables retained and it provides a robust model with good estimation accuracy. This demonstrates the potential of NIR spectroscopy with multivariate calibration methods to quickly detect the bioactive component in green tea. © 2011 Society for Applied Spectroscopy. Source


Huang W.,Beijing Institute of Technology | Huang W.,National Engineering Research Center for Information Technologies in Agriculture | Zhang C.,National Engineering Research Center for Information Technologies in Agriculture | Li J.,National Engineering Research Center for Information Technologies in Agriculture | And 2 more authors.
American Society of Agricultural and Biological Engineers Annual International Meeting 2012, ASABE 2012 | Year: 2012

Because the images of bruises often exhibit patterns and intensity values similar to the stem-end/calyx, it is important to discriminate the bruises from the stem-end/calyx in an apple sorting system. To solve this problem, a push-broom hyperspectral imaging system was developed to acquire reflectance images of apple between 400 nm and 1100 nm. A total of 60 apples were used, 30 of them were sound, 15 of them with bruises near the calyx, and 15 of them with bruises near the stem-end. The full wavelength region from 450 to 980 nm was segmented into the visible region 450 to 780 nm and the near-infrared region from 780 to 980 nm. Then the principal component analysis (PCA) was conducted on the full region and the two segmented regions respectively. The PC images were used to detect the bruise and the effective wavebands were selected according to the PC images' loading plots. The PC images from 450 to 780 nm could not be used to detect the bruises. The PCA was used again on the effective wavebands selected from 780 to 980 nm and 450 to 980 nm. Results show that the effective wavebands from 780 to 980 nm could be used to discriminate bruises from stem-end and calyx. None of the healthy apple was misclassified. None of the bruises were misclassified as stem-end or calyx. 93.3% of the bruises near the stem-end were correctly classified, and only 86.7% of the bruises near the calyx were correctly classified using the PC images resulted from the effective wavebands 820 and 970nm. The classification error of the bruises near the stem-end/calyx would be caused by the strong light spot on the sample. Moreover, the only 2 effective wavebands 820 and 970 nm from the NIR region would decrease the cost to establish a multispectral imaging system. Source

Discover hidden collaborations