Publications (34)46.99 Total impact
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Article: Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array.
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ABSTRACT: A novel artificial olfactory technique based on colorimetric sensor array was developed for the classification of Chinese rice wine according to different marked ages. The sensor array was composed of nine porphyrins or metalloporphyrins materials and six pH indicators. When the sensor array was exposed to rice wine for several minutes, a colour change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to the head-gas of the sample. The values of RGB (i.e., red, green, and blue) colour components were extracted from each dye in colour change profiles, and they were analysed using principal component analysis (PCA) and linear discriminant analysis (LDA). In contrast to PCA, LDA obtained an optimum classification result. This research shows that the artificial olfactory technique based on colorimetric sensor array has a powerful potential in the quality evaluation of rice wine.Food Chemistry 06/2013; 138(2-3):1320-4. · 3.65 Impact Factor -
Article: Pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants using near-infrared reflectance spectroscopy.
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ABSTRACT: The morphological symptoms of phosphorus (P) deficiency in the leaves of mini-cucumber plants at early stages of development have features similar to that of early stage development in healthy plants. That similarity may lead to inappropriate visual diagnostics of phosphorus deficiency in analyzed samples. Because the differences in spectral properties of leaf tissues between phosphorus-deficient and healthy plants can be demonstrated, the feasibility of using near-infrared (NIR) spectroscopy for rapid and nondestructive diagnostics of phosphorus deficiency in mini-cucumber plants was investigated. Leaf reflection spectra in the wavelength range of 10 000-4000 cm(-1) were measured before the appearance of morphological changes caused by phosphorus deficiency. Least-squares support vector machine (LS-SVM), a method for recognizing patterns, was applied to identify phosphorus-deficient plants. Parameters (γ, σ(2)) of LS-SVM were optimized by cross-validation, and several conventional, two-class classification methods such as linear discrimination analysis and K-nearest neighbors were also used comparatively for identification. Identification rates in excess of 86% were achieved with the LS-SVM model for both the training set and the prediction set. The overall results indicated that NIR spectra combined with LS-SVM could be used efficiently for pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants.Applied Spectroscopy 12/2012; 66(12):1426-32. · 1.66 Impact Factor -
Article: Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools.
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ABSTRACT: Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.Food Chemistry 11/2012; 135(2):590-5. · 3.65 Impact Factor -
Article: The socio-economic importance of wild vegetable resources and their conservation: a case study from China
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ABSTRACT: In the Xiangxi region of western Hunan province, China, 335 taxa belonging to 87 families and 119 genera are utilised as wild vegetables. In order to take advantage of this naturally occurring resource we examined the horticultural and the associated socio-economic aspects of these taxa. Wild vegetables, as the mainstay of human diet and Chinese traditional medicines, have played an important role in the daily life and income of local ethnic groups for centuries. We examine candidate species for their prevalence and their potential to offer returns, for example in cereal production and tourism, and indicate horticultural management and processing technologies which may exploit wild vegetable availability. Key wordsChina–seed plants–western Hunan–wild vegetable–XiangxiKew Bulletin 04/2012; 65(4):577-582. -
Article: Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy.
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ABSTRACT: Total acid content (TAC) and soluble salt-free solids content (SSFSC) in Chinese vinegar are 2 important indicators in the assessment of its quality. This paper shows the feasibility to determine TAC and SSFSC in Chinese vinegar by near-infrared (NIR) spectroscopy. Synergy interval partial least square (Si-PLS) algorithm was performed to calibrate the regression model. The number of PLS factors and the number of intervals were optimized simultaneously by cross-validation. The performance of the model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in the prediction set. The optimum Si-PLS model for TAC was achieved with RMSEP = 0.264 g/100 mL and R(p) = 0.9655; the optimum Si-PLS model for SSFSC was achieved with RMSEP = 1.93 g/100 mL and R(p) = 0.9302. The overall results demonstrated that NIR spectroscopy combined with Si-PLS could be utilized to determinate TAC and SSFSC in Chinese vinegar, and NIR spectroscopy has a potential to be used in vinegar industry.Journal of Food Science 02/2012; 77(2):C222-7. · 1.66 Impact Factor -
Article: Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration.
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ABSTRACT: A quantitative structure-activity relationship (QSAR) model of angiotensin-converting enzyme- (ACE-) inhibitory peptides was built with an artificial neural network (ANN) approach based on structural or activity data of 58 dipeptides (including peptide activity, hydrophilic amino acids content, three-dimensional shape, size, and electrical parameters), the overall correlation coefficient of the predicted versus actual data points is R = 0.928, and the model was applied in ACE-inhibitory peptides preparation from defatted wheat germ protein (DWGP). According to the QSAR model, the C-terminal of the peptide was found to have principal importance on ACE-inhibitory activity, that is, if the C-terminal is hydrophobic amino acid, the peptide's ACE-inhibitory activity will be high, and proteins which contain abundant hydrophobic amino acids are suitable to produce ACE-inhibitory peptides. According to the model, DWGP is a good protein material to produce ACE-inhibitory peptides because it contains 42.84% of hydrophobic amino acids, and structural information analysis from the QSAR model showed that proteases of Alcalase and Neutrase were suitable candidates for ACE-inhibitory peptides preparation from DWGP. Considering higher DH and similar ACE-inhibitory activity of hydrolysate compared with Neutrase, Alcalase was finally selected through experimental study.International Journal of Peptides 01/2012; 2012:620609. -
Article: In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging.
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ABSTRACT: The objective of this study was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation and distribution in leaves using hyperspectral imaging. The hyperspectral imaging data cube of cucumber (Cucumis sativus) leaves in the range of 450-850 nm was investigated and preprocessed. Sixty optical signatures or indices as a function of the associated reflectance (R(λ)) at the special wavelength (λ) nm which proposed in the literatures were used to predict the total chlorophyll content in cucumber leaves. Finally, R(710)/R(760), (R(780)-R(710))/(R(780)-R(680)), (R(750)-R(705))/(R(750)+R(705)), (R(680)-R(430))/(R(680)+R(430)), R(860)/(R(550)×R(708)), (R(695-705))(-1)-(R(750-800))(-1), and REP-LEM (a index based on red edge position and estimated with a linear extrapolation method) were identified as optimum indices. Red-edge waveband (680-780 nm) appeared in all these optimum indices, indicating the importance of REP (red edge position) in chlorophyll estimation. When (R(695-705))(-1)-(R(750-800))(-1), the best index was applied to an independent validation set, chlorophyll content (r=0.8286) were reasonably well predicted, indicating model robustness. Depending on the sample, this technique enables to identify and characterize the relative content of various chlorophyll that distribution in the cucumber leaves. The map shows a relatively low level of chlorophyll at margins. Higher level can be noticed in the regions along the main veins and in some areas exhibiting dark green tissue. Our results indicate that hyperspectral imaging has considerable promise for predicting pigments in leaves and, the pigments can be detected in situ in living plant samples non-destructively.Analytica chimica acta 11/2011; 706(1):105-12. · 4.31 Impact Factor -
Article: Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy.
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ABSTRACT: 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.Journal of pharmaceutical and biomedical analysis 10/2011; 60:92-7. · 2.45 Impact Factor -
Article: Discrimination of Radix Pseudostellariae according to geographical origins using NIR spectroscopy and support vector data description.
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ABSTRACT: Near infrared (NIR) spectroscopy combined with support vector data description (SVDD) was attempted to identify geographical origins of Radix Pseudostellariae. Original spectra of eggs in wavelength range of 10000-4000 cm(-1) were acquired. SVDD was performed to calibrate discrimination model, and some parameters of SVDD model were optimized. Meanwhile, conversional two-class classification method-support vector machine (SVM) was used comparatively for classification. Compared with SVM classification, SVDD model showed its superior ability in dealing with imbalance training samples. When the proportion of the number of Radix Pseudostellariae from Anhui province (the area where genuine crude Radix Pseudostellariae was cultivated) and other provinces was one to sixteen, the identification rate of SVDD model was 92.5% in prediction set. This work indicates that NIR spectroscopy combined with SVDD is an excellent choice in building one-class calibration model for discrimination of genuine crude Radix Pseudostellariae.Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 05/2011; 79(5):1381-5. · 2.10 Impact Factor -
Article: Detection of Bruise on Pear by Hyperspectral Imaging Sensor with Different Classification Algorithms
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ABSTRACT: A hyperspectral imaging sensor system was developed for the detection of bruises on pears, for these bruises were difficult to be detected by traditional computer vision technique. Hyperspectral imaging sensor technique is susceptible to the effects of uneven illumination due to a spherical object of pear. The data of hyperspectral image is a 3-dimension cube, which contains a huge amount of information. So it requires a suitable algorithm to extract some useful information from the 3-dimension data cube. In this work, Principal Component Analysis (PCA) was firstly used to extract some useful information, then several other classification algorithms were used comparatively to process the 3-dimension data cube. These classification algorithms were Maximum Likelihood Classification (MLC), Euclidean Distance Classification (EDC), Mahalanobis Distance Classification (MDC) and Spectral Angle Mapper (SAM), respectively. Results show that MDC and SAM have well performance, with detection accuracy of 93.8% and 95.0% respectively. Compared with the other classification algorithms, MDC and SAM can overcome the effects of uneven illumination in detecting bruise of pear by hyperspectral imaging sensor technique. This work demonstrates that it is feasible to detect the bruised region on the surface of pear by hyperspectral imaging sensor technique combined with MDC and SAM.Sensor Letters 07/2010; 8(4):570-576. · 0.82 Impact Factor -
Article: Genetic algorithm interval partial least squares regression combined successive projections algorithm for variable selection in near-infrared quantitative analysis of pigment in cucumber leaves.
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ABSTRACT: Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A method based on a genetic algorithm interval partial least squares regression (GAiPLS) combined successive projections algorithm (SPA) was proposed for variable selection in NIR spectroscopy. GAiPLS was used to select informative interval regions among the spectrum, and then SPA was employed to select the most informative variables and to minimize collinearity between those variables in the model. The performance of the proposed method was compared with the full-spectrum model, conventional interval partial least squares regression (iPLS), and backward interval partial least squares regression (BiPLS) for modeling the NIR data sets of pigments in cucumber leaf samples. The multiple linear regression (MLR) model was obtained with eight variables for chlorophylls and five variables for carotenoids selected by SPA. When the SPA model was applied to the prediction of the validation set, the correlation coefficients of the predicted value by MLR and the measured value for the validation data set (r(p)) of chlorophylls and carotenoids were 0.917 and 0.932, respectively. Results show that the proposed method was able to select important wavelengths from the NIR spectra and makes the prediction more robust and accurate in quantitative analysis.Applied Spectroscopy 07/2010; 64(7):786-94. · 1.66 Impact Factor -
Article: Measurement of total flavone content in snow lotus (Saussurea involucrate) using near infrared spectroscopy combined with interval PLS and genetic algorithm.
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ABSTRACT: NIR spectroscopy technique was attempted to measure total flavone content in snow lotus in this work. Interval partial least square with genetic algorithm (iPLS-GA) was used to select the efficient spectral regions and variables in model calibration. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R(c)) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R(p)) in prediction set. The optimal iPLS-GA model was obtained with 6 PLS factors, when 5 spectral regions and 53 variables were selected. The measurement results of final model were achieved as follow: RMSEC (%)=0.8347/R(c)=0.9444 in the calibration set, and RMSEP (%)=1.0766/R(p)=0.9006 in the prediction set. Finally, iPLS-GA moded showed its excellent performance, when compared with other 5 different PLS models. This work demonstrated that total flavone content in snow lotus could be measured by NIR spectroscopy technique, and iPLS-GA revealed its superiority in model calibration.Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 03/2010; 76(1):50-5. · 2.10 Impact Factor -
Chapter: MACHINE VISION ON-LINE DETECTION QUALITY OF SOFT CAPSULES BASE ON SVM
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ABSTRACT: Nowadays the quality inspect of soft capsules is mainly by manual. Despite the intensive of this work, the accuracy of inspection by manual is very low. This paper proposed soft capsules online sorting system based on machine vision. The inspection process are following: (1) soft capsules were placed on rollers are rotating while moving. The image of each soft capsule was grabbed. (2) automatic threshold based on ostu was used to segmentation capsule image from background, and morphological filter was used to eliminate noise and regional markings. (3) 4 features were extracted which were perimeter, area, girth, altitude diameter and latitude diameter. Support Vector Machine (SVM) and was used to analyze these features. 15460 soft capsules were tested by the online sorting system. The overall grading accuracy was up to 94.1%. Furthermore, the grading speed of the sorting line resches10 capsules per second.12/2009: pages 1369-1378; -
Article: Automated tea quality classification by hyperspectral imaging.
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ABSTRACT: A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.Applied Optics 08/2009; 48(19):3557-64. · 1.41 Impact Factor -
Article: Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations.
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ABSTRACT: Near infrared (NIR) spectroscopy combined with multivariate calibration was attempted to analyze free amino acid content of Radix Pseudostellariae. The original spectra of Pseudostellariae samples in wavelength range of 10000-4000 cm(-1) were acquired. Partial least squares (PLS), kernel PLS (k-PLS), back propagation neural network (BP-NN), and support vector regression (SVR) algorithms were performed comparatively to develop calibration models. Some parameters of the calibration models were optimized by cross-validation. The performance of BP-NN model was better than PLS, k-PLS, and SVR models. The root mean square error of prediction (RMSEP) and the correlation coefficient (R) of BP-NN model were 0.687 and 0.889 in prediction set respectively. Results showed that NIR spectroscopy combined with multivariate calibration has significant potential in quantitative analysis of free amino acid content in Radix Pseudostellariae.Journal of pharmaceutical and biomedical analysis 07/2009; 50(5):803-8. · 2.45 Impact Factor -
Chapter: On-Line Detection of Defects on Fruit by Machinevision Systems Based on Three-Color-Cameras Systems
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ABSTRACT: How to identify apple stem-ends and calyxes from defects is still a challenging project due to the complexity of the process. It is know that the stem-ends and calyxes could not appear at the same image. Therefore, a contaminated apple distinguishing method is developed in this article. That is, if there are two or more doubtful blobs on an apple´s image, the apple is contaminated one. There is no complex imaging process and pattern recognition in this method, because it is only need to find how many blobs (including the stem-ends and calyxes) in an apple´s image. Machine vision systems which based 3 color cameras are presented in this article regarding the online detection of external defects. On this system, the fruits placed on rollers are rotating while moving, and each camera which placed on the line grabs 3 images from an apple. After the apple segmented from the black background by multi-thresholds method, defect´s segmentation and counting is performed on the apple´s images. Good separation between normal and contaminated apples was obtained for threecamera system (94.5%), comparing to one-camera system (63.3%), twocamera system (83.7%). The disadvantage of this method is that it could not distinguish different defects types. Defects of apples, such as bruising, scab, fungal growth, and disease, are treated as the same.06/2009: pages 2231-2238; -
Chapter: ON-LINE DETECTING SIZE AND COLOR OF FRUIT BY FUSING INFORMATION FROM IMAGES OF THREE COLOR CAMERA SYSTEMS
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ABSTRACT: On the common systems, the fruits placed on rollers are rotating while moving, they are observed from above by one camera. In this case, the parts of the fruit near the points where the rotation axis crosses its surface (defined as rotational poles) are not observed. Most researchers did not consider how to manage several images representing the whole surface of the fruit, and each image was treated separately and that the fruit was classified according to the worse result of the set of representative images. Machine vision systems which based 3 color cameras are presented in this article regarding the online detection of size and color of fruits. Nine images covering the whole surface of an apple is got at three continuous positions by the system. Solutions of processing the sequential image’s results continuously and saving them into database promptly were provided. In order to fusing information of the nine images, determination of size was properly solved by a multi-linear regression method based on nine apple images’ longitudinal radius and lateral radius, and the correlation coefficient between sorting machine and manual is 0.919, 0.896 for the training set and test set. HSI (hue-saturation-intensity) of nine images was used for apple color discrimination and the hue field in 0o~80o was divided into 8 equal intervals. After counting the pixel in each interval, the total divided by 100 was treated as the apple color feature. Then 8 color features were got. PCA and ANN were used to analysis the 8 color features. There is a little overlapped in the three-dimensional space results of PCA. An ANN was used to build the relationship between 8 color characters and 4 apple classes with classification accuracy for the training/test set 88%/85.6%.06/2009: pages 1087-1095; -
Article: Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition.
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ABSTRACT: Rapid discrimination of roast green tea according to geographical origin is crucial to quality control. Fourier transform near-infrared (FT-NIR) spectroscopy and supervised pattern recognition was attempted to discriminate Chinese green tea according to geographical origins (i.e. Anhui Province, Henan Province, Jiangsu Province, and Zhejiang Province) in this work. Four supervised pattern recognitions methods were used to construct the discrimination models based on principal component analysis (PCA), respectively. The number of principal components factors (PCs) and model parameters were optimized by cross-validation in the constructing model. The performances of four discrimination models were compared. Experimental results showed that the performance of SVM model is the best among four models. The optimal SVM model was achieved when 4 PCs were used, discrimination rates being all 100% in the training and prediction set. The overall results demonstrated that FT-NIR spectroscopy with supervised pattern recognition could be successfully applied to discriminate green tea according to geographical origins.Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 01/2009; 72(4):845-50. · 2.10 Impact Factor -
Article: Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition.
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ABSTRACT: High performance liquid chromatography (HPLC) was identified green tea's quality level by measurement of catechins and caffeine content. Four grades of roast green teas were attempted in this work. Five main catechins ((-)-epigallocatechin gallate (EGCG), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECG), (-)-epicatechin (EC), and (+)-catechin (C)) and caffeine contents were measured simultaneously by HPLC. As a new chemical pattern recognition, support vector classification (SVC) was applied to develop identification model. Some parameters including regularization parameter (R) and kernel parameter (K) were optimized by the cross-validation. The optimal SVC model was achieved with R=20 and K=2. Identification rates were 95% in the training set and 90% in the prediction set, respectively. Finally, compared with other pattern recognition approaches, SVC algorithm shows its excellent performance in identification results. Overall results show that it is feasible to identify green tea's quality level according to measurement of main catechins and caffeine contents by HPLC and SVC pattern recognition.Journal of Pharmaceutical and Biomedical Analysis 10/2008; 48(5):1321-5. · 2.97 Impact Factor -
Article: Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms.
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ABSTRACT: This paper attempted the feasibility to determine content total polyphenols content in green tea with near infrared (NIR) spectroscopy coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate regression model. The number of PLS components and the number of intervals were optimized according to root mean square error of cross-validation (RMSECV) in calibration set. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of siPLS model is the best in contrast to PLS and iPLS. The optimal model was achieved with R=0.9583 and RMSEP=0.7327 in prediction set. This study demonstrated that NIR spectroscopy with siPLS algorithm could be used successfully to analysis of total polyphenols content in green tea, and revealed superiority of siPLS algorithm in contrast with other multivariate calibration methods.Journal of Pharmaceutical and Biomedical Analysis 03/2008; 46(3):568-73. · 2.97 Impact Factor
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2006–2010
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Jiangsu University
- School of Food and Biological Engineering
Zhenjiang, Jiangsu Sheng, China
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