Jiewen Zhao

Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques

Total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content in pork meat using integrating near infrared spectroscopy (NIRS), computer vision (CV), and electronic nose (E-nose) techniques. In the experiment, 90 pork samples with different freshness were collected for data acquisition by three different techniques, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from 3 different sensors data. Back-propagation artificial neural network (BP-ANN) was used to construct the model for TVB-N content prediction, and the top principal components (PCs) were extracted as the input of model. The result of the model was achieved as follows: the root mean square error of prediction (RMSEP) = 2.73 mg/100g and the determination coefficient (R(p)(2)) = 0.9527 in the prediction set. Compared with single technique, integrating three techniques, in this paper, has its own superiority. This work demonstrates that it has the potential in nondestructive detection of TVB-N content in pork meat using integrating NIRS, CV and E-nose, and data fusion from multi-technique could significantly improve TVB-N prediction performance.

Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition

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.

Measurement of total flavone content in snow lotus (Saussurea involucrate) using near infrared spectroscopy combined with interval PLS and genetic algorithm.

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.

Classification of tea category using a portable electronic nose based on an odor imaging sensor array

A developed portable electronic nose (E-nose) based on an odor imaging sensor array was successfully used for classification of three different fermentation degrees of tea (i.e., green tea, black tea, and Oolong tea). The odor imaging sensor array was fabricated by printing nine dyes, including porphyrin and metalloporphyrins, on the hydrophobic porous membrane. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to teas volatile organic compounds (VOCs). Multivariate analysis was used for the classification of tea categories, and linear discriminant analysis (LDA) achieved 100% classification rate by leave-one-out cross-validation (LOOCV). This study demonstrates that the E-nose based on odor imaging sensor array has a high potential in the classification of tea category according to different fermentation degrees.

Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy

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.

Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools.

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.

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

High performance liquid chromatography (HPLC) was identified green teas 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 teas quality level according to measurement of main catechins and caffeine contents by HPLC and SVC pattern recognition.

Enhancing the antimicrobial activity of natural extraction using the synthetic ultrasmall metal nanoparticles

The use of Catechin as an antibacterial agent is becoming ever-more common, whereas unstable and easy oxidation, have limited its application. A simple and low-energy-consuming approach to synthesize highly stable and dispersive Catechin-Cu nanoparticles(NPs) has been developed, in which the stability and dispersivity of the NPs are varied greatly with the pH value and temperature of the reaction. The results demonstrate that the optimal reaction conditions are pH 11 at room temperature. As-synthesized NPs display excellent antimicrobial activity, the survival rates of bacterial cells exposed to the NPs were evaluated using live/dead Bacterial Viability Kit. The results showed that NPs at the concentration of 10 ppm and 20 ppm provided rapid and effective killing of up to 90% and 85% of S. aureus and E. coli within 3 h, respectively. After treatment with 20 ppm and 40 ppm NPs, the bacteria are killed completely. Furthermore, on the basis of assessing the antibacterial effects by SEM, TEM, and AFM, it was found the cell membrane damage of the bacteria caused by direct contact of the bacteria with the NPs was the effective mechanism in the bacterial inactivation.

Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy

Total flavonoids content is often considered an important quality index of Ginkgo biloba leaf. The feasibility of using near infrared (NIR) spectra at the wavelength range of 10,000-4000cm(-1) for rapid and nondestructive determination of total flavonoids content in G. biloba leaf was investigated. 120 fresh G. biloba leaves in different colors (green, green-yellowish and yellow) were used to spectra acquisition and total flavonoids determination. Partial least squares (PLS), interval partial least squares (iPLS) and synergy interval partial least squares (SiPLS) were used to develop calibration models for total flavonoids content in two colors leaves (green-yellowish and yellow) and three colors leaves (green, green-yellowish and yellow), respectively. The level of total flavonoids content for green, green-yellowish and yellow leaves was in an increasing order. Two characteristic wavelength regions (5840-6090cm(-1) and 6620-6880cm(-1)), which corresponded to the absorptions of two aromatic rings in basic flavonoid structure, were selected by SiPLS. The optimal SiPLS model for total flavonoids content in the two colors leaves (r(2)=0.82, RMSEP=2.62mg g(-1)) had better performance than PLS and iPLS models. It could be concluded that NIR spectroscopy has significant potential in the nondestructive determination of total flavonoids content in fresh G. biloba leaf.

Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations

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.