Identification of Transgenic Soybean Varieties Using Mid-Infrared Spectroscopy.

Transgenic technology has enormous significance in increasing food production, protecting biodiversity and reducing the use of chemical pesticides and so on. However, there may be some security risks; therefore, research on genetically modified crop identification technology is attracting more and more attention. Mid-infrared spectroscopy combined with feature extraction methods were used to investigate the feasibility of identifying different kinds of transgenic soybeans in the wavelength range of 3 818～734 cm-1. For this purpose, partial least squares-discriminant analysis (PLS-DA) was employed as pattern recognition methods to classify three non-GMO parent soybeans(HC6, JACK and W82)and their transgenic soybeans. The results of the calibration set were 96.67%, 96.67% and 83.33% for three non-GMO parent soybeans and their transgenic soybeans, and the results of the prediction set were 83.33%, 85% and 85%. X-loading weights, variable importance in the projection (VIP) algorithm and second derivative (2-Der) algorithm were applied to select sensitive wavenumbers. The sensitive wavelengths selected with x-loading weights were used to build PLS-DA model, the classification accuracy of the calibration set were 91.11%, 91.67% and 81.67%, and the results of the prediction set were 80%, 80% and 75%. By using the VIP algorithm, the classification accuracy of the calibration set were 94.44%, 95% and 76.67%, and the results of the prediction set were 80%, 85% and 75%. By using the 2-Der algorithm, the classification accuracy of the calibration set were 88.89%, 81.67% and 80%, and the results of the prediction set were 76.67%, 75% and 75%. Principal components analysis (PCA) and independent component analysis (ICA) were applied to extract feature information. The principal components were combined with PLS-DA model. The classification accuracy of the calibration set were 96.67%, 90% and 80%, and the results of the prediction set were 80%, 90% and 80%. The independent components were combined with PLS-DA model. The classification accuracy of the calibration set were 93.33%, 83.33% and 83.33% while the results of the prediction set were 83.33%, 75% and 75%. The overall results indicated that mid-infrared spectroscopy could accurately identify the varieties of the non-GMO parent soybeans, which provided a new idea for nondestructive testing of transgenic soybeans. Feature extraction methods could be used to build more concise models and reduce the amount of program operations combined with sensitive wavenumbers selection methods.

[Fast Detection of Camellia Sinensis Growth Process and Tea Quality Informations with Spectral Technology: A Review].

The research achievements and trends of spectral technology in fast detection of Camellia sinensis growth process information and tea quality information were being reviewed. Spectral technology is a kind of fast, nondestructive, efficient detection technology, which mainly contains infrared spectroscopy, fluorescence spectroscopy, Raman spectroscopy and mass spectroscopy. The rapid detection of Camellia sinensis growth process information and tea quality is helpful to realize the informatization and automation of tea production and ensure the tea quality and safety. This paper provides a review on its applications containing the detection of tea (Camellia sinensis) growing status(nitrogen, chlorophyll, diseases and insect pest), the discrimination of tea varieties, the grade discrimination of tea, the detection of tea internal quality (catechins, total polyphenols, caffeine, amino acid, pesticide residual and so on), the quality evaluation of tea beverage and tea by-product, the machinery of tea quality determination and discrimination. This paper briefly introduces the trends of the technology of the determination of tea growth process information, sensor and industrial application. In conclusion, spectral technology showed high potential to detect Camellia sinensis growth process information, to predict tea internal quality and to classify tea varieties and grades. Suitable chemometrics and preprocessing methods is helpful to improve the performance of the model and get rid of redundancy, which provides the possibility to develop the portable machinery. Future work is to develop the portable machinery and on-line detection system is recommended to improve the further application. The application and research achievement of spectral technology concerning about tea were outlined in this paper for the first time, which contained Camellia sinensis growth, tea production, the quality and safety of tea and by-produce and so on, as well as some problems to be solved and its future applicability in modern tea industrial.

[Fast Identification of Transgenic Soybean Varieties Based Near Infrared Hyperspectral Imaging Technology].

Near-infrared hyperspectral imaging technology combined with chemometrics was applied for rapid and non-invasive transgenic soybeans variety identification. Three different non-GMO parent soybeans(HC6, JACK, TL1)and their transgenic soybeans were chosen as the research object. The developed hyperspectral imaging system was used to acquire the hyperspectral images in the spectral range of 874~1 734 nm with 256 bands of soybeans, and the reflectance spectra were extracted from the region of interest (ROI) in the images. After eliminating the obvious noises, the moving average(MA)was applied as smooth pretreatment, and the wavelengths from 941~1 646 nm were used for later analysis. Partial least squares-discriminant analysis (PLS-DA)was employed as pattern recognition method to class the three different non-GMO parent soybeans. The classification accuracy of both the calibration set and the prediction set were 97.50% and 100% for the HC6, 100% and 100% for the JACK, 96.25% and 92.50% for the TL1, which indicated that hyperspectral imaging technology could identify the varieties of the non-GMO parent soybeans. Then PLS-DA was applied to classify non-GMO parent soybean and its transgenic soybean cultivars for building discriminant models. For the full spectra, the classification accuracy of both the calibration set and the prediction set were 99.17% and 99.17% for the HC6 and its transgenic soybean cultivars, 87.19% and 81.25% for the JACK and its transgenic soybean cultivars, 99.17% and 98.33% for the TL1 and its transgenic soybean cultivars, respectively. The sensitive wavelengths were selected by x-loading weights, and the classification accuracy of the calibration set and prediction set of PLS-DA models based on sensitive wavelengths were 72.50% and 80% for the HC6 and its transgenic soybean cultivars, 80.63% and 79.38% for the JACK and its transgenic soybean cultivars, 85% and 85% for the TL1 and its transgenic soybean cultivars, respectively. These results showed that the pattern recognition for non-GMO parent soybean and their transgenic soybeans was feasible, and the selected sensitive wavelengths could be used for the pattern recognition of non-GMO parent soybeans and transgenic soybeans. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the pattern recognition of the non-GMO parent soybeans varieties, non-GMO parent soybean and its transgenic soybeans. This study also provided a new alternative for rapid and non-destructive accurate identification of transgenic soybean.

[Rapid detection of nitrogen content and distribution in oilseed rape leaves based on hyperspectral imaging].

Visible and near infrared (Vis-NIR) hyperspectral imaging system was carried out to rapidly determinate the content and estimate the distribution of nitrogen (N) in oilseed rape leaves. Hyperspectral images of 420 leaf samples were acquired at seedling, flowering and pod stages. The spectral data of rape leaves were extracted from the region of interest (ROI) in the wave- length range of 380-1,030 nm. Different spectra preprocessing including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivatives were applied to improve the signal to noise ratio. Among 471 wavelengths, only twelve wavelengths (467, 557, 665, 686, 706, 752, 874, 879, 886, 900, 978 and 995 nm) were selected by successive projections algorithm(SPA) as the effective wavelengths for N prediction. Based on these effective wavelengths, partial least squares(PLS) and least-squares support vector machines (LS-SVM) calibration models were established for the determination of N content. Reasonable estimation accuracy was obtained, with Rp of 0.807 and RMSEP of 0.387 by PLS and Rp of 0.836 and RMSEP of 0.358 by LS-SVM, respectively. Considering the simple structure and satisfying results of PLS model, SPA-PLS model was used to generate the distribution maps of N content in rape leaves. The concentrations of N were calculated at each pixel of hyperspectral images at the selected effective wavelengths by inputting its correspond- ing spectrum into the established SPA-PLS model. Different colour represented the change in N content in the rape leaves under different fertilizer treatments. By including all pixels within the selected ROI, the average N status can be displayed in more detail and visualised. The visualization of N distribution could be helpful to understanding the change in N content in rape leaves during rape growth period and facilitate discovering the difference of N content within one sample as well as among the samples from different fertilising plots. The overall results revealed that hyperspectral imaging is a promising technique to detect N content and distribution within oilseed rape leaves rapidly and nondestructively.

[Early diagnosis of gray mold on tomato stalks based on hyperspectral data].

Early diagnosis of gray mold on tomato stalks based on hyperspectral data was studied in the present paper. A total of 112 samples' hyperspectral data were collected by hyperspectral imaging system. The study spectral region was from 400 to 1,030 nm. Combined with image processing and chemometric methods, the tomato stalk gray mold diagnosis models were built. Seven effective wavelengths were selected by analysis of variable load distribution in PLS model. The experimental results showed that the excellent results were achieved by EW-LS-SVM model with standard normal variate (SNV) spectral and multiplicative scatter correction (MSC) spectral, and the accuracy of diagnosing gray mold on tomato stalks was satisfied and better than PLS model with whole band. Hence, it is feasible to early diagnose gray mold on tomato stalks using hyperspectral imaging technology, which provides a new early diagnosis and warning method for tomato disease.

[Analysis of main risk factors causing foodborne diseases in food catering business].

OBJECTIVE: To study main risk factors that cause foodborne diseases in food catering business. METHODS: Data from references and investigations conducted in food catering units were used to establish models which based on @Risk 4.5 with Monte Carlo method referring to food handling practice model (FHPM) to make risk assessment on factors of food contamination in food catering units. The Beta-Poisson models on dose-response relationship to Salmonella (developed by WHO/FAO and United States Department of Agriculture) and Vibrio parahaemolyticus (developed by US FDA) were used in this article to analyze the dose-response relationship of pathogens. RESULTS: The average probability of food poisoning by consuming Salmonella contaminated cooked meat under refrigeration was 1.96 × 10(-4) which was 1/2800 of the food under non-refrigeration (the average probability of food poisoning was 0.35 at room temperature 25°C). The average probability by consuming 6 hours stored meat under room temperature was 0.11 which was 16 times of 2 hours storage (6.79 × 10(-3)). The average probability by consuming contaminated meat without fully cooking was 1.71 × 10(-4) which was 100 times of consuming fully cooked meat (1.88 × 10(-6)). The probability growth of food poisoning by consuming Vibrio parahaemolyticus contaminated fresh seafood was proportional with contamination level and prevalence. CONCLUSION: The primary contamination level, storage temperature and time, cooking process and cross contamination are important factors of catering food safety.

[Detection of pear injury based on visible-near infrared spectroscopy and multispectral image].

A new approach to detect the injury degree and time of pear based on visible-near infrared spectroscopy and multispectral image has been proposed. Firstly, visible-near infrared spectroscopy combined with partial least squares (PLS) and least squares-support vector machine (LS-SVM) was used for pear injury degree and time prediction. The result indicated that these two methods both have good performances in predicting pear injury degree in the late period. The LS-SVM method is more accurate in predicting the injury time of light pear injury, but its overall result of injury time prediction is not as good as that for the PLS method. Then, the multispectral image was used to predict the time of pear injury. The result shows that for different degrees of pear injury, the prediction models based on LS-SVM have a better performance with correlation coefficients around 5.85. The result of this study can be used to detect the injury degree and time of pear rapidly and non-destructively, and provide a new approach to pear classification.

[Quickly determination of titanium dioxide content in juice based on Vis/NIR spectroscopy technique].

In order to quickly and accurately detect the content of titanium dioxide in the juice, a method combining chemometrics and Vis/NIR spectroscopy technique was used in the present study. First, the content of titanium dioxide in the juice sample was determined by using spectrophotometer and standard curve of titanium dioxide. Then, different amount of pure titanium dioxide was adulterated into the juice collected from the market to prepare eight different content samples. A total of 320 juice samples were studied. Two hundred samples (25 samples for each content) were randomly selected from the 320 samples to be the calibration set while the other 120 samples (15 samples for each content) were selected as the validation set. The spectra of juice were within near infrared (NIR) and mid-infrared (MIR). First six different preprocessing methods were compared, such as standard normal variate (SNV), moving average, derivative and multivariate scatter correction (MSC). The optimal partial least squares(PLS)was built after the performance comparison of different preprocessing methods. Another algorithm, principal component-artificial neural network (PC-ANN), was also used: first, the original spectral date was processed using principal component analysis, the best number of principal components (PCs) was selected, and the scores of these PCs would be taken as the input of the artificial neural network (ANN). The PC-ANN was trained with samples in the calibration collection and the samples in prediction set were predicted. After comparison, MSC was found to be the most appropriate spectral preprocessing method and the best number of PCs is 7. The correlation coefficients (R2) between the real values and predicted ones by discriminant analysis model were 0.9008 (PLS) and 0.8684 (PC-ANN) respectively. The root mean standard errors of prediction (RMSEP) by PLS and PC-ANN were 0.05 (PLS) and 0.04 (PC-ANN) respectively. The result indicated that the content of titanium dioxide in the juice powder to be quickly detected by nondestructive determination method was very feasible and laid a solid foundation for setting up the titanium dioxide content forecasting model of juice powder.

[Research on the method for rapid detection of soil moisture content using spectral data].

Spectroscopy technique is one of the qualitative and quantitative analytical techniques developed quickly in recent years. The spectral analysis is a fast and non-destructive method and has been used in many fields such as oil industry, food industry and so on. In the present paper, the spectral band sensitive to soil moisture content was found from the visible/near infrared spectra and a monadic linear regression model based on the data of sensitive spectral band was applied to develop a method for rapid detection of soil moisture content. The spectral data of 52 soil samples were collected by using FieldSpec HandHeld spectroradiometer made by ASD (Analytical Spectral Device) company in the US, and the data of soil moisture content were obtained by experiment. The spectral band sensitive to soil moisture content was achieved by correlation coefficient method. Then, the data of sensitive spectral band were used to build monadic linear regression model of soil moisture content. Finally, the model was employed for the prediction of soil moisture content. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation standards. The results indicated that the r and RMSEP for the prediction of soil moisture content were 0.966 5 and 0.012 1 respectively. Thus, it is concluded that the method used in this paper is an available method for the rapid detection of soil moisture content based on the visible/near-infrared spectra.

[Application of the distributed and parallel computation in spectroscopy signal processing].

The distributed and parallel computation was introduced to spectroscopy signal processing. The reflection spectra of 4 different varieties of sugar including sucrose, xylitol, maltose and dextrose were measured with FI/IR-4100 Fourier infrared spectral equipment. Each type of sugar consisted of 39 samples. The distributed and parallel algorithm was executed on 2 computers with the same hardware and software systems. First, the distributed and parallel algorithm was used to read original spectral data from the text files generated by FT/IR-4100 device. Second, the data were preprocessed by distributed and parallel algorithm. The preprocessing methods include standard normalization to the maximum peak, Savizky-Golay smoothing denoising, etc. Third, search for the key discriminative wave numbers in mass spectrometry data was performed by distributed and parallel genetic algorithm (GA). At the end, the discriminative features of 24 wave numbers extracted by GA were applied as BP neural network inputs and a 3-layer neural network was built up. The computing results generated by distributed and parallel algorithm are the same as the serial computing results generated by single personal computer. The processing efficiency using 2 personal computers is 33.6% higher than that of serial computation. So the paper presents a creative method for the complex scientific computation and enhancing the computing efficiency in spectroscopy signal processing.

[Nondestructive test on predicting sugar content and valid acidity of mango by spectroscopy technology].

Mango is a kind of popular tropic fruit in the word, and its quality will affect the health of consumers. Unsaturated acid is an important component in mango. So it is very important and necessary to detect the sugar content and valid acidity in mango fast and non-destructively. Visible and short-wave near-infrared reflectance spectroscopy (VIS/SWNIRS) was applied in the present study to predict sugar content and valid acidity of mango. Because of the non-linear information in spectral data characteristics of the pattern were analyzed by neural network optimized by genetic algorithm (GA-BP). Spectral data were compressed by the partial least squares (PLS). The best number of principal components (PCs) was selected according the accumulative reliabilities (AR). PCs could be used to replace the complex spectral data. After some preprocessing and through full cross validation, 17 principal components presenting important information of spectra were confirmed as the best number of principal components for valid acidity, and 18 PCs as best number of principal components for sugar content. Then, these best principal components were taken as the input of GA-BP neural network. One hundred thirty five samples were randomly collected as modeling, and the remaining 45 as samples to check the forecast results by the model. For the sake of testing the GA-BP model, at the same time we took the BP neural network on the same PCs. The quality of the calibration model was evaluated by the correlation coefficients (R) and standard error of calibration (SECV), and the prediction results were assessed by correlation coefficients (R) and standard error of prediction (SEP). Comparing PLS-BP model with PLS-GA-BP model, the coefficients of determination (R) of 0.788/0.83699 and standard errors of prediction (SEP) of 0.133312/0.109447 were calculated in valid acidity. The sugar content result was calculated by the coefficients of determination (R) = 0.75705/0.85409 and standard errors of prediction (SEP)0.864676/0.60934. Thus, it is obvious that this model is reliable and practicable. And the PLS-GA-BP model based on the spectroscopy technology is a better pattern to predict sugar content and valid acidity of mango, giving a new method for detecting fruit's sugar content and valid acidity.

[Study on fast discrimination of brands of soy sauce using near infrared spectra].

A new method for the fast discrimination of brands of soy sauce by means of near infrared spectroscopy (NIRS) was developed and these eight kinds of soy sauce had got its "identity card". The experiment adopted typical eight brands of soy sauce which we bought in the market. Total 3 942 frequencies from 7 625 to 3 684 cm(-1) transmit wavelength were gotten to set up a analyses model. In order to handle these data efficiently, after pretreatment, firstly, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and to describe the body of the spectra, the analysis suggested that the cumulate reliabilities of the first eight components was more than 99.99. According to the first eight components, the authors could distinguish some brand of the soy sauce but could not deal with all of them. So the authors chose ANN-BP as further research method. The eight components were secondly applied as ANN-BP inputs. The experiment took total 242 examples of 8 kinds of soy sauce as original model examples and left 10 every kind as unknown samples to predict. Finally, the result indicated the distinguishing rate is 98.75% in 0.98 reliable area. This paper could offer a new method to the discrimination of varieties of soy sauce.

[Application of visible/near infrared spectroscopy to discriminating honey brands based on independent component analysis and BP neural network].

Visible/near infrared spectroscopy (Vis/NIRS) appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. In the present study, a nondestructive method for the classification of honey brands was developed using Vis/NIRS. The honey brands studied in the research were Feng boshi, Tian ranfeng and Guan shengyuan. The sample set comprised 30 of each brand. Independent component analysis (ICA) was put forwarded to select several optimal wavelengths based on loading weights. Two types of preprocessing (Savitzky-Golay combined with multiplicative scatter correction) were used before the spectral data were analyzed with multivariate calibration methods of artificial neural network (ANN). The absorbance values log (1/T) (T= transmission), corresponding to the wavelengths of 408, 412, 409, 1 000, 468, 462, 408, 400, 997 and 998 nm were chosen as the input data of ANN. The ANN model with three layers was built, and the transfer function of sigmoid was used in each layer. After several trials, the best neural network architecture was obtained with 10 nodes in hidden layers. In the model, the node of input layer, hidden layer, output layer was set to be 9, 10, and 3 respectively, and the goal error was set to be 0. 000 1, the speed of learning was set to be 0.2, the time of training was set to be 1 500. Seventy five samples (25 with each brand) from three brands were selected randomly as calibration set, and the left 15 samples (5 with each brand) were as perdition set. The discrimination rate of 100% was achieved, and the fitting residual was 8. 245 365 x 10(-5). These indicated that the result of honey discrimination was very good based on ICA method, and offer a new approach to the fast discrimination of varieties of honey.

[Fast discrimination of varieties of bayberry juice based on spectroscopy technology].

Visible and near-infrared reflectance spectroscopy (NIRS) was applied in the discrimination of bayberry juice varieties. Characteristics of the pattern were analyzed by partial least square. Through full cross validation, nine principal components presenting important information of spectra were confirmed as the best number of principal components. Then, these nine principal components were taken as the input of BP neural network. Through the training and prediction, three different varieties of bayberry juice were classified according to the outputs of BP neural network. Besides, the work on building mathematic model and optimizing the algorithm was completed. In the process of BP neural network modeling, 60 samples were gained from the local market and each species has 20 samples. Fifty one samples were used as the training set and the reminder samples (total 9 samples) formed the prediction set. With a proper network training parameter, a 100% accuracy was obtained by BP neural network. Thus, it is concluded that PLS analysis combined with BP neural network is an available alternative for pattern recognition based on the spectroscopy technology.

[Spectral characterization and N content prediction of soil with different particle size and moisture content].

The present work was focused on analyzing the influence of moisture content, particle size, light source incidence angle and observation height on a loamy mixed soil spectra Meanwhile, prediction models for N content with different moisture and particle sizes were obtained, and the influence of these properties on N prediction was studied. The future applicability of NIR spectroscopy as a technique able to make prediction on the spot was analyzed. Observation height 100 mm and light source angle 45 degrees were chosen to present a sharpest spectra. Moisture content and particle size were found to affect strongly the absorbance of the spectra, and an accurate N prediction was obtained when the particle sizes varied from 0. 5-1. 0, 1. 0-2.0 and 2-5 mm with r of 0. 82, 0. 81 and 0. 81, respectively. Poor N prediction was obtained when the soil kept its natural moisture with r of 0. 57 and SECV of 3. 06 compared with the performance when it was dry with r of 0. 81 and SECV of 2. 40.

[Construction and identification of a vector inserted with gene of T7 RNA polymerase].

OBJECTIVE: To develop a system to rescue virus by intracellular expression of T7 RNA Polymerase. METHODS: The gene of T7 RNA Polymerase was amplified and cloned to VR1012 by molecular biological technology. The expression plasmid VR-1a was then identified. VR-1a and EV71 infectious plasmid were co-transfected in Vero cell. CPE was observed and viral gene viral antigen were detected. RESULTS: The gene of T7 RNA Polymerase was successfully cloned into vector VR1012. Vero cell developed to CPE after being transfected VR-1a and EV71 infectious plasmid. EV71 gene was amplified by RT-PCR from the culture. EV71 antigen was also detected by ELISA. CONCLUSION: The method can be used to rescue virus. It could apply to immunologic research of EV71 DNA vaccine.