| The tremendous recent progress in biotechnology and particularly in genetic engineering techniques has enabled the introduction of exogenous sequences which confer new characteristics, such as herbicide tolerance, resistance to insects and solutions to other problems associated with commercial agriculture. It made revolutionary impacts on every aspects of human activities include agriculture, livestock, industry and medicine. The scope and number of genetically modified (GM) crops planted each year continues to grow. However, the potential problems of GM organisms (GMOs) for environmental, ethical and religious impact are unknown. It is necessary to research on the detection of GMOs and detection technique, which is one of the most important consumer concerns regarding food safety and quality. There are several commonly used GMOs testing protocols mainly including nucleic acid-based and protein-based detection methods. Some of the drawbacks of those methods include the use of multiple procedres in the protocol and high costs. Protein-based methods are only suited to the inspection of raw materials. Near infrared (NIR) spectroscopy is sensitive to major organic compounds (e.g. vibration overtones of C-H, O-H and N-H). It has increasingly been adopted due to its advantages:rapidity of analysis, no need for complex sample preparation or processing, low cost, and its suitability for on-line process monitoring and quality control.Transgenic tomato is the first transgenic produce that received administrative approval. Utilizing the knowledge of different fields, such as spectral analysis, molecular biology, optics and chemometrics, etc, combining independent innovation, taking transgneic tomatoes and their parents as object investigated, this research focuses on non-invasive discriminant methods and physiological property (chlorophyll content and ethylene content) detection based on visible/near infrared (Vis/NIR) spectroscopy. In this dissertation, classifications of transgenic tomato leaf, fruit, juice, seed and their parent were studied using Vis/NIR spectroscopy technique and pattern recognition methods. The relationship between physiological property indexes and Vis/NIR spectra was analyzed. Quantitative models were established based on Vis/NIR spectra for chlorophyll and ethylene content determination. This study is to prove the feasibility of transgenic product discrimination and build a rapid and non-invasive detection method to quantify chlorophyll and ethylene content based on spectroscopy technology. It will provide basis to research and develop high throughput and rapid detection equipment to realize the rapid discrimination of transgenic tomato.The main results and conclusions were listed as follows:(1) The influence of spectral acquisition parameters on spectra and modeling results were analyzed. Taking an FT-NIR spectrometer as an example, the influence of resolution and scan number on tomato fruit spectra and modeling results were analyzed. The results indicated that:The discriminant rate was relatively high when the resolution was 4 cm-1 and 8 cm-1, the value was 78.89%. With the increasing of scan number, the spectra was smoother and the root mean square noise (RMSN) and variation value decreased. The discriminant rate increased with scan number. The difference of spectral absorbance at different resolutions and scan number was not distinct (a= 0.05). For RMSN, the difference was distinct (a= 0.05).(2) The difference of spectra acquired from transgenic tomato leaf, fruit, juice, seed and their parent was analyzed. The results indicate:1) The Vis/NIR diffuse reflectance spectra of transgenic leaves absorb less light than their parents below 1380 nm. The trend altered above 1380 nm. The transmittance of transgenic leaf spectra is higher than their parents.2) The NIR diffuse reflectance spectra of transgenic red tomato fruits at the same ripeness stage absorb more light than their parents. The Vis/NIR diffuse reflectance spectra of transgenic red tomato fruits absorb more light than their parents. There is a cross for transgenic green tomato fruits and their parents at 578 nm. The spectra of transgenic green tomato fruits absorb more light than their parents before 578 nm. The trend alters below 578 nm.3) The original transmission spectra of transgenic tomato juice and its parent are similar. The slight difference could be found by magnification.4) Slight difference between transgenic tomato seeds and their parents diffuse reflectance spectra exists. The results indicate that the difference between the spectra of transgenic tomato leaf, fruit, juice and seed and the spectra of their parents does exist. (3) The influence of different spectrometers (or detectors) and detection modes on discriminant accuracy was compared.1) For transgenic tomato leaves and their parents, discriminant accuracy of models based on diffuse reflectance spectra using a miniature optic fiber spectrometer USB4000 were 100%, which was more suitable for transgenic tomato leaves and their parents discriminant.2) For transgenic tomatoes and their parents at the same ripeness stage, discriminant accuracy of models using USB4000 was highest. For tomatoes at the different ripeness stages, discriminant accuracy of models based on diffuse reflectance spectra using USB4000 was much better than the accuracy of models using an FT-NIR spectrometer.(4) The influence of different pattern recognition methods, including discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), discriminant partial least squares (DPLS), back propagation neural networks (BPNN), radial basis function neural networks (RBFNN) and least squares support vector machines (LS-SVM) was compared.1) For discriminating transgenic tomato leaves and their parents, the correct classifications for transgenic and non-transgenic tomato leaves were both 100% using DA and DPLS after derivative or standard normal variate spectral pretreatment.2) For discriminating transgenic tomato fruits and their parents at the same ripeness stage, discriminant accuracy of DA models was highest. The rate was 94%. The discriminant results of BPNN and LS-SVM models were same. For tomatoes at the different ripeness stages, discriminant accuracy of SIMCA models was highest. The value was 86.08%.3) For juice, RBFNN and LS-SVM models using second derivative spectral data produced the highest level of classification rate. An overall classification accuracy of 100% was reached, both for transgenic and non-transgenic tomato juice, which demonstrated the perfect discriminatory power to differentiate transgenic and non-transgenic samples.4) For seed, DA model after 25 points smoothing using diffuse reflectance spectra in 800-2500 nm turned out highest results. An overall classification accuracy of 95.81% was reached. It can be concluded that transgenic tomatoes (leaf, fruit, juice and seed) can be classified from their parents based on Vis/NIR spectroscopy technique. (5) Successive projections algorithm (SPA) was used to extract characteristic wavelength. Discriminant accuracy increased with the number of wavelengths for transgenic and non-transgenic tomatoes. But the time cost for modeling also increased. Models were rebuilt using several characteristic wavelengths. The results indicate that for DPLS rebuilt model, when transgenic tomatoes and their parents at the same ripeness stage were discriminated, discriminant accuracy of calibration and validation increased. The performance of other methods was reverse. Models were rebuilt using 7 characteristic wavelengths when transgenic and non-transgenic tomatoes at different ripeness stages were discriminated. The results indicates that discriminant accuracy of linear and non-linear pattern recognition methods decreased. The discriminant accuracy of SIMCA models was highest. The value was 84.36%. The results indicate that transgenic tomatoes and their parents discriminant models can be built only with characteristic wavelengths by SPA to shorten classification time.(6) Quantitative models were established based on Vis/NIR spectra for determination of chlorophyll content in tomato leaves and ethylene content in tomato fruits. Based on the removal of spectra and concentration outliers, models by different calibration methods of partial least squares regression (PLSR), principal components regression (PCR) and multi linear regression (MLR) were built. The influence of different spectral pretreatments on model performance was analyzed. The influence of different wave bands on ethylene content deternimation models was also researched. I) The performance of PLSR chlorophyll content model established in 670～1100 nm with original spectra was much better, the correlation coefficients (r) was 0.961. Root mean square error of calibration (RMSEC), root mean square standard error of prediction (RMSEP) and root mean square error of cross validation were 1.50,2.25 and 3.00, respectively.2) After multiplictive scatter correction pretreatment, the performance of PLSR ethylene content models in full spectral region was much better, correlation coefficients of PLSR model was 0.904. It could be concluded that chlorophyll content in tomato leaves and ethylene content in tomato fruits can be determinated based on Vis/NIR spectra.
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