Determination of Component Contents of Blend Oil Based on Characteristics Peak Value Integration.

Edible blend oil market is confused at present. It has some problems such as confusing concepts, randomly named, shoddy and especially the fuzzy standard of compositions and ratios in blend oil. The national standard fails to come on time after eight years. The basic reason is the lack of qualitative and quantitative detection of vegetable oils in blend oil. Edible blend oil is mixed by different vegetable oils according to a certain proportion. Its nutrition is rich. Blend oil is eaten frequently in daily life. Different vegetable oil contains a certain components. The mixed vegetable oil can make full use of their nutrients and make the nutrients more balanced in blend oil. It is conducive to people's health. It is an effectively way to monitor blend oil market by the accurate determination of single vegetable oil content in blend oil. The types of blend oil are known, so we only need for accurate determination of its content. Three dimensional fluorescence spectra are used for the contents in blend oil. A new method of data processing is proposed with calculation of characteristics peak value integration in chosen characteristic area based on Quasi-Monte Carlo method, combined with Neural network method to solve nonlinear equations to obtain single vegetable oil content in blend oil. Peanut oil, soybean oil and sunflower oil are used as research object to reconcile into edible blend oil, with single oil regarded whole, not considered each oil's components. Recovery rates of 10 configurations of edible harmonic oil is measured to verify the validity of the method of characteristics peak value integration. An effective method is provided to detect components content of complex mixture in high sensitivity. Accuracy of recovery rats is increased, compared the common method of solution of linear equations used to detect components content of mixture. It can be used in the testing of kinds and content of edible vegetable oil in blend oil for the food quality detection, and provide an effective reference for the creation of national standards.

[Research on Spectroscopy Spectrum De-Noising of Mineral Oil Based on Lifting Scheme Wavelet Transform].

The use of the mineral oil is an important cause of air pollution such as fog. The effectiveness and rapidity of the de-noising processing in mineral oil fluorescence spectroscopy detection system is a hot issue of the online real-time monitoring system. The de-noising method of the lifting wavelet transform (LWT) in the application of mineral oil fluorescence spectrum is proposed. Compared with traditional discrete wavelet transform (DWT), this wavelet transform method decomposes the existing wavelet filter module into the basic construction modules and steps to complete the transform with simplicity and a fast speed. There are characteristics of low computational complexity, in situ operation and the easy implement in the denoising process of mineral oil fluorescence spectra. The LWT can effectively solve the problems in these respects. The three methods of LWT, DWT and EMD are applied to the fluorescence spectra of 0# diesel oil, 97# gasoline and kerosene. The indicators evaluating de-noising effect such as the Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE) and Normalied Correlation Coefficient (NCC) of the three kinds of mineral oil in the fluorescence spectra denoising prove the effectiveness of the lifting scheme wavelet transform in the application of mineral oil fluorescence spectrum. Meanwhile, the lifting scheme transform can improve the flexibility of structure and operation simplicity that makes the de-noising time reduced by 62%, validating the speediness of the de-noising method of the LWT in the application of mineral oil fluorescence spectrum and it is suitable for mineral oil fast de-noising processing system in real time.

[Research on Analysis of Oil in Water Based on the Joint Optimization of Savitzky-Golay Smoothing and IBPLS Models].

Rapidly and accurately detection of the type and content of mineral oil in water pollution has important significance for the timely screening and control of pollution sources. The use of infrared spectral analysisi technology to detect mineral oil has advantanges of efficient, fast and pollution-free. Infrared spectrum technology is very for the detection of mineral oil in the water. In order to obtain a more reliable results, Fourier transforms attenuated total reflection infrared spectrometry (FITR-ATR) technology is used to get the spectral information of the mineral oil sample, and SPXY method is used to divide the sample set. The paper not only analyzed partial least squares (PLS) and iterative Bagging partial least squares (IBPLS) the two different methods to build regression model, also compared the difference of using the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) regression model. Based on the comparison of the predictive regression curve, we can get that the SG smooth has a better reflection on the results. And when the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) is used to build the regression model, the gasoline model parameters RMSEP is 0.001 125 g x mL(-1), R is 0.992 5; diesel model parameters RMSEP is 0.001 384 g x mL(-1), R is 0.989 3.

[A De-Noising Algorithm for Fluorescence Detection Signal of Mineral Oil in Water by SWT].

Fluorescence analysis is an important means of detecting mineral oil in water pollutants because of high sensitivity, selectivity, ease of design, etc. Noise generated from Photo detector will affect the sensitivity of fluorescence detection system, so the elimination of fluorescence signal noise has been a hot issue. For the fluorescence signal, due to the length increase of the branch set, it produces some boundary issues. The dbN wavelet family can flexibly balance the border issues, retain the useful signals and get. rid of noise, the de-noising effects of dbN families are compared, the db7 wavelet is chosen as the optimal wavelet. The noisy fluorescence signal is statically decomposed into 5 levels via db7 wavelet, and the thresholds are chosen adaptively based on the wavelet entropy theory. The pure fluorescence signal is obtained after the approximation coefficients and detail coefficients quantified by thresholds reconstructed. Compared with the DWT, the signal de-noised via SWT has the advantage of information integrity and time translation invariance.

[Determination of the Sodium Methylparaben Content Based on Spectrum Fluorescence Spectral Technology and GA-BP Neural Network].

Sodium methylparaben as one kind of preservatives is widely used in our life, but it will do harm to health if it is eaten too much. So there are strict rules on the dosage of sodium methylparaben in every country. The fluorescence spectral properties of sodium methylparaben in aqueous solution and orange juice solution are analyzed with FS920 fluorescence spectrometer. The research result shows that the fluorescence characteristic peak of sodium methylparaben solution is in λ(ex)/λ(em) = 380/5 10 nm, while sodium methylparaben orange juice solution has two fluorescence characteristic peaks which are in λ(ex)/λ(em) = 440/520 nm and 470/530 nm, and its best excitation wavelength is 440 nm. So it can be concluded from the result that there is a significant change between the characteristic peaks of sodium methylparaben in the two solution. Compared with the fluorescence characteristic peak of sodium methylparaben solution, thoses of sodium methylparaben orange juice solution are changed significantly, which are caused by the interference of orange juice fluorescence characteristics. In order to determine the content of sodium methylparaben in the fresh orange juice, a detection model of sodium methylparaben content in orange juice is built based on GA-BP neural network, according to the relationship between fluorescence intensity in λ(ex) = 440 nm and the content of sodium methylparaben orange juice solution. When the accuracy of the mean square error in the process of network training reaches 10(-3), the correlation coefficient of network output and that of the expected is 0.996. At the same time, a better prediction result can be obtained that the average recovery of the forecast samples is 98.67% and the average relative standard deviation is 0.86%. When the concentration ranges from 0.02 to 1.0 g x L(-1), the results testify that detection method based on fluorescence spectroscopy and GA-BP neural network can accurately determine the content of sodium methylparaben in orange juice. This method has the features of novelty and simplicity and it is expected to be applied to the determination of sodium methylparaben in other kinds of drink.