[Analysis method of the fingerprint of LiuWei DiHuang pills by near infrared spectroscopy].

The purpose of the present paper is to establish a method of the fingerprint of LiuWei DiHuang pills by the near infrared spectroscopy. First, the authors established the fingerprint of LiuWei DiHuang pills by high performance capillary electrophoresis (HPCE), and calculated its similarity. At the same time, the authors scanned its near infrared spectrogram. Then the authors established the mathematical model between the similarity of fingerprint and the near infrared spectrogram. Through the optimization of the model, the correlation(r), calibration standard deviation and the average relative error of the modeling set were 0.9046, 0.058 and 6.12%, respectively. It proved that the linearity between calculated and forecast of fingerprint was clear. The results showed that the method of the fingerprint of LiuWei DiHuang pills by the near infrared spectroscopy was feasible.

[Study of microstructure of pectin and whey protein isolate mixtures under incompatible conditions using dynamic light scattering and transmission electron microscopy].

The microstructure of the pectin/whey protein isolate mixtures under incompatible conditions was investigated using dynamic light scattering spectroscopy, transmission electron microscopy and shear-viscosity model. Under the condition of 90 degrees C and pH 7.4, the presence of negatively charged pectin could induce depletion aggregation in a 5% protein solution, and promote phase separation; precisely, when the mass ratio of pectin/whey protein isolate was lower than 0.08, the hydrodynamic size of the aggregates was less than 300 nm, and the system showed Newtonian properties; when the mass ratio was higher than 0.08, the viscosity of the solution increased rapidly, the shear thinning properties became obvious and the size of the aggregates was close to 700 nm.

[Nondestructive identification of different oil content maize kernels by near-infrared spectra].

Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.

[Study on the projection method based on principal component of tobacco near-infrared spectrum and application in redrying model].

The present paper briefly describes the application of near-infrared spectroscopy technology in tobacco. Two methods for solving projection vector based on the principal component of near-infrared spectrum are reported. They are named as projection of basing on principal component and Fisher criterion by principal component analysis method (PPF_PCA for short) and projection of basing on principal component and Fisher criterion by solving eigenvalue (PPF_Eig for short), and they are studied and compared in the application and evaluation of the redrying model. The result of the first-dimensional projection on 9 kinds of tobacco leaf grading samples shows that, the diversity of the first-dimensional projection values of inter-class and intra-class by the PPF_PCA method is both larger than that by the PPF_Eig method, and the mean absolute deviation of the mean projection values of inter-class by the PPF_PCA method is about 1.26 times that of the PPF_Eig method. At the same time, this result is interpreted by using the contribution rate of the first-dimensional projection values. That is, the contribution rate of first-dimensional projection values by the PPF_PCA method is 93%, while the contribution rate of first-dimensional projection values by the PPF_Eig method is 77%. The former is about 1.21 times the later. Therefore, the first-dimensional projection values by PPF PCA method include more information of diversity of both inter-class and intra-class. The similarity of samples inter-class and the diversity of samples intra-class can be evaluated more objectively from first-dimensional projection figure(on 9 kinds of tobacco leaf grading samples, 33 kinds of tobacco leaf grading samples and 6 redrying blending models), so it is more convenient to be used as a reference for the redrying model of tobacco, and it has a good application prospect in other formulation design of agricultural products (traditional Chinese medicine etc.).

[The rapid analysis of fatty acids in vegetable oils by near infrared spectrum].

In this research, The functional components of vegetable oils were analyzed by near infrared (NIR) spectral technology. The optimum conditions of mathematics model of four components (C16 : 0, C18 : 0, C18 : 1, C18 : 2) were studied, including the sample set selection, chemical value analysis, the detection methods and condition. Chemical value was analyzed by HPLC. 52 samples were selected, 41 for modeling set and 11 for testing set. All samples were placed in 5mm thick sample pools and swept by near infrared (NIR) with discrimination factor 8 cm(-1) without any other disposal. Using PLS methods sated model. Data were processed by first derivative method and centering method. 5 000-9 000 cm(-1) spectral region was analyzed. Correlating index (r), RMSECV and RMSEP were chose as evaluation index. The result demonstrated that the correlation between the reference value of the modeling sample set and the near infrared predictive value were r(C16 : 0) = 0.891, r(C18 : 0) = 0.837, r(C18 : 1) = 0.982, r(C18 : 2) = 0.971, respectively. And the correlation between the reference value of the testing sample set and the near infrared predictive value were 0.921, 0.891, 0.946 and 0.949, respectively. It proved that the near infrared predictive value was linear with chemical value and the mathematical model established for components of vegetable oils was feasible. For validation, 8 unknown samples were selected to be analysis by infrared (NIR). The result demonstrated that error between predict value and chemical value was less than 10%. That was to say infrared (NIR) had a good veracity in analysis components of vegetable oil. Because infrared (NIR) spectral technology is convenient, rapid than HPLC in oil components analysis, moreover, infrared (NIR) can analyze many components at the same time. It must have great application prospect in vegetable oil components analysis.

[Application of wavelet packet entropy and Fisher classifier to the identification of medicinal rhubarbs with near-infrared spectrum].

The diffused-reflectance near-infrared (NIR) spectrum of medicinal rhubarbs was collected by Fourier transform spectroscopy instrument Principal components (PC) and wavelet packet entropy (WPE) were then calculated from the spectrum. Based on these two kinds of features, the models of identification of medicinal rhubarbs were developed using Fisher classifier. The results show that the error rates of cross-validation and prediction using WPE are all lower than those using PC. The model was built by WPE feature extraction method combined with Fisher classifier, the error rate of cross-validation is 6.52%, while that for prediction is 2.04%. The research result provides a method for identifying medicinal rhubarbs quickly.

[Influence of sample loading and test conditions on NIR veracity and study of analysis error source].

The influence of sample test conditions on the NIR veracity was studied with homemade grating diffuse NIR instrument using Yunnan flue-cured tobacco. Deducing analysis error was achieved by model self-emendation when a global NIR model was set up. Without regarding the influence of loading samples and test conditions, the test repetition error, re-loading error and samples tightness error, which were brought by instrument S/N, accounted for 50%, 30% and 20% of the total error, respectively. Depressing sample could reduce errors brought by sample tightness. Changes in test conditions could bring more analysis error, which was larger than the total of repetition error. These results theoretically explain the influence of sample test conditions on the NIR analysis veracity, which can provide basic theory data for farther improvement of homemade instrument and offer a new idea for resolving this problem.

[Influence of sample annum and distribution of chemical values on NIR veracity].

The influence of sample annum and the distribution of sample component on NIR veracity was studied with homemade grating diffuse NIR instrument using Yunnan flue-cured tobacco. Results showed that sample annum had an obvious influence on the total sugar and nicotine models, but had an unconspicuous influence on the total-nitrogen model. Models set up by samples, whose component content distribution was normal school, was better than those set up by even distribution. The conclusion in this study has a significant referenced value for the method and principle to select representative samples to modeling from a large amount of specimens.

[Identification model of cultivated and wild Chinese medical herbs erigeron breviscapus with near-infrared spectroscopy].

Forty three cultivated and wild Chinese medical herbs erigeron breviscapus were scanned on two Fourier transform near-infrared spectroscopy instruments. Twenty samples were used to set up the BP-NN models and the others were used to validate the models. Fifteen principal components, whose variance contribution rate is above 99%, were collected as input nodes for BP-NN models. The correct identification rates of calibration samples were 100% for the models on both the two instruments, and the correct identification rates of validation samples were 100% and 95.7%, irrespectively. The results showed that using NIR to fast detect cultivated and wild Chinese medical herbs erigeron breviscapus was feasible.

[Study on application of Fourier transformation near-infrared spectroscopy analysis with support vector machine (SVM)].

Support Vector Machine (SVM) is a method for the research on identifying two types of problem. It is the latest branch in the statistics study theories, and the identification model has a strict mathematics foundation. In this paper, the basic principle and method of SVM are not only introduced, but also applied to chemometrics. One hundred and three rhubarb samples were used as experimental materials. The identification models were established with near-infrared spectroscopy and SVM training method with the intention of identifying whether the rhubarb samples are true or false. The thirty-three samples in training set were identified by the identifying models with the accurate rate of 100%, while seventy estimate samples had an accurate rate of 96.77%. The research result provided the method of identifying the traditional Chinese medicine rhubarb quickly. So, it shows the feasibility of establishing the models with near-infrared spectroscopy and SVM method to identify biological samples. This paper introduced the theme of SVM training method in order to beget the attention of the research members who deal with chemometrics.