[Study on the Analysis of the Proportion of Mixed Samples with Near Infrared Spectroscopy and Non-Negative Coefficients Regression].

In the process of practical production, it is important to accurately analyze the proportion of mixed samples with high speed, which plays a great role for quality control and formulation design in food and agricultural processing. Traditional solution is to build statistical model with a large number of representative samples, which is both labor-intensive and time-consuming. In this paper, the proportion of alcohol and acids mixed samples, and their dilute solution mixed samples(used carbon tetrachloride (CCl4) which has no near-infrared absorption characteristics as the solvent medium)，as well as sheet tobacco leaf mixed samples are respectively analyzed by using near infrared spectroscopy, SG smooth and non-negative coefficients regression, which verifies the feasibility of analyzing the proportion of the mixed samples. The results show that, the analytic proportion of transmission spectra of alcohol and acids according to non-negative coefficients regression is closer to actual molar proportion with result error less than 4%. The result of the dilute solution is much better with error less than 4%. The analytic proportion of diffuse reflectance spectra of sheet tobacco leaf according to non-negative coefficients regression is highly accurate with error less than 10%. In the meantime, it has a highly consistency between actual spectra and analytic spectra of mixed samples; and the result of F-test and T-test shows that there is no significant difference between them and the confidence level is 0.01. It has the reliability of analytical proportion in theory. With the spectral data of several pure samples, the proportion of mixed samples can be thus analyzed, which has a good application prospect for quality control and formulation design in food and agricultural processing.

[Structure and Anti-Tumor Activity of Bovine α-Lactalbumin after Binding Linoleic Acid].

The structure changes of α-lactalbumin after binding oleic acid and linoleic acid, including hydrophobic amino acids, hydrophobic regions, tertiary structure, secondary structure, was studied by intrinsic fluorescence, ANS-binding intrinsic fluorescence and circular dichroism spectrum, respectively. The anti-tumor activity of the lactalbumin-oleic acid complex and lactalbumin-linoleic acid complex was measured using the methylene blue method. It can be seen from the fluorescence spectra that a significant red-shift from 331.07 to 337.67 nm and 337.60 nm of α-lactalbumin occurred after binding of oleic acid and linoleic acid, respectively. Together with the ANS-binding spectra, which exhibited a blue-shift (from 516.20 to 514.10 nm and 508.50 nm, respectively) with an increased fluorescence intensity, it can be indicated that binding of oleic acid and linoleic acid lead to the exposure of hydrophobic amino acids and hydrophobic regions. Results of circular dichroism spectra indicated the partial loss of the tertiary structure, and an decrease of ß-turn and random coil, which turn to the ß-sheet structure. Furthermore, the anti- tumor activity of the two complexes was verified on the three types of tumor cells. This study laid a theoretical basis for the development of anti-tumor drugs.

[Quality anlysis of the before redrying raw tobacco & after redrying sheet tobacco by using online near infrared spectroscopy].

In this paper, the 7 different origin before redrying raw tobacco & after redrying sheet tobacco's online near infrared spectroscopy were collected from sorting & redrying production line specifically for "ZHONGHUA" brand. By using the projection model bulit by different origin tobacco's online spectroscopy and the method of variance and correlation analysis, we studied the uniformity and similarity quality characteristics change before and after the redrying of tobacco, which can provide support for understanding the quality of the tobacco material and cigarette product formulations. This study show that selecting about 10,000 by equally spaced sampling time from a huge number of online near infrared spectroscopy, for modeling are feasible, and representative. After manual sorting, threshing, and redrying, the uiformity of each origin tobacco near-infrared spectroscopy can be increased by 10%~35%, homogeneity of the tobacco leaf has been significantly improved. After redrying, the similar relationship embodied in the origin also have significant changes, overall it reduce significantly, that shows the quality differences embodied by origin significantly improve, which can provide greater space for formulations, it shows the need for high-quality Chinese cigarette production requires large amounts of financial and human resources to implement cured tobacco processing. The traditional means of chemical analysis, it takes a lot of time and effort, it is difficult to control the entire processing chain, Near Infrared Spectroscopy with its rapid, non-destructive advantage, not only can achieve real-time detection and quality control, but also can take full advantage of near-infrared spectroscopy information created in the production process, which is a very promising online analytical detection technology in many industries especially in the agricultural and food processing industries.

[Analysis of tobacco site features using near-infrared spectroscopy and projection model].

In this paper, total of 5170 flue-cured tobacco samples collected from 2003 to 2012 in the domestic and foreign origin by Shanghai Tobacco Group Technical Center were tested by near infrared spectroscopy, including the typical upper leaves 1394, central 2550, the lower part of 1226. Using projection model of based on principal component and Fisher criterion (PPF), follow the projected results to get no statistically significant differences at adjacent principal components, and the number of principal components as little as possible, in this paper, four main components to build projection analysis model, the model results show that: the near-infrared spectral characteristics of the upper and lower leaves have a significant difference that can be achieved almost entirely distinguished; while the middle leaves with upper and lower have a certain degree of overlap, which is consistent to the actual situation of the continuity of tobacco leaf. At the same time, Euclidean distance between the predicted sample projection values and the mean projection values of each class in the model, a description is given for the prediction samples to quantify the extent of the site features, and its first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typically upper, upper to central, central to upper, typical central, central to the lower, the lower to central, typically the lower, or super-model range. The model was validated by 34 tobacco samples obtained from the re-drying process in 2012 with different origins and parts. This kind of analysis methods, not only can achieve discriminant analysis, and get richer feature attribute information, can provide guidance on the raw tobacco processing and formulations.

[Analysis of tobacco style features using near-infrared spectroscopy and projection model].

In the present paper, a total of 4,733 flue-cured tobacco samples collected from 2003 to 2012 in 17 provincial origins and 5 ecological areas were tested by near infrared spectroscopy, including the NONG(Luzhou) flavor 1,580 cartons, QING (Fen) flavor 2004 cartons and Intermediate flavor 1 149 cartons. Using projection model based on principal component and Fisher criterion (PPF), Projection analysis models of tobacco ecological regions and style characteristics were established. Reasonableness of style flavor division is illustrated by the model results of tobacco ecological areas. With the Euclidean distance between the predicted sample projection values and the mean projection values of each class in style characteristics model, a description is given for the prediction samples to quantify the extent of the style features, and their first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typical NONG, NONG to Intermediate, Intermediate to NONG, typical Intermediate, Intermediate to QING, QING to Intermediate, typical QING, QING to NONG, NONG to QING, or super-model range. The model was validated by 35 tobacco samples obtained from the re-dryingprocess in 2012 with different origins and parts. This kind of analysis methods not only can achieve discriminant analysis, but also can get richer feature attribute information and provide guidance to raw tobacco processing and formulations.

[Interaction of lactoferrin and its peptides with DPPC and DPPG liposomes studied by Raman spectroscopy].

Interaction of lactoferrin and its peptides LfcinB4-14 and LfampinB with dipalmitoylglycero-phosphocholine (DPPC) and dipalmitoylglycero-phosphoglycerol (DPPG) liposomes were studied by means of Raman spectroscopy. In our study, conformational changes in the phospholipid molecules were investigated by measuring the intensities of 2 847 and 2 882 cm(-1) Raman bands which are assigned to acyl chains' symmetric and asymmetric C-H stretching vibrations. The addition of lactoferrin and its peptides LfcinB4-14 and LfampinB caused a decrease in the 2 882 cm(-1) intensity of DPPG liposomes, thus the order parameter for the lateral interactions between chains S(lat) decreased from 0.19 to 0.17, 0.14 and 0.12 respectively. On the contrary, the intensities at 2 847 and 2 882 cm(-1) of DPPC liposomes were poorly affected by lactoferrin and its peptides. The results show that lactoferrin and its peptides present a stronger effect on the molecular structure and order degree of anionic lipid DPPG than that of zwitterionic lipid DPPC. This suggests that lactoferrin, LfcinB4-14 and LfampinB can interact and combine with the negatively charged DPPG liposomes by electrostatic interaction and perform its antibacterial activity. Besides, LfcinB4-14 and LfampinB can affect the lipid more strongly than lactoferrin.

[The highest proportion of tobacco materials in the blend analysis using PPF projection method for the near-infrared spectrum and Monte Carlo method].

The present paper builds a model based on Monte Carlo method in the projection of the blending tobacco. This model is made up of two parts: the projecting points of tobacco materials, whose coordinates are calculated by means of the PPF (projection based on principal component and Fisher criterion) projection method for the tobacco near-infrared spectrum; and the point of tobacco blend, which is produced by linear additive to the projecting point coordinates of tobacco materials. In order to analyze the projection points deviation from initial state levels, Monte Carlo method is introduced to simulate the differences and changes of raw material projection. The results indicate that there are two major factors affecting the relative deviation: the highest proportion of tobacco materials in the blend, which is too high to make the deviation under control; and the quantity of materials, which is so small to control the deviation. The conclusion is close to the principle of actual formulating designing, particularly, the more in the quantity while the lower in proportion of each. Finally the paper figures out the upper limit of the proportions in the different quantity of materials by theory. It also has important reference value for other agricultural products blend.

[Tobacco plant parts similarity analysis based on near-infrared spectroscopy and SIMCA algorithm].

The appearance features of tobacco reflect its inner quality. Many factors, such as different plant parts, variety and maturity, provide standard and foundation for tobacco production processing. According to the different position of tobacco plant parts, tobacco plants leaves can be divided into five parts as tip, upper-middle, middle, lower-middle and priming leaf respectively. Five hundred tobacco leaf samples (100 each for one of five tobacco plant parts) from Yunnan province in 2008 were collected using near infrared spectroscopy, which all belong to tobacco varieties of K326. The similarity analysis of tobacco plant parts was carried out using mathematical model of SIMCA similarity analysis. The conclusion showed that the tobacco plant parts similarity results based on near-infrared spectroscopy corresponded to the relative tobacco plant parts in Yunnan province. The farther two tobacco plant parts were away from each other, the lower the similarity of corresponding parts was. And the similarity results of adjacent tobacco plant parts were different. The study discussed a method of confirming PC numbers and realized the quantitative similarity analysis between classes. It is instructive in replacement or adjustment of tobacco leaf blending and evaluation of tobacco industrial grading.

[The application of near-infrared diffuse reflection spectra based on the principle of linear additive in tobacco redrying formula].

In the process of analyzing and designing tobacco redrying formula using near infrared spectroscopy, a great deal of spectra for different ratio mixed samples are badly needed. However these sample spectra are very hard to obtain in the actual production process. Furthermore, for the samples of different grades it is difficult to achieve the goal of even mixing, in consequence of introducing inevitable errors. In order to solve the above problems, the present paper proposes to use "theory of even mixed spectrum" produced by linear additive near infrared spectroscopy in place of the spectra of actual mixed samples. This way can not only eliminate the errors caused by uneven mixture, but also leave out the course of mixing samples and measuring spectra and save time, effort, and material simultaneously. This article analyzes the comparison between linear additive spectra and the spectra of actual mixed samples from the following four aspects: original spectra, derivative spectra, principal components, and the data of PPF projection, accordingly verifing the feasibility and superiority of the linear additive spectra.

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

[Study on a method of selecting calibration samples in NIR spectral analysis].

In the present paper, a simple but novel method based on maximum linearly independent group was introduced into near-infrared (NIR) spectral analysis for selecting representative calibration samples. The experiment materials contained 2,652 tobacco powder samples, with 1,001 samples randomly selected as prediction set, and the others as representative sample candidate set from which calibration sample set was selected. The method of locating maximum linearly independent vectors was used to select representative samples from the spectral vectors of representative samples candidate set. The arithmetic was accomplished by function rref(X,q) in Matlab. The maximum linearly independent spectral vectors were treated as calibration samples set. When different calculating precision q was given, different amount of representative samples were acquired. The selected calibration sample set was used to build regression model to predict the total sugar of tobacco powder samples by PLS. The model was used to analyze 1001 samples in the prediction set. When selecting 32 representative samples, the model presented a good predictive veracity, whose predictive mean relative error was 3.6210%, and correlation coefficient was 0.9643. By paired-samples t-test, we found that the difference between the predicting result of model obtained by 32 samples and that obtained by 146 samples was not significant (alpha=0.05). Also, we compared the methods of randomly selecting calibration samples and maximum linearly independent selection by their predicting effects of models. In the experiment, correspondingly, six calibration sample sets were selected, one of which included 28 samples, while the others included 32, 41, 76, 146 and 163 samples respectively. The method of maximum linearly independent selecting samples turned out to be obviously better than that of randomly selecting. The result indicated that the proposed method can not only effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of samples required for cockamamie and expensive chemical measurement, but also improve the analysis accuracy. In conclusion, this method can be applied to select representative samples in near-infrared spectral analysis.

[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.).

[Quantity analysis of information decomposition for near-infrared diffuse reflectance spectra].

Near infrared (NIR) diffuse reflection spectra can be used to obtain samples' chemical component content and physical parameters with few pretreatments. As a fast and nondestructive technique, NIR has been widely accepted. The diffuse reflection light is detected after interaction with sample. Generally, it is thought that the light carries the interior information of sample, but no one can definitely depict how much information of inner sample can be collected. In the present study, three kinds of flue-cured tobacco were used to design our tests. Each kind of tobacco was prepared in two forms, slice and powder. Then we got three slice tobacco samples and three powder tobacco samples. Every experimental sample consisted of one slice tobacco and one powder tobacco. The slice tobacco was put in the bottom of the sample-cup and the powder tobacco was placed on top of the slice tobacco. Combining different slice tobaccos with different powder tobaccos, the authors can get nine experimental samples. Every experimental sample was scanned from the bottom of the sample-cup for each sample using Bruker MPA FT-NIR instrument four times repeatedly, then we got 36 pieces of NIR spectra totally. In order to find the relationship between the deep light penetration and variance contribution rate of different principal components (PC) of NIR spectra, cluster analysis was carried out using different combination of PC. When the first and second PC were chosen, the samples were clustered according to the exterior slice tobacco; Using the third and fourth PC, the samples were clustered according to the interior powder tobacco. Combining the variance contribution rates of different PC, we could elementarily describe NIR spectra information composition according to the path length of the light penetration quantitatively. Results showed that the first and the second PC contained about 98% spectra information which represented the exterior information, and the third and the fourth PC contained about 1.5% which represented the interior information of the sample. These results can help us profoundly understand the importance of PC selection in NIR qualitative and quantitative analysis.

[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.

[Influence of LPLS algorithm parameters on NIR veracity].

The theory of local partial least square (LPLS) algorithm was described based on locally weighted regression algorithm (LWR). The influence of data processing parameters, such as principal component numbers and local set-up sample number in LPLS mode, on the NIR veracity was studied with homemade grating diffuse NIR instrument using Yunnan flue-cured tobacco. Results showed that for nicotine model, the principal component number decided by cross validation was not the best choice, and better results could be achieved by reducing the principal component number; using 30-50 samples to set up NIR model, the veracity of total sugar, total nitrogen, and nicotine could be improved by 7%, 14% and 10%, respectively. So, LPLS algorithm can effectively improve NIR model's veracity, and is a good method to set up robust NIR models.