[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.

[Determination of Wine Varieties with NIR and Fusion of Multiple Classifiers].

The conventional qualitative analysis of near infrared spectroscopy (NIR) commonly uses one single classification model. This paper focused on the fusion of multiple classifiers based on different single classifiers by using the fused classifier to determine different varieties of red-wines. NIR spectra of 170 red-wine samples were collected by using Fourier transform near-infrared spectrometer. Red-wine classification models were established respectively, based on PLS-DA, SVM, Fisher and Ada-Boost. Then these models were selected to obtain some different base classifiers according to Diversity Measure Feature Selective (DMFS). The highest accuracy rate of determining different varieties of red-wine test samples of four single base classifiers was up to 88.24%, and at the same time the lowest discriminant accuracy rate was 81.18%. At last, we got the fused classifier, which combined four base classifiers with weighted voting principle, and determined its test set again by using the fused classifier. The final classification accuracy rate for red-wine varieties increased to 92.94%, In contrast with one single classifier, the lowest misjudged number of fused classifiers decreased from 9 to 6.These results suggested that the performance of fused classifier is superior to one single classifier. It is feasible to use fused classifier combined with near infrared spectroscopy to determine different varieties of red-wines.

[Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry].

To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NIR) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NIR spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95.69% and validation set is 94.12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99.46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines.

[Identification and classification of disease severity of wheat stripe rust using near infrared spectroscopy technology].

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici, is an economically important disease in the world. It is of great significance to assess disease severity of wheat stripe rust quickly and accurately for monitoring and controlling the disease. In this study, wheat leaves infected with stripe rust pathogen under different severity levels were acquired through artificial inoculation in artificial climate chamber. Thirty wheat leaves with disease severity equal to 1%, 5%, 10%, 20%, 40%, 60%, 80% or 100% were picked out, respectively, and 30 healthy leaves were chosen as controls. A total of 270 wheat leaves were obtained and then their near infrared spectra were measured using MPA spectrometer. According to disease severity levels, 270 near infrared spectra were divided into 9 categories and each category included 30 spectra. From each category, 7 or 8 spectra were randomly chosen to make up the testing set that included 67 spectra. The remaining spectra were treated as the training set. A qualitative model for identification and classification of disease severity of wheat stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with discriminant partial least squares (DPLS). The effects of different preprocessing methods of obtained spectra, ratios between training sets and testing sets, and spectral ranges on qualitative recognition results of the model were investigated. The optimal model based on DPLS was built using cross verification method in the spectral region of 4000-9000 cm(-1) when "centralization" was used as the preprocessing method of spectra and the spectra were divided into the training set and the testing set with the ratio equal to 3:1. Accuracy rate of the training set was 95.57% and accuracy rate of the testing set was 97.01%. The results showed that good recognition performance could be acquired using the model based on DPLS. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for identification and classification of disease severity of wheat stripe rust. A new method was provided for monitoring and assessment of wheat stripe rust.

[Detection of Puccinia strii formis f. sp. tritici latent infections in wheat leaves using near infrared spectroscopy technology].

To realize the early detection of P. strii formis f. sp. tritici latent infections in wheat leaves while no disease symptoms appear, a qualitative model for identification of the wheat leaves in the incubation period of stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with qualitative partial least squares (DPLS). In this study, 30 leaf samples infected with P. strii formis f. sp. tritici were collected each day during the eleven-day incubation period. And 30 healthy leaf samples and 30 leaf samples showing disease symptoms infected with P. strii formis f. sp. tritici, were also collected as controls. In total, there were 390 leaf samples that were divided into thirteen categories. Near infrared spectra of 390 leaf samples were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using cross verification method. The effects of different spectral ranges, samples for building the model, preprocessing methods of spectra and number of principal components on NIRS prediction results for qualitative identification were investigated. The optimal identification results were obtained for the model built in the combined spectral region of 5 400-6 600 and 7 600-8 900 cm(-1) when the spectra were divided into the training set and the testing set with the ratio equal to 4 : 1, "scatter correction" was used as the preprocessing method and the number of principal components was 14. Accuracy rate, misjudgment rate and confusion rate of the training set were 95.51%, 1.28% and 3.21%, respectively. And accuracy rate, misjudgment rate and confusion rate of the testing set were 100.00%, 0.00% and 0.00%, respectively. The result showed that using near infrared reflectance spectroscopy technology, P. strii formis f. sp. tritici latent infections in wheat leaves could be detected as early as one day after inoculation (or 11 days before symptoms appearing) and the number of days when the leaf has been infected could also be identified. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for the identification of wheat leaves latently infected by P. strii formis f. sp. tritici. A new method based on NIRS was provided for the early detection of wheat stripe rust in this study.

[Application of near infrared spectroscopy to qualitative identification and quantitative determination of Puccinia strii formis f. sp. tritici and P. recondita f. sp. tritici].

To realize qualitative identification and quantitative determination of Puccinia strii formis f. sp. tritici (Pst) and P. recondita f. sp. tritici (Prt), a qualitative identification model was built using near infrared reflectance spectroscopy (NIRS) combined with distinguished partial least squares (DPLS), and a quantitative determination model was built using NIRS combined with quantitative partial least squares (QPLS). In this study, 100 pure samples including 50 samples of Pst and 50 samples of Prt were obtained, and 120 mixed samples including three replicates of mixed urediospores of the two kinds of pathogen in different proportions (the content of Pst was within the range of 2. 5% 100% with 2. 5% as the gradient) were obtained. Then the spectra of the samples were collected using MPA spectrometer, respectively. Both pure samples and mixed samples were divided into training set and testing set with the ratio equal to 2:1. Qualitative identification model and quantitative determination model were built using internal cross-validation method in the spectral region 4,000--10,000 cm(-1) based on the training sets from pure samples and mixed samples, respectively. The results showed that the identification rates of the Pst-Prt qualitative identification model for training set and testing set were both up to 100. 00% when scatter correction was used as the preprocessing method of the spectra and the number of principal components was 3. When 'range normalization + scatter correction' was used as the preprocessing method of the spectra and the number of principal components was 6, determination coefficient (RZ), standard error of calibration (SEC) and average absolute relative deviation(AARD) of the Pst-Prt quantitative determination model for training set were 99.36%, 2.31% and 8.94%, respectively, and R2, standard error of prediction (SEP) and AARD for testing set were 99.37%, 2.29% and 5. 0%, respectively. The results indicated that qualitative identification and quantitative determination of Pst and Prt using near infrared spectroscopy technology are feasible and that the Pst-Prt qualitative identification model and the Pst-Prt quantitative determination model built in this study were reliable and stable. A new method based on NIRS was provided for qualitative identification and quantitative determination of plant pathogen in this study.

[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.

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

Analysis of tobacco color and location features using visible-near infrared hyperspectral data.

In the present paper, six categories of standard industrial grading tobacco provided by Hongta Group are taken as experimental samples, including three different tobacco locations-upper (B), middle(C) and lower(X) parts, with each part containing two kinds of tobacco colors-orange (O) and lemon yellow (L). Two methods including projection model method based on principal component and Fisher criterion (PPF) and support vector machine (SVM) method are used to analyze color and location features of tobacco based on visible-near infrared hyperspectral data. The results of projection model method indicate that in the projection and similarity analysis of tobacco color, location and six tobacco groups classified by color and location, two kinds of color can be fully differentiated, of which the similarity value is -1.000 8. Tobacco from upper and lower parts can also be fully differentiated with similarity value 0.405 3, but they both have intersections with tobac- co from middle part. Six tobacco groups classified by color and location can be fully differentiated as well and their projection positions meet the actual external features of tobacco. The results of support vector machine method indicate that in the discriminant analysis of tobacco color, location and six tobacco groups classified by color and location, the average recognition rate of tobacco colors reaches 98%. The average recognition rate of tobacco location is 96%. The average recognition rate of six tobacco groups is 94%. Therefore, it's feasible to analyze color and location features of tobacco using visible-near infrared hyperspectral data, which can provide reference for tobacco quality evaluation, computer-aided grading and tobacco intelligent acquisition, and also offers a new approach to the analysis of exterior features of other agricultural products.

[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.

[Early diagnosis of wheat stripe rust and wheat leaf rust using near infrared spectroscopy].

In the present study, near-infrared reflectance spectroscopy (NIRS) technology was applied to implement early diagnosis of two kinds of wheat rusts, i. e. wheat stripe rust and wheat leaf rust, by detecting wheat leaves as disease symptom has not appeared. The wheat leaves were divided into five categories including healthy leaves, leaves in the incubation period infected with P. strii formis f. sp. tritici, leaves showing symptom infected with P. strii formis f. sp. tritici, leaves in the incubation period infected with P. recondita f. sp. tritici and leaves showing symptom infected with P. recondita f. sp. tritici. Near infrared spectra of 150 wheat leaves were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using distinguished partial least squares (DPLS). For building the model, second-order derivative method was regarded as the best preprocessing method of the spectra and the spectral region 4000 - 8000 cm(-1) was regarded as the optimal spectral region. Using the model with different training sets and testing sets, the average identification rate of the training sets was 96.56% and the average identification rate of the testing sets was 91.85%. The results proved the model's stability. The optimal identification rates were obtained while the ratio of training set to testing set was 2 : 1 and the number of principal components was 10. The identification rate of the training set was 97.00% and the identification rate of the testing set was 96.00%. The results indicated that the identification method based on the NIRS technology developed in this study is feasible for early diagnosis of wheat stripe rust and wheat leaf rust.

[Tobacco quality analysis of producing areas of Yunnan tobacco using near-infrared (NIR) spectrum].

In the present study, tobacco quality analysis of different producing areas was carried out applying spectrum projection and correlation methods. The group of industrial classification data was near-infrared (NIR) spectrum in 2010 year of middle parts of tobacco plant from Hongta Tobacco (Group) Co., Ltd. Twelve hundred seventy six superior tobacco leaf samples were collected from four producing areas, in which three areas from Yuxi, Chuxiong and Zhaotong, in Yunnan province all belong to tobacco varieties of K326 and one area from Dali belongs to tobacco varieties of Hongda. The conclusion showed that when the samples were divided into two parts by the ratio of 2 : 1 randomly as analysis and verification sets, the verification set corresponded with the analysis set applying spectrum projection because their correlation coefficients by the first and second dimensional projection were all above 0.99. At the same time, The study discussed a method to get the quantitative similarity values of different producing areas samples. The similarity values were instructive in tobacco plant planning, quality management, acquisition of raw materials of tobacco and tobacco leaf blending.

[Study of selecting characteristic wavelengths in qualitative analysis of near infrared spectroscopy].

The present article proposed a method of stepwise selecting characteristic wavelengths based on minimum sum of correlation coefficients (SMCC). The maximization of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances was used as evaluation basis in qualitative analysis of near infrared spectroscopy. Seventeen kinds of grading tobacco leaf in 2012, provided by Hongta Group, were used as experimental samples to verify the effectiveness of this new method. CO1 was selected as the reference category and ten points were selected as characteristic wavelengths. The results indicated that the average value of inner-class Euclidean distance, calculated by characteristic wavelengths, was 1.69 times as large as that calculated by all wavelengths. The average value of inter-class Euclidean distance, calculated by characteristic wavelengths, was 3.70 times as large as that calculated by all wavelengths. The average value of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances, calculated by characteristic wavelength, was 2.21 times as large as that calculated by all wavelengths. The ratio of characteristic wavelengths was increased. The characteristic wavelengths can express the classical differences. It was showed that SMCC was an effective way to select characteristic wavelengths in qualitative analyses of near infrared spectroscopy.

[Tobacco quality analysis of industrial classification of different years using near-infrared (NIR) spectrum].

In this study, tobacco quality analysis of main Industrial classification of different years was carried out applying spectrum projection and correlation methods. The group of data was near-infrared (NIR) spectrum from Hongta Tobacco (Group) Co., Ltd. 5730 tobacco leaf Industrial classification samples from Yuxi in Yunnan province from 2007 to 2010 year were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of HONGDA. The conclusion showed that, when the samples were divided to two part by the ratio of 2:1 randomly as analysis and verification sets in the same year, the verification set corresponded with the analysis set applying spectrum projection because their correlation coefficients were above 0.98. The correlation coefficients between two different years applying spectrum projection were above 0.97. The highest correlation coefficient was the one between 2008 and 2009 year and the lowest correlation coefficient was the one between 2007 and 2010 year. At the same time, The study discussed a method to get the quantitative similarity values of different industrial classification samples. The similarity and consistency values were instructive in combination and replacement of tobacco leaf blending.

[Tobacco quality analysis of industrial classification of different producing area using near-infrared (NIR) spectrum].

In this study, tobacco quality analysis of industrial classification of different producing area was carried out applying spectrum projection and correlation methods. The group of industrial classification data was near-infrared (NIR) spectrum in 2010 year from different tobacco plant parts and colors of Hongta Tobacco (Group) Co., Ltd. 6 064 tobacco leaf samples of 17 classes from Yuxi, Chuxiong and Zhaotong, in Yunnan province and 6 industrial classifications were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of K326. The conclusion showed that, the probability of the grading belonging by the first dimension was 84%, the probability of the producing area belonging by the second dimension was 71%. The study can explain the difference of tobacco quality of industrial classification and producing area by a projection method to get the quantitative similarity values. The quantitative similarity values were instructive in combination of tobacco leaf blending.

[Discrimination of different aging methods of grape wine based on ATR infrared spectroscopy].

A total of 96 red wines aged with 3 kinds of methods were included in this study, including 44 wines aged in oak barrel, 26 wines aged in stainless steel tank added with oak chips and 26 wines aged in stainless steel tanks. The infrared spectra of the wines were scanned by Fourier transform infrared spectrometer with attenuated total reflection (ATR) accessories. To classify the 96 different aged wines, discriminant partial least squares (DPLS) method and support vector machine (SVM) method were used to establish models respectively. In order to examine the stability of the discriminant model, modeling was repeated 10 times with two-thirds of samples randomly selected as cross-validation. All the models had high discriminating power with the classification accuracy of the cross-validation and the validation all higher than 90%. These results suggest that the infrared ATR spectroscopy combined with pattern recognition method is a promising tool for discriminating different aging wines.

[Research on characteristic of tobaccos from different ecological environments base on near infrared spectroscopy].

Four hundred ninety five samples from 3 parts of tobacco were collected from 11 different ecological environments in 2010 to analyze the characteristics of tobaccos in different ecological environments with near infrared spectroscopy. The results indicate that the differences are remarkable both between lower and upper parts and between lower and middle parts of tobacco, while negligible between upper and middle parts of tobacco under the same ecological environment. Good stability and consistency were showed by the similarity of characteristics of tobaccos under different ecological environments, and the projection correlation coefficient of analysis set and validation set is over 98%. This article also proposes a method of measuring the similarity between characteristics of tobaccos from different ecological environments. The results can provide reference data to quantify tobacco's planting programming, production processing and quality management.

[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.