Density Functional Theory Calculation and Raman Spectroscopy Studies of Carbamate Pesticides.

In order to obtain the molecular structure vibration information of carbamate pesticide, three carbamate pesticides (carbaryl, carbofuran and aldicarb) were optimized and calculated with B3LYP hybrid functional and 6-31G(d,p) basis set, and their experimental spectra were collected with the Raman spectrometer. The theoretically calculated spectra were compared with the experimental spectra carefully. The results indicated that the theoretically calculated spectra have a very good match with the experimental spectra. The vibrational peaks of three carbamate pesticides were assigned between the range of 400～3 200 cm-1, and the characteristic peaks of carbamate pesticide were found at 874, 1 014, 1 162 and 1 716 cm-1. The characteristic peaks of three carbamate pesticides were found by the contrast of the experimental spectra. The results can provide a theoretical basis for the detection of carbamate pesticide, and will be applied to the identification of carbamate pesticide residues in agricultural products.

[Density Functional Theory Calculation and Raman Spectroscopy Studies of Organophosphorus Pesticides].

Organophosphorus pesticides were often used for prevention and control disease and insect of plant, and are acute toxic to human and livestock by anti-ache activity. The molecular geometry of three organophosphorus pesticides(dimethoate, trichlorfon and phosalone) were constructed on Gauss View3.07, and Density functional theory (DFT) was used to optimize and calculate the vibrational wavenumbers of three organophosphorus pesticides by B3LYP hybrid functional and 6-31G(d, p) basis set. The experimental spectra of three organophosphorus pesticides were compared with the theoretically calculated spectra and Surface-enhanced Raman Scattering spectra (SERS). The results indicated that the experimental spectra and theoretically calculated spectra of three organophosphorus pesticides have a very good match. The Raman peaks of three organophosphorus pesticides were roundly assigned between the range of 400～1 800 cm(-1), and the characteristics peaks of three organophosphorus pesticides were found. The Raman vibration peak of organophosphorus pesticide may appear similar characteristic peak. The pesticide contained PO is between 1 140 and 1 320 cm(-1), the pesticide contained PS is in the range 535～750 cm(-1), and the organophosphorus pesticide contained P­O­C is n the range 920～1 088 cm-1. The different characteristic peaks of three pesticides were found by the contrast of the surface enhanced Raman spectra. This shows that the SERS method can be used to identify the organophosphorus pesticide. The results can furnish a theoretical support for qualitative and quantitative analysis of organophosphorus pesticide.

[Study on the Rapid Detection of Triazophos Residues in Flesh of Navel Orange by Using Surface-Enhanced Raman Scattering].

Surface enhanced Raman spectroscopy (SERS) and quick pre-treatment technology were used to detect triazophos residues in flesh of navel orange. Quantitative analysis model was developed by partial least squares (PLS) algorithm. SERS of different concentration (0.5 to 20 mg x L(-1)) triazophos juice solution with flesh extract as the matrix were collected by laser Raman spectrometer. Three preprocessing methods such as normalization, MSC and SNV were used to optimize Raman signals and PLS models were set up. The results showed that minimum detection concentration for triazophos in navel orange below 0.5 mg L(-1). The model built with normalization pre-processing gave the best result; the values of correlation (R(p)) and Root mean square error of prediction set (RMSEP) were 1.38 and 0.976 6, respectively. The predict recoveries were 95.97%-103.18%, and the absolute values of relative errors were below 5%. T-test (t = -0.018) showed that there was no significant difference between the true values and prediction values. This study demonstrates that this method is accurate and reliable.

[Study on the Enhancement Intensity of Cd in Rice with Microwave-Assisted Laser-Induced Breakdown Spectroscopy].

As food safety problem has become the focus of attention all over the world, green detection methods of the contaminants in food is in accordance with the sustainable development of environment. Heavy metal pollutant Cd element in rice was used as the object of study in this work, laser induced breakdown spectroscopy (LIBS) and microwave assisted laser induced breakdown spectroscopy (MA-LIBS) were utilized to detect the blank and laboratory polluted rice samples respectively. The characteristic line of Cd Ⅰ 228.802 nm was employed as analytical line to discuss the enhancement effect of plasmas emission intensity for the analytical line of target element. Meanwhile, the actual concentration of Cd in rice was measured by anodic stripping voltammetry. The result displayed that LIBS can just detect the plasmas signals of the sample which contained 13.69 µg·g-1 cadmium for the laboratory polluted rice samples which concentration range from 2.16 to 13.69 µg·g-1, however, in the same experimental conditions, MA-LIBS can detect the plasmas signals of Cd in all of the contaminated rice samples successfully, and compared with LIBS, the plasmas emission intensity of Cd element was enhanced from 9 to 27 times. The results showed that the plasmas emission intensity of Cd element in rice can be enhanced effectively by MA-LIBS, and the detection sensitivity can be effectively improved.

[Quantitative Detection of Iron in Soybean Oils with Laser Induced Breakdown Spectroscopy and Simple Regression Methods].

LIBS (laser-induced breakdown spectroscopy) was used to detect Fe element content in soybean oil quantitatively. In this experiment, a series of soybean oil samples with different concentrations of Fe were used; LIBS spectra were collected with a two-channel high precision spectrometer. According to the LIBS spectrum of samples, two characteristic wavelength of Fe (404.58 and 406.36 nm) were determined, and different simple regression methods (exponential regression, linear regression and quadratic regression) were used to establish the quantitative analysis models of Fe content using each characteristic spectral line. The results indicate that the average relative error of Fe I 404.58 and Fe I 406.36 in simple exponential regression, linear regression and quadratic regression models were 29.49%, 8.93%, 8.70% and 28.95%, 8.63%, 8.44%, respectively. The results of Fe I 406.36 regression models is better than that of Fe I 404.58, and the quadratic regression model is optimal among the three regression models. According to these results, LIBS technology has certain feasibility for detecting Fe in soybean oil; the quadratic linear regression model can improve the prediction accuracy of Fe element effectively.

[Model Optimization of Ternary System Adulteration Detection in Camellia Oil Based on Visible/Near Infrared Spectroscopy].

Visible/near infrared spectroscopy combined with chemometrics methods was used to detect ternary system adulteration in camellia oil quantificationally. In order to get adulterated samples, rapeseed oil and peanut oil were added to pure camellia oil in different proportion. Visible/near infrared spectroscopy data of pure and adulterated camellia oil samples were acquired in the wavelength range of 350~1800nm, and samples were randomly divided into calibration set and prediction set. The adulteration models were optimized by comparing different wavelength ranges, pretreatment methods and calibration methods The results show that the optimal modeling wavelength ranges and pretreatment methods for the prediction models of rapeseed oil, peanut oil and total adulteration amount are 750~1 770, 900~1 770, 870~1 770 nm and Multiple scattering correction (MSC), Standard normal variate (SNV) and second order differentia, and the best modeling method is Least square support vector machine (LSSVM). The correlation coefficient (R(P)) in prediction set and the root mean square error predictions(RMSEPs) of optimal adulteration models for rapeseed oil, peanut oil and total adulteration are 0.963, 0.982, 0.993 and 2.1%, 1.5%, 1.8%, respectively. Thus it can be seen that visible /near infrared spectroscopy combined with chemometrics methods can be used for quantitative ternary system adulteration detection in camellia oil.

[Qualitative Detection of Procymidone in Edible Vegetable Oils by Near Infrared Spectroscopy and Variable Selection Methods].

In this research, near infrared (NIR) spectroscopy was used to detect procymidone in edible vegetable oils qualitatively. Edible vegetable oil samples with different procymidone contents were classified to two groups according to boundary line of maximum residue limit of procymidone in national standard. QualitySpec spectrometer was used to acquire spectra of two group samples, then uninformative variable elimination (UVE) and subwindow permutation analysis (SPA) were used to select informative wavelength variables. At last, several methods such as linear discriminant analysis (LDA), partial least squares-linear discriminant analysis (PLS-LDA) and discriminant partial least squares (DPLS) were used to develop classification models. The results indicate that NIR spectroscopy is feasible to classify the two group samples. UVE method can select informative wavelength variables effectively, and improve the performance of classification model. The best model is developed by UVE-DPLS method, the classification correct rate, sensitivity and specificity of prediction samples in this model are 98.7%, 95.0% and 100.0%, respectively.

Identification Study of Edible Oil Species with Laser Induced Fluorescence Technology Based on Liquid Core Optical Fiber.

A laser- induced fluorescence detection set based on liquid core optical fiber was established in this study. Eight edible oils were discriminated by using this detection set combined with chemometrics method. The effect of length of liquid core optical fiber on laser induced fluorescence spectrum was explored, and the differences between the spectra of different edible oils were analyzed. The fluorescence spectra of 320 samples covering 8 types of edible oil were measured in 1 meter liquid core optical fiber. Principal component analysis was used in fluorescence data dimensionality reduction process. Partial least squares discriminant analysis (PLS-DA) method was used to develop the identification model to distinguish edible oil species. The results showed that the oil fluorescence intensity is greatly enhanced when liquid core optical fiber was used. With the increase of liquid core optical fiber length，the peaks of laser induced edible oil fluorescence spectra increased and the fluorescence spectra will produce red shift. The red shift tended to a constant value when the fiber length was more than 80 cm. The fluorescence spectra of different edible oils were quite different, its can be used to distinguish different types of edible oil. Principal component scores chart were get using PC1 and PC2 of edible oils fluorescence data which result in a trend of certain gather of same type of edible oil. The recognition rates of PLS-DA model for the calibration set and prediction set were both 100%. The study shows that the developed device in this study has high accuracy for identifying the edible oil species.

Detection of Chromium Content in Soybean Oil by Laser Induced Breakdown Spectroscopy and UVE Method.

In order to monitor chromium (Cr) content in soybean oil, laser induced breakdown spectroscopy (LIBS) was used to detect Cr content in this research. Pine wood chips was used to enrich heavy metal of Cr, and the spectra of pine wood chips were acquired in the wavelength range of 206.28~481.77 nm by a two-channel high-precision spectrometer. Then, uninformative variable elimination (UVE) method was used to select sensitive wavelength variables for heavy metal of Cr, and calibration model of Cr in soybean oil was developed with partial least squares (PLS) regression, the performance of the calibration model was compared to univariate and full PLS calibration models. The results indicate that the performance of UVE-PLS calibration model is better than that of univariate and full PLS calibration models, the correlation coefficient, root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) are 0.990, 0.045 mg·g-1, 0.050 mg·g-1 and 0.054 mg·g-1, respectively. After UVE variable selection, the number of wavelength variables in UVE-PLS calibration model is about 2% of wavelength variables in full PLS calibration model. This means UVE is an effective variable selection method which can select correlative variables for heavy metal of Cr.

[DFT and Raman Scattering Studies of Benzimidazole].

The Raman spectra of carbendazim, thiabendazole and benomyl were collected by laser Raman spectrometer. The molecules of the three pesticides were optimized and calculated by B3LYP hybrid functional and 6-31G(d, p) basis set. The results showed that the calculated value anastomosed preferably to measure value. Vibrational modes of pesticide molecules were assigned between 200 and 1 600 cm(-1) range, and found three characteristic peaks of benzimidazole about at 1 015, 1 265 and 1 595 cm(-1). The comparative analysis on the differences of normal Raman spectra, found different characteristic peaks in three pesticide molecules. The results can provide theoretical for analysis Raman spectra of benzimidazole pesticide. This work will promote the research of benzimidazole pesticide residue in food and agricultural products based on Raman spectra.

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

[Surface-enhanced Raman spectroscopy analysis of thiabendazole pesticide].

Surface-enhanced Raman spectroscopy (SERS) technique was used to analyze the Raman peaks of thiabendazole pesticides in the present paper. Surface enhanced substrates of silver nanoparticle were made based on microwave technology. Raman signals of thiabendazole were collected by laser Micro-Raman spectrometer with 514. 5 and 785 nm excitation wavelengths, respectively. The Raman peaks at different excitation wavelengths were analyzed and compared. The Raman peaks 782 and 1 012 at 785 nm excitation wavelength were stronger, which were C--H out-of-plane vibrations. While 1284, 1450 and 1592 cm(-1) at 514.5 nm excitation wavelength were stronger, which were vng and C==N stretching. The study results showed that the intensity of Raman peak and Raman shift at different excitation wavelengths were different And strong Raman signals were observed at 782, 1012, 1284, 1450 and 1592 cm(-1) at 514.5 and 785 nm excitation wavelengths. These characteristic vibrational modes are characteristic Raman peaks of carbendazim pesticide. The results can provide basis for the rapid screening of pesticide residue in agricultural products and food based on Raman spectrum.

[Determination of Cu in Shell of Preserved Egg by LIBS Coupled with PLS].

In this work, the content of copper in the shell of preserved eggs were determined directly by Laser induced breakdown spectroscopy (LIBS), and the characteristics lines of Cu was obtained. The samples of eggshell were pretreated by acid wet digestion, and the real content of Cu was obtained by atomic absorption spectrophotometer (AAS). Due to the test precision and accuracy of LIBS was influenced by a serious of factors, for example, the complex matrix effect of sample, the enviro nment noise, the system noise of the instrument, the stability of laser energy and so on. And the conventional unvariate linear calibration curve between LIBS intensity and content of element of sample, such as by use of Schiebe G-Lomakin equation, can not meet the requirement of quantitative analysis. In account of that, a kind of multivariate calibration method is needed. In this work, the data of LIBS spectra were processed by partial least squares (PLS), the precision and accuracy of PLS model were compared by different smoothing treatment and five pretreatment methods. The result showed that the correlation coefficient and the accuracy of the PLS model were improved, and the root mean square error and the average relative error were reduced effectively by 11 point smoothing with Multiplicative scatter correction (MSC) pretreatment. The results of the study show that, heavy metal Cu in preserved egg shells can be direct detected accurately by laser induced breakdown spectroscopy, and the next step batch tests will been conducted to find out the relationship of heavy metal Cu content in the preserved egg between the eggshell, egg white and egg yolk. And the goal of the contents of heavy metals in the egg white, egg yolk can be knew through determinate the eggshell by the LIBS can be achieved, to provide new method for rapid non-destructive testing technology for quality and satety of agricultural products.

[Effect of Optical Length on Detection Accuracy of Camellia Oil Adulteration by Near Infrared Spectroscopy].

In this research, near infrared spectroscopy was used to detect adulterated percent of camellia oil adulterated with soybean oil quantificationally at different optical lengths, and the effect of optical length on detection accuracy of adulterated percent was investigated. Soybean oil was put into camellia oil according to different mass fraction, the adulterated mass fraction was ranged from 1% to 50%. Transmission spectra of samples were acquired by a Quality Specspectrometer at different optical lengths (1, 2, 4, 10 mm), and effect of optical length on detection accuracy of adulterated percent was analyzed by comparing quantitative prediction models that developed at different calibration methods, pretreatment methods and wavelength range. The results indicate that the performance of quantitative prediction model of adulterated percent is improved as the optical length is increasing from 1 to 4 mm, while the performance of quantitative prediction model of adulterated percent is deteriorated as the optical length is increasing from 4 to 10 mm. 4 mm is a better optical length for camellia oil adulteration. The coefficients of determination of prediction (R2(P)) and root mean square error of prediction (RMSEP) in quantitative prediction models of adulterated percent for optical lengths of 1, 2, 4, 10 mm are 0.923, 0.977, 0.989, 0.962 and 4.58%, 2.54%, 1.72%, 3.20%, respectively.

[Effect of Characteristic Variable Extraction on Accuracy of Cu in Navel Orange Peel by LIBS].

Heavy metals pollution in foodstuffs is more and more serious. It is impossible to satisfy the modern agricultural development by conventional chemical analysis. Laser induced breakdown spectroscopy (LIBS) is an emerging technology with the characteristic of rapid and nondestructive detection. But LIBS' s repeatability, sensitivity and accuracy has much room to improve. In this work, heavy metal Cu in Gannan Navel Orange which is the Jiangxi specialty fruit will be predicted by LIBS. Firstly, the navel orange samples were contaminated in our lab. The spectra of samples were collected by irradiating the peel by optimized LIBS parameters. The laser energy was set as 20 mJ, delay time of Spectral Data Gathering was set as 1.2 micros, the integration time of Spectral data gathering was set as 2 ms. The real concentration in samples was obtained by AAS (atom absorption spectroscopy). The characteristic variables Cu I 324.7 and Cu I 327.4 were extracted. And the calibration model was constructed between LIBS spectra and real concentration about Cu. The results show that relative error of the predicted concentrations of three relational model were 7.01% or less, reached a minimum of 0.02%, 0.01% and 0.02% respectively. The average relative errors were 2.33%, 3.10% and 26.3%. Tests showed that different characteristic variables decided different accuracy. It is very important to choose suitable characteristic variable. At the same time, this work is helpful to explore the distribution of heavy metals between pulp and peel.

[Laser Raman spectrum analysis of carbendazim pesticide].

Raman signal of solid and liquid carbendazim pesticide was collected by laser Raman spectrometer. The acquired Raman spectrum signal of solid carbendazim was preprocessed by wavelet analysis method, and the optimal combination of wavelet denoising parameter was selected through mixed orthogonal test. The results showed that the best effect was got with signal to noise ratio (SNR) being 62.483 when db2 wavelet function was used, decomposition level was 2, the threshold option scheme was 'rigisure' and reset mode was 'sln'. According to the vibration mode of different functional groups, the de-noised Raman bands could be divided into 3 areas: 1 400-2 000, 700-1 400 and 200-700 cm(-1). And the de-noised Raman bands were assigned with and analyzed. The characteristic vibrational modes were gained in different ranges of wavenumbers. Strong Raman signals were observed in the Raman spectrum at 619, 725, 964, 1 022, 1 265, 1 274 and 1 478 cm(-1), respectively. These characteristic vibrational modes are characteristic Raman peaks of solid carbendazim pesticide. Find characteristic Raman peaks at 629, 727, 1 001, 1 219, 1 258 and 1 365 cm(-1) in Raman spectrum signal of liquid carbendazim. These characteristic peaks were basically tallies with the solid carbendazim. The results can provide basis for the rapid screening of pesticide residue in food and agricultural products based on Raman spectrum.

[Quantitative analysis of Cu in water by collinear DP-LIBS].

The purpose of this research is to study the influence of double pulse laser induced breakdown spectroscopy (DP-LIBS) on the sensitivity of Cu in water. The water solution of Cu was tested by collinear DP-LIBS in this article. The results show that spectral intensity of Cu can be enhanced obviously by DP-LIBS, compared with single pulse laser induced breakdown spectroscopy (SP-LIBS). Besides, the experimental results were significantly impacted by delay time between laser pulse and spectrometer acquisition, delay time of double laser pulse and energy of laser pulse and so on. The paper determined the best conditions for DP-LIBS detecting Cu in water. The optimal acquisition delay time was 1 380 ns. The best laser pulse delay time was 25 ns. The most appropriate energy of double laser pulse was 100 mJ. Characteristic analysis of spectra of Cu at 324.7 and 327.4 nm was done for quantitative analysis. The detection limit was 3.5 microg x mL(-1) at 324.7 nm, and the detection limit was 4.84 microg x mL(-1) at 327.4 nm. The relative standard deviation of the two characteristic spectral lines was within 10%. The calibration curve of characteristic spectral line, established by 327.4 nm, was verified with 500 microg x mL(-1) sample. Concentration of the sample was 446 microg x mL(-1) calculated by the calibration curve. This research shows that the detection sensitivity of Cu in water can be improved by DP-LIBS. At the same time, it had high stability.

[Rapid determination of ractopamine content in pork by using three-dimensional synchronous fluorescence spectrum coupled with APTLD].

In order to realize the rapid determination of ractopamine content in pork, quantitative determination model of ractopamine content in pork was established by using three-dimensional synchronous fluorescence spectrum coupled with alternating penalty trilinear decomposition (APTLD). Firstly, the generation mechanism of the fluorescence spectrum for ractopamine and three-dimensional synchronous fluorescence spectrum for samples were analyzed. Secondly, concentration quenching phenomenon of fluorescence of ractopamine in pork extract was investigated. Thirdly, the number of components for three linear decomposition of APTLD was set as 2 by using the core consistency diagnostic method, and the calibration curve of the relative fluorescence intensity of ractopamine between pork extract and the training sample was established for the correction of relative fluorescence intensity of prediction samples. Finally, three-dimensional synchronous fluorescence spectrum combined with APTLD was used to build the prediction model of ractopamine content in pork. The experimental results showed that the method adopted in the paper could better solve the problem of serious synchronous fluorescence spectrum overlapping between ractopamine in pork samples and backgrounds, and leave out some trivial process of chemical separation for the identification of ractopamine in pork. The determination coefficient (R2) and the root mean squared error of prediction (RMSEP) for the model proposed in this paper were 0.986 3 and 0.496 6 mg x L(-1), respectively. The method in this paper has achieved the goal of rapid quantitative detection of ractopamine content in pork.

[Huanghua pear soluble solids contents Vis/NIR spectroscopy by analysis of variables optimization and FICA].

The purpose of this study was to establish a mathematical model of the visible/near-infrared (Vis/NIR) diffuse transmission spectroscopy with fine stability and precise predictability for the non destructive testing of the soluble solids content of huanghua pear, through comparing the effects of various pretreatment methods, variable optimization method, fast independent principal component analysis (FICA) and least squares support vector machines (LS-SVM) on mathematica model for SSC of huanghua pear, and the best combination of methods to establish model for SSC of huanghua pear was got. Vis/NIR diffuse transmission spectra of huanghua pear were acquired by a Quality Spec spectrometer, three methods including genetic algorithm, successive projections algorithm and competitive adaptive reweighted sampling (CARS) were used firstly to select characteristic variables from spectral data of huanghua pears in the wavelength range of 550~950 nm, and then FICA was used to extract factors from the characteristic variables, finally, validation model for SSC in huanghua pears was built by LS-SVM on the basic of those parameters got above. The results showed that using LS-SVM on the foundation of the 21 variables screened by CARS and the 12 factors selected by FICA, the CARS-FICA-LS-SVM regression model for SSC in huanghua pears was built and performed best, the coefficient of determination and root mean square error of calibration and prediction sets were RC(2)=0.974, RMSEC=0.116%, RP(2)=0.918, and RMSEP=0.158% respectively, and compared with the mathematical model which uses PLS as modeling method, the number of variables was down from 401 to 21, the factors were also down from 14 to 12, the coefficient of determination of modeling and prediction sets were up to 0.023 and 0.019 respectively, while the root mean square errors of calibration and prediction sets were reduced by 0.042% and 0.010% respectively. These experimental results showed that using CARS-FICA-LS-SVM to build regression model for the forecast of SSC in huanghua pears can simplify the prediction model and improve the detection precision.

[Determination of cotton content in cotton/ramie blended fabric by NIR spectra and variable selection methods].

Rapid detection of textile fiber components is very important for production process of quality control, trading and market surveillance. The objective of this research was to assess cotton content in cotton/ramie blended fabric quickly by near infrared (NIR) spectrum technology and variable selection methods. Reflectance spectra of samples were acquired by a NIRFlex N-500 Fourier spectroscopy in the range of 4000~10,000 cm(-1), primary election of spectral range and pretreatment analysis were conducted first. Then, three variable selection methods such as UVE (uninformative variables elimination), SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) were used to select sensitive variables. After that, PLS (partial least squares) was used to develop calibration model for cotton content of cotton/ramie blended fabric, and the best calibration model was used to predict cotton content of samples in prediction set. The result indicates that range of 4052~8000 cm(-1) is optimal spectral range for cotton content modeling. CARS method is an efficient method to improve model performance, the correlation coefficient and root mean square error of CARS-PLS for calibration and prediction sets are 0.903, 0.749 and 8.01%, 12.93%, respectively. So NIR spectra combined with CARS method is feasible for assessing cotton content in cotton/ramie blended fabric, and CARS method can simplify model, improve model performance.