[Correction Multiplicative Effects in Raman Spectra through Vector Angle Transformation].

The linear relationship between the Raman spectral intensity and the analyte amount is frequently disrupted for a variety of complex reasons, which include these variations in laser source, focusing effect, sample scattering and refracting, so that causes poor quantitative results. As a whole, these disturbing effects can be divided to be additive and multiplicative, and the multiplicative effects are generally more difficult to be eliminated. A spectrum is a series data, also can be treated as a vector. In principle, unstable motions in spectrum intensity/amplitude corresponding to the module shifts for a vector, doesn't impact the vector direction which is the essence of the vector, so it is reasonable to rewrite the data form on module to on space angle for the same measurement. This thesis employed a data transformation to eliminate the multiplicative effects within spectra, i. e. , the spectrum signal on its amplitude has been transformed to be on the vector angles. The first step of the transformation is the selection of a stand vector which is near to the analyte and almost orthogonal to the background within the sample space; and the next step is to define a moving window, then to find out the angle between the sample vector (i. e. the transformed spectrum) and the stand vector within the window; while the window is moved along the spectrum data series, the transformation for vector angle (VA) series has been finished. The thesis has proved that an approximate linear quantitative relationship has been remained in the VA series. Multivariate calibration need full rank matrix which is combined by spectrum from variety samples, and variety VA series also can combine a full rank VA matrix, so the approximate linear VA matrix still perfectly meeting the demand for multivariate calibration. A mixed system consisted by methanol-ethanol-isopropanol has been employed to verify the eliminations to the multiplicative effects. These measuring values of the system are obtained at different Raman integral times and have remarkable multiplicative effects. In predicting results, the correlation coefficient (r) and the root mean squared error of prediction (RMSEP) from class PLS respectively are 0.911 9 and 0.110 2, and 0.906 0 and 0.100 8 are for the preprocessing by multiplicative scatter correction (MSC). In contrast, r and RMSEP under the VAPLS, presented by this thesis, respectively are 0.998 7 and 0.015 2 and are significantly better than others. The VAPLS has eliminated the multiplicative effects of Raman spectra and improved the accuracy of Raman quantitative analysis and it owes to the preprocessing of the vector angle transformation.

[Study on the Fast Quantitative Analysis of the Content of DME Adulterated in LPG].

A new analytical method of analyzing dimethyl ether (DME) content in liquefied petroleum gas (LPG) is proposed in this paper. An unsolved problem about quick detection of the composition of LPG has been settled with this method. A set of precise preparation apparatus for DME/ LPG solution and a set of quickly analytical system of LPG based on near infrared technology were designed. The analytical equipment can be conveniently connected to the sampling cylinder because it can bear 3.5 MPa pressure. Oblique projection algorithm was used to separate the pure spectra of DME from that of the LPG's solutions. The standard curve of the concentration of DME (c) has been built by using the Intensity (I) of pure signal of DME in the LPG solution and the concentrations. The correlation coefficient of the equation is 0.999 4. The result of external validation shows that the relative error is less than 2.0%. The new method has the advantages such as fast, easy and noneed of expensive multivariate modeling.

[Determination of Alcoholysis Degree and Volatile Matter of Poly-Vinyl Alcohol Using Diffuse-Reflection Near Infrared Spectroscopy].

A new method using reflection NIR technology was developed to determine the alcoholysis degree and volatile matter of Poly-vinyl alcohol (PVA). 120 samples were used in this research. NIR spectra of the sample were scanned by the spectrometer from 1 000 to 1 800 nm. The alcoholysis degree and volatile matter were determined by the national standard method of volumetric and gravimetric method respectivily. Partial least squares (PLS1) was used to establish the quantitative correction model of alcoholysis degree and volatile matter of PVA. The corrected relationship (Rc) of alcoholysis degree and volatile matter was 0.976 and 0.981 respectively. The corrected standard deviation(SEC) was 0.176 and 0.197. The predicted relationship (R(p)) was 0.967 and 0.969. The predicted deviation(SEP) was 0.202 and 0.193. The test for actual samples showed that the NIR method was fitted for the requirement of PVA analysis.

[Rapid FTIR-ATR Method for the Quantification of Bitumen Property].

The quality of bitumen directly affects road performance and road life. Traditional analytical methods-for wax content, softening point and penetration of bitumen are tedious and time-consuming. A new fast method, with which the three properties can be determined at same time, is proposed in this paper. The spectra of 220 bitumen were collected and their wax content, softening point and penetration data were determined according to the standard JTJ052-2000. The quantitative calibration models for wax content, softening point and penetration were established using partial least squares (PLS), with SECV 0.13, 0.88, 3.18 and SEP 0.14, 1.06, 3.90, less than the reproducibility error stipulated in the standard method. Three samples were in random selected to test the repeatability, the results met the precision requirement of the standard method. With its advantages of better repeatability, fast, easy operation, the new method can be used as an alternative for the determination of wax content, softening point and penetration of bitumen.

[Preparation and characterization of kaolinite-potassium dihydrogen phosphate intercalation composite].

Thepotassium dihydrogenphospiate (KDP) intercalated kaolinite (K-KDP) was prepared by a three-step reaction. Firstly, polar dimethyl sulphoxide (DMSO) was introduced into the layers of kaolinite by ultrasonic method and the product K-DMSO was obtained as precursor; secondly DMSO was replaced by potassium acetate (KAc) and the product K-KAc was gotten as intermediate; finally KAc was replaced by KDP. The intercalation ratio of the final product K-KDP reached 81.3%. The structure of products at differentreaction process was characterized by Fourier-transformed infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscope (SEM) in detail. FTIR results showed the existence of P==O in the final product but moved from 1300 to 1201 cm(-1). XRD results documented that the interlayer spacing of kaolinite was enlarged during the whole intercalation reaction. SEM indicated that the agglomeration of kaolinite was destroyed and the particle size distribution became more uniform.

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

[Rapid Quantitative Analysis of Content of the Additive in Gasoline for Motor Vehicles by Near-Infrared Spectroscopy].

A new rapid quantitative method for the determination of oxygenates and the compounds not included in the national standard in gasoline using near-infrared spectroscopy is raised by this paper. This method combine near-infrared spectroscopy with oblique projection. This experiment choose four different types of gasoline, including reconcile gasoline, FCC refined gasoline, reformed gasoline and desulfurizing gasoline. Prepare series gasoline samples containing different concentrations and different types of compounds. Using FTIR spectrometer to measure those samples and got transmission spectrums. Oblique projection method could separate quantity spectral signal from mixed spectrum signal, and using projection to calculate and analyze the separated signal to obtain the content of measured component. The deviation between this method and the real content is low, the absolute error is less than 0.8 and the relative error is less than 8%. For the actual gasoline samples, compare results of this method with gas chromatography, the absolute error are less than 0.85 and the relative error are less than 6.85%. This method solves the problem of general multivariate calibration methods. It is very significant for the development of rapid detection technology using NIR suitable for on-site and the improvement of the quality of gasoline.

[A correction method of baseline drift of discrete spectrum of NIR].

In the present paper, a new correction method of baseline drift of discrete spectrum is proposed by combination of cubic spline interpolation and first order derivative. A fitting spectrum is constructed by cubic spline interpolation, using the datum in discrete spectrum as interpolation nodes. The fitting spectrum is differentiable. First order derivative is applied to the fitting spectrum to calculate derivative spectrum. The spectral wavelengths which are the same as the original discrete spectrum were taken out from the derivative spectrum to constitute the first derivative spectra of the discrete spectra, thereby to correct the baseline drift of the discrete spectra. The effects of the new method were demonstrated by comparison of the performances of multivariate models built using original spectra, direct differential spectra and the spectra pretreated by the new method. The results show that negative effects on the performance of multivariate model caused by baseline drift of discrete spectra can be effectively eliminated by the new method.

[An optimal selection method of samples of calibration set and validation set for spectral multivariate analysis].

The side effects in spectral multivariate modeling caused by the uneven distribution of sample numbers in the region of the calibration set and validation set were analyzed, and the "average" phenomenon that samples with small property values are predicted with larger values, and those with large property values are predicted with less values in spectral multivariate calibration is showed in this paper. Considering the distribution feature of spectral space and property space simultaneously, a new method of optimal sample selection named Rank-KS is proposed. Rank-KS aims at improving the uniformity of calibration set and validation set. Y-space was divided into some regions uniformly, samples of calibration set and validation set were extracted by Kennard-Stone (KS) and Random-Select (RS) algorithm respectively in every region, so the calibration set was distributed evenly and had a strong presentation. The proposed method were applied to the prediction of dimethylcarbonate (DMC) content in gasoline with infrared spectra and dimethylsulfoxide in its aqueous solution with near infrared spectra. The "average" phenomenon showed in the prediction of multiple linear regression (MLR) model of dimethylsulfoxide was weakened effectively by Rank-KS. For comparison, the MLR models and PLS1 models of MDC and dimethylsulfoxide were constructed by using RS, KS, Rank-Select, sample set partitioning based on joint X- and Y-blocks (SPXY) and proposed Rank-KS algorithms to select the calibration set, respectively. Application results verified that the best prediction was achieved by using Rank-KS. Especially, for the distribution of sample set with more in the middle and less on the boundaries, or none in the local, prediction of the model constructed by calibration set selected using Rank-KS can be improved obviously.

[Determination of degree of polymerization of natural cellulose pulp using near-infrared diffuse reflectance spectroscopy].

A new method of near-infrared (NIR) diffuse reflectance spectroscopy is proposed to rapidly determine the degree of polymerization (DP) of natural cellulose (cotton and wood) pulp produced by a new clean pulping process. One hundred and ninety five samples were collected and their DP data were determined by standard method GB/T 9107-1999. The spectroscopy measurement method of the samples was studied and their near-infrared diffuse reflectance spectra were collected. The quantitative DP calibration models of one mixed cotton & wood and two separate cotton and wood pulps were established by partial least squares (PLS). The optimum models were developed using the spectra pretreated by derivative, autoscaling and mean-centering, and their performance is as follows: correlation coefficient of 0.980, 0.993 and 0.886, and RMSEP of 147, 143 and 53, respectively. The accuracy of NIR method was also studied. The results show that the accuracy of the two separate models of cotton and wood is better than that of the mixed model, and the precision of the two separate models is better than that of GB/T9107-1999. The identification model of cotton and wood was also established using principal component analysis (PCA). The result shows that the spectra of cotton and wood pulp have obvious difference, and the model can identify successfully the two kinds of pulp. The result indicates that the new NIR method is feasible to realize the on-line analysis of polymerization degree of natural cellulose pulp with its advantage of rapidness and easy operation.

[Fast determination of induction period of motor gasoline using Fourier transform attenuated total reflection infrared spectroscopy].

A new method is proposed for the fast determination of the induction period of gasoline using Fourier transform attenuated total reflection infrared spectroscopy (ATR-FTIR). A dedicated analysis system with the function of spectral measurement, data processing, display and storage was designed and integrated using a Fourier transform infrared spectrometer module and chemometric software. The sample presentation accessory designed which has advantages of constant optical path, convenient sample injection and cleaning is composed of a nine times reflection attenuated total reflectance (ATR) crystal of zinc selenide (ZnSe) coated with a diamond film and a stainless steel lid with sealing device. The influence of spectral scanning number and repeated sample loading times on the spectral signal-to-noise ratio was studied. The optimum spectral scanning number is 15 times and the optimum sample loading number is 4 times. Sixty four different gasoline samples were collected from the Beijing-Tianjin area and the induction period values were determined as reference data by standard method GB/T 8018-87. The infrared spectra of these samples were collected in the operating condition mentioned above using the dedicated fast analysis system. Spectra were pretreated using mean centering and 1st derivative to reduce the influence of spectral noise and baseline shift A PLS calibration model for the induction period was established by correlating the known induction period values of the samples with their spectra. The correlation coefficient (R2), standard error of calibration (SEC) and standard error of prediction (SEP) of the model are 0.897, 68.3 and 91.9 minutes, respectively. The relative deviation of the model for gasoline induction period prediction is less than 5%, which meets the requirements of repeatability tolerance in GB method. The new method is simple and fast. It takes no more than 3 minutes to detect one sample. Therefore, the method is feasible for implementing fast determination of gasoline induction period, and of a positive meaning in the evaluation of fuel quality.

[A new multivariate quantitative method of spectral analysis for multicomponent system].

In the spectral analysis, a large-scale application of the traditional multivariate analysis methods has been limited by both high cost and poor applicability of the calibration models. A new multivariate analysis method was proposed for multicomponent systems in the present paper. Determining MTBE content in gasoline solution by infrared spectroscopy was studied. The spectra of five kinds of gasoline and their 50 narrow distillation fractions were used to build the background library. The oblique projection algorithm was applied to the spectra of MTBE gasoline solution samples to extract the purespectral signal of MTBE in the solution. A unary linear regression curve was built between the pure spectral signals of MTBE and their concentrations with a correlation coefficient of 0.995 2 and an intercept of 0.025. Compared with the orthogonal projection algorithm method and PLS model method, a large amounts of calibration samples and complex model are no longer needed by the new method which is simpler, more accurate and with better applicability.

[Rapid quantitative analysis of hydrocarbon composition of furfural extract oils using attenuated total reflection infrared spectroscopy].

A set of rapid analysis system for hydrocarbon composition of heavy oils was designed using attenuated total reflection FTIR spectrometer and chemometrics to determine the hydrocarbon composition of furfural extract oils. Sixty two extract oil samples were collected and their saturates and aromatics content data were determined according to the standard NB/SH/T0509-2010, then the total contents of resins plus asphaltenes were calculated by the subtraction method in the percentage of weight. Based on the partial least squares (PLS), calibration models for saturates, aromatics, and resin+asphaltene contents were established using attenuated total reflection FTIR spectroscopy, with their SEC, 1.43%, 0.91% and 1.61%, SEP, 1.56%, 1.24% and 1.81%, respectively, meeting the accuracy and repeatability required for the standard. Compared to the present standard method, the efficiency of hydrocarbon composition analysis for furfural extract oils is significantly improved by the new method which is rapid and simple. The system could also be used for other heavy oil analysis, with excellent extension and application foreground.

[Study on nondestructive rapid identification method of foods containing trans-fatty acids using diffuse near infrared spectroscopy].

A rapid nondestructive method for identifying intact foods containing trans fatty acids (TFA) using diffuse near infrared spectroscopy (NIR) was proposed in the present paper. The diffuse Fourier transform near infrared (FT-NIR) spectra of intact samples were collected by fiber probe, and the reference data of TFA content were determined by Chinese standard method GB/T 22110-2008 (gas chromatography (GC) method). In this work, all the samples were classified into two categories: foods with TFA and foods without TFA according to the TFA content of the foods. The identification models were established by different supervised pattern recognition algorithms including partial least square discriminant analysis (PLSDA), support vector machine (SVM), soft independent modeling of class analogy (SIMCA) and K-nearest neighbor method (KNN) etc. The performances of the established models employing different algorithms, data pretreatments and wavelength bands were compared. The results show that PLSDA and SVM algorithms have the ability of identifying intact foods with TFA, and the performance of identification models established by PLSDA is better than that of SVM. The PLSDA models established by the wavelength bands of 4 138-4 428, 5 507-5 963 and 7 794-8 960 cm(-1) which were pretreated with pretreatment methods of auto scaling and second derivative have the best performance. The correct classification percentages of its calibration and validation set are 96.4% and 88%, respectively, which indicates that this method is feasible for the identification of foods with TFA. This NIR method above mentioned has the characteristics of rapidness, non-destruction and easy operation due to the elimination of sample pretreatment such as oil extraction and grinding, therefore it is very suitable for on-line and in-site detection application.

[Determination of alpha-cellulose content of natural cellulose pulp in a new clean pulping process using near infrared diffuse reflectance spectroscopy].

A new near infrared diffuse reflectance spectroscopy method is proposed to rapidly detect alpha-cellulose content of natural cellulose (plant fiber: cotton, wood) pulp in a new clean pulping process. One hundred forty two samples were collected and their alpha-cellulose content data were determined by standard method GB/T 9107-1999. The samples were homogenized by grinding pretreatment to improve spectroscopy measurement accuracy. Effective classification models were built by SIMCA, with the total correct identification. Using partial least squares (PLS) quantitative calibration, alpha-cellulose of the whole and separate cotton and wood pulp was established, with the correlation coefficients of 0.954, 0.911, 0.839, SEP, 0.024, 0.012 and 0.016, respectively. The repeatability results obtained by the new method are in agreement with the results from GB/T 9107-1999. The new method is feasible for determining alpha-cellulose content of natural cellulose (plant fiber: cotton, wood) in clean pulping process.

[Sugar characterization of mini-watermelon and rapid sugar determination by near infrared diffuse reflectance spectroscopy].

In the present paper, the distribution of sugar level within the mini-watermelon was studied, a new sugar characterization method of mini-watermelon using average sugar level, the highest sugar level and the lowest sugar level index is proposed. Feasibility of nondestructive determination of mini-watermenlon sugar level using diffuse reflectance spectroscopy information was investigated by an experiment. PLS models for measuring the 3 sugar levels were established. The results obtained by near infrared spectroscopy agreed with that of the new method established above.

[Study on rapid quantitative analysis of the active ingredient in ABC extinguishing agent and type identification of extinguishing agent powders using near infrared spectroscopy].

A new quantitative method to determine the NH4H2PO4 in ABC powder extinguishing agent and to distinguish between ABC and BC powder extinguishing agents using near infrared diffuse reflectance spectroscopy is proposed. A PLS calibration model for the NH4H2PO4 content in extinguishing agent powder was established, with RMSECV = 2.1, RMSEP = 2.4. An identification model for ABC and BC powder extinguishing agents was built by SIMCA and the identification accuracy rate is 100%. This method, compared to the present standard method, has the characteristics of rapidness and easy operation, whichis fit for the quantitative analysis and type distinguishing of the fire products on site.

Chitosan/sodium polyborate based micro-nano coating with high flame retardancy and superhydrophobicity for cotton fabric.

In this work, a sustainable flame retardant and superhydrophobic cotton fabric was prepared by a two-step process: the cotton fabric was firstly treated with a chitosan/sodium polyborate polyelectrolyte complex water solution to obtain a flame retardant layer, and then treated with a polydimethylsiloxane (PDMS) tetrahydrofuran solution to construct a superhydrophobic layer. The phase-separated chitosan with a micro-nano roughness structure was covered by PDMS, which synergistically improved the hydrophobicity of the cotton fabric. The flammability evaluation indicated that the limiting oxygen index value of the treated fabric was increased to 40.0% from 18.2%, the peak of heat release rate was reduced by 63.8%, and the total heat release was reduced by 57.6% compared with that of the control sample. The enhanced flame retardancy was attributed to the excellent charring ability in the condensed phase. The treated fabric also showed anti-sticking, self-cleaning, and oil/water-separating properties. This coating treatment without any F, Cl, Br, P elements involved is regarded as a clean methodology for producing flame retardant and superhydrophobic cotton fabrics.

[Fast determination of contents of nutrients and stone powder in compound fertilizer using near infrared diffuse reflectance spectroscopy].

In the present paper, a new approach to fast determination of contents of nutrients, including total nitrogen content (N), P2O5 content (P) and K2O content (K), and of stone powder content in compound fertilizer composed of urea, ammonium dihydrogen phosphate, potassium chloride and stone powder was proposed using near infrared diffuse reflectance spectroscopy. PLS models of N, P and stone powder content were built with the SEP values of 0.8, 0.8 and 1.4 respectively. The information on which stone powder content model was built is the spectrum of crystal water existing in stone powder. K content was calculated using other ingredientcontents by normalization principle with a SEP value of 1.5. Although the SEP values are a little larger than the reproducibility errors of the GB/T methods which are conventional methods, the new method can be accepted by situ quality control in the production process of compound fertilizer.

[Study of the over-fitting in building PLS model using orthogonal signal correction].

In the present paper, the over-fitting phenomenon in building PLS model using orthogonal signal correction (OSC) was studied through establishment of quantitative calibration models for the peanut oil content in blending edible oils, and for the dimethylsulfoxide concentration in water solution. The cross validation results and the predication results of PLS models using OSC and without using OSC were compared to evaluate the effectiveness of OSC for improving the performance of PLS1 model. The results show that the application of OSC to PLS modeling will lead to an over-fitting phenomenon. According to the principles of their algorithms, when OSC and PLS are used together, the signals which are not correlated to the interested property are removed twice from the raw spectra. This leads to deleting the parts of useful information in spectra, and to spoiling the predictive ability of PLS models to some extent.