CN108982468B - Raman analysis method for trace impurities in p-chlorotoluene - Google Patents
Raman analysis method for trace impurities in p-chlorotoluene Download PDFInfo
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Abstract
The invention discloses a Raman analysis method of trace impurities in p-chlorotoluene, which comprises the following steps: firstly, measuring the pure substance spectrum, and selecting the impurity characteristic spectrum section through a simulation structure. Secondly, collecting a modeling sample and measuring the Raman spectrum of the modeling sample, and preprocessing the measured Raman spectrum. Because the impurities are trace, when the peaks of the impurities do not overlap, in order to improve the prediction accuracy, the spectral decomposition is carried out in a segmented mode to obtain a weighting coefficient, and a regression model of the weighting coefficient and the molar concentration is established. And finally, decomposing the Raman spectrum of the application sample, and substituting the weighting coefficient into the regression model to obtain the concentration of the impurities in the sample. The method of the invention utilizes the segmented spectrum decomposition technology, can obviously improve the analysis precision of trace impurities, has the characteristics of less modeling samples, high detection speed and good repeatability, and has important significance for real-time detection of trace impurities in industrial production.
Description
Technical Field
The invention belongs to the field of chemometrics, relates to a spectral detection method for impurity content in a chemical product, and particularly relates to a quantitative analysis method for trace isomer impurities in p-chlorotoluene.
Background
The p-chlorotoluene is an important raw material and an intermediate of pesticides, dyes, medicines and other fine organic chemical products. A series of important fine chemical intermediates can be derived through a series of reactions such as oxidation, chlorination, ammoxidation on a p-chlorotoluene side chain, chlorination on a ring, sulfonation, nitration, chloromethylation and the like, and more than 100 pesticide, medicine and dye products can be developed through the intermediates. Such as the pesticides butachlor, paclobutrazol, trifluralin, pyrethroid insecticides; the drug indomethacin; aqueous dyes, dyeing media, and the like. China is a large population country, and industries such as medicine, pesticide and the like have large scale, and the market demand of p-chlorotoluene is very vigorous as a basic synthetic intermediate.
Because the boiling point difference between the p-chlorotoluene and isomers thereof (o-chlorotoluene and m-chlorotoluene) is small and is only within 4 ℃, the p-chlorotoluene obtained by rectification still contains trace impurities, namely the o-chlorotoluene and the m-chlorotoluene. Although the domestic current production capacity of p-chlorotoluene is improved year by year, the market has higher requirements on the concentration of the p-chlorotoluene, and the concentration of superior products of the p-chlorotoluene needs to reach more than 99.5 percent, so that the process for preparing the p-chlorotoluene has high requirements, and the quality detection in the preparation process also has higher requirements.
The p-chlorotoluene has the highest application value in three isomers of the monochlorotoluene, the application range is the widest, and the price is the most expensive. If the online real-time detection of the system can be realized, not only can the energy consumption be reduced, but also the factory profit can be improved.
The existing analysis method for the content of p-chlorotoluene mainly comprises gas chromatography. The gas chromatography is a chromatographic analysis method using gas as a mobile phase, which utilizes a chromatographic column to carry out component separation on mixed gas (firstly carrying out vaporization on mixed liquid), and then utilizes a special detector to obtain chromatographic signals of components, thereby finally realizing qualitative and quantitative analysis on the composition of the mixed gas. However, the online gas chromatography has a long sampling period, harsh working conditions, a complex measurement process and large maintenance workload, and is difficult to apply to online rapid analysis.
Raman spectroscopy is a new detection technology and has wide application in the fields of chemical processes, pharmaceutical industry, biochemical reactions and the like. The Raman spectroscopy analyzes the composition of a sample based on the difference of Raman scattering spectra of the sample, has higher instrument resolution, small required sample number, is maintenance-free on site, and is more suitable for online detection in an industrial process. Therefore, the Raman analysis method for trace impurities in p-chlorotoluene is of great significance to the quality control of p-chlorotoluene in the production process.
Disclosure of Invention
The invention aims to provide a method for analyzing trace impurities in p-chlorotoluene based on Raman spectrum, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a Raman analysis method for trace impurities in p-chlorotoluene comprises the following steps:
(1) according to the Raman spectra of three substances of p-chlorotoluene, m-chlorotoluene and o-chlorotoluene, a mixed spectrum of the p-chlorotoluene containing two impurities of the m-chlorotoluene and the o-chlorotoluene with known trace molar concentration is constructed in a simulation way; determining a characteristic peak corresponding to each impurity according to correlation analysis;
(2) adding a small amount of m-chlorotoluene and o-chlorotoluene into p-chlorotoluene, configuring training samples with different concentrations, adjusting integration time, and measuring Raman spectra of the training samples;
(3) performing smooth filtering and baseline correction pretreatment on the Raman spectrum of the training sample, and then selecting a reference peak of the chlorotoluene for normalization;
(4) and (3) segmented modeling:
intercepting the spectrum of the wave number of the characteristic peak of the o-chlorotoluene, performing spectral decomposition by using a spectral decomposition technology and the spectra of pure substances of the o-chlorotoluene and the p-chlorotoluene, and establishing a regression model 1 between a weighting coefficient and molar concentration;
intercepting the spectrum of the wave number of the characteristic peak of the m-chlorotoluene, performing spectral decomposition by using a spectral decomposition technology and m-chlorotoluene and p-chlorotoluene pure substance spectra, and establishing a regression model 2 between a weighting coefficient and molar concentration;
(5) measuring the Raman spectrum of the applied sample, and then carrying out pretreatment and normalization as shown in the step (3);
(6) carrying out spectral decomposition on a spectral band where the characteristic peak of the o-chlorotoluene in the Raman spectrum of the application sample is located to obtain a weighting coefficient, substituting the weighting coefficient into the regression model 1 obtained in the step (4), and obtaining a predicted value of the concentration of the o-chlorotoluene in the application sample according to normalization conditions; and (4) performing spectral decomposition on the spectrum section where the characteristic peak of the chlorotoluene in the middle of the Raman spectrum of the application sample is located to obtain a weighting coefficient, substituting the weighting coefficient into the regression model 2 obtained in the step (4), and obtaining a predicted value of the concentration of the chlorotoluene in the middle of the application sample according to the normalization condition.
Further, in the step (1), the simulation structure specifically includes: benefit toSuperposing a main component p-chlorotoluene spectrum and two impurity (m-chlorotoluene and o-chlorotoluene) spectra according to different molar concentrations by using a spectrum superposition principle; after the simulation construction, the characteristic peak of the o-chlorotoluene is determined to be located in [1035, 1055 ]]cm-1The characteristic peak of m-chlorotoluene is positioned at [980, 1030 ]]cm-1To (3).
Further, in the step (2), the micro-molar concentration of the m-chlorotoluene and the o-chlorotoluene is less than 5000 ppm.
Further, in the step (2), the adjustment of the integration time needs to satisfy [1170, 1190 ]]cm-1The characteristic peak of p-chlorotoluene corresponding to the peak is amplified as much as possible and is not saturated.
Further, in the step (3), an iterative least square method is used to fit the baseline, and then the baseline is subtracted from the spectrum, so as to realize baseline correction.
Further, in the step (3), the position [1170, 1190 ] is selected]cm-1The characteristic peak of p-chlorotoluene is taken as a reference peak, and the Raman spectrum value corresponding to each wave number is divided by the maximum value of the reference peak to realize normalization.
Further, in the step (4), the spectral decomposition specifically includes:
Rd(v)＝KpRp(v)+KiRi(v) (1)
wherein R isd(v) Shows the Raman spectrum after pretreatment and normalization of the mixture, KpRepresents a weight coefficient of p-chlorotoluene, Rp(v) Denotes the pure substance normalized spectrum, K, of p-chlorotolueneiRepresents impurity Ii(i is 1, 2), Ri(v) Represents impurity IiPure substance normalized Spectrum of (1)1Represents o-chlorotoluene, I2Represents m-chlorotoluene. The mixture spectrum and the pure substance spectrum are substituted into formula (1) to obtain a weighting coefficient KpAnd Ki。
Further, in the step (4), the establishing of the model specifically includes:
wherein x isiRepresents impurity IiMolar concentration of (a), xpRepresents the molar concentration of p-chlorotoluene; due to Kp/KiTo concentration ratio xi/xpIs linear relation, the corresponding regression model can be established to obtain βiAnd bi。
The invention has the beneficial effects that:
(1) the simulation structure is carried out before sample preparation, so that the position of the characteristic peak of the impurity can be known in advance, whether a subsequent experiment based on the Raman spectrum can be carried out or not can be judged, and the integration time range during spectrum measurement can also be determined. If a new spectral modeling method is invented later, the feasibility of the method can be verified in advance through simulation construction, and sample preparation time is saved.
(2) The accuracy of analyzing the concentration of trace impurities can be improved by using segmented modeling. If the spectra of three substances, namely p-chlorotoluene, m-chlorotoluene and o-chlorotoluene, are used for modeling in a whole section, the standard error of prediction of the two impurities is about 100ppm, and because the characteristic peaks of the two impurities do not overlap in the example, the standard error of prediction can be less than 50ppm if the modeling is carried out in a sectional manner;
(3) compared with a gas chromatography, the Raman decomposition detection method for the impurities has the characteristics of high detection speed, good repeatability, cleanness and environmental protection, can detect the content of the impurities in the mixture with high precision, requires few modeling samples, and has important significance for monitoring the production process of the substances in industry.
Drawings
FIG. 1 is a Raman spectroscopy measurement platform for use with the present invention;
FIG. 2 pure substance spectra of the main component and two impurities;
FIG. 3 impurity I1-o-chlorotoluene correlation analysis;
FIG. 4 impurity I2-m-chlorotoluene correlation analysis;
FIG. 5 models sample spectrum local 1 and the decomposed spectrum;
FIG. 6 models sample spectral local 2 and decomposed spectra;
FIG. 7 applies a sample raw spectrum;
fig. 8 applies the sample normalized spectrum.
Detailed Description
The process of the present invention is further illustrated below with reference to examples and figures.
In the example, the raman spectroscopy platform is shown in fig. 1, and the optical components used are specifically: a laser 5, which is a laser having a center wavelength of 785nm used in this example; an excitation fiber 3 and a collection fiber 4, in this embodiment, a silica fiber having a core diameter of 105um is used; a Raman probe 2, a customized 785nm fiber optic probe is used in the embodiment; spectrometer 6, in this example a TEC refrigerated fiber spectrometer produced by Ocean Optics; a sample cell 1, which is a stainless steel cavity used in this embodiment, and in which a cuvette containing a sample is placed; and the embedded computer 7 processes the Raman spectrum and is responsible for establishing a regression model and predicting the concentration.
[ examples ] A method for producing a compound
The modeling part comprises the following steps:
(1) respectively measuring main components of p-chlorotoluene and 2 impurities I by using a Raman spectrometer1(o-chlorotoluene) and I2Pure substance spectrum of (m-chlorotoluene): respectively adding p-chlorotoluene as main component and 2 impurities (I)1O-chlorotoluene, I2M-chlorotoluene) is added into a cuvette and placed into a sampling pool for shading treatment. The probe was adjusted to focus the laser on the mixture in the cuvette, the integration time was set to 5s, and the spectrum was stored. The spectrum is shown in FIG. 2.
(2) According to the pure substance spectrum, a mixed spectrum of the main component and the trace impurities with different concentrations is constructed in a simulation mode. Mixing the mixed spectrum with impurity I1And impurity I2The concentration of (b) was subjected to correlation analysis, and the results are shown in FIGS. 3 and 4. As can be seen from FIG. 3, the mixed spectrum is [1035, 1055 ]]cm-1The prescription difference is large, the correlation coefficient reaches 0.999, and the area is suitable for analyzing the impurity I1Characteristic spectrum of concentration. As can be seen from FIG. 4, the mixed spectrum is [980, 1030 ]]cm-1The variance is large and the correlation coefficient is high, so that the subsequent impurity I is carried out by using the region2And (4) analyzing the concentration.
(3) Adding trace impurity I into p-chlorotoluene1And impurity I2And 3 mixed solutions are prepared to be used as modeling samples. Impurity I in sample1Has a concentration of 2000ppm, 1200ppm, 1333ppm, impurity I2The concentration of (B) is 4000ppm, 4800ppm and 2667 ppm. To amplify the impurity peak, the integration time was amplified by 4 times, i.e., 20 s. At this integration time, some of the large peaks of p-chlorotoluene are saturated, while the characteristic peaks of both impurities are amplified. The original spectrum is saved.
(4) The spectra of the modeled samples and the pure material spectra are subjected to basic pre-processing including smoothing filtering, baseline correction, and the like. The principal component reference peak then needs to be selected for normalization.
In order to filter out high-frequency noise in the spectrum signal, a moving window polynomial smoothing filter is required to avoid the influence of noise in subsequent processing, and the robustness of the system is improved.
In order to reduce the influence of fluorescence and the like on the raman spectrum, baseline correction is required. Based on the characteristic that the spectrum base line is smoother, polynomial curve fitting can be carried out by using an iterative least square method. The baseline was finally subtracted from the spectrum.
In order to avoid the influence of instruments such as a laser and the like and environmental factors, the raman spectrum after the wavelength adjustment is required to be normalized. A peak of the main component which is less disturbed by impurities is selected near the characteristic peak of the impurities, and is guaranteed not to be saturated. Based on the previous correlation analysis, the selection bit is [1170, 1190 ]]cm-1Characteristic peak of (2). And dividing the Raman spectrum value corresponding to each wave number by the maximum value of the reference peak to realize normalization.
(5) Assuming that the Raman spectrum of the mixture satisfies the linear additivity, i.e. the mixture spectrum is
Rd(v)＝KpRp(v)+K1R1(v)+K2R2(v) (3)
Wherein R isd(v) After pretreatment and normalization of the mixtureRaman spectrum of (D), KpRepresents a weight coefficient of p-chlorotoluene, Rp(v) Denotes the pure substance normalized spectrum, K, of p-chlorotoluene1Represents the weight coefficient of o-chlorotoluene, R1(v) Denotes the pure substance normalized spectrum of o-chlorotoluene, K2Represents the weight coefficient of m-chlorotoluene, R2(v) Pure substance normalized spectra of m-chlorotoluene are shown.
Since the impurities are trace and in this example the peaks of the impurities do not overlap, in order to improve the prediction accuracy, a piecewise modeling is chosen, i.e. at [1035, 1055 ]]cm-1Only choose I1Calculating with p-chlorotoluene; at [980, 1030]cm-1Only choose I2And p-chlorotoluene. And obtaining a weighting coefficient.
For the modeling samples, known components and spectra, the spectra were decomposed, two local spectra of the training sample 1 were shown in fig. 5 and 6, and a regression model between the ratio of the weighting coefficients and the concentration ratio was established.
The application part comprises the following steps:
(6) adding trace impurity I into p-chlorotoluene1And impurity I25 mixed solutions with different compositions are prepared to be used as application samples. Impurity I in sample1In concentrations of 333ppm, 800ppm, 400ppm, 200ppm and 0ppm, respectively, of impurity I2The concentrations of (A) were 667ppm, 3200ppm, 1600ppm, 800ppm and 0ppm, respectively. The spectrum of the applied sample was measured under the same conditions and the raw spectrum is shown in fig. 7.
(7) The same pre-treatment as shown in step (4) was performed on the raman spectrum of the applied sample, followed by normalization with the same reference peak, and the results are shown in fig. 8.
(8) And (3) performing spectral decomposition on the normalized spectrum, substituting the weighted coefficient ratio into a regression model, and then obtaining the molar concentration of the impurities in the application sample according to the normalization condition.
To verify the reliability of the method of the invention, a model accuracy test is performed below.
The evaluation indexes of model accuracy verification comprise a prediction standard error SEP and a corresponding complex correlation coefficient R2. The definition is as follows:
wherein y (k) is the actual impurity concentration of the kth sample, yp(k) For the predicted impurity concentration, n, of the kth samplepIn order to predict the number of samples,
is the average of the actual impurity mass fractions of the predicted samples.
The results of prediction of two impurities in this example are shown in tables 1 and 2. Impurity I1The prediction standard error SEP of (o-chlorotoluene) is 35ppm, and the complex correlation coefficient R2Is 0.982, impurity I2The prediction standard error SEP of (m-chlorotoluene) is 43ppm, and the complex correlation coefficient R2The value is 0.998, so that the method has small measurement error and high precision.
TABLE 1 prediction of applied samples
Table 2 impurities I in the application samples1、I2Accuracy of prediction of
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A Raman analysis method for trace impurities in p-chlorotoluene is characterized by comprising the following steps:
(1) according to the Raman spectra of three substances of p-chlorotoluene, m-chlorotoluene and o-chlorotoluene, a mixed spectrum of the p-chlorotoluene containing two impurities of the m-chlorotoluene and the o-chlorotoluene with known trace molar concentration is constructed in a simulation way; determining a characteristic peak corresponding to each impurity according to correlation analysis; the simulation structure specifically comprises: superposing a main component p-chlorotoluene spectrum and two impurities, namely m-chlorotoluene and o-chlorotoluene, spectra according to different molar concentrations by using a spectrum superposition principle; after the simulation construction, the characteristic peak of the o-chlorotoluene is determined to be located in [1035, 1055 ]]cm-1The characteristic peak of m-chlorotoluene is positioned at [980, 1030 ]]cm-1At least one of (1) and (b);
(2) adding a small amount of m-chlorotoluene and o-chlorotoluene into p-chlorotoluene, configuring training samples with different concentrations, adjusting integration time, and measuring Raman spectra of the training samples;
(3) performing smooth filtering and baseline correction pretreatment on the Raman spectrum of the training sample, and then selecting a reference peak of the chlorotoluene for normalization;
(4) and (3) segmented modeling:
intercepting the spectrum of the wave number of the characteristic peak of the o-chlorotoluene, performing spectral decomposition by using a spectral decomposition technology and the spectra of pure substances of the o-chlorotoluene and the p-chlorotoluene, and establishing a regression model 1 between a weighting coefficient and molar concentration;
intercepting the spectrum of the wave number of the characteristic peak of the m-chlorotoluene, performing spectral decomposition by using a spectral decomposition technology and m-chlorotoluene and p-chlorotoluene pure substance spectra, and establishing a regression model 2 between a weighting coefficient and molar concentration;
(5) measuring the Raman spectrum of the applied sample, and then carrying out pretreatment and normalization as shown in the step (3);
(6) performing spectral decomposition on a spectral band where the characteristic peak of the o-chlorotoluene in the Raman spectrum of the application sample is located to obtain a weighting coefficient, and substituting the weighting coefficient into the regression model 1 obtained in the step (4) to obtain a predicted value of the concentration of the o-chlorotoluene in the application sample; and (4) performing spectral decomposition on the spectrum section where the characteristic peak of the chlorotoluene in the middle of the Raman spectrum of the application sample is located to obtain a weighting coefficient, and substituting the weighting coefficient into the regression model 2 obtained in the step (4) to obtain a predicted value of the concentration of the chlorotoluene in the middle of the application sample.
2. The method for Raman analysis of trace impurities in p-chlorotoluene according to claim 1, wherein in the step (2), the trace molar concentrations of the m-chlorotoluene and the o-chlorotoluene are both less than 5000 ppm.
3. The method of claim 1, wherein in step (2), the integration time is adjusted to satisfy the requirement [1170, 1190 ]]cm-1The characteristic peak of p-chlorotoluene corresponding to the peak is amplified as much as possible and is not saturated.
4. The method of claim 1, wherein in step (3), the Raman analysis of trace impurities in p-chlorotoluene is performed by selecting the impurity group as [1170, 1190 ]]cm-1The characteristic peak of p-chlorotoluene is taken as a reference peak, and the Raman spectrum value corresponding to each wave number is divided by the maximum value of the reference peak to realize normalization.
5. The Raman analysis method for trace impurities in p-chlorotoluene according to claim 1, wherein in the step (4), the spectral decomposition specifically comprises:
Rd(v)＝KpRp(v)+KiRi(v)，(i＝1，2) (1)
wherein R isd(v) Shows the Raman spectrum after pretreatment and normalization of the mixture, KpRepresents a weight coefficient of p-chlorotoluene, Rp(v) Denotes the pure substance normalized spectrum, K, of p-chlorotolueneiRepresents impurity IiWeighting coefficient of Ri(v) Represents impurity IiPure substance normalized Spectrum of (1)1Represents o-chlorotoluene, I2Represents m-chlorotoluene; the mixture spectrum and the pure substance spectrum are substituted into formula (1) to obtain a weighting coefficient KpAnd Ki。
6. The Raman analysis method for trace impurities in p-chlorotoluene according to claim 1, wherein in the step (4), the establishment of the model specifically comprises the following steps:
wherein x isiRepresents impurity IiMolar concentration of (a), xpRepresents the molar concentration of p-chlorotoluene; due to Ki/KpTo concentration ratio xi/xpIs linear relation, the corresponding regression model can be established to obtain βiAnd bi(ii) a And substituting the decomposition weighting coefficient of the application sample into the regression model, and obtaining the molar concentration of the impurities in the sample according to the normalization condition.
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