CN111948191B - Multi-light-source Raman spectrum analysis method and application thereof - Google Patents
Multi-light-source Raman spectrum analysis method and application thereof Download PDFInfo
- Publication number
- CN111948191B CN111948191B CN202010753412.8A CN202010753412A CN111948191B CN 111948191 B CN111948191 B CN 111948191B CN 202010753412 A CN202010753412 A CN 202010753412A CN 111948191 B CN111948191 B CN 111948191B
- Authority
- CN
- China
- Prior art keywords
- raman
- component
- sample
- laser
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
Landscapes
- Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a multi-light source Raman spectrum analysis method and application thereof, wherein the multi-light source Raman spectrum analysis method comprises the following steps: collecting a standard sample by Raman spectrum; establishing a database; and (4) analyzing components. The invention combines visible light, near infrared light and far infrared laser to obtain a multi-laser Raman spectrum analysis method, and combines data analysis processing modes such as a principal component analysis method, so as to obtain the Raman analysis method which is not influenced by the concentration, the multiple components, the detection condition and the environment of an object to be detected and has high sensitivity, resolution and timeliness.
Description
Technical Field
The invention relates to the technical field of spectrum detection, and mainly relates to a multi-light-source Raman spectrum analysis method and application thereof.
Background
The spirit has the saying that the spirit is fermented by producing aroma, the aroma is extracted by distillation, and the liquor is extracted by liquor extractors, so the importance of quality-measuring liquor-extracting work can be seen. The quality measurement of liquor is to take out the head of the liquor, then take the head of the liquor while tasting the head of the liquor, and accurately grade the head of the liquor. The rescue is classified into four grades of super-excellent, first-grade excellent and second-grade excellent through first-smelling, second-watching and third-tasting. The alcohol content of the liquor is observed by experience. Therefore, the existing manual liquor picking technology is low in efficiency, not beneficial to industrial production, greatly influenced by the subjective of liquor picking people, and needs to be developed to adapt to the intelligent automatic development trend of liquor production.
The Raman spectrum can directly and rapidly provide a large amount of detailed chemical group information of target molecules, and has more advantages compared with other optical analysis methods such as absorption, fluorescence, elastic scattering analysis and other methods, and the excellent detection characteristics are as follows: (1) The Raman spectrum has relatively sharp vibration peak, narrow half-peak width and high spectral resolution. And the Raman spectrum has wide range, can realize one-time analysis and detection even in a high-flux and multi-component complex system, and is very suitable for molecular qualitative analysis. (2) The Raman analysis can be carried out in real time, in situ, nondestructive and separation-free, and is rapid, simple and convenient. And the laser facula is adjustable in micron level, and the online micro-area detection can be realized, so that the method is applied to the fields of high-precision analysis such as most industrial production catalysis, archaeology, geological exploration, biology, medicine, pharmacy and the like. (3) In raman analysis, the signal of the aqueous solvent and the glass substrate itself is extremely weak. In addition, background fluorescence of protein substances in a biological system cannot be ignored, but Raman analysis can realize effective acquisition of molecular information of a target object by replacing excitation lasers with other wavelengths and signal processing modes (Fourier transform Raman spectroscopy). That is, raman detection has more advantages in analytical applications of biological systems and other aqueous reaction systems than infrared spectroscopy and other spectroscopic analyses.
The Raman spectrum technology takes photons as a probe to carry out nondestructive detection, obtains the information of the molecular structure in a substance, and can be a powerful detection means in the trace multicomponent detection process.
The raman spectrum is generated by the inelastic scattering phenomenon generated when light irradiates on a target substance, and the inelastic scattering caused by light with a fixed wavelength at the moment is related to the chemical groups and the concentration of the material, so that qualitative and quantitative analysis can be carried out on a target sample by the raman spectrum. But besides the disadvantages of raman spectroscopy analysis itself, raman spectroscopy also has inevitable interference in the multi-component raman spectra of a single excitation light source. These interferences arise from the phenomenon of overlapping vibrational peaks that are highly likely to occur in a single-light-source spectrum, because the raman shifts of the same type of chemical groups are close, the signals of high-concentration components will inevitably cover the signals of low-concentration components, and further, the qualitative analysis of the components will be interfered.
Besides extracting and counting the slight changes of the spectrum by using a mathematical statistical method, a digital-analog analysis method and the like, the single-light-source Raman spectrum analysis cannot simply and directly analyze a multi-component target sample. The resonance Raman principle is effectively utilized, target components of different types are excited by using multiple light sources in a staggered mode, sample signals are collected, multispectral signals are compared and analyzed, and the multispectral signals are equivalent to signals for selectively strengthening or weakening components of a special type under different conditions, so that component property and concentration information are extracted from different angles, and the accuracy and the practicability of Raman spectrum analysis can be effectively improved.
Then, the analysis data is collated by means of a mathematical statistical method, and the trace multicomponent components in the target sample are further analyzed by taking a principal component analysis method as reference, so that the quality, the grade and the like of the target sample are judged. Firstly, a standard Raman spectrum database of sample components is constructed, then the signal intensity and the ratio of different samples under the same test parameters are compared and calculated, a judgment basis is made according to the spectral fluctuation caused by the component change in the samples, and the contents of main components and trace components in a target sample are analyzed, so that the method can be used for identifying the quality information of the samples.
Application No.: CN 201810586689.9-a Raman spectrum-method for rapidly identifying the authenticity of white spirit by principal component analysis. The method for rapidly identifying the authenticity of the white spirit by Raman spectrum-principal component analysis comprises a training set and a testing set. Collecting Raman spectrum data of true white spirit and false white spirit and dividing the Raman spectrum data into a training set and a testing set; expressing the original Raman spectrum data of the collected samples by using a matrix, wherein each column of the matrix represents the Raman scattering intensity of each white spirit sample in a training set at a certain wave number; extracting a plurality of principal component data on the training set data by adopting a principal component analysis method; carrying out normalization treatment, then carrying out linear regression, and importing the obtained data to obtain a true and false wine predicted value Z of the white wine sample in the test set; the invention calculates the difference of the true and false white spirit by using a principal component analysis method, thereby distinguishing the quality and the true and false of the white spirit through the spectrum difference.
Application No.: CN 201910446804.7-a Raman spectrum quantitative analysis technique based on half-peak height distance method. The invention is based on the Raman spectrum quantitative analysis technology of the half-peak distance method, uses the strongest ratio of two characteristic peaks in the Raman spectrogram of a sample to be detected as the basis for judging the quantitative analysis of substances, and maximally reduces the interference of other substances; the peak type and the peak intensity are fully considered, and errors caused by the change of a spectrum peak along with the change of the concentration of a substance are eliminated to the maximum extent. However, the invention is only suitable for the detection of a sample with a single component or a target substance with a concentration much higher than other components, and is not very suitable for the Raman spectrum analysis detection of a multi-component sample.
Application No.: CN 201811572981.1-Raman spectroscopy based multicomponent gas hydrate quantitative analysis method. The method comprises the steps of testing a multi-component gas phase in a reaction system at an initial moment by using a Raman spectrum, and calculating the Raman peak area of each component gas in the gas; testing multi-component gas in a reaction system at the initial moment by using a gas chromatography sampler to perform gas chromatography concentration analysis, and obtaining concentration values of each component gas in the gas at the initial moment; selecting any gas in the gas as a reference gas, and calculating a relative Raman quantitative factor of the gas relative to the reference gas based on the Raman peak areas and concentration values of all component gases in the gas at the initial time; and measuring a Raman spectrogram at the moment to be measured of the reaction system, calculating the Raman spectrum peak area of each component gas at the moment to be measured, and calculating the concentration of each component gas according to the relative Raman quantitative factor and the Raman spectrum peak area of each component gas at the moment to be measured.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
In view of the above-mentioned prior art, the present invention aims to provide a multi-light source raman spectroscopy analysis method and an application thereof, the present invention combines visible light, near infrared light and far infrared laser to obtain a multi-laser raman spectroscopy analysis method, and combines with data analysis processing methods such as principal component analysis method, etc., thereby obtaining a raman analysis method which is not affected by the concentration, multi-components, detection conditions and environment of an object to be detected and has high sensitivity, resolution and timeliness, and aims to solve the problem that the detection result is inaccurate due to the influence of the concentration, multi-components, etc. of the object to be detected in the existing detection technology.
The technical scheme of the invention is as follows:
a multi-light source raman spectroscopy method comprising the steps of:
and (3) standard sample Raman spectrum collection: collecting standard Raman spectra by taking known or possible components in a target sample as component standard samples, and determining the integral time of Raman signal collection of different component standard samples; the collected standard Raman spectrums comprise Raman spectrums excited by the same laser with different concentrations of the standard sample and Raman spectrums excited by the same laser with different concentrations of the standard sample;
establishing a database: summarizing Raman spectra and vibration peak data of all component standard samples, selecting a target characteristic peak according to the Raman spectra excited by different lasers of the component standard samples, and using the target characteristic peak as a component confirmation basis in component analysis; collecting the intensity of a characteristic peak according to the Raman spectrum excited by the same laser with different concentrations of the component standard sample and according to the concentration gradient from high to low and the signal difference, and performing linear fitting on the signal intensity by the component concentration to obtain the semi-quantitative linearity of the standard sample to form a quantitative fitting curve of the component standard sample, wherein the quantitative fitting curve can be used as a quantitative basis in component analysis;
component analysis: acquiring a Raman spectrum of a target test sample according to parameter conditions of a component standard sample, and summarizing obtained spectrum information, wherein the spectrum information comprises a Raman spectrogram and characteristic peak signal intensities of different components; and comparing the Raman spectrogram and the quantitative fitting curve of each component standard sample in the established database by taking Raman digital-analog analysis and principal component analysis as references, analyzing components corresponding to the characteristic peak in the Raman signal of the target test sample and the vibration peak intensity difference of the characteristic peak, calculating the vibration peak intensity and content change of each component in the target sample to be tested by taking semi-quantitative linearity as a reference, and identifying and distinguishing the target test sample according to the difference.
The multi-light source Raman spectrum analysis method comprises the step of selecting two or more than two of 514nm green laser, 532nm green laser, 632.8nm near infrared laser and 785nm near infrared laser as excitation light sources for detection.
The multi-light source Raman spectrum analysis method comprises the following steps of:
when 514nm laser is used as an excitation light source, the Raman signal integration time is 1s, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged is the signal of the corresponding component standard sample;
when 532nm and 632.8nm lasers are used as excitation light sources, the Raman signal integration time is 2s, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged are the signals of the standard samples with corresponding components;
when 785nm laser is used as an excitation light source, the Raman signal integration time is 5s respectively, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged are signals of corresponding component standard samples.
The multi-light source Raman spectrum analysis method is characterized in that the collected standard Raman spectrum further comprises standard Raman spectra of target samples of different grades, and the standard Raman spectra are used as one of quantitative bases in component analysis.
The multi-light source Raman spectrum analysis method comprises the following steps of:
in the same dark detection environment, 1-2mL of liquid sample to be detected is taken as an object, a quartz or glass sample cell is taken as a substrate, and the Raman full-wave band spectrum of the liquid sample is collected.
The multi-light source Raman spectrum analysis method comprises the steps of collecting Raman spectrum signals for at least 3 times for each component standard sample, and then summarizing to establish a database.
The application of the multi-light source Raman spectrum analysis method is characterized in that the multi-light source Raman spectrum analysis method is used for judging the liquor picking process of white spirit.
The application of the multi-light source Raman spectrum analysis method comprises the following steps:
and (3) standard sample Raman spectrum acquisition: taking ethanol, acetic acid and ethyl acetate as standard samples, and collecting standard Raman spectra of the ethanol, the acetic acid and the ethyl acetate, wherein the collected standard Raman spectra comprise Raman spectra excited by the same laser with different concentrations of the standard samples and Raman spectra excited by the same laser with different concentrations of the standard samples;
establishing a database: summarizing Raman spectra and vibration peak data of all component standard samples, selecting a target characteristic peak according to the Raman spectra excited by different lasers of the component standard samples, and using the target characteristic peak as a component confirmation basis in component analysis; collecting the intensity of a characteristic peak according to the Raman spectrum excited by the same laser with different concentrations of the component standard sample and according to the concentration gradient from high to low and the signal difference, and performing linear fitting on the signal intensity by the component concentration to obtain the semi-quantitative linearity of the standard sample to form a quantitative fitting curve of the component standard sample, wherein the quantitative fitting curve can be used as a quantitative basis in component analysis;
sampling: taking a single wine outlet process as an object on a wine making production line, carrying out in-situ collection every 2 min, and collecting more than 5mL of base wine samples to be detected each time;
and (3) component analysis: collecting the Raman spectrum of the base wine sample to be detected according to the parameter conditions of the component standard sample, and summarizing the obtained spectrum information, including Raman spectrogram and characteristic peak signal intensity of different components; and comparing the Raman spectrogram and the quantitative fitting curve of each component standard sample in the established database by taking Raman digital-analog analysis and principal component analysis as references, analyzing components corresponding to characteristic peaks in Raman signals of the target test sample and the vibration peak intensity difference of the characteristic peaks, calculating the vibration peak intensity and the relative content of ethanol, acetic acid and ethyl ester in the target sample to be tested by taking semi-quantitative linearity as a reference, and identifying and distinguishing the target test sample according to the difference.
Has the advantages that: the original Raman spectrum analysis method only uses unique monochromatic laser as an excitation light source, the obtained Raman spectrum is relatively unique, and Raman information of constant and trace multi-component components is limited by a single spectrum and is overlapped, so that the constant and trace multi-component components are not easy to distinguish. On the basis of the original single spectrum analysis, the Raman spectrum signals of the visible, near-infrared, far-infrared and other multi-excitation lasers are combined, the same sample is subjected to comprehensive statistical analysis, the components of the sample can be accurately distinguished and analyzed, more components and concentration information of the sample to be detected can be obtained, and the method is more suitable for on-site in-situ rapid analysis.
Drawings
FIG. 1 shows Raman spectra of ethanol excited by different lasers.
FIG. 2 is a Raman spectrum of ethanol standard samples with different concentrations under 532nm laser.
FIG. 3 is a curve of a quantitative fit of an ethanol solution (532 nm laser, 2899 cm) -1 At the vibration peak).
FIG. 4 shows the Raman spectrum of ethanol (785 nm laser).
FIG. 5 is a graph showing the variation trend of the intensity of the corresponding vibration peak (785 nm laser).
FIG. 6 shows Raman spectra of three pure samples of ethanol, acetic acid and ethyl acetate under the same excitation light (785 nm laser).
FIG. 7 is a Raman spectrum of a mixed sample at different mixing ratios under a 785nm laser.
FIG. 8 is a graph showing the trend of Raman spectrum change (532 nm laser) of a base wine sample.
FIG. 9 is a graph showing the variation trend of the spectral vibration peak intensity of the finished base wine sample (532 nm laser).
FIG. 10 is a graph showing the trend of Raman spectrum change (785 nm laser) of the base wine sample.
FIG. 11 is a graph showing the variation trend of the spectral vibration peak intensity of the finished base wine sample (785 nm laser).
Detailed Description
The present invention provides a multi-light source raman spectroscopy method and applications thereof, and the present invention will be described in further detail below in order to make the objects, technical solutions, and effects of the present invention clearer and clearer. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The raman spectrum is generated by the inelastic scattering phenomenon generated when light irradiates on a target substance, and the inelastic scattering caused by light with a fixed wavelength at the moment is related to the chemical groups and the concentration of the material, so that qualitative and quantitative analysis can be carried out on a target sample by the raman spectrum. However, besides the disadvantages of raman spectroscopy itself, raman spectroscopy also has inevitable interference in multicomponent raman spectroscopy with a single excitation light source. These interferences arise from the phenomenon of overlapping vibrational peaks that are highly likely to occur in a single-light-source spectrum, because the raman shifts of the same type of chemical groups are close, the signals of high-concentration components will inevitably cover the signals of low-concentration components, and further, the qualitative analysis of the components will be interfered.
It is known that most biomolecules have small effective Raman scattering cross-sections, i.e. conventional Raman signals are weak, and the appearance of laser Raman makes the biomolecules have great advantages in the biological detection direction. For example, when the wavelength of the excitation laser is matched with the electron transition energy level of the target molecule, the charge distribution of the excited molecule is excited, that is, the polarizability is increased, and resonance raman scattering with stronger signals is obtained. Therefore, the raman signals collected under different laser excitation conditions will also differ. Meanwhile, spectral signals of different components can be obtained by analyzing signal changes in Raman spectra excited by different lasers. Even when multi-component components with larger concentration difference are analyzed, the multispectral Raman signals are combined with contrast analysis, and more sample information can be obtained.
Besides extracting and counting the slight changes of the spectrum by using a mathematical statistical method, a digital-analog analysis method and the like, the single-light-source Raman spectrum analysis cannot simply and directly analyze a multi-component target sample. The resonance Raman principle is effectively utilized, target components of different types are excited by using multiple light sources in a staggered mode, sample signals are collected, multispectral signals are compared and analyzed, and the multispectral signals are equivalent to signals for selectively strengthening or weakening components of a special type under different conditions, so that component property and concentration information are extracted from different angles, and the accuracy and the practicability of Raman spectrum analysis can be effectively improved.
Then, the analysis data is collated by means of a mathematical statistical method, and the trace multicomponent components in the target sample are further analyzed by taking a principal component analysis method as reference, so that the quality, the grade and the like of the target sample are judged. Firstly, a standard Raman spectrum database of sample components is constructed, then the signal intensity and the ratio of different samples under the same test parameters are compared and calculated, a judgment basis is made according to the spectral fluctuation caused by the component change in the samples, and the contents of main components and trace components in a target sample are analyzed, so that the method can be used for identifying the quality information of the samples.
Specifically, the multi-light source Raman spectrum analysis method provided by the invention comprises the following steps:
1. raman spectrum collection of standard sample
Taking known or possible components in the target sample as component standard samples, collecting standard Raman spectra, and determining the integral time of Raman signal collection of different component standard samples. Furthermore, the standard Raman spectrums of target samples with different grades can be collected and can be used as one of quantitative bases in subsequent component analysis, the Raman spectrums of the component standard samples are referred to first, and then the standard Raman spectrums of the target samples are referred to, so that the analysis result is ensured to be more accurate.
The collected standard Raman spectrums comprise Raman spectrums excited by the same laser with different concentrations of the standard sample and Raman spectrums excited by the same laser with different concentrations of the standard sample.
The detection process can be as follows: in the same dark detection environment, approximately 1-2mL of approximately static liquid sample to be detected is taken as an object, a quartz (glass) sample cell is taken as a substrate, and the Raman full-waveband spectrum (the wave number range is 0-4000 cm) of the liquid sample is collected -1 ）。
The default of the Raman spectrometer used in the invention is Thermo Scientific Silmer flying table type Raman spectrum, and the Raman spectrometer is provided with a plurality of matched laser excitations as light sources, namely 514nm green laser, 532nm green laser, 632.8nm near infrared laser and 785nm near infrared laser, and the same optical path can be used for detection during detection, but different optical filters in the instrument need to be switched. The four lasers provided by the invention are adopted to carry out combined light source, so that the detection result is more comprehensive and accurate.
The raman spectroscopy method referred to in the present invention will use different signal acquisition times depending on the different types of constituent standard samples. The signal acquisition time is mainly determined according to the optical signal data of the component standard sample, and if the main vibration peak intensity of the component standard sample is weaker, the signal acquisition time is properly prolonged.
In the invention, aiming at the judgment of the liquor-picking process of white liquor, the following signal acquisition time is provided as default test time, when 514nm laser is used as an excitation light source, the Raman signal integration time is 1s, the test is repeated twice, and the obtained spectrum data after the obtained signals are averaged is the signal of the corresponding component standard sample. Similarly, when 532nm and 632.8nm lasers are used as excitation light sources, the raman signal integration time is 2s, and the test is repeated twice, and the obtained signals are averaged to obtain spectral data which are the signals of the corresponding component standard samples. And finally, when 785nm laser is used as an excitation light source, the Raman signal integration time is respectively 5s, the test is repeated twice, and the obtained signals are averaged to obtain spectral data which are signals of the corresponding component standard sample.
2. Building a database
According to the sample detection method mentioned in the above point, the target sample standard sample and each known or possible component standard sample in the target sample are subjected to raman spectrum signal collection at least 3 times, the obtained standard raman spectra are summarized, and the difference of the vibration peak intensities at the same raman shift position in different spectra is counted, so as to establish an analysis database.
Firstly, summarizing Raman spectra and vibration peak data of all component standard samples, selecting a target characteristic peak according to the Raman spectra excited by different lasers of the component standard samples (pure samples), and using the target characteristic peak as a component confirmation basis in component analysis; and collecting the intensity of the characteristic peak according to the Raman spectrum excited by the same laser with different concentrations of the component standard sample and according to the concentration gradient from high to low and the signal difference, and performing linear fitting on the signal intensity by the component concentration to obtain the semi-quantitative linearity of the standard sample to form a quantitative fitting curve of the component standard sample, wherein the quantitative fitting curve can be used as a quantitative basis in component analysis.
Taking an alcohol standard sample as an example, 514nm green laser, 532nm green laser, 632.8nm near-infrared laser and 785nm near-infrared laser are respectively adopted as excitation light sources, and raman spectra excited by the same concentration of different lasers are collected according to the default test time provided by the invention, as shown in fig. 1.
Collecting Raman spectrum signals of an alcohol standard sample for at least 3 times, summarizing the obtained standard Raman spectrum, determining characteristic peaks, then collecting the signal intensity of each characteristic peak, and setting the average value of multiple data as the signal intensity of the vibration peak of each group in the standard sample:
(1) 514 nm/532 nm (spectra obtained by two lasers are similar) laser excited alcohol standard sample, and in Raman spectrum excited by laser, group vibration peak is 313 cm -1 ，1418 cm -1 ，2855 cm -1 ，2899 cm -1 ，2941 cm -1 ，3465 cm -1 The vibration peak represents the signal intensity of the collected characteristic peak;
(2) 632.8 nm/785 nm (the spectra obtained by two lasers are similar) laser excitation alcohol standard sample, and in the Raman spectrum of the laser excitation, the vibration peak of the group is 554 cm -1 ，884 cm -1 ，1054 cm -1 ，1093 cm -1 ，1458 cm -1 ，2878 cm -1 ，2927 cm -1 The vibration peak is the signal intensity representing the collected characteristic peak.
Alcohol Raman spectrum 2899 cm under excitation of 532nm laser -1 Taking the vibration peak as an example, the signal intensity of the characteristic peak of ethanol standard samples with different concentrations is collected, wherein the concentrations of the ethanol standard samples are respectively 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% (the solute is ethanol and the solvent is water); collecting Raman spectrum signals at least 3 times, summarizing the obtained standard Raman spectrum, setting the average value of multiple data as signal intensity, as shown in figure 2, collecting Raman spectrum signals 6 times for each concentration, wherein 6 Raman spectrum signals represent 6 times when the concentration is 5% between 5 and 1~5-6, 6 times when the concentration is 10% between 10-1 to 10-6, and the like. The semi-quantitative linearity of the standard sample is obtained by linear fitting of the component concentration to the signal intensity to form a quantitative fit curve of the component standard sample, as shown in FIG. 3, which can beThe method is used as a quantitative basis in component analysis.
3. Analysis of composition
Collecting the Raman spectrum of a target test sample according to the parameter conditions (a plurality of specific light sources and integration time) of the component standard sample, and summarizing the obtained spectrum information, including Raman spectrograms and characteristic peak signal intensities of different components; and comparing the Raman spectrogram and the quantitative fitting curve of each component standard sample in the established database by taking Raman digital-analog analysis and principal component analysis as references, analyzing components corresponding to the characteristic peak in the Raman signal of the target test sample and the vibration peak intensity difference of the characteristic peak, calculating the vibration peak intensity and content change of each component in the target sample to be tested by taking semi-quantitative linearity as a reference, and identifying and distinguishing the target test sample according to the difference.
Taking an alcohol sample as an example, doping a trace of same type of acid and ester, comparing various laser excitation spectrums, carrying out comparison analysis aiming at different group vibration peaks, and carrying out midpoint calculation analysis on the displacement and intensity change of a superposed peak, so as to analyze the concentration change and the difference among different components in the same sample, and thus, the property or grade of the sample is identified. For example, a raman spectrum of three pure samples of ethanol, acetic acid and ethyl acetate is acquired by using a 785nm laser, as shown in fig. 4-6 (fig. 4 is a spectrum of ethanol continuous test, fig. 5 is a trend graph of corresponding vibration peak intensity variation, and fig. 6 is a raman spectrum of three pure samples of ethanol, acetic acid and ethyl acetate under the same excitation light), the following raman characteristic peak positions can be obtained, and the characteristic peak intensity ratios are recorded:
ethanol: 889 cm -1 ，1059 cm -1 ，1420 cm -1 ，2857 cm -1 ，2883 cm -1 ，2899 cm -1 ，2928 cm -1 ；
Acetic acid: 629 cm -1 ，892 cm -1 ，1355 cm -1 ，1442 cm -1 ，2936 cm -1 ，3062 cm -1 ；
Ethyl acetate: 638 cm of -1 ，852 cm -1 ，1118 cm -1 ，1420 cm -1 ，1459 cm -1 ，1743 cm -1 ，2916 cm -1 ，2953 cm -1 。
Then, 785nm is used for laser testing of a mixed sample of the three components, for example, the volume ratio of a mixed sample of solvents of water, ethanol, acetic acid and ethyl ester is set as follows: mixed-sample 1 (mixed-1) 0.3. Recording the peak position of the vibration peak in the spectrum, calculating the proportion (intensity or peak area) of all characteristic peaks, comparing the proportion of the Raman spectrum vibration peak of the pure sample, and calculating according to the proportion to obtain the relation between the spectrum characteristic peak intensity and the solvent composition and concentration. Wherein, the concentration ratio ethanol in the mixed sample 1 (mixed-1) is roughly calculated: acetic acid: ethyl ester =0.7, 0.2, concentration in mixed-sample 2 (mixed-2) versus ethanol: acetic acid: ethyl ester =0.75: acetic acid: ethyl ester = 0.6.
4. Result verification
According to the Raman spectrum signal acquisition and data processing methods, detection and component analysis are carried out on other unknown target liquid samples so as to judge the accuracy of the analysis result. If the component analysis result is not sufficiently distinguished, more vibration peak information needs to be imported into the discussion. Finally, the parameter of the invention is modified according to the unknown component analysis results of the same type of liquid samples, and the invention can be used for component analysis of different samples.
The present invention is further illustrated by the following specific examples.
Examples
Sampling:
(1) In-situ collection is carried out every 2 min on a wine production line by taking a single wine outlet process as an object, more than 5mL of unknown base wine samples are collected each time, the base wine samples are marked in sequence and are respectively tested by using different lasers, and Raman spectrum data are collected. The unknown wine base samples are labeled 1,2,3,4,5,6,7,8,9, 10, 11 in that order.
(2) Taking 1-2mL of base wine sample into a quartz cuvette by using a liquid transfer gun, continuously testing the sample for at least 5 times by using a 532nm or 785nm light source, and collecting a test signal.
Data processing:
(1) According to the base wine raman spectrum library law, the obtained raman spectrum is mechanically subtracted from the background, the mean value of the background noise signal after processing is required to be close to zero, and all characteristic peaks and fluorescence background signals are reserved, as shown in fig. 8 and 10, fig. 8 is a raman spectrum change trend graph (532 nm) of a base wine sample, and fig. 10 is a raman spectrum change trend graph (785 nm) of the base wine sample. Wherein each base wine sample is tested at least 5 times continuously, 5 Raman spectrum signals of the base wine sample 1 are represented between 1-1~1-5, and the like.
(2) For example, the spectrum obtained by exciting the sample with 532nm laser, the sample component information is 313 cm -1 , 1418cm -1 ,2852 cm -1 , 2899 cm -1 ，2941 cm -1 ,3413 cm -1 Extracting spectral component concentration information by using the intensity of the vibration peak as a representative, averaging the results of multiple measurements of the same sample, and summarizing the intensity of the vibration peak to obtain a data group 1; 2899 cm -1 And normalizing the vibration peak as a main peak to process other vibration peaks to obtain a data group 2.
(3) For example, the spectrum obtained by laser-exciting a sample with 785nm, the sample has component information of 889 cm -1 , 1060 cm -1 , 1462 cm -1 ，1723 cm -1 , 2886 cm -1 , 2930cm -1 Extracting spectral component concentration information by using the intensity of the vibration peak as a representative, averaging the results of multiple measurements of the same sample, and summarizing the intensities of all the vibration peaks to obtain a data group 3; at 889 cm -1 And normalizing the vibration peak as a main peak to process other vibration peaks to obtain a data group 4.
(4) The above results were collated, and the data of the samples obtained by the same laser excitation were summarized, and a trend graph was drawn by the relative intensities in the main peak signal intensity combination data group 2 (data group 4) in the data group 1 (data group 3), as shown in fig. 9 and 11, for analyzing the content changes of the main component and the minor component in the base liquor. Wherein, fig. 9 is a graph of the variation trend of the spectral vibration peak intensity of the finished base wine samples 1-10 (532 nm laser), and fig. 11 is a graph of the variation trend of the spectral vibration peak intensity of the finished base wine samples 1-11 (785 nm laser).
(5) And (3) calculating the relative contents of different components such as alcohol, acid, ester, aliphatic hydrocarbon and the like in the components of the base wine by combining the Raman vibration peak ratio of the pure product of each component and the relative strength of the vibration peaks of different groups obtained by testing.
And (4) conclusion: experiments show that under the irradiation of 532nm or 785nm laser, components such as alcohols, esters, long-chain aliphatic hydrocarbons, macromolecules, micromolecular proteins and the like in a base wine sample can generate different Raman signals, and the intensity ratios of different vibration peaks are changed due to the excitation of different lasers. Therefore, the Raman spectrums of the samples are collected by different laser excitations, and the analysis and identification of the multi-component samples can be realized by combining the multispectral data.
In summary, the multi-light source raman spectroscopy method provided by the invention has the following advantages:
1. the multi-light source Raman spectrum analysis method mainly uses various lasers in visible and near infrared ranges as light sources for analysis, and a conventional detection light path can be basically universal. Ultraviolet laser and middle and far infrared laser are temporarily not suitable for the invention and on-site in-situ detection, relevant analysis software needs to be matched, and the light path needs to be readjusted.
2. The multi-light source Raman spectrum analysis method effectively utilizes the resonance Raman principle to carry out dislocation enhanced excitation on different types of components in a target sample to obtain different Raman spectrums excited by multiple light sources, and vibration peak intensity information of different components in multiple spectrums is extracted to be used for simultaneous analysis of multiple components.
3. The multi-light source Raman spectrum analysis method is combined with a data statistical analysis means, and the qualitative and quantitative analysis of the target sample containing trace multi-component components can be realized by calculating the vibration peak intensity, so that the analysis result is more accurate and targeted.
In the main components of the white spirit, except alcohol and water with the proportion of more than 99 percent, abundant organic matters contained in less than 1 percent are remained, and the style, taste and mouthfeel of the white spirit are directly influenced. For example, the alcohol (ethanol) content is high or low, which determines the intensity of the wine; esters are responsible for bouquet; acids affect flavor; aldehydes bring irritation and pungency; the polyalcohol contributes to mellow mouthfeel of the wine body; the ethyl caproate and the ethyl acetate more directly form the main fragrance bodies of different white spirit odor types. The distilled white spirit by the solid-state fermentation method of pure grains has different alcohol concentrations in different wine flowing stages and different trace components and fragrant substances in the white spirit. The multi-light source Raman spectrum analysis method is suitable for content analysis and judgment of various main components and micro components, so the invention also provides the application of the multi-light source Raman spectrum analysis method in liquor picking process judgment to realize intelligent judgment of the liquor picking process.
It will be understood that the invention is not limited to the examples described above, but that modifications and variations will occur to those skilled in the art in light of the above teachings, and that all such modifications and variations are considered to be within the scope of the invention as defined by the appended claims.
Claims (5)
1. A multi-light source Raman spectrum analysis method is characterized in that the multi-light source Raman spectrum analysis method is used for judging a liquor picking process and comprises the following steps:
and (3) standard sample Raman spectrum collection: taking ethanol, acetic acid and ethyl acetate as component standard samples, collecting the standard Raman spectra of the ethanol, the acetic acid and the ethyl acetate, and determining the integral time of the Raman signal collection of the different component standard samples; the collected standard Raman spectrums comprise Raman spectrums which are excited by the same laser with different concentrations and are used for the component standard sample, and Raman spectrums which are excited by the same laser with different concentrations and are used for the component standard sample;
establishing a database: summarizing Raman spectra and vibration peak data of all the obtained component standard samples, selecting a target characteristic peak according to the Raman spectra excited by different lasers of the component standard samples, and using the target characteristic peak as a component confirmation basis in component analysis; collecting the intensity of a characteristic peak according to the Raman spectrum excited by the same laser with different concentrations of the component standard sample and according to the concentration gradient from high to low and the signal difference, and performing linear fitting on the signal intensity by the component concentration to form a quantitative fitting curve of the component standard sample for quantitative basis in component analysis;
sampling: taking a single wine outlet process as an object on a wine brewing production line, carrying out in-situ collection every 2 min, and collecting more than 5mL of base wine samples to be tested as target test samples each time;
and (3) component analysis: acquiring a Raman spectrum of a target test sample according to parameter conditions of a component standard sample, and summarizing obtained spectrum information, wherein the spectrum information comprises a Raman spectrogram and characteristic peak signal intensities of different components; based on a Raman digital-analog analysis method, comparing a Raman spectrogram and a quantitative fitting curve of each component standard sample in an established database, analyzing components corresponding to characteristic peaks in Raman signals of a target test sample and the vibration peak intensity difference of the characteristic peaks, calculating the vibration peak intensity and relative content of ethanol, acetic acid and ethyl acetate in the target test sample, and identifying and distinguishing the target test sample according to the difference;
in the process of collecting Raman spectra excited by lasers with the same concentration and different concentrations of component standard samples, one of 514nm laser or 532nm laser and one of 632.8nm laser or 785nm laser are selected as excitation light sources for detection.
2. The multi-light-source raman spectroscopy method according to claim 1, wherein the raman signal acquisition times are as follows:
when 514nm laser is used as an excitation light source, the Raman signal integration time is 1s, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged is the signal of the corresponding component standard sample;
when 532nm and 632.8nm lasers are used as excitation light sources, the Raman signal integration time is 2s, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged are the signals of the standard samples with corresponding components;
when 785nm laser is used as an excitation light source, the Raman signal integration time is 5s respectively, the test is repeated twice, and the obtained spectral data after the obtained signals are averaged are signals of corresponding component standard samples.
3. The multi-light source raman spectroscopy method of claim 1, wherein acquiring the standard raman spectra further comprises acquiring standard raman spectra of target samples of different grades for one of the basis of quantitation in the composition analysis.
4. The multi-light-source raman spectroscopy analysis method according to claim 1, wherein the detection process of collecting the raman spectrum comprises the steps of:
in the same dark detection environment, a pipette is used to take 1-2mL of target test sample into a quartz or glass sample cell, and the Raman full-wave band spectrum of the target test sample is collected.
5. The multi-light-source raman spectroscopic analysis method of claim 1, wherein raman spectroscopic signals are collected at least 3 times per component standard sample, and are then aggregated to create a database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010753412.8A CN111948191B (en) | 2020-07-30 | 2020-07-30 | Multi-light-source Raman spectrum analysis method and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010753412.8A CN111948191B (en) | 2020-07-30 | 2020-07-30 | Multi-light-source Raman spectrum analysis method and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111948191A CN111948191A (en) | 2020-11-17 |
| CN111948191B true CN111948191B (en) | 2022-10-18 |
Family
ID=73338318
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010753412.8A Active CN111948191B (en) | 2020-07-30 | 2020-07-30 | Multi-light-source Raman spectrum analysis method and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111948191B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112394270B (en) * | 2020-11-27 | 2022-03-29 | 华南理工大学 | Online quality detection method and application of OLED device |
| CN114778519B (en) * | 2022-05-26 | 2023-07-11 | 深圳市诺安智能股份有限公司 | Material component detection device based on Raman wave |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998008066A1 (en) * | 1996-08-22 | 1998-02-26 | Eastman Chemical Company | On-line quantitative analysis of chemical compositions by raman spectrometry |
| US5741660A (en) * | 1995-02-01 | 1998-04-21 | Kyoto Dai-Ichi Kagaku Co., Ltd. | Method of measuring enzyme reaction by Raman scattering |
| US5815260A (en) * | 1995-10-18 | 1998-09-29 | Kyoto Dai-Ichi Kagaku Co., Ltd. | Urogenous component measuring apparatus for qualitatively/quantitatively measuring a plurality of urogenous components |
| CN102313730A (en) * | 2011-08-11 | 2012-01-11 | 江南大学 | Surface enhanced Raman scattering rapid screening method for methamidophos in vegetable |
| CN102495039A (en) * | 2011-10-27 | 2012-06-13 | 瓮福（集团）有限责任公司 | Raman spectrum qualitative detection method for compound fertilizer nitrogen forms |
| CN103884706A (en) * | 2014-04-08 | 2014-06-25 | 苏州优谱德光电科技有限公司 | Online original pulp liquor detection and classification system |
| CN109557071A (en) * | 2018-11-14 | 2019-04-02 | 公安部第研究所 | A kind of Raman spectra qualitative quantitative identification method of dangerous liquid mixture |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7427508B2 (en) * | 2003-10-09 | 2008-09-23 | Organotek Defense System Corporation | Method for assaying multi-component mixtures |
| US7521254B2 (en) * | 2004-04-12 | 2009-04-21 | Transform Pharmaceuticals, Inc. | Quantitative measurements of concentration and solubility using Raman spectroscopy |
| US9581493B2 (en) * | 2014-02-12 | 2017-02-28 | Bruker Optics, Inc. | Acquiring a Raman spectrum with multiple lasers |
-
2020
- 2020-07-30 CN CN202010753412.8A patent/CN111948191B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5741660A (en) * | 1995-02-01 | 1998-04-21 | Kyoto Dai-Ichi Kagaku Co., Ltd. | Method of measuring enzyme reaction by Raman scattering |
| US5815260A (en) * | 1995-10-18 | 1998-09-29 | Kyoto Dai-Ichi Kagaku Co., Ltd. | Urogenous component measuring apparatus for qualitatively/quantitatively measuring a plurality of urogenous components |
| WO1998008066A1 (en) * | 1996-08-22 | 1998-02-26 | Eastman Chemical Company | On-line quantitative analysis of chemical compositions by raman spectrometry |
| CN102313730A (en) * | 2011-08-11 | 2012-01-11 | 江南大学 | Surface enhanced Raman scattering rapid screening method for methamidophos in vegetable |
| CN102495039A (en) * | 2011-10-27 | 2012-06-13 | 瓮福（集团）有限责任公司 | Raman spectrum qualitative detection method for compound fertilizer nitrogen forms |
| CN103884706A (en) * | 2014-04-08 | 2014-06-25 | 苏州优谱德光电科技有限公司 | Online original pulp liquor detection and classification system |
| CN109557071A (en) * | 2018-11-14 | 2019-04-02 | 公安部第研究所 | A kind of Raman spectra qualitative quantitative identification method of dangerous liquid mixture |
Non-Patent Citations (2)
| Title |
|---|
| 不同激发波长拉曼光谱对古织物植物染料的分析探索;何秋菊;《首都博物馆文物科技研究》;20131231;第337-343页 * |
| 白酒质量检测的新方法——激光拉曼散射;蒋毅坚 等;《光散射学报》;19930331;第5卷(第1期);第12-18页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111948191A (en) | 2020-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wadood et al. | Recent development in the application of analytical techniques for the traceability and authenticity of food of plant origin | |
| Shaw et al. | Noninvasive, on-line monitoring of the biotransformation by yeast of glucose to ethanol using dispersive Raman spectroscopy and chemometrics | |
| Magdas et al. | Wine discrimination based on chemometric analysis of untargeted markers using FT-Raman spectroscopy | |
| Guyon et al. | Intrinsic ratios of glucose, fructose, glycerol and ethanol 13C/12C isotopic ratio determined by HPLC-co-IRMS: toward determining constants for wine authentication | |
| CN111948191B (en) | Multi-light-source Raman spectrum analysis method and application thereof | |
| CN102778442B (en) | Method for rapidly identifying type of balsam material liquid for cigarette | |
| WO1997028436A1 (en) | Mutivariate sample analysis | |
| CN101403696A (en) | Method for measuring gasoline olefin content based on Raman spectrum | |
| Cozzolino et al. | Two-dimensional correlation analysis of the effect of temperature on the fingerprint of wines analysed by mass spectrometry electronic nose | |
| Miller et al. | Review of analytical measurements facilitated by drop formation technology | |
| Yu et al. | Quality determination of Chinese rice wine based on Fourier transform near infrared spectroscopy | |
| Kou et al. | Efficient detection of edible oils adulterated with used frying oils through PE-film-based FTIR spectroscopy combined with DA and PLS | |
| Ceballos-Magana et al. | Quantitation of twelve metals in tequila and mezcal spirits as authenticity parameters | |
| CN101408501A (en) | Method for quantitatively detecting DNA base by using near-infrared spectrum-partial least squares method | |
| CN111521577B (en) | Infrared spectrum quantitative analysis method taking carbon dioxide peak area as reference | |
| CN101308120A (en) | Cigar flavour quality control method | |
| CN109030410B (en) | Construction method of royal jelly near-infrared quantitative correction model and royal jelly detection method | |
| Mehrotra | Infrared spectroscopy, gas chromatography/Infrared in food analysis | |
| Strojnik et al. | Compound-specific carbon and hydrogen isotope analysis of volatile organic compounds using headspace solid-phase microextraction | |
| Korshunova et al. | The application of spectroscopy to assess the quality of grape wines | |
| Wang et al. | Research Article Quantitative Analysis of Multiple Components in Wine Fermentation using Raman Spectroscopy | |
| Ershova et al. | Physicochemical analysis of the age of handwritten inscriptions on documents: Trends and prospects | |
| Huang et al. | In-situ analysis of trace components in proportioning distilled spirits using Raman integrating sphere spectroscopy | |
| CN101845951B (en) | Method for quickly analyzing components of drilling liquid by using ultrasonic molecular beams | |
| CN206095945U (en) | SERS detection device to aquatic contaminant detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |