CN113686811B - Spectral data processing method based on double sensors - Google Patents
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Abstract
The application discloses a spectrum data processing method based on dual sensors, which comprises the steps of firstly adopting dual-sensor near infrared spectrum equipment with different wave band ranges to collect spectrum data of a sample, then carrying out segmentation baseline on the spectrum data collected by the dual sensors, then carrying out segmentation multiple scattering correction processing on the spectrum data processed by the baseline, then carrying out sum value averaging processing on the spectrum data processed by the segmentation multiple scattering correction, finally carrying out data return value on the spectrum data processed by the sum value averaging processing, and carrying out spectrum modeling by the dual-sensor equipment by adopting return value data. The method can not only effectively process the spectrum data of the dual sensors in different wave band ranges and solve the problem of different magnitudes of the spectrum data acquired by different sensors, but also greatly simplify the spectrum data quantity under the condition of retaining the spectrum characteristics and improve the analysis efficiency of the portable dual-sensor near infrared spectrometer.
Description
Technical Field
The application relates to the technical field of spectrum data processing, in particular to a spectrum data processing method based on dual sensors.
Background
In recent years, near infrared spectrum analysis technology has been developed very rapidly, and has been applied in various fields such as chemical industry, pharmacy, military industry, food, etc. The near infrared spectrum technology belongs to the molecular spectrum technology, can show the substance composition and property information on the molecular level, has very high benefit for both economic and social influence, and has great development potential.
However, at present, most of material component and property information detection is mainly performed by using a large laboratory near infrared spectrum instrument, and although the quantitative accuracy and the sensitivity are high, the required equipment is huge, the equipment cost is expensive, the sample preparation time is long, the sample preparation method is strict, the detection equipment and the sample preparation require professional personnel to operate, the detection environment is fixed, the analysis time is long, and the method is not suitable for field detection and is inconvenient to popularize and use.
Along with the development of portable near infrared spectroscopy technology, the main stream of large near infrared spectrometer equipment in the market is developed towards the portable direction with small size and low price. However, the portable near infrared spectrometer is limited by the sensor technology, the band range covered by the single-sensor portable near infrared equipment is very limited, so that the single-sensor equipment cannot cover the primary frequency multiplication and the secondary frequency multiplication characteristic peaks of various samples containing hydrogen groups, and the prediction accuracy of the portable near infrared spectrum equipment is seriously affected. In order to increase the range of sensor wave bands and promote the application scene of the portable near infrared spectrometer, the portable near infrared spectrometer of the dual sensor is generated, the spectrum data processing mode that gathers dual sensors on the market at present only stays at the basic modeling analysis level, because the two sensor wave bands of the portable near infrared spectrometer of the dual sensor are different in scope, the magnitude of spectrum data is different, thus causing the difference of the spectrum data weight, and further affecting the prediction accuracy of the spectrum model; meanwhile, due to the fact that the number of sensors is increased, the spectrum data size is greatly increased, and the development of the portable near infrared spectrum detection technology is seriously hindered by the extra-large data size.
Disclosure of Invention
The application aims to provide a spectrum data processing method based on dual sensors, which aims to solve the related problems in the background technology. According to the method, firstly, dual-sensor near infrared spectrum equipment with different wave band ranges is adopted to collect spectrum data of a vinasse sample, then segmented baseline is carried out on spectrum data collected by the dual-sensor, segmented multi-element scattering correction processing is carried out on spectrum data after baseline processing, sum value averaging processing is carried out on spectrum data after segmented multi-element scattering correction, finally, data return value is carried out on spectrum data after sum value averaging processing, and spectrum modeling is carried out by the dual-sensor equipment through return value data. The method can not only effectively process the spectrum data of the dual sensors in different wave band ranges and solve the problem of different magnitudes of the spectrum data acquired by different sensors, but also greatly simplify the spectrum data quantity under the condition of retaining the spectrum characteristics and improve the analysis efficiency of the portable dual-sensor near infrared spectrometer.
In order to achieve the above purpose, the present application adopts the following technical scheme:
a dual sensor based spectral data processing method comprising: the method comprises the following steps:
adopting dual-sensor near infrared spectrum equipment with different wave band ranges to collect spectrum data of the sample;
the spectrum data acquired by the double sensors are segmented into baselines;
performing segmentation multiple scattering correction on spectrum data after baseline processing;
performing sum value equalization processing on the spectrum data after the segmented multielement scattering correction;
and carrying out data return on the spectrum data subjected to the sum value averaging treatment, and carrying out spectrum modeling by the double-sensor equipment by adopting the return data.
In some embodiments, the spectroscopic data acquisition is performed on the distillers' grain sample using dual-sensor near infrared spectroscopy equipment in different wavelength bands from each other. The dual-sensor near infrared spectrum equipment with different wave band ranges can greatly expand the spectrum acquisition wave band range, so that the dual-sensor near infrared spectrum equipment can cover primary frequency multiplication and secondary frequency multiplication characteristic peaks of hydrogen-containing groups, and further the accuracy of spectrum prediction can be effectively improved.
In some embodiments, the segmenting baseline the spectral data acquired by the dual sensors includes: the sensor has different types, different wave band ranges, and different magnitudes of spectrum data acquired by the sensor. Aiming at the spectrum data of different magnitudes of the dual sensors, a baseline spectrum data processing method is adopted, and the minimum light intensity value point on the spectrum data is subtracted from the spectrum data collected by each sensor in the dual sensors, so that the spectrum data of different magnitudes of the dual sensors are returned to the same magnitude, and the difference of the spectrum data weights caused by the different magnitudes of the spectrum data is avoided.
In some embodiments, the performing a segmented multiple scatter correction process on the baseline processed spectral data includes: the multivariate scattering correction is used as a multivariate correction technology, and the method is to map each spectrum and an ideal spectrum into a linear relation, so that baseline drift phenomenon caused by scattering of a sample piece is eliminated, and the accuracy of spectrum data is improved. The ideal spectrum is obtained by taking the average spectrum of the correction set, so that the linear relation of the absorbance corresponding to the transmission absorbance of all the single sample spectrums and the average spectrum thereof is obtained; the specific calculation formula is as follows:
(1) Calculating the average spectrum of the sample
(2) Sample-by-sample spectrum and average spectrum linear regression
(3) Correction of each sample spectrum
Where i=1, 2,3 … …, n is the number of samples, intercept b i Reflection of the reaction sample, slope m i Uniformity of the reaction samples.
In some embodiments, the sum value averaging process of the spectral data after the segmented multivariate scatter correction comprises: the amount of the spectrum data collected by the portable double-sensor near infrared spectrometer is increased in a multiplied way due to the increase of the number of sensors, and the corresponding processing analysis time of the spectrum data is also increased in a multiplied way. And (3) carrying out one-to-one correspondence on the spectrum data of each sensor in the dual sensors, obtaining the sum value of the spectrum data, carrying out average value processing after finishing the sum value processing, simplifying the spectrum data quantity to the original half under the condition of retaining the spectrum characteristics, and improving the spectrum analysis efficiency.
In some embodiments, the performing data back value on the spectrum data after the sum value averaging process, and performing spectrum modeling by using the back value data by the dual-sensor device includes: and carrying out data return on the spectrum data subjected to the sum value equalization treatment, enabling the processed dual-sensor spectrum data to return to the magnitude of the original spectrum data, respectively carrying out spectrum modeling on the spectrum data subjected to the return and the conventional spectrum data by the dual-sensor equipment by adopting the same modeling method, comparing model parameters and analysis efficiency, and judging the model effect.
The beneficial effects of the application are as follows: the method can effectively process the spectrum data of the dual sensors in different wave band ranges, solves the problem that the spectrum data acquired by the different sensors are different in magnitude, simplifies the spectrum data quantity to a great extent under the condition of retaining spectrum characteristics, and improves the analysis efficiency of the portable dual-sensor near infrared spectrometer.
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FIG. 1 is a schematic diagram of a dual sensor based spectral data processing method of the present application;
FIG. 2 is a data diagram of a conventional dual sensor spectral data processing method model;
FIG. 3 is a data diagram of a dual sensor spectral data processing method model of the present application;
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
On the contrary, the application is intended to cover any alternatives, modifications, equivalents, and variations as may be included within the spirit and scope of the application as defined by the appended claims. Further, in the following detailed description of the present application, certain specific details are set forth in order to provide a better understanding of the present application. The present application will be fully understood by those skilled in the art without the details described herein.
A method of dual sensor-based spectral data processing according to an embodiment of the present application will be described in detail with reference to fig. 1 to 3. It is noted that the following examples are only for explaining the present application and are not to be construed as limiting the present application.
Example 1:
a spectrum data processing method based on dual sensors comprises the following steps:
a. adopting dual-sensor near infrared spectrum equipment with different wave band ranges to collect spectrum data of the vinasse sample;
b. the spectrum data acquired by the double sensors are segmented into baselines;
c. performing segmentation multiple scattering correction on spectrum data after baseline processing;
d. performing sum value equalization processing on the spectrum data after the segmented multielement scattering correction;
e. and carrying out data return on the spectrum data subjected to the sum value averaging treatment, and carrying out spectrum modeling by the double-sensor equipment by adopting the return data.
In fig. 1, 101, spectral data acquisition is performed on a distillers' grain sample by using dual-sensor near infrared spectroscopy equipment with different wavelength ranges. The dual-sensor near infrared spectrum equipment with different wave band ranges can greatly expand the spectrum acquisition wave band range, so that the dual-sensor near infrared spectrum equipment can cover primary frequency multiplication and secondary frequency multiplication characteristic peaks of hydrogen-containing groups, and further the accuracy of spectrum prediction can be effectively improved.
In this example, the sample is a distillers grain sample whose component content is moisture, the distillers grain sample due to its sample characteristics: the components are complex, the surface of the sample is rough, the uniformity is poor, and further, some deviation exists on the prediction accuracy of the content of the near infrared spectrum predicted components. The water content of the components has characteristic peaks in the near infrared first-order frequency multiplication and the second-order frequency multiplication, and the method is suitable for the detection range of the double-sensor portable near infrared equipment.
102 in fig. 1 is the segmentation of spectral data acquired by dual sensors. The sensor has different types, different wave band ranges, and different magnitudes of spectrum data acquired by the sensor. Aiming at the spectrum data of different magnitudes of the dual sensors, a baseline spectrum data processing method is adopted, and the minimum light intensity value point on the spectrum data is subtracted from the spectrum data collected by each sensor in the dual sensors, so that the spectrum data of different magnitudes of the dual sensors are returned to the same magnitude, and the difference of the spectrum data weights caused by the different magnitudes of the spectrum data is avoided.
In this embodiment, the sensor types are different, the wave band ranges covered by the sensor types are different, and the spectrum data magnitude acquired by the sensor types are also different, and the wave band ranges of two sensors in the dual-sensor spectrum device in this patent are 1350 nm-1750 nm and 1750 nm-2150 nm respectively. Aiming at a vinasse sample, wherein the light intensity value range of spectral data acquired by a sensor with the wave band range of 1350-1750 nm is 3400 cd-4200 cd, and the light intensity value range of spectral data acquired by a sensor with the wave band range of 1750-2150 nm is 6200 cd-7200 cd; if the spectrum modeling is directly performed by adopting the spectrum data of the two sections of sensors, the weight of the spectrum data of the two sensors is different due to different magnitudes of the spectrum data, so that the spectrum prediction effect is affected. The spectrum data of the double sensors are subjected to baseline in a segmented mode, the minimum light intensity value point on the spectrum data is subtracted from the spectrum data collected by each single sensor in the double sensors, namely, the light intensity value of the spectrum data collected by the sensor with the wave band range of 1350-1750 nm is subtracted from 3400cd, namely, the light intensity value range is changed to 0 cd-800 cd, the light intensity value of the spectrum data collected by the sensor with the wave band range of 1750-2150 nm is subtracted from 6200cd, namely, the light intensity value range is changed to 0 cd-1000 cd, the light intensity values of the two are returned to the same magnitude, and the difference of the spectrum data weight caused by the difference of the spectrum data magnitude is effectively avoided.
In fig. 1, 103 is a segmented, multivariate scatter correction process performed on the baseline processed spectral data. The method is that each spectrum and an ideal spectrum are mapped into a linear relation, so that baseline drift phenomenon caused by scattering of a sample piece is eliminated, and spectrum data accuracy is improved.
In this embodiment, the multivariate scattering correction is used as a multivariate scattering correction technique, and the principle of the multivariate scattering correction technique is that each spectrum is mapped to a linear relationship with an ideal spectrum, where the ideal spectrum takes the average spectrum of the correction set, and then the linear relationship between the transmission absorbance of all the single sample spectra and the absorbance corresponding to the average spectrum is obtained. The specific calculation formula is as follows:
(4) Calculating the average spectrum of the sample
(5) Sample-by-sample spectrum and average spectrum linear regression
(6) Correction of each sample spectrum
Where i=1, 2,3 … …, n is the number of samples, intercept b i Reflection of the reaction sample, slope m i Uniformity of the reaction samples.
In fig. 1, 104 is the sum value averaging process of the spectrum data after the multi-component scattering correction. The amount of the spectrum data collected by the portable double-sensor near infrared spectrometer is increased in a multiplied way due to the increase of the number of sensors, and the corresponding processing analysis time of the spectrum data is also increased in a multiplied way. And (3) carrying out one-to-one correspondence on the spectrum data of each sensor in the dual sensors, obtaining the sum value of the spectrum data, carrying out average value processing after finishing the sum value processing, simplifying the spectrum data quantity to the original half under the condition of retaining the spectrum characteristics, and improving the spectrum analysis efficiency.
In this embodiment, the spectral data collected by the sensor with the wavelength range of 1350nm to 1750nm in the dual-sensor spectral device includes 50 light intensity value points, and the spectral data collected by the sensor with the wavelength range of 1750nm to 2150nm also includes 50 light intensity value points, so that the spectral data of the dual-sensor actually includes 100 light intensity value points, the increase of the sensor causes synchronous increase of the spectral data volume, and the value-averaged spectral data processing mode can well solve the problem, simplify the spectral data volume and simultaneously retain the spectral characteristics, and the specific processing mode is as follows: the 50 light intensity value points of the spectrum data acquired by the sensor with the wave band range of 1750 nm-2150 nm are overlapped on the 50 light intensity value points with the wave band range of 1350 nm-1750 nm, and the overlapped spectrum data is subjected to the averaging treatment, so that 100 light intensity value points with the wave band range of 1350 nm-2150 nm of the dual-sensor are simplified into 50 light intensity value points within the wave band range of 1350 nm-1750 nm, the spectrum data is simplified into half of the original data, a plurality of characteristic peak information of the dual-sensor are reserved, and finally the spectrum data is averaged, so that the same value magnitude as the initial 100 light intensity value points is reserved, and the data return value processing in the step 105 is facilitated.
In fig. 1, 105 is a data return value of the spectrum data after the sum value averaging process, and the dual-sensor device uses the return value data to perform spectrum modeling. And carrying out data return on the spectrum data subjected to the sum value equalization treatment, enabling the processed dual-sensor spectrum data to return to the magnitude of the original spectrum data, respectively carrying out spectrum modeling on the spectrum data subjected to the return and the conventional spectrum data by the dual-sensor equipment by adopting the same modeling method, comparing model parameters and analysis efficiency, and judging the model effect.
In this embodiment, after the sectional baseline, the multiple scattering correction and the sum value averaging are performed on the spectrum data of the dual sensor, the spectrum data is changed into 50 light intensity value points within the wavelength range 1350nm to 1750nm, and the light intensity value range is 0cd to 900cd. The difference of the magnitude is obvious from the light intensity value range of the original spectrum data of the double sensors, so that the spectrum data of the double sensors are required to be subjected to data return, and the specific data return mode is as follows: 50 light intensity value points of the dual-sensor spectrum data subjected to segmentation baseline, multi-element scattering correction and value averaging are added with 1350-1750 nm sensor spectrum data minimum light intensity value points point by point, the light intensity value range is changed to 3400-4300 cd, and the dual-sensor spectrum data returns to the same magnitude.
The patent adopts the same spectrum modeling method to carry out mathematical modeling on the dual-sensor return value data and the dual-sensor original data to respectively obtain the dual-sensor spectrum model data diagram which is shown in fig. 2 and is processed by the method and the dual-sensor spectrum model data diagram which is shown in fig. 3 and is processed by the conventional method.
Comparing the effects of the two models, wherein the effect of the spectrum model is the most visual reflected by the model correlation coefficient and the root mean square error, and the model quality is better as the model correlation coefficient is larger; the smaller the root mean square error, the better the model quality. Mathematical modeling is carried out on the original spectrum data of the double sensors by adopting a partial least square method to obtain a spectrum model M, then the spectrum data obtained by adopting the spectrum data processing method of the double sensors of the patent is subjected to mathematical modeling by adopting the same method to obtain a spectrum model M ', the model correlation coefficient (R2) of the spectrum model M ' is 0.5323 and the Root Mean Square Error (RMSEC) is 0.9937 by comparing the model correlation coefficients of the spectrum model M and the spectrum model M ' and the root mean square error; whereas the model correlation coefficient (R2) of the spectral model M was 0.3266, the Root Mean Square Error (RMSEC) was 1.1923. Comparing the model data between the two results shows that the model correlation coefficient is obviously increased after the spectrum data processing method of the patent, the root mean square error is obviously reduced, and the spectrum data analysis time is greatly shortened.
According to the spectrum data processing method based on the dual sensors, the spectrum data of the dual sensors in different wave band ranges can be effectively processed, the problem that the magnitudes of the spectrum data acquired by the different sensors are different is solved, meanwhile, the spectrum data quantity is greatly simplified under the condition of retaining the spectrum characteristics, and the analysis efficiency of the portable dual-sensor near infrared spectrometer is improved.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (2)
1. The spectrum data processing method based on the dual sensors is characterized by comprising the following steps of:
adopting dual-sensor near infrared spectrum equipment with different wave band ranges to collect spectrum data of the sample;
the spectrum data acquired by the double sensors are segmented into baselines;
performing segmentation multiple scattering correction on spectrum data after baseline processing;
performing sum value equalization processing on the spectrum data after the segmented multielement scattering correction;
carrying out data return value on the spectrum data subjected to the sum value averaging treatment, and carrying out spectrum modeling by adopting the return value data through the dual-sensor equipment;
the method for segmenting the spectrum data acquired by the double sensors comprises the following steps: subtracting the minimum light intensity value point on the spectrum data by the spectrum data collected by each sensor in the double sensors, so that the spectrum data of different magnitudes of the double sensors are returned to the same magnitude, and the difference of the spectrum data weights caused by the different magnitudes of the spectrum data is avoided;
the spectrum data after the multi-component scattering correction is subjected to sum value averaging processing, which comprises the following steps: the spectrum data of each sensor in the dual sensors are subjected to one-to-one correspondence and sum value, average value processing is performed after sum value processing is completed, the spectrum data quantity is simplified to be half of the original value under the condition of retaining spectrum characteristics, and spectrum analysis efficiency is improved;
the data return value is carried out on the spectrum data after the sum value averaging treatment, the dual-sensor device adopts the return value data to carry out spectrum modeling, and the method comprises the following steps: and carrying out data return on the spectrum data subjected to the sum value equalization processing, so that the processed dual-sensor spectrum data is returned to the magnitude of the original spectrum data.
2. The dual sensor-based spectrum data processing method of claim 1, wherein the performing the piecewise multi-component scatter correction on the baseline-processed spectrum data comprises: mapping each spectrum and an ideal spectrum into a linear relation, wherein the ideal spectrum takes an average spectrum of a correction set, and further obtains the linear relation of the absorbance corresponding to the transmission absorbance of all the single sample spectrum and the average spectrum thereof; the specific calculation formula is as follows:
(1) Calculating the average spectrum of the sample
(2) Sample-by-sample spectrum and average spectrum linear regression
(3) Correction of each sample spectrum
A i(MSC) ＝(A i -b i )/m i
Where i=1, 2,3 … …, n is the number of samples, intercept b i Reflection of the reaction sample, slope m i Uniformity of the reaction samples.
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