CN106018331B - The method for estimating stability and pretreatment optimization method of multi-channel spectral system - Google Patents
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Abstract
The invention discloses a kind of method for estimating stability of multi-channel spectral system and pretreatment optimization method, method for estimating stability to be：By calculating dynamic absorbance matrix, the dynamic deviation spectrum of sample, an estimation of stability index is obtained.Pre-processing optimization method is：Establish a preprocess method database, calculate the estimation of stability index of the corresponding former spectrum of various preprocess methods and its calibration spectrum, calibration prediction model based on PLS methods is established respectively to former spectrum and its calibration spectrum, error and correlation between near-infrared predicted value and measured value is calculated, finally according to estimation of stability index, error and correlation, optimal preprocess method when being detected for follow-up near infrared spectrometer is determined.The overall stability of the present invention objectively evaluation system; optimal preprocess method is selected to be detected for follow-up near infrared spectrometer; so that detection signal is more accurate, the scale application for developing inexpensive nir instrument and near infrared technology has important value.
Description
Technical Field
The invention relates to the field of research of multichannel near infrared spectroscopy, in particular to a stability evaluation method and a pretreatment optimization method of a multichannel spectroscopy system.
Background
Near infrared is an electromagnetic wave between the visible and mid-infrared. With the development of chemometrics and computer technologies, modern near infrared spectroscopy analysis technology has been widely used in the fields of agriculture, food, environment, biomedicine, etc. with its advantages of simplicity, rapidness, and easy realization of on-line analysis. The technology of the full-waveband universal near-infrared instrument is mature, but the instrument is large in size and high in price, is mainly suitable for laboratory detection, and is inconvenient for large-scale application of the near-infrared spectrum technology.
The traditional general near-infrared instrument is generally based on a single light source, light is split by adopting modes such as a grating or an optical filter, and the correlation between dynamic deviation spectrums of absorbance of various wavelengths is high. Traditionally, the wavelength signal-to-noise ratio is an evaluation index for the stability and the predictive performance of a near infrared instrument. In order to reduce the cost of a near-infrared instrument and realize the large-scale application of a near-infrared technology, a multi-channel spectrometer (such as a semiconductor laser type near-infrared instrument) with multiple light sources and multiple discrete wavelengths is an important research and development direction of the current low-cost near-infrared instrument, but for a multi-light source spectrum system, the correlation between dynamic deviation spectrums of absorbance of each wavelength is low, the signal-to-noise ratio of each wavelength is difficult to evaluate the overall stability of the system, and the overall prediction performance of the instrument is difficult to improve only by improving the signal-to-noise ratio of each wavelength channel.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a stability evaluation method of a multi-channel spectral system, which provides a system stability evaluation index for representing each discrete wavelength, can accurately evaluate the overall stability of the system through the index and can be used for quality evaluation of the subsequent multi-channel spectral system.
The invention aims to provide a preprocessing optimization method based on the stability evaluation method, which utilizes the stability evaluation index to pre-screen the preprocessing method so as to obtain an optimal preprocessing method for subsequent signal processing, so that the precision of a multi-channel spectrum system is greatly improved, and the near-infrared prediction capability is improved.
The purpose of the invention is realized by the following technical scheme: the stability evaluation method of the multi-channel spectral system comprises the following steps:
s1, selecting a representative sample, and repeatedly testing the near infrared spectrum of the sample: the near-infrared spectrometer is provided with m discrete wavelengths, and the spectrum of a fixed sample is continuously tested for n times to obtain a dynamic absorbance matrix of the sample as follows:
A＝(ak,i)m×n,k＝1,2,…,m,i＝1,2,…,n；
s2, calculating the dynamic deviation spectrum of the sample:
the average spectrum of the sample was first calculated as follows:
calculating a deviation spectrum matrix of the sample relative to the average spectrum:
D＝(dk,i)m×n,dk,i＝ak,i-ak,k＝1,2,…,m,i＝1,2,…,n；
further obtain a dynamic deviation spectrum corresponding to each discrete wavelength:
s3, determining the stability evaluation index: firstly, calculating the correlation coefficient of any two discrete wavelength dynamic deviation spectrums to obtain a correlation matrix R as follows:
R＝(Rp,q)m×m,p＝1,2,…,m,q＝1,2,…,m；
wherein R isp,qRepresenting the p-th discrete wavelength dynamic deviation spectrumAnd the qth discrete wavelength dynamic deviation spectrumThe correlation coefficient of (a); calculating the mean value R of m-1 correlation coefficients corresponding to the kth discrete wavelengthk,AveStandard deviation Rk,SD，k＝1,2,…,m；
Finally, obtaining the stability evaluation index of each discrete wavelength:
wherein,a larger value indicates a more stable system.
A pretreatment optimization method based on the stability evaluation method comprises the following steps:
s1, establishing a preprocessing method database, wherein various preprocessing methods are stored in the database, and the value selection range of each parameter related to each preprocessing method is also fixed;
s2, continuously testing the spectrum of the sample to be detected n times by adopting a near infrared spectrometer;
s3, processing the spectrums by adopting various preprocessing methods in a preprocessing method database respectively to obtain corresponding correction spectrums, and calculating the stability evaluation indexes of the original spectrums and the correction spectrums;
s4, respectively establishing a calibration prediction model based on a PLS method for the original spectrum and the correction spectrum thereof, and calculating according to the model to obtain the error and the correlation between the near infrared predicted value and the measured value;
s5, selecting a pretreatment method satisfying the following conditions at the same time as a preferable method: the stability evaluation index is larger than a preset first threshold, the error is smaller than a preset second threshold, and the correlation is larger than a preset third threshold; if a plurality of conditions are met, selecting the method with the highest stability evaluation index;
and S6, finally, using the preferred method as a pretreatment method in the subsequent detection of the near infrared spectrometer.
Preferably, in the pretreatment optimization method, after a near infrared spectrometer is adopted to continuously test the spectrum of a sample to be detected for n times, the current signal-to-noise ratio is calculated by using a method carried by the near infrared spectrometer; and if the signal-to-noise ratio is smaller than a preset fourth threshold value, executing step S3, otherwise, directly adopting the near-infrared spectrometer for detection.
Preferably, the preprocessing method database includes SG smoothing (Savitzky-Golay smoothing) method, standard normal variable transformation, multivariate scattering correction, normalization, and the like.
Preferably, the specific steps of step S3 are:
collecting sufficient representative samples, collecting spectral data by using a near-infrared spectrometer, and simultaneously measuring the index physicochemical value of the samples by using a standard analysis method;
randomly dividing the collected samples into a calibration set and a prediction set, wherein the calibration set is used for establishing a near infrared quantitative analysis model based on a Partial Least Squares (PLS) method, and the prediction set is used for evaluating the prediction effect of the model;
using predictionRoot Mean Square Error (RMSEP), correlation coefficient (R)P) And respectively evaluating the error and the correlation between the near infrared predicted value and the measured value, wherein the formula is as follows:
wherein,Cirespectively a predicted value and an actual measurement value of the ith prediction sample;CAVErespectively taking the average values of the predicted value and the measured value; m is the number of predicted samples; a smaller RMSEP value indicates a higher accuracy of the model, RPThe larger the value, the higher the correlation between the predicted value and the measured value.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides a new stability evaluation index, which can objectively evaluate the overall stability of the system and is more suitable for evaluating the overall stability of the multi-light-source spectrum system compared with the traditional signal-to-noise ratio of each wavelength.
2. According to the stability evaluation index, the invention further provides a pretreatment optimization method, and by the method, the optimal pretreatment method can be selected for subsequent detection of the near-infrared spectrometer, so that the detection signal is more accurate, and the method has important reference value for developing a low-cost near-infrared instrument and large-scale application of a near-infrared technology.
Drawings
Fig. 1 is a raw spectrum of a corn meal sample tested 20 times in duplicate.
FIG. 2 is a dynamic deviation spectrum of the original spectrum of a sample tested in 20 replicates.
Fig. 3 is a SG calibration spectrum of a sample repeatedly tested 20 times.
Fig. 4 is a dynamic deviation spectrum of SG correction spectrum of a sample repeatedly tested 20 times.
Fig. 5 shows raw spectra of 268 corn meal samples.
Fig. 6 is SG calibration spectra of 268 corn meal samples.
FIG. 7(a) is a comparison of the predicted value and the measured value of the original spectrum of corn water.
Fig. 7(b) is a comparison of predicted values and measured values of the corrected spectrum of corn moisture SG.
FIG. 8 is a flowchart illustrating a pre-processing optimization method according to this embodiment.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example 1
In this embodiment, taking NLD-D1 semiconductor laser type near-infrared instrument (guangzhou communication network technologies, china) as an example, the test samples: corn flour sample (feed raw material), detection indexes are as follows: and (4) moisture. The instrument adopts 11 semiconductor lasers with discrete wavelengths (channel for short, m is 11), the absorbance of 11 wavelengths of a sample can be correspondingly acquired, and the number n of times of repeatedly testing the sample is 20. Therefore, the specific steps of the stability evaluation method and the pretreatment optimization method of the multi-channel spectrum system are provided.
The stability evaluation method of the multi-channel spectral system comprises the following steps:
1. a representative corn meal sample was selected and the sample was tested in duplicate for a number of times n of 20 to obtain the dynamic absorbance of 11 channels as shown in figure 1.
The dynamic absorbance matrix is as follows:
A＝(ak,i)m×n,k＝1,2,…,m,i＝1,2,…,n。(1)
2. calculating the dynamic deviation spectrum of the sample.
The average spectrum of the sample was calculated as follows:
calculating a deviation spectrum matrix of the sample relative to the average spectrum:
D＝(dk,i)m×n,dk,i＝ak,i-ak,k＝1,2,…,m,i＝1,2,…,n，(3)
a dynamic deviation spectrum corresponding to each discrete wavelength is further obtained, as shown in fig. 2:
3. determination of stability evaluation index
Firstly, calculating the correlation coefficient of any two discrete wavelength dynamic deviation spectrums to obtain a correlation matrix R as follows:
R＝(Rp,q)m×m,p＝1,2,…,m,q＝1,2,…,m；
wherein R isp,qRepresenting the p-th discrete wavelength dynamic deviation spectrumAnd the qth discrete wavelength dynamic deviation spectrumThe correlation coefficient of (a); calculating the mean value R of m-1 correlation coefficients corresponding to the kth discrete wavelengthk,AveStandard deviation Rk,SD，k＝1,2,…,m；
Finally, obtaining the stability evaluation index of each discrete wavelength:
wherein,a larger value indicates a more stable system.
By adopting the method, the correlation matrix R of the original spectrum is calculatedOriginThe following were used:
the system stability evaluation index of the original spectrum is shown in table 1.
TABLE 1 evaluation index of System stability of original Spectrum
It can be seen that eachThe values are all small, the correlation of the dynamic deviation spectra of all channels is low, and the overall stability of the spectrum system is poor, so that an appropriate preprocessing method needs to be optimized, the overall stability of the spectrum system is improved, and the prediction capability of the model is further improved. The present embodiment will be described with reference to FIG. 8The pretreatment optimization method of the example is specifically described.
1. And establishing a preprocessing method database.
The preprocessing method database may include various existing methods such as SG smoothing method, standard normal variable transformation, multivariate scattering correction, normalization, etc., and the value ranges of the parameters involved in each method are also preset, and when in use, an optimal method is selected from the preprocessing methods.
The SG method takes w (odd) continuous wavelengths as a smooth window, uses a polynomial to carry out least square fitting on absorbance data in the window to obtain corresponding polynomial coefficients, and then calculates the absorbance of the central wavelength of the window and the correction value of each order derivative thereof. The window is moved in the full spectrum range, thereby obtaining the SG correction spectrum of the original spectrum (the wavelengths at the front end and the rear end adopt a correction method of backward fitting and forward fitting respectively). SG smoothing can effectively eliminate baseline wander, tilt, etc. noise. The SG method employs 3 parameters, which are the derivative order s, the polynomial degree t (t > s), and the number of smoothing points w. According to different combinations of the 3 parameters, SG smooth spectrum pretreatment methods with different modes can be obtained. In this embodiment, the limited preprocessing method database only includes the SG smoothing spectrum preprocessing method obtained by combining the above different parameters, wherein the derivative order s is selected to be in a range of 0, 1, 2, and 3, the polynomial degree t is selected to be in a range of 2, 3, 4, 5, and 6, the number w of smoothing points is selected to be in a range of 5, 7, 9, and 11, and t > s, so 68 SG smoothing modes are provided. That is, the database of the method of the present embodiment includes 68 preprocessing methods, and then an optimal preprocessing optimization method of the present embodiment is used to select one of the preprocessing methods for subsequent detection.
2. And continuously testing the spectrum of the sample to be detected n times by using a near infrared spectrometer.
3. And respectively calculating system stability evaluation indexes of 68 SG modes.
Taking derivative order s as 2, polynomial degree t as 4 and smoothing point w as 5 as an example, SG calibration spectrum of the sample repeatedly tested 20 times is first calculated, as shown in fig. 3.
Further, a dynamic deviation spectrum thereof was obtained as shown in FIG. 4.
Obtaining a correlation matrix RSG。
Further, a system stability evaluation index of the SG calibration spectrum was obtained as shown in table 2.
TABLE 2 evaluation index of system stability of SG correction spectra
It can be seen that eachThe values are greatly improved, the correlation of the dynamic deviation spectrum of each channel is higher, and the integral stability of the spectrum system is better.
4. And respectively establishing a near-infrared quantitative analysis model (calibration prediction model) of the moisture index of the corn flour sample based on the PLS method for the original spectrum and the SG correction spectrum.
A total of 268 corn meal samples were collected as a sample set, with a calibration sample count of 140 and a predicted sample count of 128. Fig. 5 and 6 are the original spectral diagram and the corresponding SG correction spectrum (s 2, t 4, w 5), respectively.
Respectively establishing a PLS-based near infrared quantitative analysis model for the original spectrum and the SG correction spectrum of the calibration set, wherein the prediction set is used for evaluating the prediction effect of the model; and respectively evaluating the difference and the correlation between the near infrared predicted value and the measured value by adopting a predicted Root Mean Square Error (RMSEP) and a correlation coefficient (RP), wherein the formula is as follows:
wherein,Cirespectively a predicted value and an actual measurement value of the ith prediction sample;CAVErespectively taking the average values of the predicted value and the measured value; m is the number of predicted samples; a smaller RMSEP value indicates a higher accuracy of the model, RPThe larger the value, the higher the correlation between the predicted value and the measured value.
The modeling results are shown in table 3, and the comparison between the predicted values and the measured values is shown in fig. 7(a) and 7 (b).
TABLE 3 prediction effect of original spectra and SG correction spectra PLS model
As can be seen from Table 2, Table 3, FIG. 7(a) and FIG. 7(b), the stability evaluation index when the original sample spectral data is subjected to the specific S-G smoothing modeWhile the improvement is remarkable, the quantitative analysis model of the method reduces the prediction Root Mean Square Error (RMSEP), improves the correlation coefficient (RP) of the predicted value and the measured value,
5. presetting a first threshold, a second threshold and a third threshold, and selecting a preprocessing method which simultaneously satisfies the following conditions as a preferable method: the stability evaluation index is larger than a first threshold, the error is smaller than a second threshold, and the correlation is larger than a third threshold. If a plurality of conditions are satisfied, the method having the highest stability evaluation index is selected.
6. Finally, the preferred method is used as a pretreatment method of a subsequent near infrared spectrometer when the sample is detected.
As can be understood from the present embodiment,the value can effectively evaluate the stability of the multi-channel spectrum system, and meanwhile, a proper method is provided for improving the near infrared prediction capability of the multi-channel spectrum system.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (5)
1. The stability evaluation method of the multi-channel spectral system is characterized by comprising the following steps of:
s1, selecting a representative sample, and repeatedly testing the near infrared spectrum of the sample: the near-infrared spectrometer is provided with m discrete wavelengths, and the spectrum of a fixed sample is continuously tested for n times to obtain a dynamic absorbance matrix of the sample as follows:
A＝(ak,i)m×n,k＝1,2,…,m,i＝1,2,…,n；
s2, calculating the dynamic deviation spectrum of the sample:
the average spectrum of the sample was first calculated as follows:
calculating a deviation spectrum matrix of the sample relative to the average spectrum:
D＝(dk,i)m×n,dk,i＝ak,i-ak,k＝1,2,…,m,i＝1,2,…,n；
further obtain a dynamic deviation spectrum corresponding to each discrete wavelength:
s3, determining the stability evaluation index: firstly, calculating the correlation coefficient of any two discrete wavelength dynamic deviation spectrums to obtain a correlation matrix R as follows:
R＝(Rp,q)m×m,p＝1,2,…,m,q＝1,2,…,m；
wherein R isp,qRepresenting the p-th discrete wavelength dynamic deviation spectrumAnd the qth discrete wavelength dynamic deviation spectrumThe correlation coefficient of (a); calculating the mean value R of m-1 correlation coefficients corresponding to the kth discrete wavelengthk,AveStandard deviation Rk,SD，k＝1,2,…,m；
Finally, obtaining the stability evaluation index of each discrete wavelength:
wherein,a larger value indicates a more stable system.
2. A pretreatment optimization method based on the stability evaluation method according to claim 1, comprising the steps of:
s1, establishing a preprocessing method database, wherein various preprocessing methods are stored in the database, and the value selection range of each parameter related to each preprocessing method is also fixed;
s2, continuously testing the spectrum of the sample to be detected n times by adopting a near infrared spectrometer;
s3, processing the spectrums by adopting various preprocessing methods in a preprocessing method database respectively to obtain corresponding correction spectrums, and calculating the stability evaluation indexes of the original spectrums and the correction spectrums;
s4, respectively establishing a calibration prediction model based on a PLS method for the original spectrum and the correction spectrum thereof, and calculating according to the model to obtain the error and the correlation between the near infrared predicted value and the measured value;
s5, selecting a pretreatment method satisfying the following conditions at the same time as a preferable method: the stability evaluation index is larger than a preset first threshold, the error is smaller than a preset second threshold, and the correlation is larger than a preset third threshold; if a plurality of conditions are met, selecting the method with the highest stability evaluation index;
and S6, finally, using the preferred method as a pretreatment method in the subsequent detection of the near infrared spectrometer.
3. The pretreatment optimization method according to claim 2, wherein in the pretreatment optimization method, after the spectrum of the sample to be detected is continuously tested n times by using the near infrared spectrometer, the current signal-to-noise ratio is calculated by using a method carried by the near infrared spectrometer; and if the signal-to-noise ratio is smaller than a preset fourth threshold value, executing step S3, otherwise, directly adopting the near-infrared spectrometer for detection.
4. The pre-processing optimization method according to claim 2, wherein the pre-processing method database comprises SG smoothing method, standard normal variable transformation, multivariate scattering correction and normalization.
5. The preprocessing optimization method according to claim 2, wherein the specific steps of step S4 are:
collecting sufficient representative samples, collecting spectral data by using a near-infrared spectrometer, and simultaneously measuring the index physicochemical value of the samples by using a standard analysis method;
randomly dividing the collected samples into a calibration set and a prediction set, wherein the calibration set is used for establishing a near-infrared quantitative analysis model based on a partial least square method, and the prediction set is used for evaluating the prediction effect of the model;
using predicted root mean square error RMSEP, correlation coefficient RPAnd respectively evaluating the error and the correlation between the near infrared predicted value and the measured value, wherein the formula is as follows:
wherein,Cirespectively a predicted value and an actual measurement value of the ith prediction sample;CAVErespectively taking the average values of the predicted value and the measured value; m is the number of predicted samples; a smaller RMSEP value indicates a higher accuracy of the model, RPThe larger the value, the higher the correlation between the predicted value and the measured value.
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Address after: 510530 the third floor, 883 vice floor, Shenzhou Road, science and Technology City, Guangdong high tech Industrial Development Zone, Guangzhou. Applicant after: GUANGZHOU SONDON NETWORK TECHNOLOGY Co.,Ltd. Address before: 510630 room 806-812, B building, 89 Zhongshan Avenue West, Tianhe District, Guangzhou, Guangdong. Applicant before: GUANGZHOU SONDON NETWORK TECHNOLOGY Co.,Ltd. |
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| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180828 |