CN111965140A - Wavelength point recombination method based on characteristic peak - Google Patents
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- 238000005215 recombination Methods 0.000 title claims abstract description 23
- 230000006798 recombination Effects 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title claims abstract description 15
- 230000003595 spectral effect Effects 0.000 claims abstract description 69
- 238000001228 spectrum Methods 0.000 claims abstract description 20
- 230000000694 effects Effects 0.000 claims abstract description 11
- 238000009795 derivation Methods 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000012935 Averaging Methods 0.000 description 4
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 2
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000002329 infrared spectrum Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 235000015096 spirit Nutrition 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- 238000005457 optimization Methods 0.000 description 1
- 238000005464 sample preparation method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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Abstract
The invention discloses a wavelength point recombination method based on characteristic peaks, which comprises the following steps: collecting spectral data of a sample to be tested, and modeling to obtain a spectral model M; performing second-order derivation on the spectral data of the sample to be detected to obtain a characteristic peak of the sample to be detected; changing the density of wavelength points near the characteristic peak according to the wavelength point distribution characteristics of the portable near-infrared spectrometer; carrying out spectrum data acquisition and spectrum modeling on the same sample to be tested again to obtain a spectrum model M'; and comparing the spectrum models M and M' to judge the model effect. According to the method, the spectral wavelength points are recombined and optimized through characteristic peak information, the acquisition amount of spectral data with small sample correlation is reduced, more spectral data which can be used for representing a sample to be detected are collected, and the effect of a spectral model is effectively improved.
Description
Technical Field
The invention relates to the technical field of spectral wavelength point recombination, in particular to a wavelength point recombination method based on a characteristic peak.
Background
In recent years, the near infrared spectrum analysis technology is developed rapidly and is applied to a plurality of fields such as chemical industry, pharmacy, military industry, food and the like. The near infrared spectrum technology belongs to the molecular spectrum technology, can indicate material composition and property information on the molecular level, and obtains very high benefit no matter for economic or social influence, thereby having great development potential.
However, most of the existing material composition and property information detection is mainly carried out by using a large laboratory near infrared spectroscopy instrument, although the methods have high quantitative accuracy and sensitivity, the required equipment has huge volume, expensive equipment cost, long sample preparation time and strict sample preparation method, the detection equipment and the sample preparation need professional operation, the detection environment is fixed, the analysis time is long, and the method is not suitable for field detection and is not convenient for popularization and use.
Along with the development of portable near infrared spectroscopy technology, the mainstream large near infrared spectrometer equipment in the market is developed towards the portable direction of small size and low price. However, the portable near-infrared spectrometer is easily affected by a light source, a detector, a detection method, environmental conditions and the like, so that the acquired spectral data has poor stability and low precision, and the effect of a spectral model is further affected. Particularly, the currently adopted wavelength-sharing portable near-infrared spectrometer collects too much data information with small sample correlation, and further causes the problems of poor spectrum model effect, inaccurate data analysis and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a wavelength point recombination method based on a characteristic peak, which performs recombination optimization on spectral wavelength points through characteristic peak information, reduces the acquisition amount of spectral data with small sample correlation, collects more spectral data capable of representing a sample to be detected, and further effectively improves the effect of a spectral model.
In order to achieve the purpose, the invention adopts the technical scheme that: a wavelength point recombination method based on characteristic peaks comprises the following steps:
a. collecting spectral data of a sample to be tested, and modeling to obtain a spectral model M;
b. performing second-order derivation on the spectral data of the sample to be detected to obtain a characteristic peak of the sample to be detected;
c. changing the density of wavelength points near the characteristic peak according to the wavelength point distribution characteristics of the portable near-infrared spectrometer;
d. carrying out spectrum data acquisition and spectrum modeling on the same sample to be tested again to obtain a spectrum model M';
e. and comparing the spectrum models M and M' to judge the model effect.
Preferably, the step a is as follows:
and acquiring spectral data of the sample to be detected by using a wavelength-sharing portable near-infrared spectrometer, carrying out one-to-one correspondence between the spectral data and the component concentration of the sample to be detected, and carrying out PLS modeling after the correspondence is completed to obtain an original data spectral model.
Preferably, the step c is as follows:
setting the spectral resolution of the wavelength-sharing portable near-infrared spectrometer as P and the waveband range as W, wherein the number X of wavelength points contained in the spectrometer is as follows:
X＝W/P
increasing the density of wavelength points near the characteristic peak to N times of the original density, recombining the spectral wavelength points, and determining the wavelength range P between two wavelength points around the spectral characteristic peak after recombination1Comprises the following steps:
P1＝P/N
setting T characteristic peaks of the sample to be detected, wherein the number of wavelength points distributed by each characteristic peak is the same and is equal to the number of wavelength points of non-characteristic peaks, and the number H of the wavelength points distributed by each characteristic peak is as follows:
total wavelength range W of T characteristic peaks1Comprises the following steps:
non-characteristic peak band range W2Comprises the following steps:
W2＝W-W1
the number of the wavelength points of the non-characteristic peak is H, the wavelength range between two adjacent wavelength points after the non-characteristic peak is subjected to wavelength recombinationEnclose P2Comprises the following steps:
the invention has the beneficial effects that:
firstly, collecting spectral data of a sample to be tested, modeling to obtain a spectral model M, then carrying out second-order derivation on the spectral data of the sample to be tested to obtain a characteristic peak of the sample to be tested, changing the density of wavelength points near the characteristic peak according to the wavelength point distribution characteristic of a portable near-infrared spectrometer, then carrying out spectral data collection and spectral modeling on the same sample to be tested again to obtain a spectral model M ', and finally comparing the spectral model M and the spectral model M' to judge the model effect; the spectral wavelength points are recombined and optimized through the characteristic peak information of the sample to be tested, the density of the wavelength points around the characteristic peak is increased, the acquisition quantity of spectral data with small sample correlation is reduced, more spectral data which can be used for representing the sample to be tested are collected, and the effect of a spectral model is effectively improved.
Drawings
FIG. 1 is a block flow diagram of an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
As shown in fig. 1, a wavelength point recombination method based on characteristic peaks:
in fig. 1, 101 is a spectrum model M obtained by collecting spectrum data of a sample to be measured and modeling. And acquiring spectral data of the sample to be detected by using a wavelength-sharing portable near-infrared spectrometer, carrying out one-to-one correspondence between the spectral data and the component concentration of the sample to be detected, and carrying out PLS modeling after the correspondence is completed to obtain an original data spectral model.
In this embodiment, the wavelength range of the adopted wavelength-sharing portable near-infrared spectrometer is 1850 to 2145nm, the resolution is 5nm, the total is 60 wavelength points, the wavelength range corresponding to the 1 st to 60 th wavelength points is (1850nm, 1855nm … …,2145nm), and the acquired spectral data is actually represented as an aggregate matrix of light intensity values at the 60 wavelength points. In order to reduce measurement errors as much as possible, each sample to be measured is subjected to three times of spectral data acquisition, an average value is taken as a final spectral data value, the final spectral data value of each sample to be measured is in one-to-one correspondence with the component concentration of the sample to be measured, and then a Partial Least Squares (PLS) is adopted for data modeling to obtain an original data spectral model M.
In fig. 1, 102 is to perform second-order derivation on the spectral data of the sample to be measured to obtain the characteristic peak of the sample to be measured. The full width at half maximum of the second-order derivative spectrum is only about 1/3 of the full width at half maximum of the original spectrogram, small shoulder peaks on two sides of the strong peak can be simply distinguished, the accurate peak position determination and the shoulder peak position determination are extremely effective, and the peak value of the spectrogram of the sample to be detected, namely the position of the wavelength point of the characteristic peak, can be clearly distinguished through the second-order derivative.
In this embodiment, the samples to be tested are selected from white spirits with different alcohol concentrations, and the main components of the white spirits are alcohol (C-H-O) and water (H-O). In the wavelength range of 1850-2145 nm, a combined frequency band of H-O is near 1950nm, a combined frequency band of C-H-O is near 2080nm, spectral data of a white spirit sample are acquired by the wavelength-averaging portable near infrared spectrometer, second derivation is carried out on the spectral data, and spectral peaks can appear near 1950nm wavelength points and 2080nm wavelength points, namely characteristic peaks of the white spirit sample.
In fig. 1, 103 is the wavelength point density near the characteristic peak changed according to the wavelength point distribution characteristics of the portable near infrared spectrometer. The spectral resolution of the wavelength-averaging portable near-infrared spectrometer is fixed, and the number of the collected wavelength points is also fixed in a certain waveband range. By changing the density of the wavelength points near the characteristic peak, the portable spectrometer can acquire more spectral data capable of representing the sample to be measured.
In this embodiment, the spectral resolution of the wavelength-averaging portable near-infrared spectrometer is set to be P, and the wavelength range is set to be W, so that the number X of wavelength points included in the spectrometer in total can be calculated as:
X＝W/P
the density of wavelength points near the characteristic peak is increased to the original densityIs carried out on the spectral wavelength points, the wavelength range P between two wavelength points around the spectral characteristic peak is obtained after recombination1Comprises the following steps:
P1＝P/N
setting T characteristic peaks of the sample to be detected, wherein the number of wavelength points distributed by each characteristic peak is the same and is equal to the number of wavelength points of non-characteristic peaks, and then calculating the number H of the wavelength points distributed by each characteristic peak as follows:
further, the total wavelength range W of the T characteristic peaks can be calculated1Comprises the following steps:
further, the non-characteristic peak band range W can be calculated2Comprises the following steps:
W2＝W-W1
the number of the wavelength points of the non-characteristic peak is H, the wavelength range P between two adjacent wavelength points after the wavelength recombination of the non-characteristic peak can be calculated2Comprises the following steps:
the wavelength-sharing portable near-infrared spectrometer adopted in the embodiment has a waveband range of 1850-2145 nm, a total wavelength range of 300nm, a resolution of 5nm, and 60 total wavelength points, the density of wavelength points near an increased characteristic peak is 2 times of the original density, and a white spirit sample to be measured has 2 characteristic peaks, and the number of the wavelength points of each characteristic peak after recombination can be calculated by the above formula to be 20, and the wavelength range between two adjacent wavelength points is 2.5 nm. The number of the wavelength points of the non-characteristic peak after recombination is 20, and the wavelength range between two adjacent wavelength points is 10 nm.
In fig. 1, 104 is to perform the spectrum data collection and the spectrum modeling again on the same sample to be tested to obtain the spectrum model M'. And (c) after the portable near-infrared spectrometer changes the density of the wavelength points near the characteristic peak, acquiring the spectral data of the sample to be detected in the step a again, and modeling the data by adopting the same modeling mode in the step a, so that other errors can be effectively eliminated, and the difference of the model effects before and after the recombination of the wavelength points of the portable near-infrared spectrometer can be visually reflected.
In this embodiment, the wavelength-averaging portable near-infrared spectrometer performs wavelength point recombination, and the sample to be measured is white spirit, and the characteristic peaks thereof are 1950nm and 2080 nm. Therefore, the specific wavelength point distribution of the recombined portable near-infrared spectrometer is as follows: 1922.5 nm-1970 nm contains 20 average wavelength points, and the resolution is 2.5 nm; 2052.5 nm-2100 nm contains 20 average wavelength points, and the resolution is 2.5 nm; the remaining band ranges contain a total of 20 equally divided wavelength points with a resolution of 10 nm. And (3) after the recombination is finished, acquiring spectral data of the same white spirit sample, corresponding to the same component concentration in the step (101) one by one, and modeling by adopting the same partial least square method modeling method to obtain a spectral model M' after the wavelength point recombination.
In FIG. 1, 105 shows comparison of spectral models M and M' to determine the model effect. The spectral model quality of a Partial Least Squares (PLS) is most visually embodied as a model correlation coefficient and a root mean square error, wherein the larger the correlation coefficient is, the better the model quality is, and the worse the model quality is; the smaller the root mean square error, the better the model quality, and vice versa.
In this embodiment, comparing the model correlation coefficient and the root mean square error of the spectral model M and M ', it can be known that the model correlation coefficient of the spectral model M' is significantly increased on the basis of the spectral model M, and the root mean square error is significantly decreased.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (3)
1. A wavelength point recombination method based on characteristic peaks is characterized by comprising the following steps:
a. collecting spectral data of a sample to be tested, and modeling to obtain a spectral model M;
b. performing second-order derivation on the spectral data of the sample to be detected to obtain a characteristic peak of the sample to be detected;
c. changing the density of wavelength points near the characteristic peak according to the wavelength point distribution characteristics of the portable near-infrared spectrometer;
d. carrying out spectrum data acquisition and spectrum modeling on the same sample to be tested again to obtain a spectrum model M';
e. and comparing the spectrum models M and M' to judge the model effect.
2. The method for wavelength point recombination based on characteristic peaks according to claim 1, wherein the step a is specifically as follows:
and acquiring spectral data of the sample to be detected by using a wavelength-sharing portable near-infrared spectrometer, carrying out one-to-one correspondence between the spectral data and the component concentration of the sample to be detected, and carrying out PLS modeling after the correspondence is completed to obtain an original data spectral model.
3. The method for wavelength point recombination based on characteristic peaks according to claim 1, wherein the step c is specifically as follows:
setting the spectral resolution of the wavelength-sharing portable near-infrared spectrometer as P and the waveband range as W, wherein the number X of wavelength points contained in the spectrometer is as follows:
X＝W/P
increasing the density of wavelength points near the characteristic peak to N times of the original density, recombining the spectral wavelength points, and determining the wavelength range P between two wavelength points around the spectral characteristic peak after recombination1Comprises the following steps:
P1＝P/N
setting T characteristic peaks of the sample to be detected, wherein the number of wavelength points distributed by each characteristic peak is the same and is equal to the number of wavelength points of non-characteristic peaks, and the number H of the wavelength points distributed by each characteristic peak is as follows:
total wavelength range W of T characteristic peaks1Comprises the following steps:
non-characteristic peak band range W2Comprises the following steps:
W2＝W-W1
the number of the wavelength points of the non-characteristic peak is H, the wavelength range P between two adjacent wavelength points after the non-characteristic peak is subjected to wavelength recombination2Comprises the following steps:
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| CN114018861A (en) * | 2021-10-28 | 2022-02-08 | 四川启睿克科技有限公司 | Spectral reconstruction method based on characteristic peak |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114018861A (en) * | 2021-10-28 | 2022-02-08 | 四川启睿克科技有限公司 | Spectral reconstruction method based on characteristic peak |
| CN114280002A (en) * | 2021-12-16 | 2022-04-05 | 宜宾五粮液股份有限公司 | Abnormal fermented grain spectrum screening method based on characteristic peak determination |
| CN114279962A (en) * | 2021-12-21 | 2022-04-05 | 四川启睿克科技有限公司 | Illumination self-adaption method based on portable near-infrared spectrometer |
| CN114354537A (en) * | 2022-01-14 | 2022-04-15 | 四川启睿克科技有限公司 | Abnormal spectrum discrimination method based on American ginseng |
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