CN116720067A - Mass spectrogram global peak information feature description method and device based on oscillation signals - Google Patents
Mass spectrogram global peak information feature description method and device based on oscillation signals Download PDFInfo
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Abstract
The embodiment of the invention discloses a mass spectrogram global peak information feature description method and device based on an oscillation signal, wherein the method comprises the following steps: obtaining a target mass spectrogram, and performing smoothing treatment on the target mass spectrogram; carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero area method, intercepting spectrogram data of a peak section, and splicing the spectrogram data into first peak spectrogram data; intercepting and splicing the second peak spectrogram data from the target mass spectrogram; extracting a random decrement mass spectrum signal of the second peak spectrogram data by using an RDT algorithm; processing the random decrement mass spectrum signal by using a PCA algorithm to obtain a main component mass spectrum signal; carrying out signal identification on the main component mass spectrum signal by using a Prony algorithm to obtain an oscillation attenuation factor vector; and constructing a global peak intrinsic descriptive feature vector of the target mass spectrogram. The embodiment of the invention can solve the problem that discrete peak information data in an original mass spectrogram is not easy to describe by independent features.
Description
Technical Field
The embodiment of the invention relates to the technical field of mass spectrogram data processing, in particular to a mass spectrogram global peak information characteristic description method and device based on an oscillating signal.
Background
The existing feature construction method is mostly carried out from a mathematical perspective by weighting a certain ion feature in a mass spectrogram, compressing SVD (Single Value Decomposition singular value decomposition) and the like. When the mass spectrogram data set is subjected to data mining, most of the existing mass spectrum characteristic description methods are constructed based on mathematical statistics such as variance, mean value and correlation coefficient, and when the statistical information is used as characteristics for learning, the characteristic dimension which can be suitable for describing the mass spectrogram information is lacking, and the peak shape of an ion peak in the mass spectrogram cannot be well represented.
Disclosure of Invention
In order to overcome the defects of the prior art, the embodiment of the invention aims to provide a mass spectrogram global peak information feature description method and device based on an oscillation signal, which can solve the problem that discrete peak information data in an original mass spectrogram is not easy to describe by independent features.
In order to solve the problem, a first aspect of the embodiment of the present invention discloses a mass spectrogram global peak information feature description method based on an oscillation signal, which includes:
obtaining a target mass spectrum, and smoothing the target mass spectrum to obtain a smoothed pure mass spectrum;
Carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero area method, intercepting spectrogram data of a peak section, and splicing the spectrogram data into first peak spectrogram data;
comparing the peak interval of the pure mass spectrogram, and intercepting and splicing the peak interval from the target mass spectrogram to obtain second peak spectrogram data corresponding to the first peak spectrogram data;
extracting a random decrement mass spectrum signal of the second peak spectrogram data by using an RDT algorithm of an extremum trigger condition;
processing the random decrement mass spectrum signal by using a PCA algorithm to obtain a main component mass spectrum signal;
carrying out signal identification on the main component mass spectrum signal by using a Prony algorithm to obtain an oscillation attenuation factor vector, wherein the oscillation attenuation factor vector comprises oscillation amplitude, frequency, phase and attenuation factor of the main component mass spectrum signal;
and constructing a global peak intrinsic descriptive feature vector of the target mass spectrogram based on the principal component mass spectrum signal or the oscillation attenuation factor vector.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the smoothing processing is performed on the target mass spectrum to obtain a smoothed pure mass spectrum, including:
and processing the target mass spectrogram by using an N-order SG smoothing algorithm to obtain a smoothed pure mass spectrogram.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the processing the target mass spectrum by using an N-order SG smoothing algorithm to obtain a smoothed pure mass spectrum, and then further includes:
calculating a signal-to-noise ratio (SNR) and a Root Mean Square Error (RMSE) of the target mass spectrogram by taking the pure mass spectrogram as a pure signal and the target mass spectrogram as a sampling signal, and if the SNR is smaller than or equal to a SNR threshold sigma and the RMSE is between [ beta, gamma ], carrying out the peak searching operation, wherein beta and gamma are respectively a lower threshold limit and an upper threshold limit of the RMSE;
otherwise, comparing the current running times num with the maximum times iterMax for processing the target spectrogram by using an N-order SG smoothing algorithm, wherein the initial value of num is 1, when the current running times num is smaller than the maximum times iterMax, the size of N is reduced by 1, the size of num is increased by 1, and the target spectrogram is processed by using the N-order SG smoothing algorithm again, and if the current running times num is larger than or equal to the maximum times iterMax, the peak searching operation is carried out.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the peak searching operation is performed on the pure mass spectrum by using a symmetric zero-area method, spectrum data of a peak section is intercepted, and the spectrum data is spliced into first peak spectrum data, including:
Carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero-area method, and recording the abscissa Start of a peak starting point and the abscissa End of a peak End point;
comparing the minimum modal period T of the RDT with (Start+end)/n, and selecting a smaller value of the minimum modal period T of the RDT and the (Start+end)/n as a interception length L, wherein n is the proportion of interception peak intervals, and the minimum modal period T of the RDT and the proportion n of the interception peak intervals are preset values;
judging the relation between the peak interval S between the target peak and the adjacent following peak and the interception length L;
when S is more than or equal to L, intercepting spectrogram data with the length L from the target peak to the subsequent peak, and taking the spectrogram data as peak interval spectrogram data of the target peak;
when L/8 is less than S and less than L, intercepting all spectrogram data between the target peak and the rear peak as peak interval spectrogram data of the target peak;
when S is less than or equal to L/8, peak interval spectrum data of the target peak is not intercepted;
and splicing peak interval spectrogram data corresponding to all peaks to obtain the first peak spectrogram data.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the capturing and splicing the second peak spectrum data corresponding to the first peak spectrum data from the target mass spectrum against the peak interval of the pure mass spectrum includes:
And intercepting and splicing the peak spectrogram data of the target mass spectrogram according to the abscissa of the pure mass spectrogram corresponding to the first peak spectrogram data to obtain the second peak spectrogram data.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the extracting the randomly decremented mass spectrum signal of the second peak spectrogram data using the RDT algorithm of the extremum trigger condition includes:
smoothing the second peak spectrogram data through an N+5-order SG smoothing algorithm to obtain pure peak spectrogram data;
and extracting the random decrement mass spectrum signals of the pure peak spectrogram data by using an RDT algorithm of an extremum trigger condition.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the processing the randomly reduced mass spectrum signal using a PCA algorithm to obtain a main component mass spectrum signal includes:
and extracting the first 80% -95% of main component mass spectrum signals in the random decrement mass spectrum signals by using a PCA algorithm, and taking the main component mass spectrum signals as the main component mass spectrum signals.
In a first aspect of the embodiment of the present invention, as a preferred implementation manner, the performing signal identification on the main component mass spectrum signal by using a Prony algorithm to obtain an oscillation attenuation factor vector includes:
Carrying out signal identification on the main component mass spectrum signal by adopting a Prony algorithm to obtain an oscillation amplitude, a phase, a frequency and a damping factor of the main component mass spectrum signal, wherein the Prony algorithm adopts a model parameter estimation principle of least square sum of errors;
constructing a plurality of groups of oscillation attenuation factor vectors based on oscillation amplitude, phase, frequency and attenuation factors of the main component mass spectrum signals, wherein the expression form of the oscillation attenuation factor vectors is as follows:
P i ＝(A i ,θ i ,f i ,α i )
wherein P is i Is the i-th oscillation damping factor vector; a is that i 、θ i 、f i And alpha i The oscillation amplitude, the phase, the frequency and the attenuation factor corresponding to the ith oscillation attenuation factor vector are respectively;
constructing a global peak intrinsic descriptive feature vector of the target mass spectrum based on the principal component mass spectrum signal or oscillation attenuation factor vector, comprising:
and selecting one or more parameters in the oscillation attenuation factor vector to construct a global peak intrinsic description feature vector of the target mass spectrogram, or selecting one or more parameters in the main component mass spectrum signal as the global peak intrinsic description feature vector of the target mass spectrogram.
The second aspect of the embodiment of the invention discloses a mass spectrogram global peak information characteristic description device based on an oscillation signal, which comprises the following components:
The first processing unit is used for acquiring a target mass spectrum, and smoothing the target mass spectrum to obtain a smoothed pure mass spectrum;
the peak searching unit is used for carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero area method, intercepting spectrogram data of a peak section and splicing the spectrogram data into first peak spectrogram data;
the control unit is used for controlling the peak interval of the pure mass spectrogram, intercepting and splicing the peak interval from the target mass spectrogram to obtain second peak spectrogram data corresponding to the first peak spectrogram data;
the extraction unit is used for extracting the random decrement mass spectrum signal of the second peak spectrogram data by using an RDT algorithm of the extremum triggering condition;
the second processing unit is used for processing the random decrement mass spectrum signals by using a PCA algorithm to obtain main component mass spectrum signals;
the third processing unit is used for carrying out signal identification on the main component mass spectrum signals by using a Prony algorithm to obtain oscillation attenuation factor vectors, wherein the oscillation attenuation factor vectors comprise oscillation amplitude, frequency, phase and attenuation factors of the main component mass spectrum signals;
and the construction unit is used for constructing a global peak intrinsic descriptive feature vector of the target mass spectrogram based on the principal component mass spectrum signal or the oscillation attenuation factor vector.
A third aspect of the embodiment of the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps of the method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal disclosed in the first aspect of the embodiment of the present invention when the processor executes the computer program.
A fourth aspect of the embodiment of the present invention discloses a computer-readable storage medium storing a computer program, where the computer program causes a computer to execute the steps of the method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal disclosed in the first aspect of the embodiment of the present invention.
A fifth aspect of the embodiments of the present invention discloses a computer program product, which when run on a computer causes the computer to perform the steps of the method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal disclosed in the first aspect of the embodiments of the present invention.
A sixth aspect of the embodiment of the present invention discloses an application publishing platform, which is configured to publish a computer program product, where the computer program product when run on a computer causes the computer to execute the steps of the method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal disclosed in the first aspect of the embodiment of the present invention.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
when the embodiment of the invention is used for carrying out data mining on the spectrogram data, the constructed peak shape description feature vector, oscillation amplitude, frequency, phase and attenuation factor feature description can better represent all peak information in a spectrogram, any dimension of the feature can independently represent all peak information of a spectrogram, namely, global peak information is converted and compressed to a dimension more suitable for data mining, and the problem that discrete peak information data in an original spectrogram is difficult to describe by using independent features is solved.
Drawings
Fig. 1 is a schematic flow diagram of a method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to an embodiment of the present invention;
fig. 2 is a second flow chart of a method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a mass spectrogram global peak information feature description system based on an oscillation signal according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
This detailed description is merely illustrative of the embodiments of the invention and is not intended to limit the embodiments of the invention, since modifications of the embodiments can be made by those skilled in the art without creative contribution as required after reading the specification, but are protected by the patent laws within the scope of the claims of the embodiments of the invention.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the embodiments of the present application.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In embodiments of the application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
The preferred application of the invention is in a bioaerosol single particle mass spectrometer (Biological Aerosol Single-Particle Mass Spectrometer), which is an instrument for analyzing and detecting bioaerosol particles in air. The method adopts mass spectrometry technology, and can analyze and measure the bioaerosol in the air at a single particle level in real time. The bioaerosol single-particle mass spectrometer has important application value in the fields of environmental monitoring, air quality evaluation, bioaerosol research and the like. It can provide information about the source, route of transmission and health risks of the bioaerosol, helping to understand the impact of bioaerosols on human health and environment
The bioaerosol single-particle mass spectrometer utilizes mass spectrometry technology to ionize and analyze particles by introducing bioaerosol particles in an air sample into a mass spectrometer system. It can detect and identify different types of bioaerosol particles, including bacteria, fungi, pollen, viruses, etc. At the same time, it can also provide information about the mass, size, chemical composition and quantity of the particles. This information can be used as input data in conjunction with biometric machine learning techniques to classify and identify bioaerosol particles.
The bio-classification machine learning technique can learn the characteristics and patterns of bio-aerosol particles by training algorithms and models and categorize them into different biological categories such as bacteria, fungi, pollen, viruses, etc. Common techniques for machine learning of biological classification include Support Vector Machines (SVMs), random Forest (Random Forest), deep learning, and the like.
By combining the data acquired by the bioaerosol single-particle mass spectrometer with a trained bioaerosol classification machine learning model, the bioaerosol particles can be automatically classified and identified. This is of great importance for understanding the composition, source and impact of bioaerosols on human health and the environment, providing powerful tools and methods for related research and applications.
The impact of peak information of bioaerosol mass spectrometry on classification is important. Mass spectrometry techniques can provide information about the mass, size, and relative abundance of compounds in an aerosol. These peak information contains measurements of mass-to-charge ratios (m/z) and relative abundances of different chemical compounds in the aerosol sample. Such peak information is critical for classification and identification of bioaerosols. The following are some aspects of the impact of peak information on classification:
1. Compound characteristics: different types of bioaerosols may contain specific compounds or metabolites that exhibit different peaks in the mass spectrum. By analyzing these peaks, different types of bioaerosols can be identified and distinguished.
2. Peak morphology and distribution: the peak morphology and peak distribution pattern in the mass spectrum can also provide information of classification. Different types of bioaerosol samples may have different peak shapes and peak distribution characteristics that may be used as input features for the classification algorithm.
3. Comparison of relative abundance: by comparing the relative abundances of the different peaks, differences in the abundances of certain specific compounds in different types of bioaerosols can be found. These abundance differences can also be used for feature extraction and classification decisions of the classification algorithm.
In summary, the influence of peak information of bioaerosol mass spectra on classification is based on the combined influence of factors such as the characteristics, peak shape and distribution, and relative abundance of compounds in different types of bioaerosols. Classification and identification of bioaerosol samples can be achieved by analyzing these peak information and utilizing machine learning classification algorithms to understand the source, composition and environmental impact of bioaerosols in depth.
The original peak information can be converted into a set of feature vectors by feature extraction conversion. These feature vectors may include various statistical features such as mean, standard deviation, peak area, etc., as well as other domain-related features such as peak shape, peak-to-peak distance, etc. In a preferred embodiment of the invention, peak shape characterization feature vectors, oscillation amplitude, frequency, phase and decay factor characteristics are used to characterize the nature and characteristics of the peaks, thereby providing more information for use in classification models.
Extracting and converting the features of all peaks into the above-described peak profile feature vector, oscillation amplitude, frequency, phase and attenuation factor can bring about the following meanings:
1. and (5) reducing the dimension and compressing data: the bioaerosol mass spectrum may contain a large amount of peak information, and the feature extraction and conversion of all peaks into several features can realize the data reduction and compression. This reduces the dimensionality of the data and simplifies the complexity of data analysis and processing. This helps to reduce and reduce the risk of overfitting.
2. Feature selection and importance assessment: the peak information in a spectrogram is neither good nor good for assessing the impact on the classification model. By feature extraction and transformation, the most representative and differentiated features can be selected. Some features may be more critical to classification and identification tasks, while other features may be relatively less important. Feature selection and importance assessment can help us understand which features are meaningful to classification, thereby optimizing classification models and algorithms. The dimension of the raw data can be reduced from the number of peaks to several key feature dimensions, which helps to improve the generalization ability and classification accuracy of the model.
3. Simplifying the model and improving efficiency: the feature extraction of all peaks is converted into a plurality of features, so that the complexity of a model can be simplified, the requirements on calculation and storage resources are reduced, and the efficiency and performance of an algorithm are improved. This is important for real-time or large-scale data processing.
4. Interpretation and visualization: converting peak features into several features can provide more intuitive and interpretable results. These features can be more easily combined with help to understand the contribution and meaning of peak information to classification.
Based on this, when the embodiment of the invention performs data mining on spectrogram data, the constructed peak shape description feature vector, oscillation amplitude, frequency, phase and attenuation factor feature description can better represent all peak information in a spectrogram, any dimension of the feature can independently represent all peak information of a spectrogram, namely, global peak information is converted and compressed to a dimension more suitable for data mining, so that the problem that discrete peak information data in an original spectrogram is difficult to describe by independent features is solved, and the method and the device are described in detail below with reference to the accompanying drawings.
Example 1
Referring to fig. 1 and fig. 2, fig. 1 and fig. 2 are schematic flow diagrams of a method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to an embodiment of the invention. As shown in fig. 1 and 2, the mass spectrogram global peak information characteristic description method based on the oscillation signal includes:
S110, obtaining a target mass spectrum, and smoothing the target mass spectrum to obtain a smoothed pure mass spectrum.
The target mass spectrum is a mass spectrum to be processed input from the mass spectrum data set and is recorded as a mass spectrum S.
Before or after the acquisition of the target mass spectrogram, some parameters used in the processing of the target mass spectrogram need to be set, including a window size W and an order N for an SG (Savitzky-Golay savitz-Golay filter also called moving window least square polynomial smoothing) smoothing algorithm, where the initial order N needs to be set to be greater than or equal to 5, further includes a Signal-Noise Ratio threshold σ used by an SNR (Signal-Noise Ratio), default may be set to be 3, and of course, may also be set to other values as required, further includes a threshold upper limit β and a threshold lower limit γ of an RMSE (Root Mean Square Error root mean square error) algorithm, and a minimum modal period T of an RDT (Random Decrement Technique random decrement technique) algorithm, and a proportion N of a truncated peak interval, where the minimum modal period T and the proportion N are used to ensure independence of truncated peak intervals.
And processing the target mass spectrogram by using an N-order SG smoothing algorithm to obtain a smoothed pure mass spectrogram, and marking the smoothed pure mass spectrogram as a mass spectrogram Y. In a preferred embodiment of the invention, the SG smoothing order for the RDT is selected by constructing the difference signal through multiple SG smoothing. Specifically:
taking the pure mass spectrogram Y as a pure signal, taking the target mass spectrogram S as a sampling signal, calculating the signal-to-noise ratio SNR and the root mean square error RMSE of the target mass spectrogram S, and if the signal-to-noise ratio SNR of the target mass spectrogram S is smaller than or equal to the signal-to-noise ratio threshold sigma and the root mean square error RMSE is between [ beta, gamma ], carrying out peak searching operation of the step S120;
otherwise, the current running times num and the maximum times iterMax of processing the target mass spectrogram by using the N-order SG smoothing algorithm are required to be compared, if the current running times num is smaller than the maximum times iterMax, the size of N is reduced by 1, the size of num is increased by 1, and the processing of the target mass spectrogram by using the N-order SG smoothing algorithm (which becomes N-1 order at this time) is executed again, if the current running times num is greater than or equal to the maximum times iterMax, the peak searching operation of the step S120 is performed.
S120, carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero area method, intercepting spectrogram data of a peak section, and splicing the spectrogram data into first peak spectrogram data.
Because the symmetrical zero-area method has stronger recognition capability on weak peaks, if the number of the peaks is less than 1, the target mass spectrogram S is an invalid spectrogram, and the operation on the target mass spectrogram S is finished, so that the next spectrogram can be processed; if the number of peaks is greater than or equal to 1, the Start point Start (i.e. the abscissa value corresponding to the peak with the smallest abscissa) and the End point End (i.e. the abscissa value corresponding to the peak with the largest abscissa) of the peak of the pure mass spectrum Y are recorded, and in addition, the peak information Center of the Center point of the pure mass spectrum Y may also be recorded.
Taking different peak areas of the mass spectrogram as sample intervals of the RDT algorithm in the step S140, and ensuring that each sub-sample of the RDT has a certain time interval by calculating the interval between every two peaks, wherein the specific process is as follows:
comparing the minimum modal period T of RDT with (Start+end)/n, selecting a smaller value of the minimum modal period T of RDT and (Start+end)/n as a interception length L, determining spectrogram data of a peak interval corresponding to each peak according to the interception length L and the peak interval H, defining the peak as a target peak by taking an acquisition process of the peak interval spectrogram data of a certain peak as an example, and describing the interception process:
judging the relation between the peak interval H between the target peak and the adjacent following peak (namely the absolute value of the difference value between the abscissa of the target peak and the abscissa of the following peak) and the interception length L;
If H is larger than or equal to L, the peak-to-peak spectrum data with L length is taken as peak interval spectrum data of the target peak, the mode of taking the spectrum data with L length is from the abscissa value of the target peak to the direction of the subsequent peak (assuming that the abscissa value of the target peak is X1, the abscissa corresponding to the taken spectrum data is from X1 to X1 +L), namely all the spectrum data with the abscissa value of X1 to X1 +L.
When L/8 is less than H and less than L, intercepting all spectrum data between the target peak and the rear peak as peak interval spectrum data of the target peak. Assuming that the abscissa of the target peak and the following peak are X1 and X2, respectively, all spectrogram data between the abscissa values of X1 to X2 are intercepted.
When H is less than or equal to L/8, the peak interval spectrum data of the target peak is not intercepted, namely, the target peak does not have the peak interval spectrum data.
The peak interval spectrum data of all peaks (the peak end point can be used for not cutting the peak interval spectrum data or cutting the peak interval spectrum data of L length backwards) are spliced in the mode, and the first peak spectrum data is obtained and recorded as spectrum data Y 1 。
S130, comparing the peak interval of the pure mass spectrogram, and intercepting and splicing the peak interval from the target mass spectrogram to obtain second peak spectrogram data corresponding to the first peak spectrogram data.
In the process of intercepting the first peak spectrogram data, acquiring a plurality of abscissa ranges of the first peak spectrogram data in the pure mass spectrogram, intercepting and splicing the peak spectrogram data of the target mass spectrogram corresponding to the abscissa ranges to acquire the second peak spectrogram data, and recording the second peak spectrogram data as spectrogram data Y 2 。
For example, when one of the first peak spectrum data corresponds to X1-X2 in the abscissa range of the pure mass spectrum, then the peak spectrum data of the target mass spectrum with X1-X2 in the abscissa range is also intercepted as one of the second peak spectrum data.
S140, extracting a random decrement mass spectrum signal of the second peak spectrogram data by using an RDT algorithm of an extremum trigger condition.
Extracting second peak spectrogram data Y using RDT algorithm 2 The second peak spectrogram data Y can also be processed by an N+5-order SG smoothing algorithm before the random decrement mass spectrum signal 2 And performing smoothing treatment to obtain pure peak spectrogram data, and extracting a random decrement mass spectrum signal of the pure peak spectrogram data by using an RDT algorithm of an extremum trigger condition.
The random decrement mass spectrum signal is marked as Y 3 . Second peak spectrogram data Y obtained by peak splicing 2 May include free vibration, broken vibration, and other disturbancesThe signal, but based on the structural free-decay signal extracted by RDT, mainly focuses on the free vibration characteristics, and based on the abscissa interval of the structural free-decay signal extracted by RDT, a new signal for calculating the global peak description characteristics can be constructed.
S150, processing the random decrement mass spectrum signals by using a PCA algorithm to obtain main component mass spectrum signals.
Y can be obtained by Prony algorithm 3 Is characterized by the use of PCA (Principal Component Analysis principal component analysis) for Y before applying the Prony algorithm 3 Processing is performed to reduce the recognition error of the Prony algorithm.
In a preferred embodiment of the present invention, a PCA algorithm may be used to extract the randomly decremented mass spectral signal Y 3 A main component mass spectrum signal of a part of the signals before the main component mass spectrum signal is recorded as a mass spectrum signal Y 4 . The front part of the signal can be set as required, and can be any value between 80% and 95%, for example, the PCA algorithm can be used for extracting the random decrement mass spectrum signal Y 3 The first 95% of the signals in (a) are taken as main component mass spectrum signals Y 4 。
S160, carrying out signal identification on the main component mass spectrum signals by using a Prony algorithm to obtain oscillation attenuation factor vectors, wherein the oscillation attenuation factor vectors comprise oscillation amplitude, frequency, phase and attenuation factors of the main component mass spectrum signals.
The signal identification is carried out on the main component mass spectrum signal by adopting a Prony algorithm (adopting the principle of error square sum to minimum as model parameter estimation), so that the oscillation amplitude, frequency, phase and attenuation factor of the main component mass spectrum signal can be obtained, and a plurality of groups of oscillation attenuation factor vectors (also called peak shape description characteristic vectors) can be obtained according to the oscillation amplitude, frequency, phase and attenuation factor of the main component mass spectrum signal and are recorded as oscillation attenuation factor vector Y 5 Wherein, the expression form of the oscillation damping factor vector can be:
P i ＝(A i ,θ i ,f i ,α i )
wherein P is i Is the i-th oscillation damping factor vector; a is that i 、θ i 、f i And alpha i The oscillation amplitude, phase, frequency and damping factor corresponding to the ith oscillation damping factor vector are respectively.
Thus Y 5 The overall expression of (c) may be:
Y 5 ＝[(A 1 ,θ 1 ,f 1 ,α 1 ),(A 2 ,θ 2 ,f 2 ,α 2 ),…,(A i ,θ i ,f i ,α i ),…(A m ,θ m ,f m ,α m )]
the oscillation amplitude, frequency, phase and attenuation factor of the main component mass spectrum signal are obtained by adopting the Prony algorithm, which is the prior conventional technology, and the specific processing process is not repeated.
S170, constructing a global peak intrinsic descriptive feature vector of the target mass spectrogram based on the principal component mass spectrum signal or the oscillation attenuation factor vector.
Due to the principal component mass spectrum signal Y 4 For randomly decrement mass spectrum signal Y 3 The mass spectrum signal obtained by PCA processing is also a free decaying mass spectrum signal, which comprises more parameters besides the oscillation amplitude, frequency, phase and decay factor parameters, and the oscillation decay factor vector is obtained from the main component mass spectrum signal Y 4 Is constructed by selecting important parameters from a plurality of parameters. Any one of the oscillation damping factor vector or the principal component mass spectrum signal can be used to characterize the global peak intrinsic descriptive feature vector of the target mass spectrum.
Therefore, any one or more parameters of the oscillation attenuation factor vector and the principal component mass spectrum signal can be selected (when the classification processing is performed when the parameters are selected, the classification result can be more accurate) to construct a global peak intrinsic descriptive feature vector of the target mass spectrogram, and of course, the oscillation attenuation factor vector or the whole of the principal component mass spectrum signal can also be directly used as the global peak intrinsic descriptive feature vector of the target mass spectrogram, but since the principal component mass spectrum signal may contain dozens or hundreds of parameters, the input of all parameters of the principal component mass spectrum signal into the classification model has higher requirements on the performance of equipment, and the time taken for outputting the result is longer.
In the preferred embodiment of the invention, the damping factor vector Y can be obtained from 5 The characteristic vector of any one parameter or a plurality of parameters is selected from the oscillation amplitude, frequency, phase and attenuation factors to construct the global peak intrinsic descriptive characteristic vector of the target mass spectrogram.
The global peak intrinsic descriptive feature vector of the target mass spectrogram may be any one of oscillation amplitude, frequency, phase and attenuation factor, for example, the global peak intrinsic descriptive feature vector Q may pass through the oscillation attenuation factor vector Y 5 Characterized by an oscillation amplitude vector of (a), namely:
Q＝[A 1 ,A 2 ,…,A i ,…A m ]
it is also possible to construct by means of a plurality of parameters, for example the global peak intrinsic descriptive eigenvector Q can be represented by the oscillation damping factor vector Y 5 Is characterized by the oscillation amplitude vector and the attenuation factor, namely:
Q＝[(A 1 ,α 1 ),(A 2 ,α 2 ),…,(A i ,α i ),…(A m ,α m )]
of course, the oscillation damping factor vector Y can also be directly used 5 The feature vector Q is described in global peak as a target mass spectrum, namely:
Q＝[(A 1 ,θ 1 ,f 1 ,α 1 ),(A 2 ,θ 2 ,f 2 ,α 2 ),…,(A i ,θ i ,f i ,α i ),…(A m ,θ m ,f m ,α m )]
of course, in other embodiments, part of the eigenvalues of the above parameters may be selected as global peak intrinsic descriptive eigenvectors of the target mass spectrogram, for example, B sets (B < m) of oscillation attenuation factor vectors may be selected to construct global peak intrinsic descriptive eigenvectors of the target mass spectrogram.
After the global peak intrinsic description feature vector of the target mass spectrogram is obtained, the characteristics and modes of the bioaerosol particles can be learned by using a biological classification machine learning technology through training algorithms and models, and the bioaerosol particles are classified into different biological categories, such as bacteria, fungi, pollen, viruses and the like. Common techniques for machine learning for biological classification may be Support Vector Machines (SVMs), random Forest (Random Forest), deep learning, etc.
By combining the data acquired by the bioaerosol single-particle mass spectrometer with a trained bioaerosol classification machine learning model, the bioaerosol particles can be automatically classified and identified, which has great significance for understanding the composition, the source and the influence on human health and environment of the bioaerosol, and provides powerful tools and methods for related research and application.
Example two
Referring to fig. 3, fig. 3 is a schematic structural diagram of a mass spectrogram global peak information feature describing device based on an oscillation signal according to an embodiment of the present invention. As shown in fig. 3, the mass spectrogram global peak information characterization device based on the oscillation signal may include:
a first processing unit 310, configured to obtain a target mass spectrum, and perform smoothing processing on the target mass spectrum to obtain a smoothed pure mass spectrum;
The peak searching unit 320 is configured to perform a peak searching operation on the pure mass spectrogram by using a symmetrical zero-area method, intercept spectrogram data of a peak section, and splice the spectrogram data into first peak spectrogram data;
a comparison unit 330, configured to compare peak intervals of the pure mass spectrogram, intercept and splice the pure mass spectrogram from the target mass spectrogram to obtain second peak spectrogram data corresponding to the first peak spectrogram data;
an extracting unit 340, configured to extract a randomly decremented mass spectrum signal of the second peak spectrogram data using an RDT algorithm of an extremum trigger condition;
a second processing unit 350, configured to process the randomly reduced mass spectrum signal by using a PCA algorithm to obtain a main component mass spectrum signal;
a third processing unit 360, configured to perform signal identification on the main component mass spectrum signal by using a Prony algorithm, so as to obtain an oscillation attenuation factor vector, where the oscillation attenuation factor vector includes an oscillation amplitude, a frequency, a phase and an attenuation factor of the main component mass spectrum signal;
a construction unit 370, configured to construct a global peak intrinsic descriptive feature vector of the target mass spectrum based on the principal component mass spectrum signal or the oscillation damping factor vector.
Example III
Referring to fig. 4, fig. 4 is a schematic diagram of an electronic device that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the embodiments of the invention described and/or claimed herein.
As shown in fig. 4, the electronic device includes at least one processor 410, and a memory, such as a ROM (read only memory) 420, a RAM (random access memory) 430, etc., communicatively connected to the at least one processor 410, wherein the memory stores a computer program executable by the at least one processor, and the processor 410 can perform various suitable actions and processes according to the computer program stored in the ROM 420 or the computer program loaded from the storage unit 680 into the random access memory RAM 430. In the RAM 430, various programs and data required for the operation of the electronic device may also be stored. The processor 410, ROM 420, and RAM 430 are connected to each other by a bus 440. An I/O (input/output) interface 450 is also connected to bus 440.
A number of components in the electronic device are connected to the I/O interface 450, including: an input unit 460 such as a keyboard, a mouse, etc.; an output unit 470 such as various types of displays, speakers, and the like; a storage unit 480 such as a magnetic disk, an optical disk, or the like; and a communication unit 490, such as a network card, modem, wireless communication transceiver, etc. The communication unit 490 allows the electronic device to exchange information/data with other devices via a computer network, such as the internet, or/and various telecommunications networks.
Processor 410 can be a variety of general-purpose or/and special-purpose processing components having processing and computing capabilities. Some examples of processor 410 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 410 performs one or more steps of a mass spectrum global peak information characterization method based on an oscillating signal as described in embodiment one above.
In some embodiments, a mass spectrogram global peak information characterization method based on an oscillation signal may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 480. In some embodiments, part or all of the computer program may be loaded onto and/or installed onto the electronic device via ROM 420 or/and communication unit 490. When the computer program is loaded into RAM 430 and executed by processor 410, one or more steps of a spectrogram global peak information characterization method based on an oscillating signal as described in the above embodiment can be performed. Alternatively, in other embodiments, the processor 410 may be configured to perform a mass spectrogram global peak information characterization method based on the oscillating signal in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, or/and combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed or/and interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of embodiments of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
The method and the device for describing the global peak information characteristics of the mass spectrogram based on the oscillating signal disclosed by the invention are described in detail, and specific examples are applied to the description of the principle and the implementation mode of the invention, and the description of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. The mass spectrogram global peak information characteristic description method based on the oscillation signal is characterized by comprising the following steps of:
obtaining a target mass spectrum, and smoothing the target mass spectrum to obtain a smoothed pure mass spectrum;
carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero area method, intercepting spectrogram data of a peak section, and splicing the spectrogram data into first peak spectrogram data;
comparing the peak interval of the pure mass spectrogram, and intercepting and splicing the peak interval from the target mass spectrogram to obtain second peak spectrogram data corresponding to the first peak spectrogram data;
Extracting a random decrement mass spectrum signal of the second peak spectrogram data by using an RDT algorithm of an extremum trigger condition;
processing the random decrement mass spectrum signal by using a PCA algorithm to obtain a main component mass spectrum signal;
carrying out signal identification on the main component mass spectrum signal by using a Prony algorithm to obtain an oscillation attenuation factor vector, wherein the oscillation attenuation factor vector comprises oscillation amplitude, frequency, phase and attenuation factor of the main component mass spectrum signal;
and constructing a global peak intrinsic descriptive feature vector of the target mass spectrogram based on the principal component mass spectrum signal or the oscillation attenuation factor vector.
2. The method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to claim 1, wherein smoothing the target mass spectrogram to obtain a smoothed pure mass spectrogram comprises:
and processing the target mass spectrogram by using an N-order SG smoothing algorithm to obtain a smoothed pure mass spectrogram.
3. The method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to claim 2, wherein the method further comprises the steps of processing the target mass spectrogram by using an N-order SG smoothing algorithm to obtain a smoothed pure mass spectrogram:
Calculating a signal-to-noise ratio (SNR) and a Root Mean Square Error (RMSE) of the target mass spectrogram by taking the pure mass spectrogram as a pure signal and the target mass spectrogram as a sampling signal, and if the SNR is smaller than or equal to a SNR threshold sigma and the RMSE is between [ beta, gamma ], carrying out the peak searching operation, wherein beta and gamma are respectively a lower threshold limit and an upper threshold limit of the RMSE;
otherwise, comparing the current running times num with the maximum times iterMax for processing the target spectrogram by using an N-order SG smoothing algorithm, wherein the initial value of num is 1, when the current running times num is smaller than the maximum times iterMax, the size of N is reduced by 1, the size of num is increased by 1, and the target spectrogram is processed by using the N-order SG smoothing algorithm again, and if the current running times num is larger than or equal to the maximum times iterMax, the peak searching operation is carried out.
4. The method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to claim 1, wherein the peak searching operation is performed on the pure mass spectrogram by using a symmetrical zero area method, spectrogram data of a peak interval is intercepted, and the spectrogram data is spliced into first peak spectrogram data, and the method comprises the following steps:
Carrying out peak searching operation on the pure mass spectrogram by using a symmetrical zero-area method, and recording the abscissa Start of a peak starting point and the abscissa End of a peak End point;
comparing the minimum modal period T of the RDT with (Start+end)/n, and selecting a smaller value of the minimum modal period T of the RDT and the (Start+end)/n as a interception length L, wherein n is the proportion of interception peak intervals, and the minimum modal period T of the RDT and the proportion n of the interception peak intervals are preset values;
judging the relation between the peak interval S between the target peak and the adjacent following peak and the interception length L;
when S is more than or equal to L, intercepting spectrogram data with the length L from the target peak to the subsequent peak, and taking the spectrogram data as peak interval spectrogram data of the target peak;
when L/8 is less than S and less than L, intercepting all spectrogram data between the target peak and the rear peak as peak interval spectrogram data of the target peak;
when S is less than or equal to L/8, peak interval spectrum data of the target peak is not intercepted;
and splicing peak interval spectrogram data corresponding to all peaks to obtain the first peak spectrogram data.
5. The method of claim 4, wherein, in comparison with a peak interval of the pure mass spectrum, second peak spectrum data corresponding to the first peak spectrum data is obtained by cutting and splicing from the target mass spectrum, and the method comprises the steps of:
And intercepting and splicing the peak spectrogram data of the target mass spectrogram according to the abscissa of the pure mass spectrogram corresponding to the first peak spectrogram data to obtain the second peak spectrogram data.
6. The method of any one of claims 1-5, wherein extracting the randomly decremented mass spectrum signal of the second peak spectrogram data using an extremum triggered conditional RDT algorithm comprises:
smoothing the second peak spectrogram data through an N+5-order SG smoothing algorithm to obtain pure peak spectrogram data;
and extracting the random decrement mass spectrum signals of the pure peak spectrogram data by using an RDT algorithm of an extremum trigger condition.
7. The method for describing global peak information characteristics of a mass spectrogram based on an oscillation signal according to any one of claims 1 to 5, wherein the processing the randomly reduced mass spectrum signal by using a PCA algorithm to obtain a main component mass spectrum signal comprises:
and extracting the first 80% -95% of main component mass spectrum signals in the random decrement mass spectrum signals by using a PCA algorithm, and taking the main component mass spectrum signals as the main component mass spectrum signals.
8. The method for describing global peak information characteristics of a mass spectrum based on an oscillation signal according to any one of claims 1 to 5, wherein the signal identification is performed on the main component mass spectrum signal by using a Prony algorithm to obtain an oscillation attenuation factor vector, and the method comprises the following steps:
Carrying out signal identification on the main component mass spectrum signal by adopting a Prony algorithm to obtain an oscillation amplitude, a phase, a frequency and a damping factor of the main component mass spectrum signal, wherein the Prony algorithm adopts a model parameter estimation principle of least square sum of errors;
constructing a plurality of groups of oscillation attenuation factor vectors based on oscillation amplitude, phase, frequency and attenuation factors of the main component mass spectrum signals, wherein the expression form of the oscillation attenuation factor vectors is as follows:
P i ＝(A i ,θ i ,f i ,α i )
wherein P is i Is the i-th oscillation damping factor vector; a is that i 、θ i 、f i And alpha i The oscillation amplitude, the phase, the frequency and the attenuation factor corresponding to the ith oscillation attenuation factor vector are respectively;
constructing a global peak intrinsic descriptive feature vector of the target mass spectrum based on the principal component mass spectrum signal or oscillation attenuation factor vector, comprising:
and selecting one or more parameters in the oscillation attenuation factor vector to construct a global peak intrinsic description feature vector of the target mass spectrogram, or selecting one or more parameters in the main component mass spectrum signal as the global peak intrinsic description feature vector of the target mass spectrogram.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the oscillating signal based mass spectrum global peak information characterization method according to any one of claims 1-8 when the computer program is executed.
10. A computer-readable storage medium, characterized in that it stores a computer program, wherein the computer program causes a computer to execute the steps of the oscillation signal-based mass spectrogram global peak information characteristic describing method according to any one of claims 1 to 8.
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