CN110379466B - Thermodynamic spectrum solving method and device, electronic equipment and storage medium - Google Patents
Thermodynamic spectrum solving method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a thermodynamic spectrum solving method, a thermodynamic spectrum solving device, electronic equipment and a storage medium, wherein the thermodynamic spectrum solving method can be carried out based on an isothermal titration calorimeter, and comprises the following steps: obtaining a concentration group X of a titrant A in a sample pool, a concentration group M of a dripped object B in the sample pool and a generated reaction heat group Q after each sample introduction; obtaining each sample introduction weight array Wsigma(ii) a According to element values of X, M and Q, taking the concentration of the titrant A and the concentration of the object B of each sample as input, taking the reaction heat of each sample as output, performing thermodynamic parameter fitting through a set function for fitting, and according to the weight array WsigmaAnd adjusting thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result. According to the technical scheme of the embodiment of the disclosure, the accuracy of obtaining the fitting thermodynamic parameters can be improved, and the fitting accuracy can be improved.
Description
Technical Field
The embodiment of the disclosure relates to the field of drug research, in particular to a thermodynamic spectrum solving method, a thermodynamic spectrum solving device, electronic equipment and a storage medium.
Background
Isothermal titration microcalorimetry is an authoritative method for representing thermodynamic properties of a solution process, and is widely applied to the fields of biochemistry, supramolecular chemistry, drug research and development, material science and the like.
The typical isothermal titration calorimeter mainly comprises a measuring unit and a sample feeding system, wherein the sample feeding system comprises a titration injector with a stirring paddle and a titration injector driving handle containing a stepping motor. The stepping motor controls the titration injector to accurately deliver the titrant to the reaction cell at preset volume and time intervals, while the measuring unit measures the heat change caused by the reaction.
The original data of the isothermal titration calorimeter is thermal power, a thermal pulse is generated every time a titrant is injected, the integral heat of every sample injection can be obtained by integrating a thermal power curve of sample injection interval time, and a scatter diagram between reaction heat and reaction progress can be obtained by combining the change of solution concentration. The method for performing thermodynamic solution spectroscopy comprises the steps of fitting an integral heat scatter diagram by using a chemical equilibrium model to obtain key thermodynamic parameters of the reaction, wherein the key thermodynamic parameters comprise enthalpy, equilibrium constants and stoichiometric ratio, and obtaining the key thermodynamic parameters of the reaction after performing thermodynamic solution spectroscopy by using a thermodynamic analysis tool carried by the conventional isothermal titration calorimeter, wherein the obtained key thermodynamic parameters have large errors and the fitting precision is low.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a thermodynamic spectrum solution method, apparatus, electronic device, and storage medium, so as to improve accuracy of obtaining fitting thermodynamic parameters.
Additional features and advantages of the disclosed embodiments will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosed embodiments.
In a first aspect, an embodiment of the present disclosure provides a thermodynamic spectroscopy method, including:
obtaining the concentration of a titrant A in the sample pool after each sample introduction to obtain a concentration array X, obtaining the concentration of a dripped object B in the sample pool after each sample introduction to obtain a concentration array M, and obtaining the reaction heat generated by each sample introduction to obtain a reaction heat array Q;
obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
according to the element values of the concentration array X, the concentration array M and the reaction heat array Q, the concentration of the titrant A and the concentration of the object B which are injected for each time are respectively taken as input, the reaction heat of each injection is taken as output, thermodynamic parameter fitting is carried out through a set function for fitting, and the weight array W is used for calculating the weight of the titrant A and the reaction heat of each injectionsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
In one embodiment, the set function is used for calculating the theoretical heat of each sample injection;
according to the weight array WsigmaThe step of adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm for iterative fitting comprises the following steps: calculating the residue weighted by the difference value of the theoretical heat and the reaction heat array Q after each sample injection by the following formulaSum of squared differences:
Y＝sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma-i)2
y is the residual sum of squares after the theoretical heat quantity after each sample introduction and the difference value of the reaction heat array Q are weighted;
calling _ Q () is the set function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
and adjusting the thermodynamic parameters according to the residual sum of squares to carry out iterative fitting so as to minimize the residual sum of squares.
In one embodiment, the weight of the result of each sample injection is obtained to obtain a weight array WsigmaThe method comprises the following steps:
determining the weight array W according to historical experimental datasigma；
Or determining the weight array W according to the stability degree of experimental data of each sample injectionsigma。
In one embodiment, the weight of the result of each sample injection is obtained to obtain a weight array WsigmaThe method comprises the following steps:
the weight array WsigmaEach element is assigned the same set initial value, for example, each element is assigned an initial value of 1;
in the iterative fitting process, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaThe values of the elements.
In one embodiment, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaValue of each elementThe method comprises the following steps: adjusting the weight array W according to the stability degree of the historical experimental data or the experimental data of each sample injection and the deviation of the intermediate result of iterative fittingsigmaThe values of the elements.
In one embodiment, fitting by the set function for fitting further comprises setting an initial value of the fitted thermodynamic parameter.
In a second aspect, an embodiment of the present disclosure further provides a thermodynamic spectrum solving apparatus, including:
the experimental data acquisition unit is used for acquiring the concentration of a titrant A in the sample pool after each sample introduction to obtain a concentration array X, acquiring the concentration of a dripped object B in the sample pool after each sample introduction to obtain a concentration array M and acquiring reaction heat generated by each sample introduction to obtain a reaction heat array Q;
a weight obtaining unit for obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
a fitting iteration unit for respectively taking the concentration of the titrant A and the concentration of the dripped object B of each sample introduction as input and the reaction heat of each sample introduction as output according to the element values of the concentration array X, the concentration array M and the reaction heat array Q, performing thermodynamic parameter fitting through a set function for fitting, and fitting according to the weight array WsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
In one embodiment, the set function is used for calculating the theoretical heat of each sample injection;
the fitting iteration unit is used for: and calculating the weighted residual sum of squares of the difference values of the theoretical heat and the reaction heat array Q after each sample injection by the following formula:
Y＝sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma-i)2
y is the residual sum of squares after the theoretical heat quantity after each sample introduction and the difference value of the reaction heat array Q are weighted;
calling _ Q () is the set function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
and adjusting the thermodynamic parameters according to the residual sum of squares to carry out iterative fitting so as to minimize the residual sum of squares.
In one embodiment, the weight obtaining unit is configured to:
determining the weight array W according to historical experimental datasigma；
Or determining the weight array W according to the stability degree of experimental data of each sample injectionsigma。
In one embodiment, the weight obtaining unit is configured to:
the weight array WsigmaEach element is assigned the same set initial value, for example, each element is assigned an initial value of 1;
in the iterative fitting process, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaThe values of the elements.
In one embodiment, the weight obtaining unit is configured to: adjusting the weight array W according to the stability degree of the historical experimental data or the experimental data of each sample injection and the deviation of the intermediate result of iterative fittingsigmaThe values of the elements.
In an embodiment, the fitting iteration unit is further configured to set an initial value of the fitting thermodynamic parameter.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the instructions of the method of any one of the first aspects.
In a fourth aspect, the disclosed embodiments also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method according to any one of the first aspect.
The volume array V of each sample injection is obtained in the embodiment of the disclosureinjObtaining a concentration group X of a titrant A in the sample pool, a concentration group M of a dripped object B in the sample pool and a generated reaction heat group Q after each sample introduction; obtaining each sample introduction weight array Wsigma(ii) a According to element values of X, M and Q, taking the concentration of the titrant A and the concentration of the object B of each sample as input, taking the reaction heat of each sample as output, performing thermodynamic parameter fitting through a set function for fitting, and according to the weight array WsigmaAnd adjusting thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result. According to the technical scheme of the embodiment of the disclosure, the accuracy of obtaining the fitting thermodynamic parameters can be improved, and the fitting accuracy can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly described below, and it is obvious that the drawings in the following description are only a part of the embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the contents of the embodiments of the present disclosure and the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a thermodynamic spectroscopy method provided by an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram of another thermodynamic spectroscopy method provided by embodiments of the present disclosure;
FIG. 3 is a graphical representation of experimental results of experimental data reflecting heat and fitted curve images provided by an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a thermodynamic spectroscopy apparatus provided by an embodiment of the present disclosure;
FIG. 5 illustrates a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
In order to make the technical problems solved, technical solutions adopted and technical effects achieved by the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments, but not all embodiments, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
It should be noted that the terms "system" and "network" are often used interchangeably in the embodiments of the present disclosure. Reference to "and/or" in embodiments of the present disclosure is meant to include any and all combinations of one or more of the associated listed items. The terms "first", "second", and the like in the description and claims of the present disclosure and in the drawings are used for distinguishing between different objects and not for limiting a particular order.
It should also be noted that, in the embodiments of the present disclosure, each of the following embodiments may be executed alone, or may be executed in combination with each other, and the embodiments of the present disclosure are not limited specifically.
The technical solutions of the embodiments of the present disclosure are further described by the following detailed description in conjunction with the accompanying drawings.
Fig. 1 shows a schematic flowchart of a thermodynamic spectroscopy solution method provided in an embodiment of the present disclosure, which may be applied to a case of performing thermodynamic spectroscopy based on an isothermal titration calorimeter to obtain a fitted thermodynamic parameter, where the method may be performed by a thermodynamic spectroscopy solution apparatus configured in an electronic device, as shown in fig. 1, the thermodynamic spectroscopy solution method according to the embodiment includes:
in step S110, the concentration of the titrant a in the sample cell after each sample injection is obtained to obtain a concentration array X, the concentration of the dripped object B in the sample cell after each sample injection is obtained to obtain a concentration array M, and the reaction heat generated by each sample injection is obtained to obtain a reaction heat array Q.
When the titration calorimetry method is adopted for calorimetric measurement or analysis, the steps of obtaining the concentration of the titrant A in the sample pool after each sample introduction, obtaining the concentration of the dripped object B in the sample pool after each sample introduction and obtaining the reaction heat generated by each sample introduction are very conventional. In this embodiment, the step may use any method in the prior art to obtain the above data, which is not limited in this embodiment.
For example, the concentration X of titrant A in a titration syringeSInitial concentration M of object B in sample cell0Volume array V for each sample introductioninjAnd the effective volume V of the reaction cellCAnd determining the concentration of the titrant A in the sample pool after each sample introduction to obtain a concentration array X.
Specifically, the concentration of the titrant a in the sample cell after each sample injection can be calculated by adopting the following formula:
wherein, XiThe concentration of the titrant A in the sample pool after the ith sample introduction;
Xi-1the concentration of the titrant A in the sample cell before the ith sample introduction is shown;
Xsis the concentration of titrant A in the titration syringe;
Vithe volume of the ith sample injection;
Vcis the effective reaction volume of the sample cell.
For another example, the concentration X of titrimetric agent A in a titration syringe can be usedSInitial concentration M of object B in sample cell0Volume array for each sample introductionVinjAnd the effective volume V of the reaction cellCAnd determining the concentration of the dripped object B in the sample cell after each sample introduction to obtain a concentration array M.
Specifically, the concentration of the dripped object B in the sample cell after each sample injection can be calculated by the following formula:
wherein the content of the first and second substances,
Mithe concentration of the object B in the sample pool after the ith sample introduction;
Vithe volume of the ith sample injection;
Vcis the effective reaction volume of the sample cell;
Mi-1the concentration of the object B in the sample pool after the i-1 injection.
For another example, the heat of reaction generated after each sample injection can be calculated by the following formula:
Qi＝ΔH·n·[αi·Mi·Vc-αi-1·Mi-1·(Vc-Vi)]
wherein Q isiThe reaction heat generated for the ith sample injection;
Δ H is the molar reaction enthalpy;
n is a stoichiometric ratio;
Mithe total concentration of the object B in the sample pool after the ith sample introduction;
Mi-1the concentration of the object B in the sample pool after the i-1 th sample introduction;
αithe active site proportion of the dripped matter B which has participated in the reaction after the ith sample introduction;
αi-1the active site proportion of the dripped matter B which has participated in the reaction after the i-1 th sample introduction;
wherein alpha isiThe following formula can be used for calculation:
Kais the binding constant of the chemical reaction;
it should be noted that, the arrays and the parameters in this step are not limited to a specific obtaining manner in this embodiment, and various obtaining methods can be adopted, and the calculation formula involved in the obtaining method is only an example manner, and the disclosure range of the present application cannot be limited accordingly.
In step S120, the result weight of each sample injection is obtained to obtain a weight array WsigmaAnd the result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result.
Obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe weight array W may be determined in a number of ways, for example from historical experimental datasigmaAnd if the weight array W can be determined according to the stability degree of the experimental data of each sample injectionsigma。
For another example, first, the weight array WsigmaCan be set to a uniform value and then adjusted in the calculation process. If, the weight array WsigmaAll elements are assigned with the same set initial value, for example, an initial value 1, and in the iterative fitting process, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaThe values of the elements.
Specifically, the adjustment may be performed according to the stability of the historical experimental data or the experimental data of each sample injection, and the deviation of the intermediate result of the iterative fitting.
In step S130, according to the element values of the concentration array X, the concentration array M, and the reaction heat array Q, the concentration of the titrant a and the concentration of the dripped object B in each sample are respectively used as input, the reaction heat in each sample is used as output, thermodynamic parameter fitting is performed through a set function for fitting, and the weight array W is used for fittingsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
For example, the set function is used to calculate the theoretical heat for each injection. According to the weight array WsigmaThe step of adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm for iterative fitting comprises the following steps: and calculating the weighted residual sum of squares of the difference values of the theoretical heat and the reaction heat array Q after each sample injection by the following formula:
Y＝sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma-i)2
y is the residual sum of squares after the theoretical heat quantity after each sample introduction and the difference value of the reaction heat array Q are weighted;
calling _ Q () is the set function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
and adjusting the thermodynamic parameters according to the residual sum of squares to carry out iterative fitting so as to minimize the residual sum of squares.
Further, in order to improve the convergence rate in the iterative fitting process and improve the efficiency of iterative fitting, an initial value of the fitting thermodynamic parameter may be set, the thermodynamic parameter is adjusted according to the deviation of the intermediate result of iterative fitting in the iterative fitting process so as to fit to the minimum sum of squared residuals, and the final thermodynamic parameter is determined according to the fitting result at this time.
In the embodiment, a concentration group X of a titrant A in a sample cell, a concentration group M of a dripped object B in the sample cell and a generated reaction thermal array Q are obtained after each sample introduction; obtaining each sample introduction weight array Wsigma(ii) a According to element values of X, M and Q, respectively in each stepTaking the concentration of a titrant A and the concentration of a dripped object B of the sample as input, taking the reaction heat of each sample introduction as output, carrying out thermodynamic parameter fitting through a set function for fitting, and carrying out thermodynamic parameter fitting according to a weight array WsigmaAnd adjusting thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result. According to the technical scheme of the embodiment of the disclosure, the accuracy of obtaining the fitting thermodynamic parameters can be improved, and the fitting accuracy can be improved.
Fig. 2 shows a schematic flow chart of another thermodynamic de-spectroscopy method provided by the embodiment of the present disclosure, and the embodiment illustrates a technical solution of the embodiment of the present disclosure in a view point of a program.
The main input parameters of the program include: initial concentration Xs of titrant A and initial concentration M of dripped object B0Heat array Q, fitting parameter initial value array (parameters0, default), sample volume array (V)inj) Effective volume V of reaction tankc。
The main output parameters of the program include: thermodynamic parameter array [ H Ka n]。
As shown in fig. 2, the process of the thermodynamic solution spectrum method according to the present embodiment to obtain the output variable according to the input variable includes:
in step S210, the concentration array X of the titrant a and the concentration array M of the dripped object B in the reaction cell after each dripping are calculated.
According to the initial concentration Xs of the titrant A and the initial concentration M of the object B to be dripped0Sample introduction volume array VinjEffective volume V of reaction tankcAnd calculating the concentration of the titrant A in the reaction tank after each dripping to obtain a concentration array X and the concentration of the dripped object B to obtain a concentration array M. The step S110 of the previous embodiment is referred to for a specific calculation method, which is not described herein again.
The concentration X of A and B reaching chemical equilibrium is dripped each timei，MiThe respective sets of numbers X, M are assembled, the units of which are converted to millimoles per liter.
In step S220, a least squares fit objective function is constructed, denoted by "call _ Q".
Namely, the reaction heat Q is calculated according to the reactant concentration by using an n site independent combination model. The purpose of this step is to construct the reaction concentration as a function of the heat of reaction, Q, in preparation for the next step of fitting.
The main input parameters of the step comprise: A. b concentration array X, M, sample volume array VinjAnd the effective volume Vc of the reaction tank.
The main output parameters of the step include: heat array Q.
The parameters to be fitted mainly include: h, Ka,n。
The detailed description does not refer to step S110 in the above embodiment, and this embodiment is not described herein.
In step S230, the integrated heat is fitted nonlinearly by iterative weighted least squares.
The purpose of the step is to obtain the values H and K of three unknown parameters in the function called _ Q in a fitting modeaAnd n is the final output result of the program.
The function call _ Q serves as an input to this step.
A fitting function is set, denoted by "Fit _ Q". The Fit _ Q function is used for performing nonlinear fitting on the integrated heat by an iterative weight least square method, and the fitted target function, namely the "called _ Q" constructed in step S220, obtains the thermodynamic parameter Ka、H、n。
The main input variables in this step include: heat array Q, A, B concentration array X, M, fitting parameter initial value array, weight array WsigmaSample introduction volume array VinjEffective volume V of reaction tankcAnd fitting an objective function call _ Q ().
The main output variables in this step include: fitting parameter array including thermodynamic parameters H, KaAnd n, wherein the covariance matrix, the fitting detailed information, the intermediate variable and the like can be selectively output.
WsigmaThe array can be designated by a user or automatically assigned by a program according to a statistical result, and the specific implementation method is that the program assigns the weight array WsigmaAssigning initial values to 1, and then performing pre-simulation on the heat array QFor example, the theoretical heat array can be calculated by fitting with ordinary least square method, and the weight array W can be calculated according to the absolute value of the deviation between the input array Q and the theoretical heat arraysigmaAnd (6) re-assigning.
The optimization function of the iterative least squares method at this time is as follows:
min[sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma)2]
wherein, call _ Q () is the setting function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
min [ ] is the minimum.
In step S240, the fitting parameters are output.
The main information output by the program is experimental data reaction Heat "and a Fitted curve" image as shown in fig. 3, the abscissa is the number of instillations (Injection number) at the time of sample Injection, the ordinate is the reaction Heat, and the blue line is the Fitted curve. Under the curve, the fitting parameter K is outputaΔ H, n as shown in fig. 3, other detailed fitting parameters may optionally be output in text form.
The embodiment is suitable for a mainstream isothermal titration calorimeter, can improve the accuracy of obtaining fitting thermodynamic parameters, and can improve the fitting accuracy.
Fig. 4 shows a schematic structural diagram of a thermodynamic spectrum solution apparatus provided in an embodiment of the present disclosure, and as shown in fig. 4, the thermodynamic spectrum solution apparatus according to this embodiment includes an experimental data obtaining unit 410, a weight obtaining unit 420, and a fitting iteration unit 430.
The experimental data acquisition unit 410 is configured to acquire the concentration of the titrant a in the sample cell after each sample introduction to obtain a concentration array X, acquire the concentration of the dripped object B in the sample cell after each sample introduction to obtain a concentration array M, and acquire the reaction heat generated by each sample introduction to obtain a reaction heat array Q;
the weight obtaining unit 420 is configured to obtain a weight of each sample to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
the fitting iteration unit 430 is configured to perform thermodynamic parameter fitting according to a set function for fitting according to the element values of the concentration array X, the concentration array M and the reaction heat array Q by taking the concentration of the titrant A and the concentration of the dripped object B of each sample introduction as input and the reaction heat of each sample introduction as output respectively, and according to the weight array WsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
Further, the setting function is used for calculating the theoretical heat of each sample introduction;
the fitting iteration unit 430 is configured to calculate a difference weighted sum of squared residuals of the theoretical heat and the reaction heat array Q after each injection by the following formula:
Y＝sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma-i)2
y is the residual sum of squares after the theoretical heat quantity after each sample introduction and the difference value of the reaction heat array Q are weighted;
calling _ Q () is the set function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
and adjusting the thermodynamic parameters according to the residual sum of squares to carry out iterative fitting so as to minimize the residual sum of squares.
Further, the weight obtaining unit 420 is configured to:
determining the weight array W according to historical experimental datasigma；
Or determining the weight array W according to the stability degree of experimental data of each sample injectionsigma。
Further, the weight obtaining unit 420 is configured to:
the weight array WsigmaEach element is assigned the same set initial value, for example, each element is assigned an initial value of 1;
in the iterative fitting process, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaThe values of the elements.
Further, the weight obtaining unit 420 is configured to:
adjusting the weight array W according to the stability degree of the historical experimental data or the experimental data of each sample injection and the deviation of the intermediate result of iterative fittingsigmaThe values of the elements.
Further, the fitting iteration unit 430 is further configured to set an initial value of the fitting thermodynamic parameter.
The thermodynamic spectrum solving device provided by the embodiment can execute the thermodynamic spectrum solving method provided by the method embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method.
Referring now to FIG. 5, a block diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 501.
It should be noted that the computer readable medium described above in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the disclosed embodiments, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the disclosed embodiments, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
obtaining the concentration of a titrant A in the sample pool after each sample introduction to obtain a concentration array X, obtaining the concentration of a dripped object B in the sample pool after each sample introduction to obtain a concentration array M, and obtaining the reaction heat generated by each sample introduction to obtain a reaction heat array Q;
obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
according to the element values of the concentration array X, the concentration array M and the reaction heat array Q, the concentration of the titrant A and the concentration of the object B which are injected for each time are respectively taken as input, the reaction heat of each injection is taken as output, thermodynamic parameter fitting is carried out through a set function for fitting, and the weight array W is used for calculating the weight of the titrant A and the reaction heat of each injectionsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The foregoing description is only a preferred embodiment of the disclosed embodiments and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure in the embodiments of the present disclosure is not limited to the particular combination of the above-described features, but also encompasses other embodiments in which any combination of the above-described features or their equivalents is possible without departing from the scope of the present disclosure. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method of thermodynamic spectroscopy, comprising:
obtaining the concentration of a titrant A in the sample pool after each sample introduction to obtain a concentration array X, obtaining the concentration of a dripped object B in the sample pool after each sample introduction to obtain a concentration array M, and obtaining the reaction heat generated by each sample introduction to obtain a reaction heat array Q;
obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
according to the element values of the concentration array X, the concentration array M and the reaction heat array Q, the concentration of the titrant A and the concentration of the object B which are injected for each time are respectively taken as input, the reaction heat of each injection is taken as output, thermodynamic parameter fitting is carried out through a set function for fitting, and the weight array W is used for calculating the weight of the titrant A and the reaction heat of each injectionsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
2. The method of claim 1, wherein the set function is used to calculate the theoretical heat of each injection;
according to the weight array WsigmaThe step of adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm for iterative fitting comprises the following steps: and calculating the weighted residual sum of squares of the difference values of the theoretical heat and the reaction heat array Q after each sample injection by the following formula:
Y＝sum((Calculate_Q(Xi,Mi)-Qi)/Wsigma-i)2
y is the residual sum of squares after the theoretical heat quantity after each sample introduction and the difference value of the reaction heat array Q are weighted;
calling _ Q () is the set function;
Xiis the ith element of the concentration array X;
Mithe element is the ith element of the concentration array M;
Qiis the ith element of the reaction heat array Q;
Calculate_Q(Xi,Mi) The theoretical heat of the ith sample introduction calculated by the set function is calculated;
Wsigma-iis the weight array WsigmaThe ith element of (1);
sum () is the summation function;
and adjusting the thermodynamic parameters according to the residual sum of squares to carry out iterative fitting so as to minimize the residual sum of squares.
3. The method of claim 1, wherein obtaining the resulting weight of each sample provides a weight array WsigmaThe method comprises the following steps:
determining the weight array W according to historical experimental datasigma；
Or determining the weight array W according to the stability degree of experimental data of each sample injectionsigma。
4. The method of claim 1, wherein obtaining the resulting weight of each sample provides a weight array WsigmaThe method comprises the following steps:
the weight array WsigmaEach element is assigned with the same set initial value;
in the iterative fitting process, the weight array W is adjusted according to the deviation of the intermediate result of the iterative fittingsigmaThe values of the elements.
5. The method of claim 4, wherein the weight array W is adjusted according to a deviation of intermediate results of the iterative fittingsigmaEach element value includes: adjusting the weight array W according to the stability degree of the historical experimental data or the experimental data of each sample injection and the deviation of the intermediate result of iterative fittingsigmaThe values of the elements.
6. The method of claim 1, wherein fitting thermodynamic parameters through a set function for fitting further comprises setting initial values for the thermodynamic parameters.
7. A thermodynamic spectroscopy device, comprising:
the experimental data acquisition unit is used for acquiring the concentration of a titrant A in the sample pool after each sample introduction to obtain a concentration array X, acquiring the concentration of a dripped object B in the sample pool after each sample introduction to obtain a concentration array M and acquiring reaction heat generated by each sample introduction to obtain a reaction heat array Q;
a weight obtaining unit for obtaining the result weight of each sample introduction to obtain a weight array WsigmaThe result weight of each sample introduction represents the importance degree of the experimental data of each sample introduction to the finally obtained spectrum resolving result;
a fitting iteration unit for respectively taking the concentration of the titrant A and the concentration of the dripped object B of each sample introduction as input and the reaction heat of each sample introduction as output according to the element values of the concentration array X, the concentration array M and the reaction heat array Q, performing thermodynamic parameter fitting through a set function for fitting, and fitting according to the weight array WsigmaAnd adjusting the thermodynamic parameters by adopting an iterative weight least square algorithm to carry out iterative fitting, and determining the thermodynamic parameters according to an iterative fitting result.
8. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
instructions which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1-6.
9. The electronic device of claim 8, wherein the electronic device is a fitting device on an isothermal titration calorimeter.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120065948A1 (en) * | 2010-09-13 | 2012-03-15 | Mks Instruments, Inc. | Monitoring, Detecting and Quantifying Chemical Compounds in a Sample |
| CN103914594A (en) * | 2014-03-26 | 2014-07-09 | 河海大学 | Concrete thermodynamic parameter intelligent recognition method based on support vector machine |
-
2019
- 2019-07-12 CN CN201910629126.8A patent/CN110379466B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120065948A1 (en) * | 2010-09-13 | 2012-03-15 | Mks Instruments, Inc. | Monitoring, Detecting and Quantifying Chemical Compounds in a Sample |
| CN103914594A (en) * | 2014-03-26 | 2014-07-09 | 河海大学 | Concrete thermodynamic parameter intelligent recognition method based on support vector machine |
Non-Patent Citations (2)
| Title |
|---|
| 基于第一原理力场预测热力学参数的探讨;孙淮等;《化工学报》;20060531;第57卷(第05期);全文 * |
| 金属铜的晶体结构与热力学性质的第一性原理计算;陈春彩等;《原子与分子物理学报》;20121030;第29卷(第05期);全文 * |
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