Mass spectrometer anti-pollution performance dynamic monitoring method and system and medium

Abstract

The invention belongs to a mass spectrometry technology, and particularly provides a mass spectrometer anti-pollution performance dynamic monitoring method which comprises the following steps: A1, alternately injecting samples with the same concentration and blank products, and collecting a mass spectrum full-scanning spectrogram in real time; a2, extracting a time sequence pollution characteristic index according to the spectrogram, wherein the index comprises a background noise rise rate delta Nt / N0; a3, calculating a dynamic pollution index according to the characteristic index; a4, establishing a method for predicting a future cleaning critical point based on a dynamic pollution index sequence; and A5, a pollution boundary response threshold value is set, and a pollution cleaning strategy is fed back. The method has the advantages of high pollution monitoring capability and the like.

Description

Technical Field

[0001] This invention relates to mass spectrometry technology, and in particular to a method, system, and medium for dynamic monitoring of the anti-contamination performance of a mass spectrometer. Background Technology

[0002] Contamination resistance is a key performance indicator for mass spectrometers, directly affecting the transmission efficiency of target ions and leading to problems such as decreased sensitivity, increased risk of false negatives or false positives in screening, and ultimately interfering with the qualitative and quantitative analysis of substance composition. The contamination resistance of mass spectrometers can be improved through methods such as ion source design, transmission path optimization, and system maintenance strategies (especially in systems coupled with direct ionization sources).

[0003] Mass spectrometer contamination resistance can be evaluated through stability testing and maintenance cycle assessment. Common methods include: cross-contamination testing, which uses the peak intensity loss rate as an evaluation index to verify cross-contamination resistance and avoid false positives, but can only assess contamination along the ion transport path; matrix effect assessment, which calculates matrix enhancement and inhibition rates by adding target analytes and evaluates signal response differences using relative standard deviation, but relies on sample pretreatment purification; continuous injection stability testing, which determines instrument cleaning time and the number of cycles based on background signal accumulation trends, but has long testing cycles and high requirements; and matrix resistance quantification methods, which collect signal drift through high-concentration organic samples and provide sensitivity attenuation rates, but are not suitable for use with open direct ionization ion sources coupled to mass spectrometers. These methods generally focus on analyzing the contamination resistance performance of traditional laboratory mass spectrometers.

[0004] Miniaturized mass spectrometers are typically used in conjunction with open direct ionization sources, making them particularly suitable for rapid on-site detection in fields such as drug control, food safety, and battlefield chemical defense. They perform direct ionization and sample introduction analysis in an open environment under normal pressure. However, due to the low vacuum level and limited space for anti-contamination design of miniaturized mass spectrometers, their anti-contamination performance faces significant challenges. Furthermore, the lack of a dynamic quantitative evaluation system for contamination of miniaturized mass spectrometers leads to excessive maintenance or detection failure. However, the aforementioned evaluation methods are primarily geared towards the design of large laboratory mass spectrometers, which presuppose a stable laboratory environment, ample maintenance windows, and high hardware redundancy. When applied to portable, miniaturized mass spectrometers weighing less than 20 kg and with a vacuum chamber volume of less than 0.5 m³, they face fundamental challenges: 1. On-site testing requires continuous injection of multiple samples, and the contamination accumulates dynamically. The above-mentioned static or long-cycle testing methods cannot quantify the contamination status in real time, causing the timing of maintenance to depend entirely on the operator's experience.

[0005] 2. To accommodate miniaturization, the ion transport path is shortened and the vacuum system is simplified, which, while improving sensitivity, makes them more susceptible to contamination and less tolerant. Existing methods cannot achieve dynamic optimization between "ensuring detection sensitivity" and "performing anti-contamination cleaning".

[0006] 3. In mobile or harsh environments such as vehicle-mounted or field environments, vibrations and changes in temperature and humidity can introduce additional signal noise and pollution artifacts. Traditional evaluation systems cannot distinguish between such environmental interference and actual instrument contamination.

[0007] Therefore, there is an urgent need for a pollution resistance performance evaluation and control system designed specifically for miniaturized mass spectrometers, capable of real-time dynamic monitoring of pollution, distinguishing interference, and accurately predicting maintenance needs. Summary of the Invention

[0008] To address the shortcomings of the existing technical solutions, this invention provides a method for dynamic monitoring of the anti-pollution performance of a mass spectrometer.

[0009] The objective of this invention is achieved through the following technical solution: A method for dynamic monitoring of the anti-contamination performance of a mass spectrometer, including the following steps: A1. Alternately inject samples of the same concentration and blanks, and acquire full-scan mass spectra in real time; A2. Extract time-series pollution characteristic indicators from the spectrum, including the background noise rise rate Δ. N t / N 0; A3. Calculate the dynamic pollution index based on the aforementioned characteristic indicators; A4. Establish a sequence prediction method for future cleaning critical points based on dynamic contamination index; A5. Set the pollution boundary response threshold and provide feedback on the pollution cleaning strategy.

[0010] The present invention also provides a computer-readable storage medium, which is achieved through the following technical solution: when the program is executed by a processor, a method for dynamic monitoring of the anti-contamination performance of a mass spectrometer is implemented.

[0011] The present invention also provides a miniaturized mass spectrometer anti-contamination dynamic monitoring system, which is achieved through the following technical solution: The system includes: The mass spectrometry detection module is used to acquire spectral data; The data processing module is configured to perform dynamic monitoring of the mass spectrometer's anti-contamination performance. The cleaning execution module is configured to perform corresponding cleaning operations based on the cleaning instructions output by the data processing module.

[0012] Compared with the prior art, the present invention has the following beneficial effects.

[0013] This invention establishes a mapping relationship between spectral temporal characteristics and physical contamination, forming a closed-loop dynamic control for anti-contamination maintenance. This achieves a complete chain innovation, from real-time quantification of contamination status to accurate prediction of maintenance timing and automatic execution of graded actions. 1. Using the LSTM-ARIMA collaborative model, the pollution index S(t) is calculated in real time and the trend after 5-10 future injections is predicted with a prediction error of less than 5 injections, thus achieving true dynamic pollution monitoring and trend prediction. 2. A three-level response threshold (0.3, 0.6) is set up, and cleaning is triggered only when the predicted limit is exceeded. This reduces the frequency of unnecessary deep cleaning by more than 40%, extends the life of key components, and realizes predictive and adaptive hierarchical maintenance. 3. It features three miniaturized closed-loop control strategies, exhibits environmental robustness, and is particularly suitable for field applications such as miniaturized, portable, and vehicle-mounted applications; 4. While removing contamination, ensure that the target ion transmission efficiency decreases during continuous detection, establish a predictive maintenance method, form a synergistic optimization of anti-contamination and high sensitivity, and overcome over-maintenance (waste of resources) or delayed maintenance (risk of false positives). 5. By changing the target ion and interference peak database, it can be quickly adapted to the detection of VOCs, explosives, pesticide residues and other fields, and build an scalable multimodal pollution identification framework to realize multimodal pollution identification. It is particularly suitable for pollution feature databases in multiple fields such as environment (VOCs), security inspection (explosives, drugs, chemical warfare agents), medical treatment (poisoning treatment toxin identification, biomarkers), food rapid testing (agricultural and veterinary drug residues, illegal additives), and drug counterfeiting. Attached Figure Description

[0014] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are merely illustrative of the technical solutions of this invention and are not intended to limit the scope of protection of this invention. In the drawings: Figure 1 This is a schematic diagram of the dynamic monitoring method of Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the dynamic monitoring method of Embodiment 2 of the present invention. Detailed Implementation

[0015] Figures 1-2The following description illustrates optional embodiments of the invention to teach those skilled in the art how to implement and reproduce the invention. Some conventional aspects have been simplified or omitted to teach the technical solutions of the invention. Those skilled in the art should understand that variations or substitutions derived from these embodiments will be within the scope of the invention. Those skilled in the art should understand that the following features can be combined in various ways to form multiple variations of the invention. Therefore, the invention is not limited to the optional embodiments described below, but is defined only by the claims and their equivalents.

[0016] Example 1

[0017] This embodiment describes a method for dynamically monitoring the anti-contamination performance of a mass spectrometer, such as... Figure 1 As shown, the steps include: A1. Alternately inject samples of the same concentration and blanks (such as blank solvents or blank matrix samples) and acquire full-scan mass spectra in real time.

[0018] A2. Extract time-series pollution characteristic indicators from the spectrum, including the background noise rise rate Δ. N t / N 0. Characteristic interference peak intensity ratio I int / I ref The target ion signal attenuation slope k was obtained by linearly fitting the target ion intensity after five or more consecutive injections.

[0019] A3. Calculate the dynamic pollution index based on the aforementioned feature indicators using machine learning or deep learning algorithms. S ( t )= α ·(Δ N t / N 0) + β ·( I int / I ref ) + γ ·| k |, α + β + γ =1.

[0020] A4. Establish a method for predicting future cleaning critical points based on dynamic pollution indices, such as using time series prediction algorithms.

[0021] A5. Set the contamination boundary response threshold and provide feedback on the contamination cleaning strategy. The contamination boundary response threshold is a combination of third-order fitness values: (0, 0.3), continue injection; [0.3, 0.6), inert gas pulse cleaning; [0.6, 1], start solvent cleaning.

[0022] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in this embodiment.

[0023] A miniaturized mass spectrometer anti-contamination dynamic monitoring system includes: The mass spectrometry detection module is used to acquire spectral data; The data processing module is configured to execute the methods described in this embodiment. The cleaning execution module is configured to perform corresponding cleaning operations based on the cleaning instructions output by the data processing module.

[0024] The cleaning execution module includes: An inert gas pulse cleaning unit is used to trigger upon receiving a first-stage cleaning command; The solvent deep cleaning unit is triggered upon receiving a second-level cleaning command.

[0025] Example 2

[0026] Application example of the monitoring method according to Example 1.

[0027] like Figure 2 As shown, the dynamic monitoring method for the anti-contamination performance of the mass spectrometer is as follows: A1. Sample preparation: 1) Positive sample: hair sample containing methamphetamine (0.2 ng / mg) + sebum mimic, or prepare hair methamphetamine sample 0.2 ng / mg + sebum mimic by mixing methamphetamine sample with blank hair sample; 2) Blank sample: a mixed solution of methanol and water, wherein methanol:water = 7:3.

[0028] Contamination induction: 50 alternating injections, i.e., positive, empty, positive, empty... After every 5 cycles, a full scan is performed, with the scan range being m / z = 50~500.

[0029] A2. Pollution Feature Extraction: Background noise was set at m / z = 80~150; methamphetamine amine side chain characteristic fragment m / z = 58 (miniaturized mass spectrometers, especially ion trap mass spectrometers, are easily interfered with by indole in the hair matrix at m / z = 91.05 at m / z = 91, and characteristic fragments at m / z = 58 (C3H8N) can also be generated according to the fragmentation pathway). + It exhibits less interference and higher specificity in the low-quality region; the interference peak intensity ratio is I(m / z=86.1) / I refBecause the characteristic fragment of decanoic acid in sebum has a m / z value of 86.1, it can easily interfere with the m / z values ​​of methamphetamine (91 and 119).

[0030] The output contaminated fingerprint feature library is: ΔN / N0, k(m / z=58), I(m / z=86.1) / I ref .

[0031] A3. Taking the LSTM-ARIMA algorithm combination as an example, by optimizing parameters to adapt to the hair matrix, the contamination index S(t) and prediction S(t+1) are calculated, and the dynamic scoring model training is completed, solving the problems of time dependence and abrupt response in mass spectrometry contamination prediction: 1. Input sequence: [ΔN / N0, k(m / z=58), I(m / z=86.1) / Iref]×20 injections, input into the LSTM algorithm; 2. Calculate the contamination index S(t) using the LSTM algorithm: Set the network structure to 2 layers (128 neurons per layer), and plan to obtain and output S(t); A4. Calculate the prediction exponent S(t+1) using the ARIMA algorithm: Set the model order to ARIMA(1,1,1), where p=1 indicates dependence on S(t) at the previous time point; d=1 indicates that the first-order difference makes the sequence stationary; q=1 indicates that the prediction error at the previous time point is considered. Simultaneously, based on the physical characteristics of mass spectrometry contamination and data-driven optimization, the regression coefficient φ1=0.8 and the moving average coefficient θ1=-0.6 are chosen. S(t+1) is then calculated.

[0032] The model was trained and tested using 200 sample sequences covering normal, gradual, and sudden contamination. Results showed that the root mean square error (RMSE) between the predicted and actual values ​​of the contamination index S(t) was <0.05; for the prediction of events requiring cleaning, the accuracy was >95% and the false alarm rate was <5%.

[0033] A5. Operational strategy based on S(t+1) feedback cleaning timing. For example: At t-1, S(t-1)=0.4 (normal), and the predicted S(t)=0.42 (continue injection).

[0034] At time t, a sudden change occurs in the on-site environmental detection, S(t)=0.55 (sudden change), resulting in a prediction error S(t)=0.13.

[0035] At t+1, S(t+1)=0.52, calculate the predicted value S(t+1)=0.8×0.55+(-0.6)×0.13=0.102, and output the predicted value S(t+1)=0.4+0.102=0.502.

[0036] Feedback does not trigger deep cleaning (normal to avoid false triggers).

[0037] Example 3

[0038] Based on Example 2, reliability testing was conducted through continuous on-site detection of actual samples: Ten hair samples were input, and the hair supernatant was extracted according to the pretreatment method for drugs in laboratory hair or the general pretreatment method for miniaturized mass spectrometers.

[0039] The spectrum was obtained by detecting the sample using a miniaturized mass spectrometer.

[0040] Start the algorithm combination program and perform sample testing.

[0041] LSTM is used to calculate the real-time S(t) for each hair sample.

[0042] Use ARIMA to predict the pollution trend S(t+i) for the next 5 periods, i=1, 2, 3, 4, 5.

[0043] Within 10 parts, S(t) < 0.6 and ∪S(t+i) < 0.6 respectively.

[0044] As a control, the same test was performed using the traditional strategy of washing every 5 samples. The traditional strategy triggered two deep washes, taking a total of 20 minutes; while the prediction-based strategy of this invention did not trigger deep washes at all, only triggering a pulse wash (taking 2 seconds) after sample 7, reducing the total detection time by 18%, and no false negatives or false positives were found in the detection results of any samples due to contamination.

[0045] The feedback indicates that deep cleaning is not required.

[0046] If the threshold for the number of ARIMA predictions is 10, then the optimized ARIMA(1,2,1) is obtained with φ1=0.75 (strong sebum adsorption persistence) and θ1=-0.7 (strong correction is needed for sudden sweat interference).

[0047] Example 4

[0048] According to the application example of the monitoring method in drug enforcement scene according to Example 2, a mobile detection vehicle is used to continuously screen hair samples from suspects at the scene. The difference from Example 2 is: With the same mass spectrometry system parameter settings as the on-site stationary miniaturized mass spectrometer, vibration compensation will be added to the acquired mass spectrometry data under vibration interference in the vehicle-mounted system. Add a vibration sensing module in front of the LSTM layer.

[0049] In a simulated vehicle vibration (5-10Hz) environment, the effects of enabling and disabling the vibration compensation module were compared. When disabled, the vibration noise caused severe fluctuations in S(t), resulting in 7 false triggers of the cleaning alarm within 100 sample injections. When enabled, the number of false triggers decreased to 1, and the accuracy of predicting the actual contamination trend remained comparable to that in the static scenario.

[0050] Variable-order ARIMA (V-ARIMA) transforms ARIMA(1,1,1) into ARIMA(2,2,1), utilizing the addition of differential stages to counteract the effects of mutations.

[0051] The predicted cleaning threshold was lowered from 0.6 to 0.5.

[0052] Example 5

[0053] The application example of the monitoring method in this embodiment 2 in monitoring multiple target substances in the matrix differs from that in embodiment 2 in the following ways: Take the simultaneous rapid screening of two common pesticide residues on fruit and vegetable surfaces—chlorpyrifos and acetamiprid—as an example. Components such as chlorophyll and organic acids in the fruit and vegetable matrix can cause complex interference with the ion source, and the pollution effects of different pesticides may be cumulative, placing higher demands on the anti-contamination performance of the mass spectrometer.

[0054] Positive samples: Apple peel extract was used as the matrix, and chlorpyrifos (characteristic ion pair: m / z 314→258) and acetamiprid (characteristic ion pair: m / z 223→126) standards were added to prepare double-positive mixed samples (concentration of 0.1 mg / L for both). Chlorophyll a mimic was added as the main matrix interference source.

[0055] Blank sample: Apple peel extract (without pesticides and chlorophyll mimics).

[0056] The samples were injected 60 times in alternating order of "mixed positive sample - blank sample" to simulate the accumulation of compound contamination during continuous screening. After every 10 injections, a full scan (m / z 50-350) was performed to monitor global changes.

[0057] For each target analyte, an independent contamination fingerprint was established, and common matrix interference indicators were extracted: by linearly fitting the intensity decay trend of the quantitative ion (m / z 258), matrix peaks (m / z 191) that may share fragments or cause mass interference were monitored; combined with linearly fitting the intensity decay trend of the quantitative ion (m / z 126), m / z 136 was monitored. Background noise in the m / z 70-120 range was collected, a strong common matrix peak (m / z 149) was monitored, and its intensity ratio was calculated.

[0058] The data processing module runs two independent LSTM-ARIMA analysis threads in parallel, receiving inputs of 191 and 136 [ΔN / N0, k, I] respectively. int / I ref The system outputs the pollution index of chlorpyrifos and acetamiprid. A comprehensive pollution index S(t) is set up. Cleaning decisions are made based on S(t) and its predicted value to ensure that maintenance is triggered if the pollution of any target or common matrix exceeds the standard.

[0059] To verify the effectiveness of the method in this embodiment, a fixed strategy of deep cleaning after every 20 samples was adopted. Eighty simulated samples containing different concentrations of pesticides and matrix interference were continuously injected and compared. The results showed that the number of false positive alarms was reduced from 11 to 1 in this method and the cleaning time was shortened by 21%.

Claims

1. A method for dynamic monitoring of the anti-contamination performance of a mass spectrometer, characterized in that, The monitoring method is as follows: A1. Alternately inject samples of the same concentration and blanks, and acquire full-scan mass spectra in real time; A2. Extract time-series pollution characteristic indicators from the spectrum, including the background noise rise rate Δ. N t / N 0; A3. Calculate the dynamic pollution index based on the aforementioned characteristic indicators; A4. Establish a sequence prediction method for future cleaning critical points based on dynamic contamination index; A5. Set the pollution boundary response threshold and provide feedback on the pollution cleaning strategy.

2. The monitoring method according to claim 1, characterized in that, The feature indicators also include: Characteristic interference peak intensity ratio I int / I ref The target ion signal attenuation slope k was obtained by linearly fitting the target ion intensity of 5 or more consecutive injections. The dynamic pollution index S ( t )= α ·(Δ N t / N 0) + β ·( I int / I ref ) + γ ·| k |, α + β + γ =1.

3. The monitoring method according to claim 1, characterized in that, In steps A3 and A4, the joint prediction algorithm is run.

4. The monitoring method according to claim 3, characterized in that, The joint prediction algorithm includes LSTM-ARIMA, where the output sequence of the LSTM model is used as the input of the ARIMA model to predict pollution trends.

5. The method according to claim 1 or 4, characterized in that, The ARIMA algorithm is used to predict the future cleaning critical point, and the order of the ARIMA model can be adaptively adjusted. When the external environmental interference is detected to exceed the preset level, the model order is automatically switched from ARIMA(1,1,1) to the ARIMA(p,2,q) model with a higher difference order.

6. The monitoring method according to any one of claims 1 to 5, characterized in that, The method is applicable to vacuum chambers weighing no more than 20 kg and with a volume of less than 0.5 m³. 3 A miniaturized mass spectrometer.

7. The monitoring method according to claim 1, characterized in that, The contamination boundary response threshold is a combination of third-order fitness values: (0, 0.3), continue injection; [0.3, 0.6), inert gas pulse cleaning; [0.6, 1], start solvent cleaning.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

9. A miniaturized mass spectrometer anti-contamination dynamic monitoring system, characterized in that, include: The mass spectrometry detection module is used to acquire spectral data; A data processing module is configured to perform the method as described in any one of claims 1 to 7; The cleaning execution module is configured to perform corresponding cleaning operations based on the cleaning instructions output by the data processing module.

10. The system according to claim 9, characterized in that, The cleaning execution module includes: An inert gas pulse cleaning unit is used to trigger upon receiving a first-stage cleaning command; The solvent deep cleaning unit is triggered upon receiving a second-level cleaning command.

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