Microscale organophosphorus pesticide detection instrument and detection method thereof

Abstract

The invention discloses an organophosphorus pesticide trace detection instrument and a detection method thereof, and relates to the technical field of pesticide detection, the organophosphorus pesticide trace detection instrument comprises an analyzer, a tube furnace, a flow controller, a heat insulation box and a steam generation device; a liquid organophosphorus pesticide in the steam generation device volatilizes to generate steam under the temperature control of ice water in a heat insulation box, and carrier gas with the flow rate regulated and controlled by a flow controller carries the steam to enter a cross pipeline to be mixed to form uniform detection gas; the detection gas enters the tubular furnace through a pipeline to be heated to eliminate interference components in the detection gas; the processed detection gas is in contact with the gas sensor and the biosensor, the gas sensor generates electrical parameter change data after electrical parameter change is caused by the adsorption effect of the nano gas-sensitive material on organophosphorus pesticide steam, and the effect is that the detection result is ensured to be reliable in practical application, and whether pesticide residues exceed the standard or not is helped to be judged.

Description

Technical Field

[0001] This invention relates to the field of pesticide detection technology, and more specifically, to an instrument and method for detecting trace amounts of organophosphorus pesticides. Background Technology

[0002] With the widespread use of organophosphorus pesticides in agricultural production, their residues pose an increasingly prominent threat to food safety, the ecological environment, and human health, making the demand for accurate and efficient trace detection ever more urgent. In actual testing scenarios, complex sample matrices can introduce interfering components, such as impurities in agricultural products and other pollutants in environmental water samples, thus affecting detection accuracy. Furthermore, existing detection methods fail to fully integrate the complementary advantages of multi-source data, making it difficult to accurately identify organophosphorus pesticide residues in low-concentration, complex matrices. Therefore, accurate and rapid detection of organophosphorus pesticides is not possible, making detection of organophosphorus pesticides inconvenient. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide an instrument and method for detecting trace amounts of organophosphorus pesticides.

[0004] To achieve the above objectives, the present invention provides the following technical solution: An instrument for detecting trace amounts of organophosphorus pesticides includes an analyzer, a tubular furnace, a flow controller, an insulation box, and a steam generator; In the steam generator, liquid organophosphorus pesticides evaporate and generate steam under the temperature control of ice water in the insulated box. The steam is carried by the carrier gas whose flow rate is regulated by the flow controller and enters the cross pipe to mix and form a uniform detection gas. The detection gas is piped into a tubular furnace for heating treatment to eliminate interfering components in the detection gas; The processed detection gas comes into contact with the gas sensor and the biosensor. The gas sensor generates electrical parameter change data by causing changes in electrical parameters through the adsorption of organophosphorus pesticide vapor by the nano-gas-sensitive material. The biosensor converts chemical signals into electrical signals through the biological reaction between the gate surface and the organophosphorus pesticide. The analyzer collects real-time data on changes in electrical parameters and electrical signals, and detects organophosphorus pesticides by analyzing the data and signals.

[0005] A method for detecting trace amounts of organophosphorus pesticides, comprising the following steps: Acquire the electrical parameter changes and electrical signal data of the sample to be tested; The first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested is obtained by analyzing the change data of electrical parameters, and all the first pre-selected detection indices are combined to obtain the first detection index set. The electrical signal data is analyzed to obtain the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, and all the second pre-selected detection indices are combined to obtain the second detection index set. The first detection index set and the second detection index set are compared and analyzed to obtain the candidate detection indexes of the sample to be detected, and the candidate detection coefficients corresponding to the candidate detection indexes are obtained according to the first pre-selection detection coefficient and the second pre-selection detection coefficient. The detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the candidate detection sample is obtained based on the detection index response threshold. The detection of organophosphorus pesticides is then performed based on the target detection index.

[0006] Preferably, the electrical parameter change data includes resistance change data and capacitance change data of the sample under test during the testing process; The electrical signal data includes voltage signal data and current signal data corresponding to the sample to be detected.

[0007] Preferably, the analysis of electrical parameter variation data yields the first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested, specifically including the following steps: Obtain the resistance change amplitude and resistance change rate corresponding to the resistance change data; The resistance amplitude coefficient corresponding to the resistance change is obtained based on the resistance change amplitude. The resistance rate coefficient is obtained based on the rate of change of resistance. The resistance detection coefficient corresponding to the resistance change is obtained based on the resistance amplitude coefficient and the resistance rate coefficient. The capacitance change data is processed and analyzed to obtain the capacitance detection coefficient; Both the detection index corresponding to the resistance change and the detection index corresponding to the capacitance change are recorded as the first pre-selected detection index. Based on the resistance detection coefficient, resistance detection weight, capacitance detection coefficient, and capacitance detection weight, the first pre-selected detection coefficient corresponding to the first pre-selected detection index is obtained.

[0008] Preferably, the capacitance change data is processed and analyzed to obtain the capacitance detection coefficient, specifically including the following steps: Based on the capacitance change data, obtain the capacitance change amplitude and capacitance change rate corresponding to the capacitance change. The capacitance amplitude coefficient corresponding to the capacitance change is obtained based on the capacitance change amplitude. The capacitance rate coefficient is obtained based on the rate of change of capacitance. The capacitance detection coefficient corresponding to the capacitance change is obtained based on the capacitance amplitude coefficient and capacitance rate coefficient.

[0009] Preferably, the analysis of electrical signal data yields the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, specifically including the following steps: Based on the voltage signal data, the voltage signal feature points in the sample to be detected and the number of times the signal features corresponding to the voltage signal feature points appear are obtained; The voltage signal coefficients corresponding to the voltage signal feature points are obtained based on the frequency of occurrence of the signal features. The current signal data is processed and analyzed to obtain the current signal coefficients; Both voltage signal feature points and current signal feature points are recorded as the second pre-selected detection index; The second preselected detection coefficient is obtained based on the voltage signal coefficient, voltage signal weight, current signal coefficient, and current signal weight of the second preselected detection index.

[0010] Preferably, the current signal data is processed and analyzed to obtain the current signal coefficients, specifically including the following steps: Based on the current signal data, the current signal feature points in the sample to be detected and the number of times the signal features corresponding to the current signal feature points appear are obtained; The current signal coefficients corresponding to the current signal feature points are obtained based on the frequency of occurrence of the signal features.

[0011] Preferably, the first detection index set and the second detection index set are compared and analyzed to obtain the candidate detection indexes for the sample to be detected, and the candidate detection coefficients corresponding to the candidate detection indexes are obtained according to the first pre-selected detection coefficient and the second pre-selected detection coefficient. Specifically, this includes the following steps: The sample to be tested contains at least one candidate detection indicator, and the candidate detection indicators include overlapping candidate detection indicators and independent candidate detection indicators. The pre-selected detection indicators that are the same in the first detection indicator set and the second detection indicator set are marked as overlapping candidate detection indicators; Based on the first pre-selected detection coefficient and the first detection weight, and the second pre-selected detection coefficient and the second detection weight of the overlapping pre-selected detection indicators, the candidate detection coefficients corresponding to the overlapping candidate detection indicators are obtained; Pre-selected detection indicators that are different from both the first and second detection indicator sets are marked as independent candidate detection indicators; If the independent candidate detection indicator is the first pre-selected detection indicator, then the first pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator. If the independent candidate detection indicator is the second pre-selected detection indicator, then the second pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator.

[0012] Preferably, the detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the sample to be tested is obtained by analyzing the candidate detection index in the sample to be tested based on the detection index response threshold. The detection of organophosphorus pesticides based on the target detection index specifically includes the following steps: The response thresholds of the candidate detection indicators are compared to obtain the correlation between the response thresholds of the candidate detection indicators and other candidate detection indicators in the sample to be tested. The response threshold correlation is then compared with the preset threshold correlation. If the correlation degree between the candidate detection index and the response threshold of other candidate detection indicators in the sample to be detected is less than or equal to the threshold correlation degree threshold, then the candidate detection index is designated as the target detection index. If the correlation between the candidate detection index and the response threshold of other candidate detection in the sample to be detected is greater than the threshold correlation threshold, then the candidate detection priority corresponding to the candidate detection index is obtained according to the candidate detection coefficient, and the candidate detection index with the highest candidate detection priority is designated as the target detection index.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention integrates an analyzer, a tubular furnace, a flow controller, an insulated chamber, and a steam generator to construct a complete detection chain. The insulated chamber's chilled water temperature control ensures stable pesticide vapor generation; the flow controller regulates the carrier gas flow rate to ensure uniform mixing of steam and carrier gas for detection; the tubular furnace effectively eliminates interfering components, ensuring a pure detection environment for subsequent sensors. Gas sensors collect changes in electrical parameters through the adsorption of nanomaterials, biosensors convert biological reactions into electrical signals, and the analyzer collects and analyzes data in real time, thereby automating the detection process.

[0014] By extracting amplitude and rate features from electrical parameters through hierarchical processing, a first set of detection indicators is obtained using resistance and capacitance detection coefficients. Feature points and frequency of occurrence are extracted from electrical signals, and a second set of detection indicators is obtained by calculating voltage and current signal coefficients. The indicator sets are compared to distinguish between overlapping and independent candidate indicators. Coefficients from different dimensions are fused to obtain candidate detection coefficients. Target indicators are then selected based on response threshold correlation and priority, improving detection accuracy. In practical applications, this ensures reliable detection results and helps determine whether pesticide residues exceed standards. Attached Figure Description

[0015] Figure 1 A schematic diagram of an organophosphorus pesticide trace detection instrument proposed in this invention; Figure 2 This is a schematic diagram of a method for obtaining the target detection index in a trace detection method for organophosphorus pesticides proposed in this invention; Figure 3This is a schematic diagram illustrating the steps for obtaining the capacitance detection coefficient in a trace detection method for organophosphorus pesticides proposed in this invention.

[0016] 1. Analyzer; 2. Tubular furnace; 3. Flow controller; 4. Insulation box; 5. Steam generator; Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figure 1-Figure 3 As shown.

[0021] The examples further illustrate the trace detection instrument and method for organophosphorus pesticides proposed in this invention.

[0022] An instrument for detecting trace amounts of organophosphorus pesticides includes an analyzer 1, a tubular furnace 2, a flow controller 3, an insulation box 4, and a steam generator 5; In the steam generator 5, the liquid organophosphorus pesticide evaporates under the temperature control of ice water in the insulated box 4 to generate steam. The carrier gas, whose flow rate is regulated by the flow controller 3, carries the steam into the cross pipe and mixes to form a uniform detection gas. The detection gas enters the tubular furnace 2 through a pipeline for heating treatment to eliminate interfering components in the detection gas; The processed detection gas comes into contact with the gas sensor and the biosensor. The gas sensor generates electrical parameter change data by adsorbing organophosphorus pesticide vapors through nano-gas-sensitive materials. The biosensor converts chemical signals into electrical signals through biological reactions between the gate surface and the organophosphorus pesticide. The gas sensor and the biosensor are located inside the analyzer 1. The gas sensor can be a metal oxide semiconductor gas sensor or a VOC electrochemical sensor, and the biosensor can be an acetylcholinesterase biosensor or a microbial biosensor.

[0023] The analyzer 1 collects electrical parameter change data and electrical signals in real time, and detects organophosphorus pesticides by analyzing the electrical parameter change data and electrical signals.

[0024] This trace detection instrument for organophosphorus pesticides mainly consists of an analyzer 1, a tubular furnace 2, a flow controller 3, an insulated chamber 4, and a steam generator 5. In the steam generator 5, liquid organophosphorus pesticides volatilize and generate steam using the ice-water temperature-controlled environment of the insulated chamber 4. Simultaneously, a carrier gas, with its flow rate regulated by the flow controller 3, carries this steam into a cross-pipeline, ensuring thorough mixing of the steam and carrier gas to form a uniform detection gas. The carrier gas can be clean air. This detection gas is piped into the tubular furnace 2 for heating treatment to eliminate interfering components, such as impurities. These impurities can be volatile organic compounds (VOCs), such as esters and aldehydes, which typically decompose into carbon dioxide and water at 200-350℃ or directly volatilize and leave the detection gas. The treated detection gas then contacts a gas sensor and a biosensor. The gas sensor utilizes the adsorption characteristics of nanomaterials for organophosphorus pesticide vapors; adsorption causes changes in electrical parameters, resulting in electrical parameter change data. The biosensor, on the other hand, converts chemical signals into electrical signals through a biological reaction between the biosensor and the organophosphorus pesticide on its grid surface. The analyzer 1 collects real-time data on changes in electrical parameters and electrical signals. By analyzing and processing this data, it can detect organophosphorus pesticides, determine their presence and content, and so on.

[0025] A method for detecting trace amounts of organophosphorus pesticides, comprising the following steps: Acquire the electrical parameter changes and electrical signal data of the sample to be tested; The first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested is obtained by analyzing the change data of electrical parameters, and all the first pre-selected detection indices are combined to obtain the first detection index set. The electrical signal data is analyzed to obtain the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, and all the second pre-selected detection indices are combined to obtain the second detection index set. The first detection index set and the second detection index set are compared and analyzed to obtain the candidate detection indexes of the sample to be detected, and the candidate detection coefficients corresponding to the candidate detection indexes are obtained according to the first pre-selection detection coefficient and the second pre-selection detection coefficient. The detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the candidate detection sample is obtained based on the detection index response threshold. The detection of organophosphorus pesticides is then performed based on the target detection index.

[0026] The electrical parameter change data includes the resistance change data and capacitance change data of the sample under test during the test process; Electrical signal data includes voltage signal data and current signal data corresponding to the sample to be tested.

[0027] The system acquires electrical parameter and signal data for the sample under test, including resistance and capacitance changes induced by the sample during the detection process. Resistance changes originate from alterations in charge transport paths and carrier concentration within the nanomaterial after adsorption of organophosphorus pesticide vapors; capacitance changes are related to the surface charge distribution and the influence of pesticides on the dielectric properties between electrodes. Electrical signal data includes voltage and current signals corresponding to the sample. These signals are derived from the conversion of chemical signals by biological reactions on the gate surface of the biosensor; different pesticide concentrations and types will cause specific patterns in voltage and current changes.

[0028] Key characteristics reflecting the properties of organophosphorus pesticides are identified from resistance and capacitance change data, such as the magnitude and rate of resistance change, and the inflection point and trend of capacitance change. These characteristics are defined as first-selection detection indicators. The resistance and capacitance data are processed, considering the contribution of the change magnitude to detection and the influence weight of the rate, to calculate the first-selection detection coefficient corresponding to each first-selection detection indicator. The first-selection detection coefficient reflects the correlation strength between the indicator and the presence and concentration of organophosphorus pesticides. All first-selection detection indicators are integrated to form a first detection indicator set.

[0029] Feature points, such as peak voltage and voltage change period, are extracted from voltage signals, and key information, such as current stability and current fluctuation frequency, is captured from current signals. These features serve as the second pre-selected detection indicators. The second pre-selected detection coefficients, reflecting the correlation between electrical signal indicators and pesticide detection, are calculated based on the physical meaning of the voltage and current signals. All second pre-selected detection indicators are then compiled to construct a second detection indicator set.

[0030] The first and second sets of detection indicators are compared and analyzed to identify potential detection indicators for sample testing. Overlapping indicators with common indicative functions for organophosphorus pesticide detection are sought from both sets; for example, a certain resistance change characteristic and voltage signal characteristic can both reflect pesticide concentration, and each indicator is considered individually. For overlapping indicators, the first and second pre-selected detection coefficients are combined to calculate the corresponding potential detection coefficients. If an independent indicator originates from the first set of detection indicators, its first pre-selected detection coefficient is directly used as the potential detection coefficient; if the independent indicator originates from the second set of detection indicators, the second pre-selected detection coefficient is used. Through this fusion screening, the most representative potential detection indicators and their corresponding coefficients for organophosphorus pesticide detection are identified.

[0031] The detection response threshold for each candidate detection indicator is calculated based on the candidate detection coefficient. This threshold is the critical value used to determine whether an indicator effectively indicates the presence and concentration of organophosphorus pesticides. Subsequently, the correlation between the candidate detection indicators is analyzed. If an indicator has a low correlation with the response thresholds of other indicators, it indicates that it is independent and can accurately reflect pesticide characteristics, and this indicator is designated as the target detection indicator. If the correlation is high, the priority of the indicators is determined based on the candidate detection coefficient; those with higher coefficients have stronger sensitivity and specificity for pesticide detection, and the highest priority indicator is selected as the target detection indicator. Combining the correspondence between the target detection indicators and organophosphorus pesticides facilitates the detection of organophosphorus pesticides based on the target detection indicators.

[0032] The analysis of electrical parameter variation data yields the first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested. This process includes the following steps: Obtain the resistance change amplitude and resistance change rate corresponding to the resistance change data; The resistance amplitude coefficient corresponding to the resistance change is obtained based on the resistance change amplitude. The resistance rate coefficient is obtained based on the rate of change of resistance. The resistance detection coefficient corresponding to the resistance change is obtained based on the resistance amplitude coefficient and the resistance rate coefficient. The capacitance change data is processed and analyzed to obtain the capacitance detection coefficient; Both the detection index corresponding to the change in resistance and the detection index corresponding to the change in capacitance are recorded as the first pre-selected detection index. Based on the resistance detection coefficient, resistance detection weight, capacitance detection coefficient, and capacitance detection weight, the first pre-selected detection coefficient corresponding to the first pre-selected detection index is obtained.

[0033] This application first extracts the resistance change amplitude and resistance change rate from resistance change data. The resistance change amplitude reflects the total change in resistance of the nano-gas-sensitive material due to the adsorption of organophosphorus pesticide vapors during the detection process, and is a fundamental data point that directly reflects the intensity of pesticide action. The resistance change rate focuses on how quickly the resistance changes over time, and can help determine the dynamic process of pesticide-material interaction. Based on the resistance change amplitude, a resistance amplitude coefficient is obtained through a reference table, where the reference table is derived from historical data, highlighting the contribution weight of amplitude characteristics to detection. Similarly, a resistance rate coefficient is obtained based on the resistance change rate through a reference table. Considering the combined role of the resistance amplitude coefficient and the resistance rate coefficient in reflecting organophosphorus pesticide detection, a weighted resistance detection coefficient is obtained, which comprehensively represents the indicative value of resistance change data for pesticide detection.

[0034] Based on the amplitude and rate characteristics of capacitance changes, a capacitance detection coefficient is obtained through a lookup table to measure the role of capacitance change data in pesticide detection. The detection indicators corresponding to resistance changes and capacitance changes are uniformly labeled as the first pre-selected detection indicators, thus constructing an electrical-dimensional detection indicator system.

[0035] The first pre-selected detection coefficient is obtained by combining the resistance detection coefficient, resistance detection weight, capacitance detection coefficient, and capacitance detection weight through a weighted summation operation (e.g., first pre-selected detection coefficient = resistance detection coefficient × resistance detection weight + capacitance detection coefficient × capacitance detection weight). This coefficient integrates the detection information of two types of electrical parameters, resistance and capacitance, to reflect the comprehensive indicative ability of the first pre-selected detection index for organophosphorus pesticide detection.

[0036] The capacitance change data is processed and analyzed to obtain the capacitance detection coefficient, specifically including the following steps: Based on the capacitance change data, obtain the capacitance change amplitude and capacitance change rate corresponding to the capacitance change. The capacitance amplitude coefficient corresponding to the capacitance change is obtained based on the capacitance change amplitude. The capacitance rate coefficient is obtained based on the rate of change of capacitance. The capacitance detection coefficient corresponding to the capacitance change is obtained based on the capacitance amplitude coefficient and capacitance rate coefficient.

[0037] This application extracts the capacitance change amplitude and capacitance change rate from capacitance change data. The capacitance change amplitude reflects the total magnitude of capacitance change caused by the interaction between pesticide vapor and the detection element during organophosphorus pesticide detection, directly demonstrating the intensity of the pesticide's influence on the detection system. The capacitance change rate focuses on how quickly the capacitance changes over time, aiding in the assessment of the dynamic process of the interaction between the pesticide and the detection element. Based on the capacitance change amplitude, a capacitance amplitude coefficient is obtained using a pre-set lookup table. Similarly, based on the capacitance change rate, a capacitance rate coefficient is obtained using a pre-set lookup table, emphasizing the impact of the capacitance change rate characteristic on detection. Considering the combined roles of the capacitance amplitude coefficient and capacitance rate coefficient in reflecting organophosphorus pesticide detection information, a capacitance detection coefficient is obtained through weighted calculation.

[0038] The analysis of electrical signal data yields the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, specifically including the following steps: Based on the voltage signal data, the voltage signal feature points in the sample to be detected and the number of times the signal features corresponding to the voltage signal feature points appear are obtained; The voltage signal coefficients corresponding to the voltage signal feature points are obtained based on the frequency of occurrence of the signal features. The current signal data is processed and analyzed to obtain the current signal coefficients; Both voltage signal feature points and current signal feature points are recorded as the second pre-selected detection index; The second preselected detection coefficient is obtained based on the voltage signal coefficient, voltage signal weight, current signal coefficient, and current signal weight of the second preselected detection index.

[0039] The current signal data is processed and analyzed to obtain the current signal coefficients, specifically including the following steps: Based on the current signal data, the current signal feature points in the sample to be detected and the number of times the signal features corresponding to the current signal feature points appear are obtained; The current signal coefficients corresponding to the current signal feature points are obtained based on the frequency of occurrence of the signal features.

[0040] This application identifies voltage signal feature points, such as voltage peaks, valleys, and abrupt changes, from the voltage signal data of the sample to be tested. These are key locations in the voltage signal that reflect the action of organophosphorus pesticides. The frequency of occurrence of the signal feature corresponding to each voltage signal feature point is counted, thus reflecting the frequency of occurrence of that feature during the detection process and indirectly reflecting its correlation with pesticide detection. Voltage signal coefficients are obtained by looking up the frequency of occurrence of the signal feature in a pre-set lookup table.

[0041] Features related to organophosphorus pesticide detection are extracted from the current signal, and the current signal coefficients are obtained by referring to a pre-set lookup table. Voltage signal feature points and current signal feature points are then categorized as the second pre-selected detection indicators.

[0042] The second pre-selection detection coefficient is obtained by combining the voltage signal coefficient, voltage signal weight, current signal coefficient, and current signal weight through a weighted summation operation (e.g., second pre-selection detection coefficient = voltage signal coefficient × voltage signal weight + current signal coefficient × current signal weight).

[0043] The first and second sets of detection indicators are compared and analyzed to obtain the candidate detection indicators for the sample to be detected. The candidate detection coefficients corresponding to the candidate detection indicators are obtained based on the first and second pre-selected detection coefficients. The specific steps include: The sample to be tested contains at least one candidate detection indicator, and the candidate detection indicators include overlapping candidate detection indicators and independent candidate detection indicators. The pre-selected detection indicators that are the same in the first detection indicator set and the second detection indicator set are marked as overlapping candidate detection indicators; Based on the first pre-selected detection coefficient and the first detection weight, and the second pre-selected detection coefficient and the second detection weight of the overlapping pre-selected detection indicators, the candidate detection coefficients corresponding to the overlapping candidate detection indicators are obtained; Pre-selected detection indicators that are different from both the first and second detection indicator sets are marked as independent candidate detection indicators; If the independent candidate detection indicator is the first pre-selected detection indicator, then the first pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator. If the independent candidate detection indicator is the second pre-selected detection indicator, then the second pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator.

[0044] This application first clarifies that the sample to be tested will contain at least one candidate detection indicator, including overlapping candidate detection indicators and independent candidate detection indicators.

[0045] The first and second sets of detection indicators are compared, and the pre-selected detection indicators that are the same in both sets are marked as overlapping candidate detection indicators. For these overlapping indicators, their corresponding candidate detection coefficients are calculated, and then combined with the first pre-selected detection coefficient, the first detection weight, the second pre-selected detection coefficient, and the second detection weight of the overlapping candidate detection indicator. Here, the detection weights are determined through experimental verification, data statistics, or algorithm optimization based on the importance of different detection dimensions in the overall detection. The candidate detection coefficients corresponding to the overlapping candidate detection indicators are obtained through calculation (e.g., weighted summation, i.e., overlapping candidate detection coefficient = first pre-selected detection coefficient × first detection weight + second pre-selected detection coefficient × second detection weight).

[0046] Similarly, the first and second sets of detection indicators are compared, and pre-selected detection indicators that are different in both sets are marked as independent candidate detection indicators. For these indicators, their origin needs to be distinguished: if an independent candidate detection indicator belongs to the first set of pre-selected detection indicators, then the first pre-selected detection coefficient corresponding to that indicator is directly marked as the candidate detection coefficient corresponding to the independent candidate detection indicator; if an independent candidate detection indicator belongs to the second set of pre-selected detection indicators, then its corresponding second pre-selected detection coefficient is marked as the candidate detection coefficient corresponding to the independent candidate detection indicator. In this way, the candidate detection indicators in the sample to be detected are comprehensively reviewed, and corresponding reasonable candidate detection coefficients are assigned.

[0047] The detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the sample is obtained by analyzing the candidate detection index in the sample based on the detection index response threshold. The detection of organophosphorus pesticides based on the target detection index specifically includes the following steps: The response thresholds of the candidate detection indicators are compared to obtain the correlation between the response thresholds of the candidate detection indicators and other candidate detection indicators in the sample to be tested. The response threshold correlation is then compared with the preset threshold correlation. If the correlation degree between the candidate detection index and the response threshold of other candidate detection indicators in the sample to be detected is less than or equal to the threshold correlation degree threshold, then the candidate detection index is designated as the target detection index. If the correlation between the candidate detection index and the response threshold of other candidate detection in the sample to be detected is greater than the threshold correlation threshold, then the candidate detection priority corresponding to the candidate detection index is obtained according to the candidate detection coefficient, and the candidate detection index with the highest candidate detection priority is designated as the target detection index.

[0048] This application extracts the response threshold of each candidate detection indicator from the sample to be tested. The response threshold of each candidate detection indicator is compared with the response thresholds of other candidate detection indicators in the sample to obtain the correlation degree of the response threshold. This correlation degree reflects the similarity of the response patterns between indicators. The higher the correlation degree, the closer the indicators are in reflecting pesticide detection information.

[0049] A preset threshold correlation degree is set, and the response threshold correlation degree is compared with the preset threshold. If the response threshold correlation degree of a candidate detection indicator is less than or equal to the threshold correlation degree of other candidate detection indicators in the sample, it indicates that the indicator is relatively independent in response pattern and is less affected by other indicators. It is then directly marked as the target detection indicator.

[0050] If the correlation between the response thresholds of the candidate detection index and other indicators is greater than the threshold correlation threshold, it indicates a high degree of overlap in the response patterns among the indicators. The candidate detection priority for each indicator is then determined; a higher coefficient indicates a higher priority, representing stronger sensitivity and specificity for pesticide detection. The indicator with the highest candidate detection priority is marked as the target detection index.

[0051] The target detection indicators that best reflect the characteristics of organophosphorus pesticides are selected from the candidate detection indicators, providing a core basis for subsequent qualitative and quantitative detection of pesticides and ensuring the accuracy and reliability of the detection results.

[0052] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A trace detection instrument for organophosphorus pesticides, characterized in that, It includes an analyzer (1), a tubular furnace (2), a flow controller (3), an insulation box (4), and a steam generator (5); In the steam generator (5), the liquid organophosphorus pesticide evaporates and generates steam under the temperature control of ice water in the insulated box (4). The carrier gas, whose flow rate is regulated by the flow controller (3), carries the steam into the cross pipe and mixes to form a uniform detection gas. The detection gas enters the tubular furnace (2) through a pipeline for heating treatment to eliminate interfering components in the detection gas; The processed detection gas comes into contact with the gas sensor and the biosensor. The gas sensor generates electrical parameter change data by causing changes in electrical parameters through the adsorption of organophosphorus pesticide vapor by the nano-gas-sensitive material. The biosensor converts chemical signals into electrical signals through the biological reaction between the gate surface and the organophosphorus pesticide. The analyzer (1) collects electrical parameter change data and electrical signals in real time, and realizes the detection of organophosphorus pesticides by analyzing the electrical parameter change data and electrical signals.

2. A method for detecting trace amounts of organophosphorus pesticides, applied to the organophosphorus pesticide trace detection instrument of claim 1, characterized in that, The method includes the following steps: Acquire the electrical parameter changes and electrical signal data of the sample to be tested; The first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested is obtained by analyzing the change data of electrical parameters, and all the first pre-selected detection indices are combined to obtain the first detection index set. The electrical signal data is analyzed to obtain the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, and all the second pre-selected detection indices are combined to obtain the second detection index set. The first detection index set and the second detection index set are compared and analyzed to obtain the candidate detection indexes of the sample to be detected, and the candidate detection coefficients corresponding to the candidate detection indexes are obtained according to the first pre-selection detection coefficient and the second pre-selection detection coefficient. The detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the candidate detection sample is obtained based on the detection index response threshold. The detection of organophosphorus pesticides is then performed based on the target detection index.

3. The method for trace detection of organophosphorus pesticides according to claim 2, characterized in that, The electrical parameter change data includes the resistance change data and capacitance change data of the sample under test during the test process; The electrical signal data includes voltage signal data and current signal data corresponding to the sample to be detected.

4. The method for trace detection of organophosphorus pesticides according to claim 3, characterized in that, The analysis of electrical parameter variation data yields the first pre-selected detection coefficient corresponding to the first pre-selected detection index of the sample to be tested. This process includes the following steps: Obtain the resistance change amplitude and resistance change rate corresponding to the resistance change data; The resistance amplitude coefficient corresponding to the resistance change is obtained based on the resistance change amplitude. The resistance rate coefficient is obtained based on the rate of change of resistance. The resistance detection coefficient corresponding to the resistance change is obtained based on the resistance amplitude coefficient and the resistance rate coefficient. The capacitance change data is processed and analyzed to obtain the capacitance detection coefficient; Both the detection index corresponding to the resistance change and the detection index corresponding to the capacitance change are recorded as the first pre-selected detection index. Based on the resistance detection coefficient, resistance detection weight, capacitance detection coefficient, and capacitance detection weight, the first pre-selected detection coefficient corresponding to the first pre-selected detection index is obtained.

5. The method for trace detection of organophosphorus pesticides according to claim 4, characterized in that, The capacitance change data is processed and analyzed to obtain the capacitance detection coefficient, specifically including the following steps: Based on the capacitance change data, obtain the capacitance change amplitude and capacitance change rate corresponding to the capacitance change. The capacitance amplitude coefficient corresponding to the capacitance change is obtained based on the capacitance change amplitude. The capacitance rate coefficient is obtained based on the rate of change of capacitance. The capacitance detection coefficient corresponding to the capacitance change is obtained based on the capacitance amplitude coefficient and capacitance rate coefficient.

6. The method for trace detection of organophosphorus pesticides according to claim 5, characterized in that, The analysis of electrical signal data yields the second pre-selected detection coefficient corresponding to the second pre-selected detection index of the sample to be detected, specifically including the following steps: Based on the voltage signal data, the voltage signal feature points in the sample to be detected and the number of times the signal features corresponding to the voltage signal feature points appear are obtained; The voltage signal coefficients corresponding to the voltage signal feature points are obtained based on the frequency of occurrence of the signal features. The current signal data is processed and analyzed to obtain the current signal coefficients; Both voltage signal feature points and current signal feature points are recorded as the second pre-selected detection index; The second preselected detection coefficient is obtained based on the voltage signal coefficient, voltage signal weight, current signal coefficient, and current signal weight of the second preselected detection index.

7. The method for trace detection of organophosphorus pesticides according to claim 6, characterized in that, The current signal data is processed and analyzed to obtain the current signal coefficients, specifically including the following steps: Based on the current signal data, the current signal feature points in the sample to be detected and the number of times the signal features corresponding to the current signal feature points appear are obtained; The current signal coefficients corresponding to the current signal feature points are obtained based on the frequency of occurrence of the signal features.

8. The method for trace detection of organophosphorus pesticides according to claim 7, characterized in that, The first and second sets of detection indicators are compared and analyzed to obtain the candidate detection indicators for the sample to be detected. The candidate detection coefficients corresponding to the candidate detection indicators are obtained based on the first and second pre-selected detection coefficients. The specific steps include: The sample to be tested contains at least one candidate detection indicator, and the candidate detection indicators include overlapping candidate detection indicators and independent candidate detection indicators. The pre-selected detection indicators that are the same in the first detection indicator set and the second detection indicator set are marked as overlapping candidate detection indicators; Based on the first pre-selected detection coefficient and the first detection weight, and the second pre-selected detection coefficient and the second detection weight of the overlapping pre-selected detection indicators, the candidate detection coefficients corresponding to the overlapping candidate detection indicators are obtained; Pre-selected detection indicators that are different from both the first and second detection indicator sets are marked as independent candidate detection indicators; If the independent candidate detection indicator is the first pre-selected detection indicator, then the first pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator. If the independent candidate detection indicator is the second pre-selected detection indicator, then the second pre-selected detection coefficient corresponding to the independent candidate detection indicator is recorded as the candidate detection coefficient corresponding to the independent candidate detection indicator.

9. The method for trace detection of organophosphorus pesticides according to claim 8, characterized in that, The detection index response threshold of the candidate detection index is obtained based on the candidate detection coefficient, and the target detection index of the candidate detection index in the sample is obtained by analyzing the candidate detection index in the sample based on the detection index response threshold. The detection of organophosphorus pesticides based on the target detection index specifically includes the following steps: The response thresholds of the candidate detection indicators are compared to obtain the correlation between the response thresholds of the candidate detection indicators and other candidate detection indicators in the sample to be tested. The response threshold correlation is then compared with the preset threshold correlation. If the correlation degree between the candidate detection index and the response threshold of other candidate detection indicators in the sample to be detected is less than or equal to the threshold correlation degree threshold, then the candidate detection index is designated as the target detection index. If the correlation between the candidate detection index and the response threshold of other candidate detection in the sample to be detected is greater than the threshold correlation threshold, then the candidate detection priority corresponding to the candidate detection index is obtained according to the candidate detection coefficient, and the candidate detection index with the highest candidate detection priority is designated as the target detection index.

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