Automatic Matching Research Articles

An In-Run Automatic Mode-Matching Method for N = 3 MEMS Disk Resonator Gyroscope

This paper demonstrates an in-run automatic mode-matching method for a Micro-Electro-Mechanical systems (MEMS) disk resonator gyroscope (DRG) operating in the n = 3 wineglass mode. In proposed method, two sideband signals whose frequencies are located at both sides of the drive mode symmetrically, are used to excite the gyroscope, and the response of the gyroscope in the Coriolis force channel is demodulated to obtain the indicator of the frequency split for mode-matching. The input angle rate can still be measured when the proposed mode-matching loop works normally. However, due to the asymmetry of the amplitude of the double-sideband response of the sense mode, there is a tuning error, which leads to a shift of the matching frequency. To eliminate the tuning error and achieve perfect mode-matching, a novel compensation method for the amplitude correction is proposed. The theories of electrostatic tuning of the MEMS DRG, the proposed mode-matching method and the compensation method are analyzed and introduced. The simulation results show that this method can achieve in-run automatic closed-loop mode matching for gyroscopes, and the compensation method can effectively eliminate the tuning error and reduce the frequency split from ~0.5 Hz to < 0.01 Hz. The experimental results show that the bias instability (BI) and the angle random walk (ARW) is improved by 5.8 times and 1.7 times, respectively, under mode-matching. After the tuning error is eliminated with compensation method, the BI and ARW are further reduced to 1.36 °/h and 0.1562 °/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\surd \text{h}$ </tex-math></inline-formula> , respectively.

Research on Embroidery Feature Recognition Algorithm of Traditional National Costumes Based on Double-Layer Model

In order to improve the visual communication ability of traditional national costume patterns, it is necessary to carry out image texture intelligent matching processing. A traditional national costume embroidery feature recognition algorithm based on a double-layer model is proposed. The traditional national costume pattern texture intelligent information acquisition model under the double-layer model is constructed to carry out texture imaging and feature segmentation of traditional national costume patterns, extract the texture histogram of traditional national clothing pattern and national design language, carry out texture segmentation and automatic matching under the two-layer model according to the histogram distribution, enhance and optimize the texture information of traditional national clothing pattern, extract the edge contour feature points of traditional national clothing pattern, and complete the embroidery feature recognition of traditional national clothing. The experimental results show that the designed recognition algorithm has high integrity and accuracy.

Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis.

Background:The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection.Methods:This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons.Results:Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans.Conclusions:The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Development of mock-up Ion cyclotron resonance heating system with a new algorithmic approach for automatic impedance matching

This paper presents the development of a mock-up Ion Cyclotron Resonance Heating (ICRH) system along with a new algorithmic approach for fast automatic impedance matching. The mock-up is developed for low power (few watts) and frequency (182.5 MHz) i.e. five times the ICRH frequency of SST-1. The size of the mock-up is considerably reduced to be fit on the test bench using it. The main objective of the work is to develop different matching approaches in order to get the optimum speed of matching. Reducing the size of the mock-up allows frequent implementation and testing of new algorithms which is generally difficult to implement in the real system of tokamak because of its size and complexity. The system consists of indigenously developed components such as 3 dB hybrid coupler, 20 dB directional coupler, tapped transmission line, rigid coaxial stub tuners, line stretcher, service stub, RF antenna, movable water tank based load. The developed system is capable of automatically match any arbitrary load in the range, (35–260)Ω, resistive + (-j25 to +j220)Ω, reactive or VSWR in the range 1.5–4.0 within 200 ms. The matching system also consists of power detectors which automatically sense the forward and reflected power using coupler and the feedback controlled moving coaxial stubs, line stretchers respond as per developed algorithms. The matching algorithm incorporates the various matching steps based on VSWR which have been interpreted for the controller program to control the devices used in the matching network. The developed system is tested using an RF antenna facing the movable saltwater tank that emulates the plasma-like loading behavior. The developed setup is also useful for various high power RF applications like RF heating, sputtering, plasma generation, RADAR, etc. in the RF frequency range.

STC-Det: A Slender Target Detector Combining Shadow and Target Information in Optical Satellite Images

Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%.

Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images

Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.

A Novel High-Q Dual-Mass MEMS Tuning Fork Gyroscope Based on 3D Wafer-Level Packaging.

Tuning fork gyroscopes (TFGs) are promising for potential high-precision applications. This work proposes and experimentally demonstrates a novel high-Q dual-mass tuning fork microelectromechanical system (MEMS) gyroscope utilizing three-dimensional (3D) packaging techniques. Except for two symmetrically decoupled proof masses (PM) with synchronization structures, a symmetrically decoupled lever structure is designed to force the antiparallel, antiphase drive mode motion and eliminate low frequency spurious modes. Thermoelastic damping (TED) and anchor loss are greatly reduced by the linearly coupled, momentum- and torque-balanced antiphase sense mode. Moreover, a novel 3D packaging technique is used to realize high Q-factors. A composite substrate encapsulation cap, fabricated by through-silicon-via (TSV) and glass-in-silicon (GIS) reflow processes, is anodically bonded to the wafer-scale sensing structures. A self-developed control circuit is adopted to realize loop control and characterize gyroscope performances. It is shown that a high-reliability electrical connection, together with a high air impermeability package, can be fulfilled with this 3D packaging technique. Furthermore, the Q-factors of the drive and sense modes reach up to 51,947 and 49,249, respectively. This TFG realizes a wide measurement range of ±1800 °/s and a high resolution of 0.1°/s with a scale factor nonlinearity of 720 ppm after automatic mode matching. In addition, long-term zero-rate output (ZRO) drift can be effectively suppressed by temperature compensation, inducing a small angle random walk (ARW) of 0.923°/√h and a low bias instability (BI) of 9.270°/h.

A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach

AbstractIn recent years, Internet of Things (IoT) services have expanded to promote the quality of life in different areas. Cloud connectivity services are so popular now that they have prompted the experts to enhance cloud computing for its utilization in IoT, making everything online in the next few decades. For reducing latency, immediate processing, and network congestion, fog computing has emerged in which cloud computing is expanded to the edge of the network. On the other hand, concerning the limitations in fog hardware resources compared with the cloud, and the dynamic and unpredictable fog environment, the provision of dynamic fog services is a challenge. Automatic matching of the resources based on the workload oscillations of IoT applications leads to allocating minimum fog resources to IoT devices, therefore, the satisfaction of service level agreement (SLA) and quality of service (QoS) parameters.The present article introduces a method based on the control monitoring‐analysis‐planning‐execution having shared knowledge‐base loop and presents an approach for dynamic resource provisioning based on autonomic computing and reinforcement learning techniques. The proposed scheme uses learning automata as a decision‐maker in the planning phase and time series prediction model in the analysis phase. The simulation test results indicated a reduced delay in service provisioning, total cost, and SLA violation compared with other approaches, highlighting the potential of fog computing in ensuring the QoS.

REvolver: Automated running and matching of couplings and masses in QCD

In this article we present REvolver, a C++ library for renormalization group evolution and automatic flavor matching of the QCD coupling and quark masses, as well as precise conversion between various quark mass renormalization schemes. The library systematically accounts for the renormalization group evolution of low-scale short-distance masses which depend linearly on the renormalization scale and sums logarithmic terms of high and low scales that are missed by the common logarithmic renormalization scale evolution. The library can also be accessed through Mathematica and Python interfaces and provides renormalization group evolution for complex renormalization scales as well. Program summaryProgram Title:REvolverCPC Library link to program files:https://doi.org/10.17632/m6cjfmzsxb.1Developer's repository link:https://gitlab.com/REvolver-hep/REvolverCode Ocean capsule:https://codeocean.com/capsule/6150064Licensing provisions: GPLv3 or laterProgramming language:C++, Python, Wolfram LanguageNature of problem: The strong coupling and the quark masses are fundamental parameters of QCD that are scheme and renormalization-scale dependent. The choice of scheme depends on the active number of flavors and the range of scales, and is dictated by the requirements to minimize the size of corrections and to sum large logarithmic corrections to all orders. For the strong coupling and the quark masses at high scales, the MS‾ scheme with logarithmic scale dependence is used. For quark masses at low scales, short-distance mass schemes with linear scale-dependence are used. The REvolver library provides conversions for the strong coupling and the most common quark mass schemes, with renormalization scale evolution implemented such that all types of large logarithmic terms are summed to all orders, accounting for flavor threshold effects and state-or-the-art correction terms. The pole mass, which is not a short-distance mass and contains a sizable renormalon ambiguity, is treated as a derived quantity.Solution method: Renormalization group equations are solved for complex-valued scales to machine precision based on fast-converging iterative algorithms and analytic all-order expressions. Matching relations for the strong coupling at flavor thresholds are computed in a way that gives equal results for upward and downward evolution. Core objects allow to define an arbitrary number of physical scenarios for strong coupling values and quark mass spectra, where options for precision and matching scales can be set freely, and values for quark masses in all common schemes including the pole mass can be extracted. All REvolver routines are implemented entirely in C++ and can be accessed through Mathematica and Python interfaces.

A New Music Teaching Mode Based on Computer Automatic Matching Technology

The rapidly developing computer technology has been extensively applied in music teaching. The computer automatic matching technology supports the automatic generation of randomized teaching contents, providing a good tool to develop the musical thinking of students. This paper tentatively introduces this technology to music teaching, and derives a new music teaching mode. The results show that, computer technology can effectively assist in music teaching; the inclusion of computer technology in music teaching arouses students’ interest in music activities; the application of computer automatic matching technology has improved students’ professional skills, live music performance, as well as music ability. The research results greatly promote the reform of music teaching modes and methods.

Review of Wide-Baseline Stereo Image Matching Based on Deep Learning

Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automatic matching for such images. Deep convolutional neural networks, which can express high-level features and their correlation, have received increasing attention for the task of wide-baseline image matching, and learning-based methods have the potential to surpass methods based on handcrafted features. Therefore, we focus on the dynamic study of wide-baseline image matching and review the main approaches of learning-based feature detection, description, and end-to-end image matching. Moreover, we summarize the current representative research using stepwise inspection and dissection. We present the results of comprehensive experiments on actual wide-baseline stereo images, which we use to contrast and discuss the advantages and disadvantages of several state-of-the-art deep-learning algorithms. Finally, we conclude with a description of the state-of-the-art methods and forecast developing trends with unresolved challenges, providing a guide for future work.

A review of automatic history matching

History matching is one of the most complicated problems which requires long time period and many attempts to get an acceptable match because it is ill-post invers problem. For this purpose, many theories have been developed to solve this complexity.In this paper we show the automatic history matching methods and the different studies that use those methods over the years.

Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography.

We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labour. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists. LAY DESCRIPTION: Electron tomography of biological samples is used for a three-dimensional (3D) reconstruction of filamentous structures, such as microtubules (MTs) in mitotic and meiotic spindles. Large-scale electron tomography can be applied to increase the reconstructed volume for the visualisation of full spindles. For this, each spindle is cut into a series of semi-thick physical sections, from which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. Previously, we presented fully automatic approaches for this 3D reconstruction pipeline. However, large volumes often suffer from imperfections (ie physical distortions) caused by the image acquisition process, making it difficult to apply fully automatic approaches for matching and stitching of numerous tomograms. Therefore, we developed an interactive, semi-automatic solution that considers all requirements for large-scale stitching of microtubules in image stacks of consecutive sections. We achieved this by combining automatic methods, visual validation and interactive error correction, thus allowing the user to continuously improve the result by interactively correcting landmarks or matches of filaments. We present large-scale reconstructions of spindles in which the automatic workflow failed and where different steps of manual corrections were needed. Our approach is also applicable to other biological samples showing 3D distributions of MTs in a number of different cellular contexts.

Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

Photogrammetric analysis of multispectral and thermal close-range images

Sensors capable of multispectral and thermal imaging beyond visible bands offer many analysis possibilities for environmental monitoring. Different sensor images constitute an important source of information especially in the fields of agriculture, forestry, geology and energy. Photogrammetric studies have been affected by this development in recent years and have been used in the production of multispectral and thermal models besides the RGB model. However, due to geometric and radiometric resolution differences, it is difficult to combine or evaluate models produced from different types of sensors. In this study, the three-dimensional test field images obtained with RGB, multispectral and thermal sensors were oriented and modeled photogrammetrically. The accuracies of the control points on the produced models were compared and discussed. When the results are examined, control point accuracy was obtained as almost similar as in the RGB model after the orientation based on automatic feature matching. Automatic feature detection and matching in thermal images were not robustly produced due to low geometric resolution. For this reason, manual measurements were performed in thermal images, and the photogrammetric orientation and adjustment process was done accordingly. The fused evaluation approach considering RGB, multispectral and thermal images in one photogrammetric model was also implemented and discussed.

Method for automatic antenna matching with transmitter output stage

This work is devoted to the study of automatic antenna tuning units of the short-wave range. Devices of narrowband matching based on discrete sets of reactive elements are considered. A classification of the most frequently used automatic matching methods has been made. The advantages and disadvantages of each method are analyzed. Examples of using different approaches in commercially available devices are given. Particular attention is paid to the calculation method of matching, as the most promising for use in modern communications. Assumptions are made about the reasons for its rare use in serial devices. Circuits have been developed to simulate the influence of parasitic parameters of the components of the matching circuit and the body of the tuning unit on the resulting standing wave ratio when using this method. Based on the simulation results, conclusions were drawn about the reasons for the low quality of the calculation method. As an alternative, a new method of automatic tuning is proposed, combining the advantages of computation and search methods, which is based on modeling the search process using a simulation model. The conditions for its application in automatic antenna tuning units are determined. Acomparative analysis of the features of both the known methods of automatic tuning and the newly proposed one ismade.

Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm

Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts’ similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative’s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.

Expert Knowledge as Basis for Assessing an Automatic Matching Procedure

The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks.

A Technology of Surface Defects for the Solar Wafer by Dual Vision

For overcoming the defects of the existing method of surface defect for the solar wafer.A automatic measurement technology by dual vision is presented. Frist the principle and flow of the automatice measurement technology are introduced. Second the technique of camera calibration, technique of center extraction of edge and technique of automatic matching are researched. At the end, using the technology realized the rapid defects detection including crack, cavern, smudge and broken so on and so forth which can be concluded that the application of this technology helps to enhance the inspection efficiency.

Screening and confirmation of 244 pesticide residues in chilli by gas chromatography-quadrupole time-of-flight mass spectrometry

建立了辣椒中244种农药残留的QuEChERS前处理结合气相色谱-四极杆/飞行时间质谱(GC-Q-TOF/MS)快速筛查确证方法。鲜辣椒和干辣椒样品分别采用经-20 ℃冷冻的乙腈和1%(v/v)乙酸化乙腈提取,经盐析分层、分散固相萃取净化和浓缩后加入内标并复溶,HP-5MS UI色谱柱(30 m×0.25 mm×0.25 μm)分离,程序升温不分流进样,GC-Q-TOF/MS全扫描模式采集,内标法定量。比较了分析保护剂(AP)和基质匹配校准法对基质效应的补偿效果,最终选择采用基质匹配校准法来补偿基质效应并进行样品中农药残留的校准定量。设置定性筛查中的保留时间最大偏差为±0.25 min,精确质量偏差阈值为±20×10 -6。对鲜辣椒中244种农药残留和干辣椒中222种农药残留进行了定量方法验证,实验结果表明,采用建立的数据库和分析方法可以对辣椒进行农药残留的高通量筛查和定量分析。在空白辣椒样品中添加不同水平的目标化合物,以信噪比S/N≥10对应的添加水平作为定量限(LOQ)。鲜辣椒中最大残留限量(MRL)≤0.05 mg/kg的44种农药在鲜辣椒中LOQ≤0.010 mg/kg,线性范围在0.01~1.00 mg/L,在1倍和2.5倍LOQ添加水平下,回收率在60%~120%的农药种类占比分别为88.64%和100%;鲜辣椒中暂无MRL规定或MRL>0.05 mg/kg的200种农药在鲜辣椒中LOQ≤0.025 mg/kg,线性范围在0.05~1.00 mg/L,在1倍、2倍和10倍LOQ添加水平下,回收率在60%~120%的农药种类占比分别为49.50%、87.00%和89.50%; 244种农药的线性相关系数(r 2)均大于0.99。222种农药在干辣椒中LOQ≤0.15 mg/kg,线性范围在0.04~1.00 mg/L, r 2≥0.99的比例为95.46%,在1倍、2倍和10倍LOQ添加水平下,回收率在60%~120%占比分别为72.52%、73.42%和81.53%。应用建立的筛查确证方法对市售的12份鲜辣椒样品和14份干辣椒样品进行农药残留筛查分析,从9份鲜辣椒样品和3份干辣椒样品中筛查出8种农药化合物,经人工鉴定均为阳性,定量结果显示,8种农药化合物均未超过其在GB 2763-2019《食品安全国家标准食品中农药最大残留限量》所规定的MRL。方法快速、简单、高效、可靠,适用于鲜辣椒及干辣椒中多种农药残留的筛查分析。