A study of the transient gas flow affected ion transmission in atmospheric pressure interfaces based on large eddy simulation for electrospray ionization mass spectrometry.

Modern atmosphere pressure interface (API) enables high-efficiency coupling between mass analyzers in high vacuum and atmosphere ionization sources such as electrospray ionization (ESI) source. The transient gas flow entering API possesses strong compressibility and turbulent characteristics, which exerts a huge impact on ion transmission. However, the instantaneous nature and vortical morphology of the turbulence in API and its affection in ion transmission were hardly covered in the reported research. Here we conduct a transient turbulent flow-affected ion transmission evaluation for two typical APIs, the ion funnel and the S-lens, based on scale-resolving large eddy simulation and electro-hydrodynamical ion tracing simulation. In our simulation, the transient properties of the gas flow in the two APIs are illustrated and analyzed in-depth. After experimentally validated on a homemade ESI-TOF-MS platform, the results suggest that the ion funnel can achieve a higher droplet desolvation rate by introducing a unique droplet recirculation mechanism. Meanwhile, the less-dispersed gas flow in S-lens is beneficial in actuating ions axially. In conclusion, the application of the scale-resolving turbulence model helps us to understand the complicated fluid-ion interaction mechanism in APIs and is promising in the development of mass spectrometry instruments of higher performance.

Flexible-Arranged Biomimetic Array Integrated with Parallel Entropy-Driven Circuits for Ultrasensitive, Multiple, and Reliable Detection of Cancer-Related MicroRNAs.

With the emergence of microRNA (miRNA) as a promising biomarker in cancer diagnosis, it is significant to develop multiple analyses of miRNAs. However, it still faces difficulties in ensuring the sensitivity and accuracy during multiplex detection owing to the low abundance and experimental deviation of miRNAs. In this work, a flexible-arranged biomimetic array integrated with parallel entropy-driven circuits (EDCs) was developed for ultrasensitive, multiplex, reliable, and high-throughput detection of miRNAs. The biomimetic array was fabricated by arrangement of various photonic crystals (PCs) for adjustable photonic band gaps (PBGs) and specific fluorescence enhancement. Meanwhile, two cancer-related miRNAs and one reference miRNA were introduced as multiple analytes as a proof-of-concept. The parallel EDCs with negligible crosstalk were designed based on the modular property. Because of the one-to-one match between the emitted fluorescence of parallel EDCs and the PBGs of the flexible-arranged biomimetic array, the generated fluorescence signal triggered by target miRNAs can be enhanced on the corresponding domain of the array. Furthermore, the amplified signal of the array was detected with high-throughput scanning, which could reveal specific information on cancer-related miRNAs as well as reference miRNA, enhancing the abundance and reliability of the analysis. The proposed array has the merits of a modular design, flexible deployment, simple operation (nonenzymatic and isothermal), improved accuracy, high sensitivity, and multiplex analysis, showing potential in disease diagnosis.

Exhaled breath is found to be better than blood samples for determining propofol concentrations in the brain tissues of rats.

The correlation between propofol concentration in exhaled breath (CE) and plasma (CP) has been well-established, but its applicability for estimating the concentration in brain tissues (CB) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredCE,CP, andCBin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

The High-Efficiency Calculation and Analysis of Different Quadrupole Fields Based on the Method of Fundamental Solution.

The calculation and analysis of electric fields are indispensable steps in the design of mass spectrometry. In this work, an approach for this calculation was established based on the method of fundamental solution (MFS). It was proved to be much faster and more accurate than the other popular methods, and its optimum parameters were found for the calculation of different quadrupole fields. After this, quadrupole fields with round rods and different shielding covers were computed to investigate the impact of shielding covers, and a strategy of nonequilibrium allocation in the MFS was proposed to further improve the calculational efficiency. Moreover, through field calculation and mass analysis, the performances of the quadrupole fields with rectangular rods and different electrode cross section lengths were demonstrated, and their optimum sizes were also found. The proposed method and results of analysis in this work provided a highly efficient calculational approach and useful instruction for the design of a quadrupole mass filter.

Improving the Laser-Induced Breakdown Spectroscopy for Highly Efficient Trace Measurement of Hazardous Components in Waste Oils.

Improper disposal of waste oils containing hazardous components damages the environment and the ecosystem, posing a significant threat to human life and health. Here, we present a method of discharge-assisted laser-induced breakdown spectroscopy combined with filter paper sampling (DA-LIBS-FPS) to detect hazardous components and trace the source of polluting elements. DA-LIBS-FPS significantly enhances spectral intensity by 1-2 orders of magnitude due to the discharge energy deposition into the laser-induced plasma and the highly efficient laser-sample interaction on the filter paper, when compared to single-pulse LIBS with silica wafer sampling (SP-LIBS-SWS). Additionally, the signal-to-noise ratio and the signal-to-background ratio are both significantly increased. Resultantly, indiscernible lines, such as CN and Cr I, are well distinguished. In contrast with DA-LIBS combined with silica wafer sampling (DA-LIBS-SWS), the spectral signal fluctuations in DA-LIBS-FPS are reduced by up to 33%, because of the homogeneous distribution of the oil layer on the filter paper in FPS. Further examination indicates that the limit of detection for Ba is reduced from a several parts per million level in SP-LIBS-SWS to a dozens of parts per billion level in DA-LIBS-FPS, i.e., nearly 2 orders of magnitude enhancement in analysis sensitivity. This improvement is attributed to the extended plasma lifespan in DA-LIBS and the increasing electron density and plasma temperature in FPS. DA-LIBS-FPS provides a low-cost, handy, rapid, and highly sensitive avenue to analyze the hazardous components in waste oils with great potential in environmental and ecological monitoring.

Electrospun nanofiber-based indicatorpaper sensing platform for fluorescence and visualization detection of norfloxacin.

Norfloxacin (NOR) residues in water pose a serious threat to human health via the food chain, necessitating the development of a rapid on-site antibiotic detection technique. In this work, we utilize electrostatic spinning technology that combines polyacrylonitrile (PAN) fibers and adenosine triphosphate (ATP)-rare earth metal Tb3+ complexes (ATP/Tb) to construct a new ternary film-based sensor for sensitive, quick, and convenient field testing of NOR in water. The operating mechanism is that the ternary system produces gradually enhanced bright green fluorescence at increasing concentrations of NOR. The unique fluorescence property of the ternary systems is attributed to the use of ATP, rather than the commonly used adenosine monophosphate (AMP), to coordinate with Tb3+, which avoided the possible fluorescence quenching from competitive water binding. Benefiting from the PAN nanofiber's superior stability, acid, and alkali resistance, and flexibility as support, the ternary system exhibited a good linear response to NOR in a wide dynamic range of 0.04-30 µM at the detection limit of 16 nM. Additionally, the combination of a smartphone color recognition app allows for quick on-scene NOR detection. This film sensing strategy is instructive for the development of smart and portable sensing platforms for real-time detection of analytes such as antibiotics, pesticide residues, and hazardous materials in water bodies.

A preclinical study on online monitoring of exhaled ciprofol concentration by the ultraviolet time-of-flight spectrometer and prediction of anesthesia depth in beagles.

BACKGROUND: Exhaled air has been demonstrated as a reliable medium for monitoring propofol concentration. However, online monitoring of exhaled ciprofol have not been reported. METHODS: Thirty-six beagles undergoing mechanical ventilation were divided into 6 groups, including bolus injection of low (Group BL, n = 6), medium (Group BM, n = 6), and high dose of ciprofol (Group BH, n = 6) groups; as well as 1 h continuous infusion of low (Group IL, n = 6), medium (Group IM, n = 6), and high dose of ciprofol (Group IH, n = 6) groups. The ciprofol concentration in exhaled air (CE) was determined by the ultraviolet time-of-flight mass spectrometer (UV-TOFMS). The correlations of CE and plasma concentration (Cp), CE and the bispectral index (BIS) were explored. Additionally, the pharmacokinetics (PK) models of CE and Cp, the pharmacodynamics (PD) models of CE and BIS were also established. RESULTS: Online monitoring of exhaled ciprofol can be achieved with the UV-TOFMS instrument. The CE of ciprofol in beagles was found at parts per billion by volume (ppbv) level. The linear correlation of CE and Cp was weak in bolus injection groups (R2 = 0.01) nonetheless moderate in continuous infusion groups (R2 = 0.53). The i.v. bolus PK model of CE and Cp can be fitted with the non-compartment models. Additionally, the the PD models of CE and BIS can be well fitted with the inhibitory sigmoid Emax model with the estimate values of IC50 = 0.05 ± 0.01 ppbv, γ = 4.74 ± 1.51, E0 = 81.40 ± 3.75, Imax = 16.35 ± 4.27 in bolus injection groups; and IC50 = 0.05 ± 0.01 ppbv, γ = 6.92 ± 1.30, E0 = 83.08 ± 1.62, Imax = 12.58 ± 1.65 in continuous infusion groups. CONCLUSIONS: Online monitoring of exhaled ciprofol concentration in beagles can be achieved with the UV-TOFMS instrument. Good correlations can be observed between exhaled ciprofol concentration and its cerebral effects reflected by the BIS value, demonstrating the potential of exhaled ciprofol monitoring for titrating depth of anesthesia in future clinical setting.

Calibration and validation of ultraviolet time-of-flight mass spectrometry for online measurement of exhaled ciprofol.

Ciprofol (HSK 3486, C14H20O), a novel 2,6-disubstituted phenol derivative similar to propofol, is a new type of intravenous general anaesthetic. We found that the exhaled ciprofol concentration could be measured online by ultraviolet time-of-flight mass spectrometry (UV-TOFMS), which could be used to predict the plasma concentration and anaesthetic effects of ciprofol. In this study, we present the calibration method and validation results of UV-TOFMS for the quantification of ciprofol gas. Using a self-developed gas generator to prepare different concentrations of ciprofol calibration gas, we found a linear correlation between the concentration and intensity of ciprofol from 0 parts per trillion by level (pptv) to 485.85 pptv (R2 = 0.9987). The limit of quantification was 48.59 pptv and the limit of detection was 7.83 pptv. The imprecision was 12.44% at 97.17 pptv and was 8.96% at 485.85 pptv. The carry-over duration was 120 seconds. In addition, we performed a continuous infusion of ciprofol in beagles, measured the exhaled concentration of ciprofol by UV-TOFMS, determined the plasma concentration by high-performance liquid chromatography, and monitored the anaesthetic effects as reflected by the bispectral index value. The results showed that the exhaled and plasma concentrations of ciprofol were linearly correlated. The exhaled ciprofol concentration correlated well with the anaesthetic effect. The study showed that we could use UV-TOFMS to provide a continuous measurement of gaseous ciprofol concentration at 20 second intervals.

Fiber-Optic-Based Biosensor as an Innovative Technology for Point-of-Care Testing Detection of Foodborne Pathogenic Bacteria To Defend Food and Agricultural Product Safety.

Food safety is a concerning issue globally. Foodborne-pathogenic-bacteria-derived foodborne disease outbreaks have increased the threat to human health. The accurate and rapid detection of foodborne bacteria is of great significance for food safety. A fiber-optic-based biosensor has emerged as a powerful technique for the point-of-care testing of foodborne bacteria in food and agricultural products. This Perspective discusses the opportunities and challenges of fiber-optic-based biosensors for foodborne bacteria detection. The corresponding solution strategies to promote the application of this innovative technology in food and agricultural product detection for food safety and human health are also discussed and proposed.

Label-Free, High-Throughput, Sensitive, and Logical Analysis Using Biomimetic Array Based on Stable Luminescent Copper Nanoclusters and Entropy-Driven Nanomachine.

The development of an array for high-throughput and logical analysis of biomarkers is significant for disease diagnosis. DNA-templated copper nanoclusters (CuNCs) have a strong potential to serve as a label-free photoluminescence source in array platforms, but their luminescent stability and sensitivity need to be improved. Herein, we report a facile, sensitive, and robust biomimetic array assay by integrating with stable luminescent CuNCs and entropy-driven nanomachine (EDN). In this strategy, the luminescent stability of CuNCs was improved by adding fructose in CuNCs synthesis to offer a reliable label-free signal. Meanwhile, the DNA template for CuNCs synthesis was introduced into EDN with excellent signal amplification ability, in which the reaction triggered by target miRNA would cause the blunt/protruding conformation change of 3'-terminus accompanied by the production or loss of luminescence. In addition, a biomimetic array fabricated by photonic crystals (PCs) physically enhanced the emitted luminescent signal of CuNCs and achieved high-throughput signal readout by a microplate reader. The proposed assay can isothermally detect as low as 4.5 pM of miR-21. Moreover, the logical EDN was constructed to achieve logical analysis of multiple miRNAs by "AND" or "OR" logic gate operation. Therefore, the proposed assay has the advantages of label-free property, high sensitivity, flexible design, and high-throughput analysis, which provides ideas for developing a new generation of facile and smart platforms in the fields of biological analysis and clinical application.

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser-induced breakdown spectroscopy.

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

Rapid and Online Detection of Foodborne Bacteria via a Novel Ultraviolet Photoionization Time-of-Flight Mass Spectrometry.

Foodborne bacteria are widespread contaminated sources of food; hence, the real-time monitoring of pathogenic bacteria in food production is important for the food industry. In this study, a novel rapid detection method based on microbial volatile organic compounds (MVOCs) emitted from foodborne bacteria was established by using ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS). The results showed obvious differences of MVOCs among the five species of bacteria, and the characteristic MVOCs for each bacterium were selected by a feature selection algorithm. Online monitoring of MVOCs during bacterial growth displayed distinct metabolomic patterns of the five species. MVOCs were most abundant and varied among species during the logarithmic phase. Finally, MVOC production by bacteria in different food matrixes was explored. The machine learning models for bacteria cultured in different matrixes showed a good classification performance for the five species with an accuracy of over 0.95. This work based on MVOC analysis by online UVP-TOF-MS achieved effective rapid detection of bacteria and showed its great application potential in the food industry for bacterial monitoring.

Assessment of an exhaled breath test using ultraviolet photoionization time-of-flight mass spectrometry for the monitoring of kidney transplant recipients.

Continuous monitoring for immunosuppressive status, infection and complications are a must for kidney transplantation (KTx) recipients. Traditional monitoring including blood sampling and kidney biopsy, which caused tremendous medical cost and trauma. Therefore, a cheaper and less invasive approach was urgently needed. We thought that a breath test has the potential to become a feasible tool for KTx monitoring. A prospective-specimen collection, retrospective-blinded assessment strategy was used in this study. Exhaled breath samples from 175 KTx recipients were collected in West China Hospital and tested by online ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS). The classification models based on breath test performed well in classifying normal and abnormal values of creatinine, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and tacrolimus, with AUC values of 0.889, 0.850, 0.849 and 0.889, respectively. Regression analysis also demonstrated the predictive ability of breath test for clinical creatinine, eGFR, BUN, tacrolimus level, as the predicted values obtained from the regression model correlated well with the clinical true values (p < 0.05). The findings of this investigation implied that a breath test by using UVP-TOF-MS for KTx recipient monitoring is possible and accurate, which might be useful for future clinical screenings.

Development of mass spectrometry imaging techniques and its latest applications.

Mass spectrometry imaging (MSI) is a novel molecular imaging technology that collects molecular information from the surface of samples in situ. The spatial distribution and relative content of various compounds can be visualized simultaneously with high spatial resolution. The prominent advantages of MSI promote the active development of ionization technology and its broader applications in diverse fields. This article first gives a brief introduction to the vital parts of the processes during MSI. On this basis, provides a comprehensive overview of the most relevant MS-based imaging techniques from their mechanisms, pros and cons, and applications. In addition, a critical issue in MSI, matrix effects is also discussed. Then, the representative applications of MSI in biological, forensic, and environmental fields in the past 5 years have been summarized, with a focus on various types of analytes (e.g., proteins, lipids, polymers, etc.) Finally, the challenges and further perspectives of MSI are proposed and concluded.

Volatolomics analysis of exhaled breath and gastric-endoluminal gas for distinguishing early upper gastrointestinal cancer from benign.

Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds, which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.

The investigation of volatile organic compounds in diagnosing (early) esophageal squamous cell carcinoma and gastric adenocarcinoma.

PURPOSE: The diagnosis of upper gastrointestinal cancer (UGIC) and early UGIC is currently based on endoscopy and histopathology. In this study, we aimed to explore whether intraluminal and exhaled volatile organic compounds (VOCs) could be used to diagnose (early) esophageal squamous cell carcinoma (ESCC) and gastric adenocarcinoma (GC). METHODS: We prospectively recruited 259 patients and first collected intraluminal gas simples directly from upper GI tract via our designed device after passing endoscopic biopsy channel and collected exhaled gas samples in pairs. RESULTS: 509 gas samples were totally collected and VOCs composed by peak compounds detected by gas chromatography-mass spectrometry (GC-MS) were used to train and test Multilayer Perceptron Network (MPN) for discrimination. Intraluminal and exhaled gas had more than 0.95 area under the curve (AUC) to discriminate UGIC (ESCC and GC) and early UGIC from benign control with different VOCs compositions. CONCLUSION: Both intraluminal and exhaled VOCs had cancer-specific compositions to accurately discriminate early UGIC and UGIC, and the ability of intraluminal VOCs was better than that of exhaled VOCs. These suggested the potential role of VOCs in diagnosing and screening early UGIC and UGIC in the future.

Development of a headspace-solid phase microextraction gas chromatography-high resolution mass spectrometry method for analyzing volatile organic compounds in urine: Application in breast cancer biomarker discovery.

BACKGROUND AND AIM: Breast cancer (BC) is the leading cause of cancer-related death in females. The development of non-invasive methods for the early diagnosis of BC still remains challenge. Here, we aimed to discover the urinary volatile organic compounds (VOCs) pattern of BC patients and identify potential VOC biomarkers for BC diagnosis. METHODS: Urine samples were analyzed by headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-high resolution mass spectrometry (GC-HRMS). To assure reliable analysis, the factors influencing HS-SPME extraction efficiency were comprehensively investigated and optimized by combing the Plackett-Burman design (PBD) with the central composite design (CCD). The established HS-SPME/GC-HRMS method was validated and applied to analyze urine samples from BC patients (n = 80) and healthy controls (n = 88). RESULTS: A total number of 134 VOCs belonging to distinct chemical classes were identified by GC-HRMS. BC patients demonstrated unique urinary VOCs pattern. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation between BC patients and healthy controls. Eight potential VOC biomarkers were identified using multivariate and univariate statistical analysis. The predictive ability of candidate VOC biomarkers was further investigated by the random forest (RF) algorithm. The candidate VOC biomarkers yielded 76.3% sensitivity and 85.4% specificity on the training set, and achieved 76.0% sensitivity and 92.3% specificity on the validation set. CONCLUSIONS: Overall, this work not only established a standardized HS-SPME/GC-HRMS approach for urinary VOCs analysis, but also highlighted the value of urinary VOCs for BC diagnosis. The knowledge gained from this study paves the way for early diagnosis of BC using urine in a non-invasive manner.

Plasma-based ambient mass spectrometry: Recent progress and applications.

Ambient mass spectrometry (AMS) has grown as a group of advanced analytical techniques that allow for the direct sampling and ionization of the analytes in different statuses from their native environment without or with minimum sample pretreatments. As a significant category of AMS, plasma-based AMS has gained a lot of attention due to its features that allow rapid, real-time, high-throughput, in vivo, and in situ analysis in various fields, including bioanalysis, pharmaceuticals, forensics, food safety, and mass spectrometry imaging. Tens of new methods have been developed since the introduction of the first plasma-based AMS technique direct analysis in real-time. This review first provides a comprehensive overview of the established plasma-based AMS techniques from their ion source configurations, mechanisms, and developments. Then, the progress of the representative applications in various scientific fields in the past 4 years (January 2017 to January 2021) has been summarized. Finally, we discuss the current challenges and propose the future directions of plasma-based AMS from our perspective.

T-Shaped Aptamer-Based LSPR Biosensor Using Ω-Shaped Fiber Optic for Rapid Detection of SARS-CoV-2.

SARS-CoV-2, especially the variant strains, is rapidly spreading around the world. Rapid detection methods for the virus are crucial for controlling the COVID-19 epidemic. Herein, a localized surface plasmonic resonance (LSPR) biosensor based on Ω-shaped fiber optic (Ω-FO) was developed for dual assays of SARS-CoV-2 monitoring. Due to its strong ability to control the orientation and density, a new T-shaped aptamer exhibits enhanced binding affinity toward N proteins. After being combined on the fiber optic surface, the T-shaped aptamer sensitively captured N proteins of SARS-CoV-2 for a direct assay. Further, core-shell structured gold/silver nanoparticles functionalized with a T-shaped aptamer (apt-Ag@AuNPs) can amplify the signal of N protein detection for a sandwich assay. The real-time analytical feature of the dual assays endows time-dependent sensitivity enhancement behavior, which provides a guideline to save analytical time. With those characteristics, the LSPR biosensor has been successfully used to rapidly identify 39 healthy volunteers and 39 COVID-19 patients infected with the ancestral or variant SARS-CoV-2. With the help of simple pretreatment, we obtain a true negative rate of 100% and a true positive rate of 92.3% with a short analysis time of 45 min using the direct assay. Further, the LSPR biosensor could also broaden the detection application range to the surface of cold-chain foods using a sandwich assay. Thus, the LSPR biosensor based on Ω-FO was demonstrated to have broad application potential to detect SARS-CoV-2 rapidly.

Construction of classification models for pathogenic bacteria based on LIBS combined with different machine learning algorithms.

Bacteria, especially foodborne pathogens, seriously threaten human life and health. Rapid discrimination techniques for foodborne pathogens are still urgently needed. At present, laser-induced breakdown spectroscopy (LIBS), combined with machine learning algorithms, is seen as fast recognition technology for pathogenic bacteria. However, there is still a lack of research on evaluating the differences between different bacterial classification models. In this work, five species of foodborne pathogens were analyzed via LIBS; then, the preprocessing effect of five filtering methods was compared to improve accuracy. The preprocessed spectral data were further analyzed with a support vector machine (SVM), a backpropagation neural network (BP), and k-nearest neighbor (KNN). Upon comparing the capacity of the three algorithms to classify pathogenic bacteria, the most suitable one was selected. The signal-to-noise ratio and mean square error of the spectral data after applying a Savitzky-Golay filter reached 17.4540 and 0.0020, respectively. The SVM algorithm, BP algorithm, and KNN algorithm attained the highest classification accuracy for pathogenic bacteria, reaching 98%, 97%, and 96%, respectively. The results indicate that, with the support of a machine learning algorithm, LIBS technology demonstrates superior performance, and the combination of the two is expected to be a powerful tool for pathogen classification.