Device for the rapid identification of pathogenic bacterial strains by spectral fingerprinting

Abstract

A system for the rapid identification of pathogenic bacterial strains using spectral fingerprinting, the system comprising: an optical spectroscopic detection unit comprising at least one excitation source configured to emit electromagnetic radiation within a predefined wavelength range; a spectral acquisition module comprising one or more photodetectors and diffraction components configured to collect scattered or absorbed radiation from a bacterial sample and resolve it into discrete spectral signals; a microfluidic sample handling module comprising a disposable cartridge having at least one liquid inlet, a bacteria capture zone, and an optical window aligned with the detection unit, the cartridge configured for the aseptic introduction and positioning of liquid or semi-solid biological samples; an automated sample preparation subsystem integrated into the microfluidic module and configured to perform concentration, purification, and flow control of the bacterial sample prior to spectral analysis; a spectral preprocessing engine configured to receive raw spectral data from the spectral acquisition module and perform baseline correction, noise filtering, normalization, and dimensionality reduction; an AI-driven classification engine comprising at least one machine learning model trained on a curated database of bacterial spectral fingerprints, the classification engine configured to output an identification of the bacterial strain with confidence metrics based on extracted spectral features; a user interface module configured to display identification results, system status, and actionable diagnostics in real time; a controller operatively coupled to the optical detection unit, the microfluidic sample handling module, the preprocessing engine, and the classification engine, the controller configured to coordinate timing, signal acquisition, and data forwarding operations; The system is further configured to dynamically update its classification engine through network-based retrieval of newly indexed bacterial spectral data.

Description

Technical FieldThe present invention relates to the field of microbiological diagnostics and, more particularly, to an apparatus and method for the rapid and accurate identification of pathogenic bacterial strains using spectral fingerprinting techniques in combination with computer assisted analysis for biomedical, ecological and industrial applications.BackgroundCurrent methods for bacterial identification, including culture-based methods, PCR and immunoassays, are either time-consuming, labor-intensive or are limited in the range of pathogen detection. A rapid and precise identification is essential in clinical diagnostics, food safety and biosecureness in order to be able to intervene and treat in good time. Spectral fingerprints, in particular by vibrational spectroscopy such as Raman and Fourier Transform Infrared Spectroscopy (FTIR), offer a promising non-destructive and mark-free alternative for distinguishing bacterial strains on the basis of unique molecular signatures. However, the development of a stand-alone, compact, automated, and high precision system, incorporating spectral detection, sample handling, and data interpretation remains a challenge in practice.The identification of pathogenic bacterial strains is still a corner poster of microbiological diagnostics and plays a decisive role in clinic, environment, agriculture and industry. The rapid detection and characterization of these microorganisms are decisive for the treatment of infectious diseases, the prevention of outbreaks of disease, the guarantee of food and water safety and the maintenance of sterile production environments. Conventional identification methods, while historically robust, suffer from several limitations that compromise their suitability for real-time, high-throughput diagnostics. Over the course of decades, a large number of technologies-from culture-based phenotypic tests to molecular and immunological approaches-have been used for identifying bacterial pathogens. However, these systems each have inherent disadvantages that limit their effectiveness, particularly in fast lived or resource constrained environments.Conventional culture-based techniques, often considered a gold standard in bacteriological analysis, involve culturing the sample on selective or differential media followed by biochemical assays to distinguish species. These methods, while offering high specificity, are labor intensive, time consuming (often 24-72 hours) and require skilled operators for interpretation. Moreover, they often do not recognize viable, but not culturable (VBNC) organisms, which entails considerable risks, in particular in the clinical sector and in food safety. Culture-based diagnostics also require extensive infrastructures such as incubators, autoclaves and safety covers and are therefore not very suitable for field or decentralized use.Molecular methods such as polymerase chain reaction (PCR), real time PCR (qPCR), and loop mediated isothermal amplification (LAMP) have led to marked improvements in speed and sensitivity. These approaches amplify pathogen-specific nucleic acids and thus make detection possible within hours. However, these methods require sophisticated thermal cyclers, precise temperature control and careful primer design. In addition, PCR-based systems tend to have false positives due to contaminants and are difficult to distinguish closely related strains unless they are followed by high resolution melting (HRM) analysis or sequencing. While next generation sequencing techniques (NGS) have revolutionized the genomics of pathogens, their high cost, long processing times, and high computational effort have limited their use to specialized laboratories and retrospective analyses. Although total genome sequencing (WGS) provides an exemplary resolution, it is not practicable for routine point-of-care diagnostics.Immunological assays, including enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFA), and agglutination assays, utilize antigen-antibody interactions to identify specific bacterial markers. These techniques are relatively fast and easy to carry out, lateral flow devices being frequently used in field tests. Nevertheless, they frequently have a limited sensitivity and specificity, in particular in the case of complex matrices such as foods, blood or wastewater. Cross reactivity between antigens can lead to false positive results, while low antigen expression can lead to false negative results at early stages of infection. Moreover, immunological assays typically require precharacterized antibodies, limiting their adaptability to newly occurring or mutating bacterial strains.More recently, mass spectrometry, particularly matrix assisted laser desorption / ionization time-of-flight spectroscopy (MALDI-TOF), has been introduced for microbial identification by analysis of the protein mass fingerprint of cultured bacteria. MALDI-TOF allows rapid and accurate identification of a variety of bacteria after culture, but its major disadvantage is dependent on pure cultures making them unsuitable for direct analysis of samples. The need for sample preparation, spectral calibration and expensive instruments additionally restricts their use outside central laboratories. In addition, MALDI-TOF spectra can vary considerably depending on growth conditions, media composition and sample handling, leading to different results.Fluorescence in situhybridization (FISH) and other probe-based microscopic methods allow visualization of specific bacterial DNA or RNA sequences in complex samples. These methods are highly specific, but often require fluorescence microscopy, hybridization incubation steps, and custom probes. Their sensitivity is limited by probe penetration and background fluorescence and are generally not scalable for high throughput applications.Biosensor-based systems have also proven to be promising platform for pathogen detection. Electrochemical, optical and piezoelectric biosensors have proven themselves for the detection of bacterial cells or their metabolic products.However, these systems often require functionalization with biological recognition elements such as aptamers or antibodies, which degrade over time and may suffer from environmental instability. Many biosensors also have difficulties in distinguishing between strains within the same species because of the limited specificity of the recognition mechanism. In addition, many biosensor prototypes cannot be well transferred from the proof-of-concept in the laboratory for industrial reliability and reproducibility.In view of these limitations, vibrational spectroscopy techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy (FT-IR) are becoming increasingly important as powerful, mark-free methods for bacterial identification. These methods analyze the intrinsic molecular oscillations of cellular components, thus creating a "spectral fingerprint" unique to each species of bacteria or strain of bacteria. Raman spectroscopy in particular offers high spatial resolution and minimal sample preparation and is therefore suitable for single cell analysis. FT-IR, on the other hand, detects broader molecular oscillations and is suitable for the analysis of large bacterial populations. Both techniques are non-destructive and can be performed in real time. Despite these advantages, their application in clinical diagnostics or field diagnostics has been limited by several factors.A major limitation of current spectroscopic systems is their sensitivity to noise and variability in biological samples. Spectral signals can be affected by environmental conditions, media components, and artifacts of sample preparation, often obscuring the subtilizing characteristics required for accurate stem differentiation. Another limitation is the complexity of the spectral data, the interpretation of which requires sophisticated chemometric or machine learning methods. Many commercially available spectrometers are stand-alone devices that are not integrated into automated sample handling or smart classification software. This makes the spectral analysis process cumbersome and susceptible to human errors. In addition, the lack of standardized spectral libraries for pathogenic bacteria makes it difficult to validate and compare results between laboratories and devices.In addition, current devices are rarely designed for continuous automation. Spectroscopic analyses often still require trained personnel for sample preparation, spectrum acquisition, and computational preprocessing and classification. Only a few systems combine the advantages of spectral fingerprinting with a microfluidic sample concentration, purification or enrichment interface-decisive factors in working with clinical or environmental samples with low biomass. Moreover, many Raman and FTIR instruments are large, sensitive, and expensive, preventing their use in point-of-care or on-site scenarios such as field lazaretts, food processors, or contaminated water sources.Attempts to overcome these problems using artificial intelligence (AI) and machine learning (ML) are promising. Supervised learning algorithms such as support vector machines (SVM), random forests, and neural networks have shown that they can classify bacteria with high accuracy based on spectral data. Unsupervised methods such as principal component analysis (PCA) help reduce dimensions and cluster formation. However, most existing implementations are fragmented and do not have a unified platform that combines robust hardware, automated fluidics, and adaptive AI models. Only a few systems provide real-time learning or the ability to dynamically update spectral databases as new strains or mutations occur.Although many tools and methods have been developed for identifying bacterial pathogens, they each have significant limitations in time, specificity, infrastructure requirements, scalability, and adaptability. The need for a fully integrated, mark free and automated real-time system that combines fast spectral detection with intelligent classification remains unsatisfaction. To overcome these challenges, the development of a unitary device is required that combines advanced optical detection, smart fluidics, and AI-controlled pattern recognition to enable rapid and accurate bacterial identification when needed.SUMMARY OF THE INVENTIONThe invention discloses an integrated device for the rapid identification of pathogenic bacterial strains on the basis of spectral fingerprints. The apparatus includes a closed spectroscopic diagnostic chamber, an automated microfluidic sample handling unit, an adjustable excitation source, a spectrometer module, a bioinformatically controlled spectral classification unit, and a multi-core machine learning algorithm embedded processing unit. The system allows the acquisition of real-time vibration spectra of unknown bacterial samples and matches them with a predefined spectral database to identify the strain with high specificity and sensitivity.The main object of the present invention is to provide an integrated and automated apparatus for rapid identification of pathogenic bacterial strains by spectral fingerprints. This significantly reduces the diagnostic processing time in comparison with conventional methods such as culture-based, molecular or immunological assays. Another object of the invention is to provide a non-invasive, marker free diagnostic platform that requires minimal sample preparation and eliminates dependence on specific reagents such as primers, antibodies or culture media. The invention aims to improve the accuracy and specificity of bacterial strain identification by employing advanced vibrational spectroscopy techniques such as Raman and FTIR spectroscopy in combination with machine learning-based classification algorithms that can detect subtilous spectral variations in closely related pathogens.Another object of the invention is to enable high throughput analyses through the use of an automated microfluidic sample handling system. This allows precise bacterial concentration, isolation and transfer to the spectroscopy chamber under sterile and controlled conditions. The device is designed as an independent diagnostic unit with integrated computing intelligence, whereby a comprehensive laboratory infrastructure or technical personnel for operation becomes superfluous. The invention also enables real-time interpretation and reporting of diagnostic results and offers the user, via an intuitive user interface, usable findings such as bacterial strain identity, confidence levels and resistance patterns, if available.Moreover, the device is intended to allow continuous updates and adaptability by using cloud-synchronized spectral fingerprint libraries that can be dynamically augmented with newly discovered or mutated strains. It is also an object of the invention to provide operational safety and sterility through the integration of self-cleaning mechanisms, including UV-C irradiation and ozone sterilization, as well as disposable microfluidic cartridges that prevent cross-contamination between samples. The invention aims to ensure mobility and reliability in a wide variety of environments - including hospitals, mobile clinics, food processing facilities and environmental monitoring stations - by virtue of their compact, robust construction, environmental control mechanisms and emergency power supply systems. Overall, the invention aims to establish a next generation diagnostic platform that closes the gap between high-end laboratory instruments and accessible point-of-care technology for rapid, accurate, and intelligent identification of bacterial pathogens.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects and advantages of the present invention will become more fully understood by reading the following detailed description when taken in conjunction with the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: Figure 1 shows a block diagram of an apparatus for rapidly identifying pathogenic bacterial strains by spectral fingerprinting.Those skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Also, as for the construction of the apparatus, individual or plural components of the apparatus may be represented by conventional symbols in the drawing. The drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION OF THE INVENTIONIn order to promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also other steps not expressly listed or inherent in that process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.FIG. 1 shows a block diagram of a device for rapid identification of pathogenic bacterial strains by means of spectral fingerprinting. The system 100 comprises: an optical spectroscopic detection unit (102) having at least one excitation source (102a) that emits electromagnetic radiation within a predefined wavelength range; a spectral detection module (104) having one or more photodetectors and diffraction components for collecting and resolving scattered or absorbed radiation of a bacterial sample into discrete spectral signals; a microfluidic sample handling module (106) having a disposable cartridge having at least one liquid inlet, a bacterial trapping zone, and an optical window aligned with the detection unit, the cartridge configured for aseptic introduction and positioning of liquid or semi-solid biological samples; an automated sample preparation subsystem (108) integrated with the microfluidic module and configured for concentrating, purifying, and controlling the flow of the bacterial sample prior to spectral analysis; a spectral preprocessing engine (110) configured to receive raw spectral data from the spectral acquisition module and perform baseline correction, noise filtering, normalization and dimension reduction; an AI-controlled classification engine (112) comprising at least one machine learning model trained on a curved database of bacterial spectral fingerprints, the classification engine configured to output the identification of the bacterial strain with confidence metrics based on extracted spectral features; a user interface module (114) configured to display identification results, system status and implementable diagnostics in real time; a controller (116) operatively connected to the optical recognition unit, the microfluidic sample handling module, the preprocessing machine, and the classification machine, the controller configured to coordinate timing, signal acquisition, and data routing operations; the system (118) further configured to dynamically update its classification machine by network-based retrieval of re-indexed bacterial spectral data.In one embodiment, the excitation source (102a) comprises a near infrared (NIR) or visible laser operating in the range of 500 to 1100 nm, having coherence control and power modulation function, and configured to perform point focused Raman excitation with a spot size of less than 2 μm for single cell resolution.In one embodiment, the spectral detection module (104) includes a high sensitivity, back thinned charge coupled device (CCD) array detector coupled to a holographic grating and an optical fiber input and supporting a spectral resolution of better than 5 cm -1. In addition, it is thermoelectrically cooled to suppress dark current noise.In one embodiment, the microfluidic sample handling module (106) comprises: a bacterial filtration subchannel having membrane-based mechanical confinement or dielectrophoretic concentration; an in-line valve array configured to control the directional flow of reagents or cleaning solutions; an optically transparent microcompartment coated with anti-reflective materials and hydrophobic surface treatments to minimize light scattering and sample adhesion; and the disposable cartridge is comprised of biocompatible polymers with integrated RFID for tracking and contamination logging.In one embodiment, the spectral preprocessing engine (110) implements a sequential operation that includes Savitzky-Golay smoothing, polynomial baseline subtraction, vector normalization, and principal component analysis (PCA), each of which may be adjusted based on the complexity of the sample matrix and the spectral noise profile.In one embodiment, the AI-driven classification engine (112) includes an ensemble of supervised learning models including support vector machines, convolutional neural networks, and gradient-boosted decision trees, each trained using cross-valid spectral datasets labeled with metadata about bacteria taxonomy and annotations about resistance phenotype.In one embodiment, the classification engine (112) is also configured to detect out-of-distribution spectral fingerprints using an anomaly detection module that is based on variations. Autoencoder or Mahalanobis distance metrics, thereby identifying unidentified or novel bacterial strains.In one embodiment, the user interface module (114) includes a touch-sensitive graphical user interface that can display root identity, gram character, species-specific resistance markers, spectral overlays, and system confidence intervals, and also allows operator inputs regarding clinical context or environmental parameters to direct interpretation.In one embodiment, the controller (116) also includes a real-time operating system (RTOS) implementing a task scheduler for synchronizing excitation pulses, detector indicators, fluid valve actuations, and feedback calibration and configured to support remote diagnostics, firmware upgrades, and failover handling via secure network protocols.In one embodiment, the system (118) also includes a sterile self-cleaning mechanism having a UV-C LED array and a microozon generator integrated into the waste liquid path and the optical window and configured to initiate automatic cleaning cycles after each sample run based on sensor feedback or user-defined intervals.The present invention integrates a sophisticated algorithm approach for quickly and accurately identifying pathogenic bacterial strains by spectral fingerprints. The focus of this process is the machine learning classification engine that uses a thoroughly trained AI model to analyze and classify the spectral data obtained from the bacterial sample. The algorithm on which this system is based is designed to process a large number of input data and to classify bacterial strains with high accuracy. In this case, it uses advanced techniques for preprocessing spectral data, for feature extraction and for pattern recognition.The spectral data acquired by the optical spectroscopic detection unit pass through a series of preparation steps before they are transferred to the machine learning model for classification. The first step of the algorithm is spectral preprocessing, in which the raw data is cleaned and optimized for analysis. This phase includes baseline correction to eliminate low frequency noise that may arise from instrument drift or environmental factors. A Savitzky-Golay smoothing filter is then used to reduce high frequency noise while still obtaining the essential spectral characteristics. In addition, the algorithm performs normalization to account for variations in sample size, concentration, and illumination, thus ensuring that the spectral signatures of different samples are comparable. In this phase, dimensional reduction techniques such as principal component analysis (PCA) are also implemented. The PCA helps reduce the complexity of the spectral data by identifying the most important features (principal components) that make up the majority of the variance. This simplifies the subsequent classification process and reduces the computing effort.Once the spectral data is preprocessed, the algorithm performs the feature extraction. Key features of the spectral fingerprint are identified and isolated for classification. These characteristics correspond to marked peaks, valleys, or spectral signatures characteristic of the molecular composition of the bacterial sample. The process of feature extraction focuses on identifying spectral patterns corresponding to different biomolecules such as proteins, lipids and nucleic acids, which are characteristic of different bacterial species and strains. The algorithm uses advanced signal processing techniques to quantify these characteristics so as to allow differentiation between species, subspecies and even closely related bacterial strains.The heart of the classification engine is the machine learning model trained using a large, kuratted database of known bacterial spectral fingerprints. The training dataset contains a broad range of labeled samples, each with associated metadata such as parent identity, genomic characteristics, resistance profiles, and growth conditions. The model is trained using supervised learning algorithms, including decision trees, support vector machines (SVMs), and neural networks, all optimized for analysis of spectral data. These algorithms can recognize subtilous deviations in the spectral signatures corresponding to different bacterial strains and phenotypes. During training, the model learns the optimal decision boundaries between classes based on the spectral features extracted from the data. The system also includes a technique called ensemble learning in which multiple models are combined to improve classification accuracy and robustness by reducing the risk of overfitting to a particular subset of data.The machine learning model is continuously updated to improve its performance. Once a new bacterial strain is detected, the system integrates this new spectral data into the existing model using incremental learning. This approach allows the classification engine to adapt to new or newly occurring bacterial strains without requiring complete retraining. The model also utilizes cloud-based spectral libraries that are regularly updated with new data from various research facilities, hospitals, and diagnostic centers. This cloud synchronization ensures that the classification engine has access to the most up-to-date information and can recognize new strains that may not be included in the original training dataset.The algorithm also includes an anomaly detection component that is of vital importance for identifying unknown or new bacterial strains that do not correspond to any of the known patterns in the database. This is achieved by techniques such as variation analysis. Autoencoders (VAEs) or Mahalanobis distance measurements measure how far a particular spectral fingerprint deviates from the trained distribution of known bacterial strains. If the spectral data has a significant anomaly, the system identifies the sample for further analysis. This helps to avoid misclassifications and ensure that new pathogens not yet characterized are correctly identified.The user interface is a further integral part of the algorithm. Once the classification engine has identified a bacterial strain, the results are displayed to the user in real time via an intuitive graphical interface. The results include species or parent identification, gram classification and optionally antibiotic resistance markers derived from the spectral characteristics. In addition to the identification, confidence metrics are displayed that indicate the probability of the correctness of the result. These metrics are generated using statistical methods such as a probability value or a confidence interval, based on the safety of the machine learning model in the classification of the respective sample. In addition, the user interface provides tools for entering clinical context or environmental parameters that the algorithm can use to refine the results or provide additional diagnostic suggestions.Another feature of the algorithm is the ability of the system to detect spectral changes indicative of antibiotic resistance. The classification engine is trained not only to identify bacterial strains, but also to derive antibiotic resistance profiles from spectral data. This is done by analyzing specific biomarkers or spectral characteristics that are shown to correlate with resistance mechanisms in bacteria, such as changes in the composition of the bacterial cell wall or modifications of protein expression. The model can classify these resistance markers based on the patterns identified during the training phase and thus provides clinicians with real-time insight into potential therapy options.To ensure long term reliability and accuracy of the system, the algorithm is designed to be continuously trained and validated. New spectral data from user input or research co-operations is automatically integrated into the training process, so that the model can be improved with increasing data availability. The cloud-based architecture of the system ensures a smooth sequence, since updates are sent to the diagnostic units without manual interventions. In addition, the system recognizes and corrects for possible deviations in the classification model that may arise from changes in environmental factors or instrumentation.The present invention provides a system for rapidly identifying pathogenic bacterial strains by spectral fingerprinting. This novel approach enables rapid, precise and mark-free detection of bacterial species in biological samples. The system integrates several advanced components that allow a seamless diagnostic process from sample collection to strain identification - all on a compact and automated platform suitable for use in clinic, environment and field.The system comprises an optical spectroscopic detection unit forming the frog of the invention. This unit is equipped with an excitation source which emits electromagnetic radiation, for example a near-infrared or visible laser, which is specifically tuned to the excitation of the bacterial sample. The emitted light interacts with the bacterial cells and causes them to scatter or absorb the radiation. The scattered light is then detected and analyzed by a spectral detection module containing one or more photodetectors and diffraction components. These components cooperate to resolve the scattered light into discrete spectral signals representing the unique modes of vibration of the bacterial constituents such as proteins, lipids and nucleic acids. These spectral fingerprints serve as key data for identifying the bacterial strain.A central aspect of the invention is the integration of a microfluidic sample handling module. This module contains a disposable cartridge which optimizes sample preparation and eliminates the risk of contamination between samples. The cartridge has integrated functions such as a bacteria trapping zone, a liquid inlet and an optical window, all aligned to provide smooth interaction between the sample and the spectroscopic detection unit. The microfluidic system can perform various preparatory tasks, such as concentration, purification and regulation of the flow of the bacterial sample into the analysis chamber. This automation ensures consistent and high quality sample preparation, reduces human errors, and increases throughput.Once the spectral data is acquired, it passes through a spectral preprocessing engine. This engine is designed for processing raw spectral data and performs several tasks to prepare for further analysis. These tasks include baseline correction, noise filtering, normalization, and dimension reduction. These are indispensable for eliminating irrelevant data and for improving the accuracy of the subsequent classification process. The preprocessing engine helps optimize the quality of the spectral signals by minimizing environmental factor interference and artifacts from sample preparation.The processed spectral data is then fed into an artificial intelligence (AI) classification engine. This utilizes machine learning algorithms trained on a comprehensive, kuratted database with bacterial spectral fingerprinting. The classification engine analyzes the spectral characteristics and compares them to the database to identify the bacterial strain present in the sample. The AI system provides a bacterial identification containing the species, subspecies or strain of the pathogen as well as confidence metrics on the accuracy of the identification. These results can be used to aid clinical decisions, for example, determination of the appropriate antimicrobial treatment.To ensure that the system always remains up-to-date and adaptable to newly occurring pathogens, the classification engine dynamically updates its model by network-based retrieval of new bacterial spectral data. This allows the system to integrate new strains and mutations once they are identified, thus continuously improving overall system performance and accuracy. The system also includes a cloud-based spectral fingerprint database that stores metadata about each sample, including genomic identifiers, growth conditions, and antibiotic sensitivity profiles. This cloud integration enhances the ability of the system to compare and correlate spectral data from different diagnostic units, thus enabling more comprehensive diagnostic capabilities.The system is configured for user interaction and has an intuitive user interface that displays diagnostic results, system status, and real-time monitoring of the identification process. The touch sensitive surface provides clear, implementable findings such as parent identity, gram properties and resistance markers. This user-friendly design ensures that medical professionals, environmental testers and field workers can operate the system without any problem, without requiring special training in spectroscopy or machine learning.To ensure robustness and reliability, the system is equipped with multiple fail-safe mechanisms. These include a sterile self-cleaning mechanism with integrated UV-C LEDs and an ozone generator which automatically cleanses the microfluidic cartridge and the optical windows after each use to avoid cross-contamination. Moreover, the controller of the system is designed to synchronize all components, including the optical detection unit, the sample handling module, and the data processing modules, via a real-time operating system (RTOS). This ensures precise temporal coordination and coordination of all processes-from excitation and detection to data analysis and result reporting.In addition to the kernel functions, the system is particularly mobile and is therefore suitable for use in a wide variety of environments, including mobile clinics, food monitoring stations, and remote field laboratories. The system is compact, robust and operates under different environmental conditions. For additional flexibility, the system may be operated via batteries or external power sources, which ensures seamless operation even in areas with unreliable power supply.The invention also includes a number of advanced software functions. For example, the classification engine may detect anomalies or new loads by comparing the acquired spectral data to a pre-trained model and identifying significant deviations. This abnormality detection is based on machine learning models such as variation analysis. Autoencoders identify data outside the distribution and ensure that new or previously uncharacterized bacterial strains are accurately identified and detected. In addition, the system supports continuous learning in that the AI models can be retraining with new spectral data of additional bacterial strains. This leaves the system conformable and improves its diagnostic capabilities continuously.In summary, the present invention provides a mature and automated solution for the rapid identification of pathogenic bacterial strains. It integrates most advanced optical spectroscopy with advanced microfluidic handling, AI-controlled data analysis and cloud-based spectral libraries and is thus a powerful tool for real-time bacterial diagnostics on site. By eliminating traditional culture-based methods, molecular assays, and reagent-dependent assays, the system provides a faster, less expensive, and more versatile alternative to detecting and identifying bacterial pathogens over a broad range of applications.The invention relates to the field of microbiological diagnostics, in particular systems and methods for the rapid detection and identification of pathogenic bacterial strains. It uses optical spectroscopy in combination with machine learning algorithms for bacteria identification on the basis of unique spectral fingerprints. This system overcomes the limitations of conventional microbial detection methods such as culture-based methods, PCR assays, and immunological assays, and provides a more rapid, efficient, and non-invasive alternative. The invention also includes advances in microfluidic technology that facilitate sample preparation, increase throughput, and simultaneously minimize the risk of cross-contamination. The system can be used in the clinical, environmental and food safety fields and offers a robust solution for the detection of pathogens and the profiling of antimicrobial resistances.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, merit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 An apparatus for rapidly identifying pathogens from bacterial strains by means of the spectral fingerprint. 102 Optical spectroscopic detection unit 102 a Anregungs source 104 Spectral detection module 106 Microfluidic sample handling module 108 Automated sample preparation subsystem 110 Spectral preprocessing engine 112 AI-controlled classification engine 114 User interface module 116 System

Claims

A system for rapidly identifying pathogenic bacterial strains using spectral fingerprints, the system comprising: an optical spectroscopic detection unit comprising at least one excitation source configured to emit electromagnetic radiation within a predefined wavelength range; a spectral detection module comprising one or more photodetectors and diffraction components configured to collect scattered or absorbed radiation from a bacterial sample and to resolve it into discrete spectral signals; a microfluidic sample handling module comprising a disposable cartridge having at least a liquid inlet, a bacterial trapping zone and an optical window aligned with the detection unit, wherein the cartridge is configured for aseptic introduction and positioning of liquid or semi-solid biological samples; an automated sample preparation subsystem incorporated into the microfluidic module and configured to perform concentration, purification, and flow regulation of the bacterial sample prior to spectral analysis; a spectral preprocessing engine configured to receive raw spectral data from the spectral acquisition module and perform baseline correction, noise filtering, normalization, and dimension reduction; an AI-controlled classification engine comprising at least one machine learning model trained from a kuralized database of bacterial spectral fingerprints, the classification engine configured to output an identification of the bacterial strain with confidence metrics based on extracted spectral characteristics; a user interface module configured to display identification results, system status, and implementable diagnostics in real time; a controller operatively coupled to the optical recognition unit, the microfluidic sample handling module, the preprocessing engine, and the classification engine, the controller configured to coordinate timing, signal acquisition, and data forwarding operations; the system further configured to dynamically update its classification engine by network-based retrieving of re-indexed bacterial spectral data.The system of claim 1, wherein the excitation source comprises a near infrared (NIR) or visible laser operating in the range of 500 to 1100 nm, having coherence control and power modulation function, and configured to perform point focused Raman excitation with a spot size of less than 2 μm for single cell resolution.The system of claim 1, wherein the spectral detection module comprises a high sensitivity, back thinned charge coupled device (CCD) array detector coupled to a holographic grating and an optical fiber input and supporting a spectral resolution of better than 5 cm -1 and thermoelectrically cooled to suppress dark current noise.The system of claim 1, wherein the microfluidic sample handling module comprises: a bacterial filtration subchannel using membrane-based mechanical restriction or dielectrophoretic concentration; an inline valve assembly configured to control the directional flow of reagents or cleaning solutions; an optically transparent microcompartment coated with anti-reflective materials and hydrophobic surface treatments to minimize light scattering and sample adhesion; and the disposable cartridge is comprised of biocompatible polymers with integrated RFID for tracking and contamination logging.The system of claim 1, wherein the spectral preprocessing engine implements a sequential workflow comprising Savitzky-Golay smoothing, polynomial baseline subtraction, vector normalization, and principal component analysis (PCA), each of which can be adjusted based on sample matrix complexity and spectral noise profile.The system of claim 1, wherein the AI-driven classification engine comprises an ensemble of supervised learning models including support vector machines, convolutional neural networks, and gradient-boosted decision trees, each trained using cross-valid spectral datasets labeled with metadata about bacteria taxonomy and annotations about resistance phenotype.The system of claim 1, wherein the classification engine is further configured to identify out-of-distribution spectral fingerprints by using an anomaly detection module based on variations. Autoencoder or Mahalanobis distance metrics, thereby identifying unidentified or novel bacterial strains.The system of claim 1, wherein the user interface module comprises a touch sensitive graphical interface capable of displaying parent identity, gram character, species specific resistance markers, spectral overlays, and system confidence intervals, and also allowing operator inputs regarding clinical context or environmental parameters to direct interpretation.The system of claim 1, wherein the controller further comprises a real-time operating system (RTOS) implementing a task scheduler for synchronizing excitation pulses, detector indicators, fluid valve actuation, and feedback calibration and configured to support remote diagnostics, firmware upgrades, and failover handling via secure network protocols.The system of claim 1, wherein the system further comprises a sterile self-cleaning mechanism comprising a UV-C LED array and a microozon generator integrated into the waste liquid path and the optical window and configured to initiate automatic cleaning cycles after each sample run based on sensor feedback or user defined intervals.

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