A method and system for detecting 1,3-beta-d-glucan based on raman spectrum recognition index

Abstract

The present application relates to the field of biomedical in vitro detection and spectral analysis, and particularly relates to a detection method and system for 1,3-beta-D glucan based on Raman spectrum recognition index.The detection method for 1,3-beta-D glucan based on Raman spectrum recognition index provided by the present application aims at the backbone structure characteristics, conformation change characteristics and complex sample background interference problems of 1,3-beta-D glucan as a polysaccharide chain segment type marker, forms a detection result obtained based on Raman conformation fingerprint discrimination parameters of beta-1,3 glycosidic bond backbone characteristics, polysaccharide chain segment conformation characteristics and sample background adaptive deduction characteristics, and compared with only relying on single Raman peak intensity or single threshold judgment, the present application can more fully reflect the molecular structure and chain segment conformation information of 1,3-beta-D glucan, and improve the reliability of target signal recognition and the interpretability of detection results in complex samples.

Description

Technical Field

[0001] This invention relates to the field of biomedical in vitro detection and spectral analysis technology, and in particular to a method and system for detecting 1,3-β-D-glucan based on Raman spectroscopy recognition index. Background Technology

[0002] 1,3-β-D-glucan is an important component of fungal cell walls and is widely present in various fungal cell wall structures. During fungal growth, invasion, metabolic renewal, or cell wall rupture, 1,3-β-D-glucan in the fungal cell wall can be released into the surrounding environment or host body fluids. Therefore, the level of 1,3-β-D-glucan in in vitro biological samples can serve as an important molecular marker for in vitro detection of fungal infections. Detection of 1,3-β-D-glucan in samples such as serum, plasma, urine, cerebrospinal fluid, bronchoalveolar lavage fluid, tissue extracts, food extracts, and microbial cultures can help provide reference information for in vitro screening, risk assessment, efficacy observation, and disease monitoring related to fungal infections.

[0003] Existing methods for detecting 1,3-β-D-glucan typically rely on specific biochemical reagent systems, enzymatic reaction systems, colorimetric systems, or other specific reaction systems. While these methods have a certain foundation in laboratory and clinical auxiliary testing, their results are often affected by factors such as sample processing conditions, reagent batches, reaction environment, matrix background, and interpretation thresholds. In complex ex vivo biological samples, factors such as proteins, salts, non-target sugars, lipids, drug residues, inflammatory metabolites, mucus components, and sample baseline drift can all influence the output of the detection signal, leading to deficiencies in the stability, consistency, anti-interference ability, and interpretability of the results.

[0004] Furthermore, 1,3-β-D-glucan belongs to the polysaccharide class of molecules. It is not a small molecule compound with only a single fixed conformation, but rather a high-molecular-weight or segmental biomarker with structural features such as β-1,3-glycosidic bonds connecting the main chain, polysaccharide segment conformation, interchain aggregation state, and hydrogen bonding. Therefore, different sample environments, concentration states, ionic strengths, protein backgrounds, and solution conditions may affect the segment extension, coiling, aggregation, and local ordering of 1,3-β-D-glucan, thus influencing its spectral response. Therefore, characterizing 1,3-β-D-glucan solely based on total amount or single intensity signals is insufficient to fully reflect the structural information contained in its polysaccharide main chain and segment conformation.

[0005] Raman spectroscopy is a spectroscopic analysis technique based on molecular vibrational information, capable of reflecting information such as chemical bonds, molecular skeletons, sugar ring structures, glycosidic bond connections, and molecular conformations. Theoretically, Raman spectroscopy can be used for the molecular fingerprint detection of polysaccharide markers such as 1,3-β-D-glucan. By acquiring the Raman or enhanced Raman spectral intensity information of the sample to be tested, spectral characteristics related to β-1,3-glycosidic bonds, pyranose rings, C—O—C vibrations, C—O vibrations, and polysaccharide skeleton vibrations in 1,3-β-D-glucan can be obtained, providing a new technical approach for its in vitro detection.

[0006] However, directly applying mature Raman detection methods to the detection of 1,3-β-D-glucan and relying solely on a single peak position, peak height, or intensity threshold for interpretation is insufficient to meet the stable detection requirements of complex biological samples. On one hand, the Raman signal of 1,3-β-D-glucan itself is relatively weak, easily affected by fluorescence background, sample baseline drift, and detection noise. On the other hand, biological samples may simultaneously contain non-target components such as glucose, other glucans, glycoproteins, polysaccharide fragments, proteins, and metabolites, whose spectral peak positions or broad background may overlap with the characteristics associated with 1,3-β-D-glucan. If existing Raman spectroscopy acquisition methods and the extraction of single characteristic peaks for interpretation are used, problems such as difficulty in distinguishing the target signal from the background signal, large fluctuations in results between different samples, and insufficient interpretability of the detection results can easily arise. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an in vitro detection method for 1,3-β-D-glucan based on Raman spectroscopy. This method aims to solve the problems of existing 1,3-β-D-glucan detection methods, such as reliance on specific biochemical reagent systems, strong background interference from complex samples, overlapping signals from non-target sugars, impact of polysaccharide segment conformational changes on detection stability, and insufficient reliability of single peak intensity interpretation.

[0008] This invention provides a method for detecting 1,3-β-D glucose based on Raman spectroscopy recognition index, comprising the following steps:

[0009] An in vitro biological sample is obtained, and the biological sample is pretreated to obtain a test sample suitable for Raman spectroscopy detection; Collect Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested; The spectral intensity information is corrected to obtain spectral data; Extract the β-1,3 glycosidic bond backbone features, polysaccharide segment conformation features, and adaptive background subtraction features from the spectral data; Based on the main chain characteristics of β-1,3 glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of samples, Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan are constructed. Based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan, the detection results of 1,3-β-D-glucan are output.

[0010] Furthermore, the β-1,3 glycosidic bond backbone characteristics include one or more of the following: peak position, peak intensity, peak area, peak shape parameters, or characteristic band intensity vectors related to the β-1,3 glycosidic bond, C-O-C bond vibration, C-O bond vibration, pyranose ring skeleton vibration, or dextran backbone vibration in the 1,3-β-D-dextran molecule.

[0011] Furthermore, the conformational features of the polysaccharide segments include one or more of the following characteristics related to the coiling, interchain aggregation, hydrogen bonding, molecular chain extension state, solution conformational changes, or degree of segment order of 1,3-β-D-glucan segments: peak width, peak shape, peak area ratio, characteristic peak offset, band broadening parameters, or multi-band intensity distribution features.

[0012] Furthermore, the adaptive background subtraction features of the sample include one or more of the following: background subtraction amount, background similarity, background weight coefficient, or residual background intensity obtained from blank control spectrum, negative sample spectrum, non-target polysaccharide spectrum, protein background spectrum, fluorescence background spectrum, or sample baseline drift spectrum.

[0013] Furthermore, the step of obtaining the adaptive background subtraction feature of the sample includes: The same wavenumber calibration, baseline correction, smoothing, and normalization processes are performed on the spectral data of the test sample and the reference spectrum to establish a set of background reference spectra; the reference spectra include reference spectra of blank control, negative sample, non-target polysaccharide, protein background, fluorescence background, or baseline drift; Outside the preset 1,3-β-D-glucan target feature wavenumber window, the correlation, similarity or fitting error between the spectral data of the sample to be tested and each reference spectrum is calculated, and non-negative least squares, constrained regression, template matching or a combination thereof are used to determine the background weight coefficients corresponding to the current sample to be tested for each reference spectrum. According to formula S corr (ν)=S sample (ν)-Σα i B i (ν), to obtain the corrected spectrum after background subtraction; where, S sample (ν) represents the spectrum of the sample to be tested, B i (ν) represents the i-th type of background reference spectrum, α i These are background weighting coefficients automatically determined based on the current sample to be tested. An adaptive background subtraction feature is generated based on the residual background intensity, background similarity reduction, and target feature preservation in the corrected spectrum. α is then re-estimated when the residual background exceeds a preset threshold. i Or update the selected background reference spectrum.

[0014] Furthermore, the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan include one or more of the following: β-1,3 main chain feature weight, polysaccharide segment conformation weight, background subtraction weight, conformational peak shape correction amount, or multi-band comprehensive discrimination score.

[0015] Furthermore, the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan are obtained through weighted calculation, template matching, band correlation analysis, principal component analysis, partial least squares regression, support vector machine, random forest or neural network model analysis.

[0016] Furthermore, the detection results of the 1,3-β-D-glucan include one or more of the following: negative or positive results, relative content results, concentration inversion results, or fungal infection risk stratification results.

[0017] Furthermore, the correction of the spectral intensity information includes one or more of the following: dark noise correction, background subtraction, baseline correction, smoothing filtering, wavenumber calibration, normalization processing, and outlier spectrum removal.

[0018] This invention also provides a detection system for 1,3-β-D-glucan based on Raman spectroscopy recognition index, comprising: The sample processing unit is used to acquire in vitro biological samples, preprocess the biological samples, and obtain test samples suitable for Raman spectroscopy detection. The spectral acquisition unit is used to acquire Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested; A spectral correction unit is used to correct the spectral intensity information to obtain spectral data; The feature extraction unit is used to extract the β-1,3 glycosidic bond backbone features, polysaccharide segment conformation features, and sample background adaptive subtraction features from the spectral data. The data processing unit constructs Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan based on the main chain characteristics of β-1,3-glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of samples. The result output unit outputs the detection result of 1,3-β-D-glucan based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan.

[0019] In summary, the advantages and beneficial effects of the present invention are as follows: This invention provides a method for detecting 1,3-β-D glucose based on Raman spectroscopy recognition index. Addressing the challenges of main chain structure characteristics, conformational changes, and interference from complex sample backgrounds associated with 1,3-β-D-glucan as a polysaccharide segmental biomarker, the method develops a Raman conformational fingerprint discrimination parameter based on β-1,3-glycosidic bond main chain characteristics, polysaccharide segmental conformational characteristics, and adaptive background subtraction features. Compared to relying solely on a single Raman peak intensity or a single threshold for interpretation, this invention more fully reflects the molecular structure and segmental conformational information of 1,3-β-D-glucan, improving the reliability of target signal identification and the interpretability of detection results in complex samples. Attached Figure Description

[0020] Figure 1 A schematic flowchart of a method for detecting 1,3-β-D glucose based on Raman spectroscopy recognition index provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of constructing Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan in an embodiment of the present invention; Figure 3 This is a schematic diagram of the detection result output and risk stratification of Raman conformational fingerprint discrimination parameters based on 1,3-β-D-glucan in an embodiment of the present invention. Figure 4 This is a schematic block diagram of a 1,3-β-D glucose detection system based on Raman spectroscopy recognition index, provided as an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] See Figure 1 , Figure 1 The present invention provides a schematic flowchart of a method for detecting 1,3-β-D-glucan based on Raman spectroscopy recognition index, which specifically includes steps S10-S60.

[0024] S10. Obtain an isolated biological sample, preprocess the biological sample, and obtain a test sample suitable for Raman spectroscopy detection.

[0025] More specifically, ex vivo biological samples can be serum, plasma, urine, cerebrospinal fluid, bronchoalveolar lavage fluid, tissue extracts, microbial culture media, microbial extracts, or other ex vivo samples containing 1,3-β-D-glucan.

[0026] Pretreatment includes one or more of the following: centrifugation, dilution, filtration, particulate removal, buffer conditioning, sample concentration, protein background reduction, non-target sugar background reduction, sample viscosity adjustment, or sample quantitative aliquoting, in order to reduce the impact of complex sample background on Raman spectroscopy acquisition and subsequent conformational fingerprinting.

[0027] S20. Collect the Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested.

[0028] For example, the pretreated sample is subjected to Raman or enhanced Raman spectroscopy, and spectral intensity information related to 1,3-β-D-glucan is collected. The tools used to collect the spectral intensity information are not limited to a specific Raman enhancement substrate, enhancement material, sample support structure, labeling material, or Raman detection device model; as long as Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan can be obtained, they can be used to implement this invention.

[0029] S30. Correct the spectral intensity information to obtain spectral data.

[0030] More specifically, in step S30, the correction of the spectral intensity information includes one or more of the following: dark noise correction, background subtraction, baseline correction, smoothing filtering, wavenumber calibration, normalization processing, and abnormal spectrum removal.

[0031] By correcting the spectral intensity information, the impact of instrument noise, fluorescence background, baseline drift, and sample batch differences on feature extraction can be reduced.

[0032] S40. Extract the β-1,3 glycosidic bond backbone features, polysaccharide segment conformation features, and sample background adaptive subtraction features from the spectral data.

[0033] More specifically, in step S40, the β-1,3 glycosidic bond backbone characteristics include one or more of the following: peak position, peak intensity, peak area, peak shape parameters, or characteristic band intensity vectors related to the β-1,3 glycosidic bond, C-O-C bond vibration, C-O bond vibration, pyranose ring skeleton vibration, or dextran backbone vibration in the 1,3-β-D-dextran molecule.

[0034] Based on the β-1,3 glycosidic bond backbone characteristics of 1,3-β-D-glucan, Raman conformational fingerprint discrimination parameters of 1,3-β-D-glucan are established. This method differs from the single Raman peak intensity interpretation method and can improve the accuracy and stability of 1,3-β-D-glucan-related signal identification in complex samples.

[0035] More specifically, in step S40, the polysaccharide segment conformational features include one or more of the following related to the coiling, interchain aggregation, hydrogen bonding, molecular chain extension state, solution conformational changes, or degree of segment order of 1,3-β-D-glucan segments: peak width, peak shape, peak area ratio, characteristic peak offset, band broadening parameters, or multi-band intensity distribution features.

[0036] Incorporating the conformational features of polysaccharide segments into the Raman conformational fingerprinting parameters of 1,3-β-D-glucan can reflect conformational changes such as the degree of coiling, aggregation, extension, or ordering of 1,3-β-D-glucan segments, thereby improving the structural interpretability of the detection results.

[0037] More specifically, in step S40, the adaptive background subtraction feature of the sample includes one or more of the following: background subtraction amount, background similarity, background weight coefficient, or residual background intensity obtained from blank control spectrum, negative sample spectrum, non-target polysaccharide spectrum, protein background spectrum, fluorescence background spectrum, or sample baseline drift spectrum.

[0038] More specifically, the steps for obtaining the adaptive background subtraction features of the sample include: The same wavenumber calibration, baseline correction, smoothing, and normalization processes are performed on the spectral data of the test sample and the reference spectrum to establish a set of background reference spectra; the reference spectra include reference spectra of blank control, negative sample, non-target polysaccharide, protein background, fluorescence background, or baseline drift; Outside the preset 1,3-β-D-glucan target feature wavenumber window, the correlation, similarity or fitting error between the spectral data of the sample to be tested and each reference spectrum is calculated, and non-negative least squares, constrained regression, template matching or a combination thereof are used to determine the background weight coefficients corresponding to the current sample to be tested for each reference spectrum. According to formula S corr (ν)=S sample (ν)-Σα i B i (ν), to obtain the corrected spectrum after background subtraction; where, S sample (ν) represents the spectrum of the sample to be tested, B i (ν) represents the i-th type of background reference spectrum, α i These are background weighting coefficients automatically determined based on the current sample to be tested. Background adaptive subtraction feature B is formed based on the residual background intensity, background similarity reduction, and target feature preservation in the corrected spectrum. adp And re-estimate α when the residual background exceeds a preset threshold. i Or update the selected background reference spectrum.

[0039] The Raman conformational fingerprinting parameters of 1,3-β-D-glucan introduce adaptive background subtraction features, which can reduce the impact of protein, non-target sugars, mucus, salt, metabolites, drug-related background, fluorescence background and sample baseline drift on the detection results.

[0040] Since the discrimination criterion of this invention lies in extracting the main chain features of β-1,3 glycosidic bonds, the conformational features of polysaccharide segments, and the adaptive subtraction features of sample background from spectral data, and constructing Raman conformational fingerprint discrimination parameters based on the above features, rather than relying on specific Raman enhancement substrates, enhancement materials, sample support structures, labeling materials, or Raman detection device models, it can be adapted to different detection methods such as conventional Raman, enhanced Raman, and labeled Raman, and has good application scalability.

[0041] S50. Based on the main chain characteristics of β-1,3 glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of samples, Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan are constructed.

[0042] More specifically, in step S50, the Raman conformational fingerprint discrimination parameters of 1,3-β-D-glucan include one or more of the following: β-1,3 main chain feature weight, polysaccharide segment conformation weight, background subtraction weight, conformational peak shape correction amount, or multi-band comprehensive discrimination score.

[0043] More specifically, the Raman conformational fingerprint discrimination parameters of 1,3-β-D-glucan are obtained through weighted calculation, template matching, band correlation analysis, principal component analysis, partial least squares regression, support vector machine, random forest or neural network model analysis.

[0044] For example, such as Figure 2 As shown, representative Raman spectra of 1,3-β-D-glucan samples, non-target sugars or interfering components, and background or blank control samples were collected. After baseline correction, background subtraction, smoothing filtering, and normalization, the acquired spectral intensity information can be processed to extract multiple characteristic peaks or characteristic bands with discriminative significance within a preset wavenumber range.

[0045] Extract approximately 890cm -1 1000cm -1 1125cm -1 1450cm -1Peak intensity, peak area, peak height, peak width, peak shape parameters, or characteristic band intensity vectors near the same wavenumber are used as candidate variables. Among them, approximately 890 cm⁻¹ -1 The nearby features can be used as characteristic peaks in the structural region related to the β-1,3 glycosidic bond backbone; approximately 1125 cm⁻¹ -1 Nearby features can serve as characteristic peaks for segment conformations or polysaccharide backbone-related feature regions; approximately 1450 cm⁻¹ -1 Nearby features can serve as characteristic peaks in the conformation-aided correlation region; approximately 1000 cm⁻¹ -1 Nearby features can serve as auxiliary reference peaks or normalized reference peaks. The wavenumber positions described above are only used to illustrate the construction approach of the conformational fingerprint discrimination parameters and do not limit the specific peak position selection of this invention. Those skilled in the art can select other characteristic peaks or characteristic bands with discriminative significance based on sample type, detection platform, and spectral data.

[0046] For serum samples containing strong protein and fluorescence background, the spectra of the same batch of blank substrate, negative serum, albumin reference spectrum, and fluorescence baseline spectrum can be used as candidate background reference spectra. (The shielding should be approximately 890 cm⁻¹.) -1 Approximately 1125cm -1 After the target feature window is established, the weights of each candidate background are obtained by fitting the remaining background wavenumber intervals, and the corresponding weighted background is subtracted from the spectrum of the sample to be tested. Subsequently, the main chain features of β-1,3 glycosidic bonds and the conformational features of polysaccharide segments are extracted from the calibrated spectrum. If the residual background in the non-target interval is reduced after subtraction, and the peak position, peak shape, or peak area within the target feature window is not excessively weakened, then the corresponding background weight, subtraction amount, and residual background intensity are used as the background adaptive subtraction feature B. adp Components of.

[0047] The β-1,3 glycosidic bond backbone characteristic can be denoted as F. main The conformational characteristics of polysaccharide segments can be denoted as F. conf Background adaptive subtraction features can be denoted as B. adp The comprehensive discriminant parameter can be denoted as R. BDG Therefore, the discrimination parameters of the 1,3-β-D-glucan Raman conformation fingerprint can be expressed as: R BDG = f(F main F conf B adp ) Alternatively, it can be represented as a combination of features: [F main F conf B adp R BDG ] Among them, F mainUsed to characterize the backbone continuity of β-1,3 glycosidic bonds and the backbone-related spectral contribution; F conf Used to characterize polysaccharide segment conformation, band broadening, peak shape changes, peak shifts, or spectral contributions related to the polysaccharide backbone; B adp Used to characterize adaptive background subtraction results obtained from blank controls, negative samples, non-target carbohydrate backgrounds, protein backgrounds, fluorescent backgrounds, or sample baseline drift; R BDG Effective discrimination parameters for 1,3-β-D-glucan after comprehensive processing of main chain features, conformational features, and background subtraction features.

[0048] More specifically, F main It can be made of approximately 890cm -1 1125cm -1 F is obtained from the peak intensity, peak area, or characteristic band intensity vector of other β-1,3 glycosidic bond main chain related characteristic peaks. conf It can be obtained from peak width, peak shape, spectral broadening parameters, peak position shift, peak area ratio, or multi-band intensity distribution characteristics. B adp It can be obtained through background subtraction, background similarity, background weight coefficient, or residual background intensity. R BDG It can be obtained through weighted calculation, template matching, spectral band correlation analysis, principal component analysis, partial least squares regression, support vector machine, random forest, neural network model or other spectral analysis model.

[0049] S60. Based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan, output the detection result of 1,3-β-D-glucan.

[0050] More specifically, in step S60, the detection results of 1,3-β-D-glucan include one or more of the following: negative or positive determination results, relative content results, concentration inversion results, or fungal infection risk stratification results.

[0051] For example, such as Figure 3 As shown, based on the Raman conformation fingerprint discrimination parameter R BDG Output test reports and risk stratification results. Test reports may include sample information, conformational fingerprinting parameters, negative or positive results, risk stratification results, efficacy observation results, or disease course monitoring results.

[0052] In a risk stratification scenario, it can be based on R BDG Alternatively, the model outputs a score that categorizes detection results into low risk, medium risk, and high risk. For example, when R... BDG When the value is below the first preset threshold, a negative or low-level result can be output; when R... BDG When the value is between the first preset threshold and the second preset threshold, a result of doubt, medium risk, or moderate level can be output; when R... BDGWhen the result exceeds the second preset threshold, a positive, high-risk, or high-level result can be output. These thresholds can be set and optimized based on standard samples, negative samples, positive reference samples, clinical reference results, or model training results.

[0053] In dynamic monitoring scenarios, R values ​​obtained at different time points for the same subject or the same sample source can be analyzed. BDG Compare them. If R BDG A sustained increase in R, or a sustained enhancement in the backbone characteristics of β-1,3 glycosidic bonds and the conformational characteristics of polysaccharide segments after adaptive background subtraction, can serve as reference information for increased risk, increased burden, or disease progression; if R BDG A decrease or stabilization in the level of the disease can serve as a reference for observing the efficacy of treatment, reducing the risk, or stabilizing the course of the disease.

[0054] The spectral detection results obtained in this embodiment can be converted into in vitro detection results, fungal infection risk stratification results, efficacy observation results, or disease course monitoring results, and have clear medical testing application outlets.

[0055] This invention provides a method for detecting 1,3-β-D-glucan based on Raman spectroscopy recognition index. This method can be used for in vitro detection, auxiliary screening, risk assessment, efficacy observation, or disease monitoring of fungal infections, invasive fungal infections, immunosuppression-related infections, respiratory fungal infections, or other diseases related to changes in 1,3-β-D-glucan levels.

[0056] Another embodiment of the present invention provides a detection system 100 for 1,3-β-D-glucan based on Raman spectroscopy recognition index, which can realize the detection method described above, such as... Figure 4 As shown, the detection system 100 includes: The sample processing unit 101 is used to acquire in vitro biological samples, preprocess the biological samples, and obtain test samples suitable for Raman spectroscopy detection. The spectral acquisition unit 102 is used to acquire Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested; The spectral correction unit 103 is used to correct the spectral intensity information to obtain spectral data; Feature extraction unit 104 is used to extract β-1,3 glycosidic bond main chain features, polysaccharide segment conformation features and sample background adaptive subtraction features from the spectral data; Data processing unit 105 constructs Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan based on the main chain characteristics of β-1,3-glycosidic bonds, conformational characteristics of polysaccharide segments, and adaptive background subtraction characteristics of samples. The result output unit 106 outputs the detection result of 1,3-β-D-glucan based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan.

[0057] The detection system 100 provided in this embodiment acquires Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan, extracts the main chain characteristics of β-1,3-glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of the samples, constructs Raman conformational fingerprint discrimination parameters, and outputs in vitro detection results, infection risk stratification results, or dynamic monitoring results based on the Raman conformational fingerprint discrimination parameters. Therefore, without limiting the specific Raman enhancement substrate and detection device model, the stability, interpretability, and application scalability of in vitro detection of 1,3-β-D-glucan can be improved.

[0058] This invention does not simply adapt mature Raman detection methods for 1,3-β-D-glucan detection, nor does it rely solely on a single Raman peak intensity or a simple threshold as the detection basis. Instead, it establishes a Raman conformational fingerprinting method based on the continuity of the β-1,3 glycosidic bond backbone, the conformational state of polysaccharide segments, and the background interference characteristics of complex samples. This method transforms the backbone structure information, segment conformation information, and background subtraction information of 1,3-β-D-glucan into spectral discrimination parameters suitable for in vitro detection and risk assessment, thereby improving the accuracy and stability of 1,3-β-D-glucan-related signal identification in complex samples.

[0059] Finally, it should be noted that any modification or equivalent substitution of some or all of the technical features based on the device structure and the technical solutions of the embodiments of the present invention, without departing from the corresponding technical solutions of the present invention, shall fall within the patent scope of the device structure and the embodiments of the present invention.

Claims

1. A method for detecting 1,3-β-D-glucan based on Raman spectroscopy recognition index, characterized in that, include: An in vitro biological sample is obtained, and the biological sample is pretreated to obtain a test sample suitable for Raman spectroscopy detection; Collect Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested; The spectral intensity information is corrected to obtain spectral data; Extract the β-1,3 glycosidic bond backbone features, polysaccharide segment conformation features, and adaptive background subtraction features from the spectral data; Based on the main chain characteristics of β-1,3 glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of samples, Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan are constructed. Based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan, the detection results of 1,3-β-D-glucan are output.

2. The detection method according to claim 1, characterized in that, The β-1,3 glycosidic backbone features include one or more of the following: peak position, peak intensity, peak area, peak shape parameters, or characteristic band intensity vectors related to β-1,3 glycosidic bonds, C-O-C bond vibrations, C-O bond vibrations, pyranose ring skeletal vibrations, or dextran backbone vibrations in the 1,3-β-D-d-glucan molecule.

3. The detection method according to claim 1, characterized in that, The conformational features of the polysaccharide segments include one or more of the following related to the coiling, interchain aggregation, hydrogen bonding, molecular chain extension state, solution conformational changes, or degree of segment order of 1,3-β-D-glucan segments: peak width, peak shape, peak area ratio, characteristic peak offset, band broadening parameters, or multi-band intensity distribution characteristics.

4. The detection method according to claim 1, characterized in that, The adaptive background subtraction features of the sample include one or more of the following: background subtraction amount, background similarity, background weighting coefficient, or residual background intensity obtained from blank control spectrum, negative sample spectrum, non-target polysaccharide spectrum, protein background spectrum, fluorescence background spectrum, or sample baseline drift spectrum.

5. The detection method according to claim 4, characterized in that, The steps for obtaining the adaptive background subtraction features of the sample include: The same wavenumber calibration, baseline correction, smoothing, and normalization processes are performed on the spectral data of the test sample and the reference spectrum to establish a set of background reference spectra; the reference spectra include reference spectra of blank control, negative sample, non-target polysaccharide, protein background, fluorescence background, or baseline drift; Outside the preset 1,3-β-D-glucan target feature wavenumber window, the correlation, similarity or fitting error between the spectral data of the sample to be tested and each reference spectrum is calculated, and non-negative least squares, constrained regression, template matching or a combination thereof are used to determine the background weight coefficients corresponding to the current sample to be tested for each reference spectrum. According to formula S corr (ν)=S sample (ν)-Σα i B i (ν), to obtain the corrected spectrum after background subtraction; where, S sample (ν) represents the spectrum of the sample to be tested, B i (ν) represents the i-th type of background reference spectrum, α i These are background weighting coefficients automatically determined based on the current sample to be tested. An adaptive background subtraction feature is generated based on the residual background intensity, background similarity reduction, and target feature preservation in the corrected spectrum. α is then re-estimated when the residual background exceeds a preset threshold. i Or update the selected background reference spectrum.

6. The detection method according to claim 1, characterized in that, The Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan include one or more of the following: β-1,3 main chain feature weight, polysaccharide segment conformation weight, background subtraction weight, conformational peak shape correction, or multi-band comprehensive discrimination score.

7. The detection method according to claim 6, characterized in that, The Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan were obtained through weighted calculation, template matching, band correlation analysis, principal component analysis, partial least squares regression, support vector machine, random forest or neural network model analysis.

8. The detection method according to claim 1, characterized in that, The detection results of the 1,3-β-D-glucan include one or more of the following: negative or positive results, relative content results, concentration inversion results, or fungal infection risk stratification results.

9. The detection method according to claim 1, characterized in that, Correcting the spectral intensity information includes one or more of the following: dark noise correction, background subtraction, baseline correction, smoothing filtering, wavenumber calibration, normalization, and outlier spectrum removal.

10. A detection system for 1,3-β-D-glucan based on Raman spectroscopy recognition index, characterized in that, include: The sample processing unit is used to acquire in vitro biological samples, preprocess the biological samples, and obtain test samples suitable for Raman spectroscopy detection. The spectral acquisition unit is used to acquire Raman or enhanced Raman spectral intensity information related to 1,3-β-D-glucan in the sample to be tested; A spectral correction unit is used to correct the spectral intensity information to obtain spectral data; The feature extraction unit is used to extract the β-1,3 glycosidic bond backbone features, polysaccharide segment conformation features, and sample background adaptive subtraction features from the spectral data. The data processing unit constructs Raman conformational fingerprint discrimination parameters for 1,3-β-D-glucan based on the main chain characteristics of β-1,3-glycosidic bonds, the conformational characteristics of polysaccharide segments, and the adaptive background subtraction characteristics of samples. The result output unit outputs the detection result of 1,3-β-D-glucan based on the Raman conformational fingerprint discrimination parameters of the 1,3-β-D-glucan.

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