Indoor on-site bloodstain formation time identification method based on gas chromatography-ion mobility spectrometry technology

Abstract

The invention relates to an indoor on-site bloodstain formation time identification method based on a gas chromatography-ion mobility spectrometry technology, which comprises the following steps: collecting indoor on-site bloodstain samples with different deposition times, analyzing volatile organic compounds of bloodstains, and detecting and analyzing the volatile organic compounds in the bloodstain samples through the gas chromatography-ion mobility spectrometry technology. According to the method, mode recognition, principal component analysis and computer comparison modes are adopted to screen volatile markers with different formation time and characteristic changes, and a machine learning algorithm is adopted to establish an indoor field bloodstain formation time inference model; then, the volatile organic compound data of an unknown bloodstain sample is substituted into the indoor on-site bloodstain formation time inference model so as to identify the formation time of the unknown bloodstain, and the method has the detection advantages of being convenient, simple, high in sensitivity and good in reproducibility; a novel sensitive and feasible analysis and identification method is provided for detection of the bloodstain formation time in the bloodstain physical evidence.

Description

Technical Field

[0001] The present invention relates to the technical field of forensic blood stain identification, and more specifically, to an indoor on-site blood stain formation time identification method based on gas chromatography-ion migration spectrometry technology. Background Art

[0002] At the crime scene, especially in the homicide scene investigation, on-site bloodstain analysis is an important part of the investigation work. The identification of the time of bloodstain formation is of great significance for judging the bleeding time of the injured or the death time of the deceased, and can provide a basis for criminal investigators to determine the time of the crime, reconstruct the crime scene, and determine the scope of investigation. The determination of the time of bloodstain formation is an important part of forensic bloodstain analysis and a scientific issue that has received much attention in the forensic profession. At present, there are many methods for testing the time of bloodstain formation, and each has its own characteristics, such as high-performance liquid chromatography, electrophoresis, and genetic analysis technology. High-performance liquid chromatography can accurately analyze multiple components in bloodstains. However, the inspection of the sample is destructive. Once the sample is tested, it can no longer be used for other similar analyses. Electrophoresis technology can perform preliminary analysis of bloodstain samples in a relatively short time. However, the resolution of this method is low and the technical complexity is high, which requires a high level of technical skills of researchers. Genetic analysis technology can obtain accurate information from the genetic level in the test of bloodstain formation time, but its detection accuracy is easily affected by environmental interference and varies greatly between individuals. Therefore, it is particularly important to find more effective technical methods or objective biological indicators to infer the time of bloodstain formation and improve its prediction accuracy.

[0003] Gas chromatography-ion mobility spectrometry (GC-IMS) is a new gas phase separation and detection technology that has been gradually applied to military, medical, food and environmental protection fields. This technology combines the high separation ability of gas chromatography and the rapid response of ion mobility spectrometry. Samples can be directly detected without enrichment, and can accurately determine volatile and semi-volatile metabolites in unknown complex samples. It can also detect organic substances such as esters, ketones, aldehydes, alcohols, amines and phosphorus. The secondary separation function of gas chromatography-ion mobility spectrometry is powerful. It can not only obtain high-throughput two-dimensional top view and three-dimensional spectral stereo data, but also further obtain detailed information such as retention time, mobility, peak height and peak area. With the help of dynamic principal component analysis plug-in and powerful database comparison function, it can complete sample clustering and similarity analysis, quickly identify unknown samples and form a fingerprint library, realize visual detection of target objects, and derive the concentration ratio of the analyte to achieve quantitative analysis, laying a foundation for machine learning and subsequent mathematical model construction. Data and powerful conditions.

[0004] The present invention uses gas chromatography-ion mobility spectrometry technology to detect the components of volatile organic compounds during the bloodstain deposition process, and establishes a simple, rapid, and non-destructive identification method for the formation time of indoor on-site bloodstains through machine learning, improving the sensitivity of bloodstain formation time identification and the universality of application. Summary of the Invention

[0005] In order to solve the above technical problems existing in the prior art, the present invention provides an identification method for the formation time of indoor on-site bloodstains based on gas chromatography-ion mobility spectrometry technology.

[0006] In order to achieve the above invention object, the present invention provides the following technical solutions.

[0007] An identification method for the formation time of indoor on-site bloodstains based on gas chromatography-ion mobility spectrometry technology, comprising the following steps:

[0008] S1. Collect indoor on-site bloodstain samples with different formation times and detect volatile organic compounds, and use gas chromatography-ion mobility spectrometry technology to detect and analyze the components of volatile organic compounds in the bloodstain samples;

[0009] S2. Generate qualitative table data and fingerprint spectra based on the detected volatile components of the bloodstain samples, identify the characteristic markers of bloodstains with different formation times, and thus establish a database of volatile components of bloodstain samples;

[0010] S3. Screen volatile characteristic markers with characteristic changes over the deposition time, and establish an OPLS-DA model using machine learning algorithms;

[0011] S4. Substitute the volatile marker data of the bloodstain sample with unknown deposition time into the OPLS-DA model to identify the bloodstain formation time of the unknown sample.

[0012] Preferably, in S1, the chromatographic column of the gas chromatography-ion mobility spectrometry combined instrument is an MXT-5 metal capillary gas chromatography column with a length of 15 m, an inner diameter of 0.53 mm, and a film thickness of 1 μm, the ionization source is tritium, the carrier gas and the purge gas are both 99.999% high-purity nitrogen, the column temperature is 60 °C, the initial flow rate is 2 mL / min, after holding for 2 min, it is increased to 100 mL / min at 20 min; the drift tube temperature is 35-55 °C, the injector temperature is 80 °C, the analysis time of the sample is 32 min, and the drift gas flow rate of the mobility spectrometry is 150 mL / min.

[0013] Preferably, in S1, one sample is selected from each of the bloodstain samples with different deposition times, and the matrix data within the migration time of 1.0-2.0 and the retention time of 100-400 s is intercepted as a new matrix;

[0014] According to the principle of differential spectra, a total of 45 characteristic peaks were selected to characterize the variation law between bloodstain samples with different deposition times. The GC-IMS Library Search data processing software was used to process the peak intensity data of substances in the above-mentioned characteristic regions, and the volatile components in samples with different bloodstain formation times were analyzed.

[0015] Preferably, in S2, by analyzing and collecting data on relative ion peak intensity, ion migration time, and gas-phase retention time, a fingerprint spectrum is established.

[0016] Preferably, in S3, a certain number of characteristic peaks with high correlation with bloodstain formation time in the fingerprint spectrum are selected as characteristic variables to form a matrix for principal component analysis (Principal Component Analysis, PCA). The obtained principal components are sorted from high to low according to the contribution rate, and the scores of the first two principal components are taken for visual analysis;

[0017] Through heat map and hierarchical cluster analysis (Hierarchical Cluster Analysis, HCA), the relationship between bloodstain samples with different deposition times and volatile compounds in the qualitative table data is visualized to obtain a heat map for classifying bloodstains with different deposition times.

[0018] Preferably, in S3, the bloodstain samples with different deposition times are analyzed by the orthogonal partial least squares discriminant analysis method to establish an OPLS-DA model. The specific establishment process is as follows:

[0019] Sample collection and data preprocessing: The volatile compound data of bloodstain samples are preprocessed and feature extracted. By analyzing the peak intensity and retention time information in the volatile compound spectrum, characteristic variables related to bloodstain formation time are selected, and redundant and noise information is removed to enhance the stability and reliability of the model. Further, PCA is used to reduce the dimensionality of the sample data to identify potential time features.

[0020] Construction of the OPLS-DA model: The SIMCA 14.1 software is used to construct the OPLS-DA model for training and classifying bloodstain samples. The OPLS-DA method divides the variance in the data into two parts: one part is the variable related to the bloodstain time classification target, and the other part is the orthogonal part unrelated to the classification target. In this way, the model can maximize the information for time classification and effectively remove noise.

[0021] (3) Model training and verification: The OPLS-DA algorithm is adopted to train the OPLS-DA model using the calibration sample set to optimize the classification accuracy of the model. In addition, the model is evaluated by the 200-fold cross-validation method, Q 2When the regression line crosses the horizontal axis and the vertical intercept is <0, this result indicates that the model is not overfitting.

[0022] Preferably, blood stain samples with different formation times are collected on the surface of a white porcelain plate, placed in an indoor environment, and the retention time of the blood on the white porcelain plate and the quality of the samples are recorded.

[0023] Preferably, after collecting the blood sample, the headspace incubation temperature, incubation time, shaking speed, heating method, injection volume, injection needle temperature, cleaning time and carrier gas conditions are set for analysis, and the samples are analyzed in parallel 5 times.

[0024] Preferably, the headspace conditions are: incubation temperature 40-80°C, incubation time 5-15 min, incubation speed 400-600 rpm, injection volume 100-700 μL, injection needle temperature 50-85°C.

[0025] Preferably, the indoor temperature is 25±5°C and the humidity is 30-40%.

[0026] Compared with the prior art, the method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology proposed by the present invention has the following beneficial effects:

[0027] The present invention explores the relationship between the change of volatile components of bloodstains in indoor environments and the time of bloodstain formation, develops potential biomarkers related to the time of bloodstain formation, mines differential compounds suitable for inferring the time of bloodstain formation at indoor scenes, uses machine learning algorithms to predict the time of bloodstain formation at indoor scenes, and establishes a bloodstain formation time inference model (OPLS-DA model). Bloodstain samples with unknown deposition time are substituted into the model to identify the formation time of unknown bloodstain samples. The method of the present invention has the advantages of no need for sample pretreatment, short analysis time, and high sensitivity, and is universal for inferring the time of bloodstain formation in indoor scene bloodstain analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 3D fingerprint spectra of volatile organic compounds of blood samples with different deposition times in the embodiment, wherein the x-axis represents ion migration time, the y-axis represents gas chromatography retention time, and the z-axis represents peak intensity.

[0030] Figure 2Representative gas chromatography-ion mobility spectrometry topographic difference spectra of volatile organic compounds in bloodstain samples with different deposition times in the examples.

[0031] Figure 3 Gallery Plot fingerprint spectra of volatile organic compounds in bloodstain samples with different deposition times in the examples.

[0032] Figure 4 Principal component analysis diagrams of volatile organic compounds in bloodstain samples with different deposition times in the examples.

[0033] Figure 5 Cluster heat maps of volatile organic compounds in bloodstain samples with different deposition times in the examples.

[0034] Figure 6 Orthogonal partial least squares-discriminant analysis models of volatile organic compounds in bloodstain samples with different deposition times in the examples.

[0035] Figure 7 Flow chart of the identification method described in the present invention. Detailed implementation manners

[0036] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other; and all other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0037] This embodiment provides a method for identifying the formation time of indoor on-site bloodstains based on gas chromatography-ion mobility spectrometry technology. The overall technical idea is as follows:

[0038] S101: Collect indoor on-site bloodstain samples with different formation times and detect volatile organic compounds, and detect and analyze the components of volatile organic compounds in the bloodstain samples by gas chromatography-ion mobility spectrometry technology.

[0039] It should be noted that the indoor on-site bloodstain samples collected in the present invention may not only include bloodstain samples with a formation time within 70 days, but also samples at other time points outside 0h, 6h, 12h, 24h, 48h, 7d, 21d, and 70d. The greater the deposition time span of the bloodstain samples, the longer the deposition time that can be identified. The larger the number of bloodstain samples, the higher the accuracy of the method in the embodiments of the present invention.

[0040] S102: Adopt pattern recognition, principal component analysis, and computer comparison modes to screen volatile markers with characteristic changes with different formation times, and establish a formation time inference model using machine learning algorithms.

[0041] The volatile markers that can be used in this embodiment include the volatile organic compounds in Table 1.

[0042] It can be clearly seen that the bloodstain formation time inference model established by the above volatile markers can effectively estimate the bloodstain formation time at the indoor scene. Of course, the volatile markers that can be used in the present invention include, but are not limited to, the above compounds.

[0043] When establishing the bloodstain formation time inference model, the content of the volatile marker can be used as a variable, and the samples are randomly divided into a training set and a validation set according to a set ratio.

[0044] S103: Substitute the volatile marker data of the bloodstain sample with unknown deposition time into the bloodstain formation time inference model to identify the bloodstain formation time of the unknown sample.

[0045] Similarly, the volatile marker data of the unknown sample is obtained by gas chromatography-ion mobility spectrometry detection, and specifically, the same detection method as S101 can be used.

[0046] When the above method is specifically implemented, it specifically includes the following steps:

[0047] 1. Sample collection:

[0048] The collection, use and processing of the blood involved in this embodiment strictly comply with relevant regulations and systems, and are approved by the ethics committee and obtain the written informed consent of the blood donors. Divide the same collected human venous blood into 8 portions with the same volume, inject them into 8 grooves of the same white porcelain plate, and place them in an indoor environment (temperature 25±5°C, humidity 30-70%, light condition is 12-hour day and night alternation). At 0h (control group), 6h, 12h, 24h, 48h, 7d, 21d and 70d after formation, scrape and collect the corresponding bloodstain samples (1.00±0.01g) into headspace vials, press the caps and seal them for standby.

[0049] 2. Gas chromatography-ion mobility spectrometry detection:

[0050] The blood samples collected at different formation times were analyzed by gas chromatography-ion mobility spectrometry. The chromatographic column of the gas chromatography-ion mobility spectrometry instrument was a MXT-5 metal capillary gas chromatography column with a length of 15m, an inner diameter of 0.53mm, and a film thickness of 1μm. The ionization source was tritium (3H), and the carrier gas and the purge gas were both 99.999% high-purity nitrogen. The column temperature was 60°C, the initial flow rate was 2mL / min, and after retaining for 2min, it was increased to 100mL / min at 20min; the carrier gas flow gradient was set as: 0-2min, 2mL / min, 2-20min, 2mL / min-100mL / min; the migration spectrum drift gas flow rate: 150mL / min; the migration spectrum drift tube temperature: 45°C; the injection needle temperature: 70°C; the injection volume: 500μL; the analysis time: 5min.

[0051] 3. Data processing:

[0052] Analyze and collect data on relative ion peak intensity, ion migration time, and gas phase retention time, and establish a fingerprint spectrum based on these data; Figure 1 This is a three-dimensional fingerprint spectrum of volatile organic compounds in a blood sample, where the x-axis represents the ion migration time, the y-axis represents the retention time of gas chromatography, and the z-axis represents the relative ion peak intensity. The background of the entire fingerprint spectrum is blue, and the red vertical line at 1.0 on the x-axis is the RIP peak (reaction ion peak, normalized). Each point on both sides of the RIP peak represents a volatile organic compound, and the color represents the concentration of the substance. The darker the color, the greater the concentration. White indicates a lower concentration, and red indicates a higher concentration.

[0053] The GC-IMS has built-in Library Search software, which uses the retention index (RI) supported by NIST combined with IMS drift time to qualitatively confirm the compounds with two-dimensional data. This qualitative method is more accurate and efficient. Based on the qualitative table data and fingerprint spectrum of volatile organic compounds in blood samples, a database of volatile characteristic markers of blood samples with different deposition times can be established to facilitate the analysis and comparison of blood formation time.

[0054] Table 1 Qualitative table data

[0055]

[0056]

[0057] Figure 2Select the spectrogram of the bloodstain sample formed at 0 h as the reference, and subtract the reference from the spectrograms of other formation times. In the subtracted spectrograms, substances with the same concentration are offset to white, red indicates that the concentration of the substance is higher than that in the reference, blue indicates that the concentration of the substance is lower than that in the reference, and the darker the color, the greater the difference. The deposition time of the bloodstain sample can be distinguished according to the different components of volatile organic compounds in the bloodstain.

[0058] Using the comparison method, based on the difference in the signal peak intensity or color of the characteristic substances in the fingerprint spectrum, extract the characteristic regions of the fingerprint spectra of bloodstain samples with different formation times, such as Figure 3 is the fingerprint spectrum of volatile organic compounds in the bloodstain sample. Figure 3 Each row in represents all the signal peaks selected in a bloodstain sample, and each column represents the signal peaks of the same volatile organic compound in different bloodstain samples. It can be seen from the figure the complete volatile organic compound information of bloodstain samples with different formation times and the differences in volatile organic compounds between bloodstain samples.

[0059] 4. Pattern recognition:

[0060] According to the peak emergence situation in the characteristic analysis region on the fingerprint spectrum, bloodstain samples with different deposition times can be quickly identified. If the peak emergence situation in the characteristic analysis region of the measured unknown bloodstain sample is the same as or similar to that of the known bloodstain formation time sample, the bloodstain formation time of the measured sample is the same as or close to that of this bloodstain sample, otherwise it is different or not close.

[0061] Perform principal component analysis on bloodstain samples with different formation times, establish a bloodstain formation time classification model, and complete the distinction of bloodstain formation times. Such as Figure 4 is the principal component analysis of the volatile organic components of bloodstain samples with different formation times. As time goes by, the principal component differences of bloodstain samples formed at 0 h, 6 h, 12 h, 24 h, 48 h, 7 d, 21 d, and 70 d increase and can be separated by principal component analysis. Further, according to the different distribution regions of bloodstain samples with different deposition times in the principal component analysis, unknown bloodstain samples can be quickly distinguished and identified.

[0062] Perform heat map analysis and hierarchical clustering analysis (HCA) on bloodstain samples with different formation times to visualize the relationship between bloodstain samples with different formation times and 45 volatile compounds, such as Figure 5 is the heat map of bloodstain classification at different formation times, forming a significant time-dependent pattern. By analyzing the clustering relationship and color depth change of different samples in the heat map, the time characteristics of bloodstain samples can be quickly identified, providing an intuitive and reliable reference basis for inferring the bloodstain formation time.

[0063] The bloodstain samples with different deposition times were analyzed by the orthogonal partial least squares discriminant analysis (OPLS-DA) method to establish an inference model for the bloodstain formation time (OPLS-DA model) to achieve accurate discrimination of the bloodstain formation time. In this embodiment, by analyzing the characteristics of volatile organic compounds (VOCs) in the bloodstain samples formed at different time points, an accurate discrimination model for the bloodstain formation time was established. The specific process is as follows:

[0064] (1) Sample collection and data preprocessing: The VOCs data of the bloodstain samples were preprocessed and feature extracted. By analyzing the peak intensity and retention time information in the VOCs spectrogram, the characteristic variables related to the bloodstain formation time were selected, and the redundant and noise information was removed to enhance the stability and reliability of the model. Further, the principal component analysis (PCA) was used to reduce the dimensionality of the sample data to identify potential time features.

[0065] (2) Construction of the OPLS-DA model: On the basis of the above data preprocessing and feature extraction, the SIMCA software was used to construct the OPLS-DA model to train and classify the bloodstain samples. The OPLS-DA method divides the variance in the data into two parts: one part is the variables related to the bloodstain time classification target, and the other part is the orthogonal part unrelated to the classification target. In this way, the model can maximize the information of time classification and effectively remove noise.

[0066] (3) Model training and verification: The OPLS-DA algorithm was adopted to train the OPLS-DA model using the calibration sample set to optimize the classification accuracy of the model. The model was evaluated by the 200-fold cross-validation method to avoid overfitting and ensure the generalization ability of the model in different data sets.

[0067] The final model output is the classification result of the bloodstain samples, that is, the time category to which each sample belongs (such as 0h, 6h, 12h, etc.). As Figure 6 shown, the bloodstain samples formed at different time points (0h, 6h, 12h, 24h, 48h, 7d, 21d, and 70d) were analyzed by OPLS-DA, and the differences in their volatile organic components increased significantly with time. It can be observed from the OPLS-DA model that the distribution regions of the bloodstain samples with different formation times in the model have obvious discrimination effects. Based on this model, the deposition time of unknown bloodstain samples can be quickly identified, providing accurate bloodstain formation time information for case detection.

[0068] For those of ordinary skill in the art, although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology, characterized in that: The following steps are involved: S1. Collect blood samples at different formation times and detect volatile organic compounds, and use gas chromatography-ion mobility spectrometry to detect and analyze the volatile organic components in the blood samples; S2, generating qualitative table data and fingerprint spectrum according to the volatile components of the measured bloodstain samples, identifying characteristic markers of bloodstain samples formed at different times, and thereby establishing a volatile component database of the bloodstain samples; S3, screening volatile characteristic markers with characteristic changes over deposition time, and establishing an OPLS-DA model using a machine learning algorithm; S4. Substitute the volatile marker data of bloodstain samples with unknown deposition time into the OPLS-DA model to identify the bloodstain formation time of the unknown samples.

2. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology according to claim 1, characterized in that: In S1, the chromatographic column of the gas chromatography-ion mobility spectrometer is a MXT-5 metal capillary gas chromatography column with a length of 15m, an inner diameter of 0.53mm, and a film thickness of 1μm. The ionization source is tritium, and the carrier gas and the purge gas are both 99.999% high-purity nitrogen. The column temperature is 60°C, the initial flow rate is 2mL / min, and after retaining for 2min, it is increased to 100mL / min at 20min; the drift tube temperature is 35-55°C, the injector temperature is 80°C, the sample analysis time is 32min, and the migration spectrum drift gas flow rate is 150mL / min.

3. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry according to claim 2, characterized in that: In S1, one sample is selected from each of the bloodstain samples with different deposition times, and the matrix data with migration time of 1.0 to 2.0 s and retention time of 100 to 400 s are intercepted as a new matrix; According to the difference spectrum principle, a total of 45 characteristic peaks were selected to characterize the change patterns between bloodstain samples with different deposition times. The material peak intensity data of the above characteristic areas were processed using GC-IMS Library Search data processing software to analyze the volatile components in samples with different bloodstain formation times.

4. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry according to claim 1, characterized in that: In S2, a fingerprint spectrum is established by analyzing and collecting data on relative ion peak intensity, ion migration time and gas phase retention time.

5. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry according to claim 4, characterized in that: In S3, a certain number of characteristic peaks with high correlation with the time of bloodstain formation in the fingerprint spectrum are selected as characteristic variables, and a matrix is ​​formed to perform principal component analysis. The obtained principal components are sorted from high to low according to the contribution rate, and the scores of the first two principal components are taken for visual analysis; Through heat map and agglomerative hierarchical clustering analysis, the relationship between bloodstain samples with different deposition times and volatile compounds in the qualitative table data was visualized, and a heat map of bloodstain classification with different deposition times was obtained.

6. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry according to claim 5, characterized in that: In S3, the bloodstain samples with different deposition times were analyzed by orthogonal partial least squares discriminant analysis method, and the OPLS-DA model was established.

7. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology according to claim 1, characterized in that: In S1, samples with different bloodstain formation times come from the same blood.

8. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry according to claim 7, characterized in that: Samples with different bloodstain formation times were collected on the surface of a white porcelain plate and placed in an indoor environment. The deposition time of blood on the white porcelain plate and the quality of the samples were recorded.

9. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology according to claim 8, characterized in that: After collecting the blood samples, the headspace incubation temperature, incubation time, shaking speed, heating method, injection volume, injection needle temperature, cleaning time and carrier gas conditions were set for analysis, and the samples were analyzed in parallel 5 times.

10. The method for identifying the time of bloodstain formation at an indoor scene based on gas chromatography-ion mobility spectrometry technology according to claim 9, characterized in that: Headspace conditions: incubation temperature 40-60°C, incubation time 5-10 min, incubation speed 400-600 rpm, injection volume 100-300 μL, injection needle temperature 50-85°C.

Images