Method for rapid quantitative analysis of methamphetamine based on portable near infrared spectroscopy and pls modeling

Abstract

This invention discloses a method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling, characterized by: (1) collecting near-infrared diffuse reflectance spectra of methamphetamine crystals and tablets using a portable near-infrared analyzer, and preprocessing the spectral data using the S-G method; (2) performing data analysis and modeling based on the PLS method to obtain a PLS regression model; (3) detecting the sample to be tested: collecting the near-infrared diffuse reflectance spectra of the sample to be tested, and then obtaining the methamphetamine content in the sample to be tested according to the model in step (2).

Description

Technical Field

[0001] This invention relates to a method for rapid quantitative analysis of methamphetamine using portable near-infrared spectroscopy and PLS modeling, and belongs to the field of hazardous materials detection. Background Technology

[0002] Methamphetamine, commonly known as crystalline methamphetamine, is highly addictive and difficult to quit. It is a laboratory-developed drug and has become one of the most harmful drugs in the world, as well as a widely abused psychoactive substance. Methamphetamine is an amphetamine stimulant (ATS). However, drugs cannot be completely eliminated. Given the still serious drug situation, how to accurately detect methamphetamine to facilitate the effective handling of cases is a question worth exploring.

[0003] Rapid screening methods are used for the qualitative and quantitative detection of drugs, such as antibody-based screening devices and immunoassays. However, these methods have many problems, including cross-reactivity and the generation of false positive and false negative results. Therefore, confirmatory analyses are usually performed subsequently using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). The results obtained are accurate and sensitive, but sample pretreatment is cumbersome and time-consuming. Because grassroots testing units handle a large number of samples in their daily work, traditional testing methods can no longer meet the timeliness requirements of case detection. In emergency situations, the technology used for drug detection must be rapid, simple, and inexpensive.

[0004] Near-infrared spectroscopy (NIR) analysis is rapid, simple, and non-destructive, and has been widely applied in various fields. Currently, in drug detection, benchtop spectrometers are used for the detection of sedatives and anesthetics, and related qualitative detection experiments have been demonstrated in laboratory studies. However, large benchtop infrared spectrometers are expensive, inconvenient to carry, and require specialized technicians to operate the instruments and analyze the data, making them unsuitable for rapid on-site analysis of methamphetamine. With the development of microelectromechanical systems (MEMS) and micro-opto-electromechanical systems (MEMS) technologies, near-infrared technology is further miniaturizing. Fourier transform infrared (FTIR) and micro-near-infrared (NIR) have reportedly found effective applications in drug detection, food quality control, and agricultural efficiency. However, FTIR requires a stable operating environment and is very expensive. Micro-near-infrared instruments, on the other hand, have fluctuating temperatures that vary with external temperatures, resulting in less than ideal economic efficiency. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for rapid quantitative analysis of methamphetamine using portable near-infrared spectroscopy and PLS modeling, thereby solving the problems in the prior art.

[0006] The technical solution of the present invention is: a method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling, (1) collecting near-infrared diffuse reflectance spectra of methamphetamine crystals and tablets using a portable near-infrared analyzer, and preprocessing the spectral data using the SG method; (2) performing data analysis and modeling based on the PLS method to obtain a PLS regression model; (3) detection of the sample to be tested: collecting the near-infrared diffuse reflectance spectra of the sample to be tested, and then obtaining the methamphetamine content in the sample to be tested according to the model in step (2).

[0007] The near-infrared diffuse reflectance spectrum is collected in the range of 908-1676 nm, with an increment of 6.25 nm between adjacent points.

[0008] The PLS method includes: (1) a determination coefficient R2:

[0009] Where yi is the reference value of the analyte obtained by LC. It is a predicted value. is the average of the reference values, and n and k are the number of calibration sets and latent variables, respectively.

[0010] The PLS method includes: (2) root mean square error of cross-validation, confirmation, and prediction:

[0011]

[0012] Where S is the score matrix of the calibration set.

[0013] The beneficial effects of the present invention are: the portable near-infrared spectrometer has the advantages of small size, light weight, easy to carry and moderate price, and is widely used in various physical field analysis [25\u201226], and is more suitable for grassroots work in drug detection.

[0014] Portable near-infrared spectroscopy and multivariate calibrated PLS analysis were used to model the determination of methamphetamine crystals and methamphetamine tablets, respectively. The models were then validated and used to make predictions based on actual collected samples. The quantitative regression model established by this method can help the police to promptly determine the quality of drugs and meet the requirements of timely case handling.

[0015] The combination of portable near-infrared spectroscopy (NIIR) technology with chemometric tools such as PLS (partially precipitated near-infrared spectroscopy) has enabled the development of portable, rapid, and inexpensive analytical methods. Near-infrared diffuse reflectance spectra of methamphetamine (crystal and tablet samples) were collected using a portable NIIR analyzer, and data analysis and modeling were performed based on the PLS method. Data comparison and methodological validation results show that the measured results of the constructed model are consistent with the true values ​​and have good stability. Therefore, the combination of portable NIIR spectroscopy and PLS regression modeling can achieve rapid on-site detection of methamphetamine. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating the operation of the method of the present invention;

[0017] Figure 2 Near-infrared spectra of methamphetamine crystal samples: (a) original spectrum (b) preprocessed spectrum;

[0018] Figure 3 Near-infrared spectra of methamphetamine tablet samples: (a) original spectrum (b) pre-processed spectrum;

[0019] Figure 4 Modeling results for methamphetamine crystal samples: (a) calibration, (b) validation, (c) prediction. RMSECV, root mean square error for calibration; RMSEV, root mean square error validation; RMSEP, root mean square error for prediction;

[0020] Figure 5 Modeling results for methamphetamine tablet samples: (a) calibration, (b) validation, (c) prediction. Detailed Implementation

[0021] 1. Materials and Methods

[0022] 1.1 Instruments and Reagents

[0023] The methamphetamine standard was provided by the Third Research Institute of the Ministry of Public Security (Shanghai, China). Glucose powder was purchased from Kangmei Pharmaceutical (Sichuan, China). Acetonitrile (chromatographic grade, purity >99%) was purchased from Merck AG (Darmstadt, Germany). Phosphoric acid and triethylamine were purchased from China National Pharmaceutical Group Chemical Reagent Co., Ltd. (Shanghai, China) and Kemio Chemical Reagent Co., Ltd. (Tianjin, China), respectively.

[0024] A portable near-infrared spectrometer (NR-17, Kaiyuan City, Beijing, China) was used to collect spectra in the range of 908–1676 nm, with an increment of 6.25 nm between adjacent points. A linear variable filter (LVF) was used to produce a stable and reliable optical element, serving as the dispersive element to meet the portability and vibration resistance requirements of on-site analysis. An HPLC system (Waters e2695 series) with a binary high-pressure pump and a diode array detector was used. The environmental conditions for sample preparation and measurement were fixed (temperature: 22℃ ± 2℃; humidity: ≤70%), and all other related experiments were performed under these conditions.

[0025] 1.2 Sample Preparation

[0026] The methamphetamine crystal samples collected in this study were derived from real-world cases and diluted formulations, totaling 359. Of these, 249 samples were used for modeling, 80 for validation, and 30 for model prediction experiments. Glucose was a commonly detected dopant in the samples. Therefore, low-content samples with a concentration range of 7.45%–52.81% were prepared using glucose powder.

[0027] The methamphetamine tablet samples were tested directly using actual samples captured in the case. A total of 187 samples were used, divided into three parts. Of these, 127 samples were used for modeling, 40 samples for validation, and 20 samples for model prediction experiments.

[0028] 1.3 Reference Method

[0029] Liquid chromatography (LC) was used to determine the content of all modeling and validation samples. Analytes were separated using a Waters XBridge C18 column (4.6 mm × 150 mm, 5 μm) at 35 °C. Acetonitrile and water, containing 0.412% phosphoric acid and 0.556% triethylamine, were used as solvents A and B, respectively. The mobile phase gradient conditions were: 0–8.0 min 84% B, 8.0–8.2 min decreasing to 20% B, holding at 20%, 8.2–11 min, 11.0–11.2 min increasing to 84% B, holding for 2.8 min. The flow rate was 1.0 mL. min-1 The injection volume was 5 μL, and the absorbance wavelength was set to 210 nm.

[0030] 1.4 Modeling and Validation

[0031] Partial Least Squares (PLS) is a multivariate data analysis method that combines regression models based on multiple dependent and independent variables with principal component analysis. The accuracy of the regression models is analyzed by comparing evaluation parameters R², RMSECV, and RMSEV / P.

[0032] (1) Coefficient of determination R²

[0033]

[0034] Where yi is the reference value of the analyte obtained by LC. It is a predicted value. This is the average of the reference values. n and k are the number of calibration sets and latent variables, respectively.

[0035] (2) Root mean square error of cross-validation, confirmation and prediction

[0036]

[0037] Where S is the score matrix of the calibration set.

[0038] 1.5 Statistical Analysis

[0039] Spectral data were collected and analyzed using NR-17 quantitative software (v1.0, Beijing Kaiyuan Shengshi, China). Leverage was used as an identifier for outliers to remove them from the dataset, allowing the parametric statistical model to fit the training data more smoothly. Microsoft Excel 2021 was used for data processing, and IBM SPSS Statistics 27.0 was used for one-way ANOVA.

[0040] 2. Results and Discussion

[0041] Infrared spectra of 359 methamphetamine crystals and 187 methamphetamine tablets are as follows: Figure 2 As shown in 3a and 3a, it can be clearly seen that the trends of the spectral curves are consistent, and most of them have the same or similar absorption peaks, but most spectral lines overlap, and the spectrum has a more serious baseline drift problem. Spectral preprocessing can effectively reduce the interference of background noise and environmental factors, improve the correlation between spectral data and chemical composition, and improve the accuracy and applicability of prediction models [32, 2012, 33]. At the same time, the Savitaky-Golay smoothing method (SG) has the advantages of high stability, small error, and good noise reduction effect. In order to eliminate interference, the SG method is used to preprocess the spectral data to remove baseline drift, noise and stray light, so as to improve the accuracy of the prediction model before modeling. Comparison Figure 2 b and Figure 3 As can be seen from b, after spectral preprocessing, the baseline drift was effectively corrected, and the spectral peak information was clearer. This indicates that SG preprocessing can effectively eliminate the interference of environmental factors.

[0042] The accuracy of each model is expressed as the coefficient of determination (R²) and the adjusted root mean square error (RMSE). For each element and each spectral preprocessing method, the number of latent variables with the lowest RMSECV is usually selected; the larger the R² and the smaller the RMSECV, the better the model fit. Figure 4 and Figure 5 The near-infrared spectral data of methamphetamine crystals and methamphetamine tablets are shown, along with the results of PLS ​​fitting. The evaluation parameters of the PLS regression model are as follows: for the methamphetamine crystal calibration group, the evaluation indices are 99.50% R² and 2.10% RMSECV. Figure 4 a) The corresponding values ​​for methamphetamine tablets were 96.7% and 0.78%, respectively. Figure 5 a) Two models were validated for methamphetamine crystals (R2: 99.40%; RMSEV: 2.12%). Figure 4 b) and methamphetamine tablets (R2: 97.10%; RMSEV: 0.80%) Figure 5 b). Finally, 30 methamphetamine crystal samples and 20 methamphetamine tablet samples were used to test the two models ( Figure 4 The tests were conducted using c and 5c), and the RMSEP of the prediction set were 2.19% and 0.82%, respectively. Figure 4 and Figure 5 The results show that the overall performance of the model is stable, and the predicted distribution of each index is concentrated near the target line, reflecting the overall reliability of the corresponding optimized modeling algorithm. Meanwhile, the sample content used in the experiment was measured by liquid chromatography. The reference physicochemical values ​​for the methamphetamine crystal sample group ranged from 11.72% to 92.91% (Table 1), while the content range for the methamphetamine tablet samples was 7.57% to 20.30% (Table 2). In the predicted sample group, the relative difference (RE%) between the reference value and the predicted value of the quantitative model was less than 10% (Tables 1 and 2). The prediction results indicate that both quantitative models are acceptable in practical applications, and the results are consistent with those of liquid chromatography. Although the RE was less than 10%, the results for some samples were poor. This is due to the complexity of the samples, making it difficult to achieve good predictions. Although the matrix of the seized samples was relatively complex, the current model basically covers the most common matrices and can meet the needs of daily use.

[0043] Table 1. Predicted results of methamphetamine crystallization samples

[0044]

[0045]

[0046] Res, relative difference.

[0047] Table 2. Predicted results for methamphetamine tablet samples

[0048]

[0049] While the standard error of prediction (SEP) is the primary parameter for selecting the "best model," repeatability and reproducibility were also considered. In this study, three levels of methamphetamine samples (low, medium, and high) were selected and tested three times simultaneously. In the repeatability test, each sample was tested five times consecutively using the same instrument, and the results are shown in Table 3. The relative standard deviation (RSD) of the three levels of samples was controlled within the range of 0.18%–0.93%. Simultaneously, the reproducibility of each sample was tested for six consecutive days using three instruments, and the RSD of the nine samples ranged from 0.36% to 1.67% (Table 4). The model was validated by the accuracy of repeatability and reproducibility levels, indicating that the method employed is stable and feasible.

[0050] Table 3. Repeatability Test Results

[0051]

[0052] RSD, relative standard deviation.

[0053] Table 4-1. Reproducibility Test Results (Day 1)

[0054] level Sample number Instrument 01 Instrument 02 Instrument 03 Low 01 35.72 34.33 34.74 02 28.91 29.47 29.83 03 34.62 35.03 35.54 medium 01 55.14 55.45 55.92 02 60.24 60.83 61.03 03 47.92 48.43 48.65 high 01 79.22 79.63 79.91 02 80.43 80.95 81.37 03 84.50 84.93 85.47

[0055] Table 4-2. Reproducibility Test Results (Day 2)

[0056]

[0057]

[0058] Table 4-3. Reproducibility Test Results (Day 3)

[0059] level Sample number Instrument 01 Instrument 02 Instrument 03 Low 01 33.82 34.40 34.51 02 28.84 29.33 28.95 03 34.85 34.28 35.04 medium 01 55.28 55.67 56.13 02 60.36 61.06 61.02 03 47.95 48.65 48.63 high 01 79.33 79.56 79.83 02 80.60 81.08 81.34 03 84.62 85.07 85.54

[0060] Table 4-4. Reproducibility Test Results (Day 4)

[0061] level Sample number Instrument 01 Instrument 02 Instrument 03 Low 01 33.76 34.38 34.74 02 28.92 29.43 29.82 03 34.71 33.23 33.55 medium 01 55.19 55.47 55.92 02 60.21 60.83 61.07 03 47.94 48.46 48.63 high 01 79.27 79.64 79.90 02 80.42 80.93 81.33 03 84.56 84.90 85.43

[0062] Table 4-5. Reproducibility Test Results (Day 5)

[0063]

[0064]

[0065] Table 4-6. Reproducibility Test Results (Day 6)

[0066] level Sample number Instrument 01 Instrument 02 Instrument 03 RSD (%) Low 01 33.91 34.36 34.65 1,31 02 29.09 29.47 29.87 1,25 03 34.56 33.90 34.68 1,67 medium 01 55.25 55.56 56.13 0.64 02 60,89 60.71 61.00 0.51 03 48.07 48.42 48.66 0.63 high 01 79.08 79.40 79.82 0.36 02 80.25 80.87 81.15 0.43 03 84.20 84.76 85.23 0.47

[0067] in conclusion

[0068] The combination of portable near-infrared spectroscopy (NIIR) technology with chemometric tools such as PLS (partially precipitated near-infrared spectroscopy) has enabled the development of portable, rapid, and inexpensive analytical methods. Near-infrared diffuse reflectance spectra of methamphetamine (crystal and tablet samples) were collected using a portable NIIR analyzer, and data analysis and modeling were performed based on the PLS method. Data comparison and methodological validation results show that the measured results of the constructed model are consistent with the true values ​​and have good stability. Therefore, the combination of portable NIIR spectroscopy and PLS regression modeling can achieve rapid on-site detection of methamphetamine.

Claims

1. A method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling, characterized in that: (1) Near-infrared diffuse reflectance spectra of methamphetamine crystals and tablets were collected using a portable near-infrared analyzer, and the spectral data were preprocessed using the SG method; (2) Data analysis and modeling were performed on both based on the PLS method to obtain the PLS regression model; (3) Detection of the sample to be tested: Near-infrared diffuse reflectance spectra of the sample to be tested were collected, and then the methamphetamine content in the sample to be tested was obtained according to the model in step (2).

2. The method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling according to claim 1, characterized in that: The near-infrared diffuse reflectance spectrum is collected in the range of 908-1676 nm, with an increment of 6.25 nm between adjacent points.

3. The method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling according to claim 1, characterized in that: The PLS method includes: (1) a determination coefficient R2: Where yi is the reference value of the analyte obtained by LC. It is a predicted value. is the average of the reference values, and n and k are the number of calibration sets and latent variables, respectively.

4. The method for rapid quantitative analysis of methamphetamine based on portable near-infrared spectroscopy and PLS modeling according to claim 1, characterized in that: The PLS method includes: (2) root mean square error of cross-validation, confirmation, and prediction: Where S is the score matrix of the calibration set.

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