Detection and Direct Readout of Drugs in Human Urine Using

Jan 29, 2015 - It is greatly significant to detect drugs for fighting against drug crimes and stopping the drug trade, especially the detection of dru...
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Detection and Direct Readout of Drugs in Human Urine Using Dynamic Surface-Enhanced Raman Spectroscopy and Support Vector Machines Ronglu Dong,†,‡ Shizhuang Weng,† Liangbao Yang,*,†,‡ and Jinhuai Liu†,‡ †

Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China Department of Chemistry, University of Science & Technology of China, Hefei, Anhui 230026, China

‡

S Supporting Information *

ABSTRACT: A new, novel, rapid method to detect and direct readout of drugs in human urine has been developed using dynamic surface-enhanced Raman spectroscopy (D-SERS) with portable Raman spectrometer on gold nanorods (GNRs) and a classification algorithm called support vector machines (SVM). The high-performance GNRs can generate gigantic enhancement and the SERS signals obtained using D-SERS on it have high reproducibility. On the basis of this feature of DSERS, we have obtained SERS spectra of urine and urine containing methamphetamine (MAMP). SVM model was built using these data for fast identified and visual results. This general method was successfully applied to the detection of 3, 4-methylenedioxy methamphetamine (MDMA) in human urine. To verify the accuracy of the model, drug addicts’ urine containing MAMP were detected and identified correctly and rapidly with accuracy more than 90%. The detection results were displayed directly without analysis of their SERS spectra manually. Compared with the conventional method in lab, the method only needs a 2 μL sample volume and takes no more than 2 min on the portable Raman spectrometer. It is anticipated that this method will enable rapid, convenient detection of drugs on site for the police.

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The extracts are centrifuged and then separated. Finally, the concentration of MAMP in the sample is analyzed using a GC/ MS. It is greatly significant to detect drugs for fighting against drug crimes and stopping the drug trade, especially the detection of drugs on site. On the other hand, a rapid, costeffective method for detection of drugs in body fluid would be of great value for law enforcement in field tests. Surface-enhanced Raman spectroscopy (SERS) is a form of vibrational spectroscopy that is able to identify analyte substances uniquely. The capability of observing very weak normal Raman signals makes it one of the most powerful and ultra sensitive analytical tools.11−13 SERS has previously been applied to detect and identify illicit drugs successfully.14−18 Optimization of parameters for the quantitative SERS detection of mephedrone using a fractional factorial design and a portable Raman spectrometer had been reported.14 Gold-plating of Mylar lift films to capitalize on SERS for chemical extraction of drug residues provided a distinctive mode of performing ultratrace analysis.15 Meinhart presented a microfluidic device that detects trace concentrations of drugs of abuse in saliva within minutes using SERS.16 A total of 80 drugs of abuse and

owadays, the abuse and spread of drugs has been an increasingly grim problem in the world. Among all the popular drugs, especially amphetamines, such as methamphetamine (MAMP) and 3,4-methylenedioxy methamphetamine (MDMA) have been flooding globally for their low cost and ease of manufacture.1 The detection of drugs in body fluids such as blood, urine, and saliva has been accomplished by utilizing isolation technology with pretreatment. Gas chromatography with mass spectrometry (GC/MS),2,3 high-performance liquid chromatography (HPLC),4,5 and enzyme-linked immunosorbent assay (ELISA)6,7 are typically used in detecting drugs in human body fluids. However, these analytical technologies are mostly carried out in laboratories, which usually require time-consuming pretreatment steps with expensive reagents and instruments. Most importantly, the test can only be done by highly trained operators. All of these make it impossible to field test applications. On the other hand, commercial drug-screening products for testing use the marquis reagent test, a similar colorimetric reaction, or colloidal gold detection to determine the presence of drugs. Such assays usually test for only a limited number of substances and require specialized reactants and large sample volumes and may be misinterpreted by subjective color perception.8 A standard procedure9,10 for drug testing of human urine in laboratory is illustrated in Scheme 1A. NaOH or K2CO3 is first added to a test sample, followed by extracting the sample with n-hexane. © XXXX American Chemical Society

Received: November 30, 2014 Accepted: January 29, 2015

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dry nanostructure film-based methods. In solution-based SERS, the Raman probe is mixed with colloidal particles and hot spots are generated through the addition of an external agent that induces particle aggregation prior to the SERS measurement. Generally, this method hardly ever achieves high sensitivity for SERS detection. Dry nanostructure film-based SERS detection involves placing the general colloidal nanoparticles on a solid substrate (silicon, glass wafer) and drying the sample on the substrate. In general, SERS measurements are commonly performed by fabricating highly ordered nanostructure substrates and drying the sample onto the substrate. However, fabricating most of these substrates require intricate and complicated techniques. Additionally, because of the long times of the laser irradiation, the substrates suffer damage, leading to weak reproducibility and stability for detection. D-SERS is based on state translation nanoparticles from the wet state to the dry state to realize SERS measurements.29 During the transition from the wet state to dry state, an optimal surface plasmon peak of a nanostructure that resonates sharply with excitation wavelength will certainly emerge. Also, the nanostructures can self-close to form hot spots driven by the solvent capillary forces. In our former study,31 complete drying of a 1 μL droplet of Ag sols with 50 amol of rhodamine 6G (R6G) at 25 °C required approximately 600 s. During the first 300 s, no Raman signals were observed. Nevertheless, the fingerprints of R6G gradually appeared after 300 s; gradually, these weak signals became stronger and achieved their highest values at 580 s; interestingly, the peak shape also became increasingly stable and its intensity increased. However, after 600 s, the peak intensity rapidly decreased. It is the solvent that drives nanoparticles to move close together and produces a distinct 3D geometry with minimal polydispersity of particle size and maximal uniformity of interparticle distance, both of which are beneficial for ultrahigh Raman enhancement and molecular trapping in a three-phase solid/liquid/air interface. In short, this new method not only produces giant Raman enhancement of at least 2 orders of magnitude larger than that of dried substrates but also provides reproducible and stable SERS signals for at least 100 s. With the aid of D-SERS and SVM, we aim to realize the visual online detection and fast accurate identification of drugs in body fluid combining with the portable Raman spectrometer. We use the gold nanorods (GNRs)32 as D-SERS substrate for drug detection during the process from the wet state to dry state. As shown in Scheme 1B, the suspected sample without pretreatment mixed with GNRs colloidal sol is exposed directly onto a SERS chip, followed by detecting drugs from wet state to drying state. Raman spectra can be measured by inserting the SERS chip into the sample slit in a portable spectrometer and identified by SVM embedded into the system. Finally, the test result is shown on the screen, which indicates that whether there are drugs in the urine. The entire process can be completed within less than 2 min, which makes it highly practical for field applications.

Scheme 1. Comparison of Drug Detection in Urine by (A) a Standard Procedure versus (B) Our D-SERS and SVM Solution

metabolites had been successfully measured by SERS using gold and silver doped sol−gels immobilized in glass capillaries. This general method was successfully applied to the detection of a number of additional drugs in saliva.17 Application of SERS to identify 3,4-methylenedioxyamphetamine in forensic samples utilizing matrix stabilized silver halides was also reported.18 However, it is a great challenge for SERS detection of drugs in complex media. Several disadvantages of SERS make it difficult for practical application.19 Mostly, the reproducibility of SERS signal maybe very poor due to the randomly distributed hot spots,20−22 which are ascribed to the random arrangement of the nonuniform nanostructure. Second, the target analyte are always in a complex media containing multiple components in real field test applications. The signals of interferences may cover or overlap with those of analytes, making the feature peaks of target analytes indiscernible.23 When target analyte such as MAMP are in a low concentration, it runs the risk of being outcompeted for available adsorption sites on the nanostructure surface by other species. As a result, there is tiny difference of the SERS signal between drug abusers and normal people. So it is difficult to differentiate them with direct means for nonprofessionals.24 Therefore, visually effective results cannot be displayed in short time onsite. The powerful and robust spectral data processing algorithms are much needed to extract effective diagnostic information. The chemometrics methods such as principal component analysis (PCA), linear discriminant analysis (LDA), artificial neural networks (ANNs), and support vector machine (SVM) have been successfully used to develop diagnostic algorithms.15−18,25 Among them, SVM has been of great success in the task of classifying spectral data for tissue diagnosis17,18 as it is quite robust when it comes to handling noisy data and is generally not susceptible to the presence of outliers. Moreover, the excellent generalization and high efficiency make it outperform other algorithms which have been demonstrated in some experiments.26−28 Consequently, SVM is herein used for accurate identification of drugs in human urine. Very recently, our group explored a new method called the dynamic surface-enhanced Raman spectroscopy (DSERS), 29−31 which is based on the two conventional approaches for SERS detection, the solution-based and the

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EXPERIMENTAL SECTION Synthesis of Gold Nanorods (GNRs). The synthesis of GNRs was performed using a typical seed-mediated growth method previously developed by El-Sayed.33 The details are shown in the S1 experimental section part of the Supporting Information. Sampling Procedure (I). Simulative samples: 50 fresh urine samples were collected randomly in the morning in the B

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beings with and without 50, 2.5, 0.1 ppm MAMP and MDMA are utilized to build the final SVM classification model. Also the prediction accuracy of the model is tested using the spectra of the drug abuser’s urine which are not used for construction of the model. All computation and chemometric methods were implemented in MATLAB 2011b (The Mathworks Inc., Natick, MA). The free SVM toolbox (Zhiren Lin, Taiwan) is used in MATLAB to develop the classification models. The PCA is carried out by using the PCA toolbox in MATLAB.

Science Island Hospital, Hefei, China. Then different drugs were added to urine to prepare the simulative drug abuser’s urine samples. One kind was urine containing different concentration of MAMP (50, 2.5, 0.1 ppm), and the other kind was urine containing a different concentration of MDMA (50, 2.5, 0.1 ppm). (II) Real samples: the real urine samples of drug abuser were provided by the Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China, which were collected under the same conditions as above. The MAMP concentration in urine was (a) 30 ppm, (b) 3 ppm, (c) 0.4 ppm, by identification of GC, respectively. Raman Measurement. All the spectra were obtained both on a laboratory Raman spectrometer and a portable Raman spectrometer. For the Lab-RAM HR800, a 785 nm laser was used as the excitation source with a measured power of 3.5 mW at sample surfaces with a focal spot of about 1 μm in diameter. All spectra obtained from this spectrometer were recorded with a 3 s accumulation time. For the portable Raman spectrometer, shown in Scheme 1B, is equipped with a diode laser emitting at 785 nm for illumination over an area about 100 μm in diameter with a measured power of 120 mW on the sample surface. A volume of 1 μL of GNRs colloid and 1 μL of urine sample were mixed well and then dropped on the chip. With the droplet evaporation, at the edge of transition from the wet to dry state, urine sample sensing was achieved and 10 continuous spectra were collected with a 5 s integration time and no time interval. Support Vector Machine and Data Analysis. The basic principle of SVM is described as follows: given a set of data with different classes, an optimal linear classifier is constructed in the form of a hyper plane which has the maximum margin (the simultaneous minimization of the empirical classification error and maximization of the geometric margin). In the case of data sets that are not linearly separable, the original data will be mapped into a higher dimensional feature space which is implicitly given by the kernel function. Also a linear classifier is constructed in this feature space which is equivalent to constructing a nonlinear classifier in the original input space. Furthermore, for those samples (outliers) that are linearly not separated in the high dimension space, the insensitive zone is introduced to make SVM be less sensitive to the outliers. In this work, the radial basis function (RBF) is adopted as the kernel function of SVM because of its effectiveness during a fast training process.34 Moreover, as the spectra is of high dimensionality and includes the invalid information, the principal component analysis (PCA) is employed to preprocess data with the accumulating contribution rate up 95%. Also the parameters of SVM are optimized with a grid search method. Prior to construction of SVM models, the Raman spectra are linearly scaled such that the intensity values were distributed between 0 and 1. To verify the feasibility of visual detection of drugs in human body fluid combining the D-SERS method with SVM, the calibration models are first developed with SERS spectra of the urine of 50 human beings with and without different concentration of MAMP (50, 2.5, 0.1 ppm) and MDMA (50, 2.5, 0.1 ppm). Subsequently, the prediction performance of the model is evaluated by a 5-fold cross validation method. In this method, the data set is randomly partitioned into 5 equal size subsets. One subset is held out as the validation data, and the remaining subsets are as the training set every time until each sample is used once as the validation set. Finally, to validate the effect of the SVM model in practical detection, the SERS spectra of the 3 drug abusers’ urine and the urine of 50 human

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RESULTS AND DISCUSSION Characterization of the Uniform Assembled mPEG-SH Coated-GNRs for High SERS Performance. In this study, the GNRs in solution exhibit two well-defined plasmon resonance bands located at 514 and from 650 to 828 nm corresponding to electron oscillations along the short and long axis of the nanorods, respectively (Figure 1A). That is to say,

Figure 1. (A) Normalized UV−vis−NIR extinction spectra of different aspect ratios GRNs in aqueous solution. The spectra corresponding color of GNRs (inset) for different amounts of AgNO3 from 80, 90, 100, 110, 120 to 130 μL; (B) SEM images of self-assembled GNRs with LSPR at 785 nm; (C) TEM images of self-assembled GNRs with LSPR at 785 nm; and (D) SEM images of self-assembled GNRs colloid mixing with urine.

we changed the concentration of Ag+ by adding different amounts of AgNO3. The GNRs corresponding LSPR from 650 to 828 nm have been synthesized by varying the silver ion usage in growth solution from 80 to 130 μL (the details are shown in section S2 in the Supporting Information). The corresponding pictures of GNRs were also shown in the insert map at the top right. The longitudinal surface plasmon resonance peaks of the nanorods are finely tuned to overlap with the excitation source, and these colloidal Au nanorods can serve as efficient SERS substrates. We utilized gold NRs with peak plasmon resonance at 785 nm with the R = 3.8 (120 μL), designed to match our near-infrared SERS excitation source (785 nm). In this work we use mPEG-SH to displace the amount of CTAB from the surface of GNRs. Our results reveal the high stability of polymer-coated-GNRs against chemically induced aggregation and interesting induced restructuring of the polymer at the metal surface. Notably, the mPEG-SH polymer plays a double role, namely, to prevent the aggregation of GNRs and, consequently, to induce self-assembly during the C

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the two particles. In fact, in order to saving time, part of the droplet is adsorbed by filter paper. The residual part is enough. The time-resolved Raman spectra are immediately recorded until the droplet is dried. The Raman spectra are recorded continuously at an interval of 0 s. About 50 spectra are recorded before the signals keep stable except huge background noise. Then, all of the successive spectra have been put together to reflect the dynamics of the method. Here, GNRs were chosen to research the D-SERS performance during the process of the volatilization. Urea is the dominant nitrogen-containing component in the urine, followed by creatinine and uric acid.35 Also there is some albumin. From Table S1 in the Supporting Information, we can see that human urine is a complex fluid containing many components, which makes SERS analysis so difficult. To explore the potential for SERS analysis, we have used DSERS to collect SERS spectra of 50 random human urine samples. Figure 2 shows the spectra of 10 representative urine samples of 50 random human urine samples collected using DSERS on a portable Raman spectrometer. From parts A to J of

process of the solvent evaporation. The assembly method we adopted here has been proposed by our group work before.32 GNRs colloid with LSPR 785 nm conjugated with a moderate amount of mPEG-SH orderly nanostructure as solvent evaporation and long-chain mPEG-SH shells interdigitate and orientate the GNRs. The morphology and structure of assembled GNRs were examined by SEM and TEM (Figure 1B,C). As shown in the SEM and TEM image, the assembled GNRs were uniform and in good order. The average interspace of different Au nanostructures is about 5 nm (Figure 1C). Two factors are responsible for the formation of close-packed domains. On the one hand, as solvent evaporates, the surface tension at the edge of the drop increases and eventually overpowers the electrostatic repulsions between the particles, thus forcing them to agglomerate. On the other hand, during the solvent evaporation process, long-chain mPEG-SH shells on the particles are able to interdigitate and orientate the Au nanostructures to minimize the system’s energy, thus yielding close-packed domains.32 It should reinforce the point that adding urine to the mPEGSH coated-GNRs solution did not greatly affect their optical properties. Moreover, the plasmon bands do not show additional broadening, thus ruling out any possible aggregation of the particles upon polymer adsorption (Figure S1B in the Supporting Information). Here, the mixture was dropped on chip and observed after drying. Figure 1D showed the SEM of GNRs mixed with urine after drying. Compared with assembled GNRs in Figure 1B, it can be demonstrated that there was little difference. Therefore, GNRs can still assemble orderly with mPEG-SH in the presence of urine. In this work, we use GNRs as a SERS substrate. To evaluate the sensitivity of the D-SERS performance of the GNRs, SERS experiments were conducted employing crystal violet (CV) (Figure S2A,B in the Supporting Information). The detection limit of assembled GNRs is 10−10 M. The problem of reproducibility has always been a critical issue in SERS, which has hampered the progress of SERS technology toward field test application. Therefore, uniformity and reproducibility of SERS substrates must be validated. Figures S2C and S3 in the Supporting Information demonstrate that the SERS substrates are of high reproducibility. It is necessary to evaluate the stabilization of GNRs caused by storage. In this study, a set of GNRs colloid with mPEG-SH was prepared and kept under seal at room temperature for different times without special protective measures. Figure S2D in the Supporting Information showed the GNRs substrate owning a good SERS performance after 150 days. For more evidence of high SERS performance of GNRs, another model molecule 4-ATP was also used to estimate its sensitivity and reproducibility (Figures S4 and S5 in the Supporting Information). All of these results of D-SERS show excellent sensitivity, reproducibility, and stability (Supporting Information section S2, Figures S2−S5) which are the basis and preconditions for the support vector machine and data analysis. Detections of MAMP in Human Urine with D-SERS. The general method for the preparation of D-SERS substrates is to drop 2 μL of GNRs colloid mixing with urine solution (v/ v = 1/1) on a glass slide and then collect the real-time SERS spectra in situ. D-SERS is based on the state translation of nanoparticles from the wet state to the dry state. This enables time-dependent and continuous SERS measurement during the evaporating process. The evaporation rate can greatly affect the characteristics of the substrate, especially the distance between

Figure 2. Spectra of 10 representative urine samples of 50 random human urine samples collected using D-SERS on a portable Raman spectrometer. D

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Analytical Chemistry Figure 2, it can be seen that the obvious feature of all of these 10 groups is high similarity, which forms the basis of applying SVM. Here, we choose Figure 2A as one group of these to analyze the spectra. As shown in Figure 2A, the SERS signal collected using D-SERS have high reproducibility with an obvious feature of urine. According the peaks at 665 (OC−N deformation), 722 (creatinine structure transformation), 1003 (C−N stretching), 1136 (C−N stretching), 1460 (tryptophan vibration), and 1598 cm−1(ring stretching),36 it can be identified as human urine (see Table S2 in the Supporting Information). Especially for the primary band at 1003 cm−1, whose intensity is much higher than other peaks, is corresponding to urea for it is the dominant nitrogencontaining component in urine. Compared with the major component, however, the characteristic vibration bands of creatinine are also shown at 725 cm−1 as well as some protein in 1460 cm−1, though there exist several Raman shifts relative to the literature. Considering the spectral resolution is 8 cm−1, it is under reasonable range and can be accepted. Here, it indicates that D-SERS on GNRs is an efficient method to detect trace analytes in a complex system. Next, we use the same method to detect MAMP in 50 human urine samples on a portable Raman spectrometer. The details are shown in Figure 3. From parts A to J of Figure 3, the obvious feature of these spectra are also high similarity. One of the representatives obtained SERS spectra is shown in Figure 3A. With the comparison of Figure 2A and Figure 3A, it is difficult to differentiate from each other when considering the structural similarity of these spectra and overlapping peaks. The spectra still had a good reproducibility and there was little difference in between urine and the simulative drug abuser urine. Only the intensity at 1003 cm−1 increased because the major Raman bands of MAMP are also at this position. Meanwhile, some new Raman band appeared at 656 and 1208 cm−1, which maybe associated with MAMP. However, these are not obvious and cannot be used as proof to identify the drugs in urine. To validate the ability of D-SERS to detect trace drugs in urine, a set of same experiment was conducted on the laboratory Raman spectrometer. The comparison among urine, urine containing MAMP and pure MAMP were presented in Figure 4A. Obviously, there were new characteristic peaks appearing at 653, 825, 950, 1066, 1208, and 1630 cm−1. And these spectral features can be assigned as arising from characteristic vibrations of MAMP.2,36 Though, we can differentiate two kinds of spectra obtained on laboratory Raman spectrometer though comparison in detail. It cannot be achieved on portable Raman spectrometer. To further prove the advantage of D-SERS, we detected urine and simulative drug abusers’ urine sample using a conventional method at the dry state. However, as the result shown in Figure 4B, without using D-SERS, when the samples were detected in the dry state, it is too difficult to acquire the SERS signal MAMP. From SERS spectra collected in the dry state, there was little difference between urine and urine containing MAMP. Only an obvious Raman band was obtained at 1001 cm−1, which is assigned to C−N stretching of urea due to its dominated concentration. Other species were hardly detected, and the characteristic Raman bands were covered. Therefore, the conventional method was no longer available to detect trace MAMP in human urine. Combination of D-SERS with SVM for Detection of MAMP and MDMA in Human Urine. SVM has been an

Figure 3. Corresponding spectra of 10 simulative drug abusers’ urine sample collected containing 50 ppm MAMP using D-SERS on a portable Raman spectrometer.

Figure 4. (A) Comparison of spectra of MAMP (blue line), urine (red line), and urine containing MAMP (black line) obtaining by “D-SERS” on a laboratory Raman spectrometer; (B) comparison of spectra of urine (red line) and urine containing MAMP (black line) obtaining using the conventional method which uses GNRs to form thin solid films on a portable Raman spectrometer.

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Table 1. Results of High Concentration MAMP (50 ppm) Obtained by SVM and SERS Spectra Measured via Two Different Methods sample 1

sample 2

sample 3

mean value

data set

C-SERS

D-SERS

C-SERS

D-SERS

C-SERS

D-SERS

C-SERS

D-SERS

specificity (%) sensitivity (%) accuracy (%)

83.2 84.4 83.8

96.6 96.4 96.5

85.4 86.8 86.1

95.2 96.4 95.8

87.8 82.2 85.0

96.4 95.6 96.0

85.5 84.5 85.0

96.1 96.1 96.1

excellent learning machine with optimal classified ability and generalization ability and combined SERS to diagnose cancer, acquired immune deficiency syndrome (AIDS), pharmaceutical data analysis.17,24,37 As the performance of SVM is superior to other algorithms because of its valid generalization and high efficiency,26−28 it is selected to solve the classification issue in the paper. In this study, we aim to identify the drugs in human urine combining the D-SERS method with SVM. Herein, the method we used is superior to present testing methods because it does not involve additional pretreatment for analysis (separation, dilution, or mixing with any other reagents). However, as the result of this experiment, it is still difficult to analyze SERS spectra and time-consuming for nonprofessionals. Accordingly, the chemometrics method SVM is herein adopted to solve these problems. To verify the feasibility and effectiveness of the approach for our issue, 50 volunteers were chose for the urine samples collection, and each person was sampled 3 times. Samples were randomly divided into three kinds and named sample 1, sample 2, and sample 3. Subsequently, every urine sample was equally divided into two parts, and one part was prepared to be with 50 ppm MAMP. So we obtained 300 samples in total. Also 10 SERS spectra were obtained for each sample using the conventional SERS (C-SERS, which is using colloidal nanoparticles to form thin solid films for SERS detection) method and the D-SERS method every time. Also all the spectra are processed by PCA with the accumulating contribution rate up to 95%. Subsequently, the classification models were built by combining SVM with the processed data and evaluated by 5fold cross validation methods (Table 1). From Table 1, it can be known that the mean prediction accuracy of the classification models built by combining SVM with the DSERS spectra was up to 96.1% and almost 11% higher than the model developed with the C-SERS spectra. As the residue in the urine of the drug abuser is about from 0.1 to 100 ppm, we also obtain the spectra of the urine with 2.5 and 0.1 ppm MAMP using D-SERS. As we focus on identification of the presence or absence of MAMP in urine, the spectra of urine were labeled as one class, and the spectra of urine with MAMP were labeled as the other class. Subsequently, all the spectra of urine and different concentrations of MAMP in urine were adopted to develop the classification model. Similarly, the model was also evaluated via 5-fold cross validation methods. From Table S3 in the Supporting Information, we know that the prediction accuracy of the models decrease slightly with the induction of the spectra of low concentration samples. The phenomenon mainly due to that the samples of low concentration are relatively difficult to be recognized from the urine. Nevertheless, the accuracy still keeps on the high level of 94.2% for 0.1 ppm MAMP in urine (Table S3 in the Supporting Information), which reviews our approach and possesses excellent robustness and high classification capacity. Accordingly, it was feasible and effective for the accurate

identification of the drug (MAMP) in urine using D-SERS and SVM. To view the difference of the spectra using two different methods visually, the space distribution of the 100 spectra handled by PCA with the accumulating contribution rate up 95% is shown in Figure 5. As seen in Figure 5A,B, the SERS

Figure 5. Space distribution of SERS spectra were processed by PCA. (A) The spectra of 50 ppm MAMP are measured by the C-SERS method. (B) The spectra of 50 ppm MAMP are obtained with the DSERS method. (C) The spectra of 2.5 ppm MAMP are obtained with the D-SERS method. (D) The spectra of 0.1 ppm MAMP are obtained with the D-SERS method. PC1, the first principal component; PC2, the second principal component.

spectroscopy of the two classes which were measured by DSERS were more concentrated and the margin between classes is bigger compared with the conventional method. In fact, the phenomenon confirms the reproducibility and stability of SERS spectra that is obtained using our method have gained a significant improvement. Additionally, when the concentration of MAMP in urine is becoming low, the distribution of the corresponding samples is gradually getting close to the pure urine (Figure 5B−D), which can explain the slight decrease of classification accuracy for the latter model. To validate the universality of our method, a similar set of experiments was carried out and exemplified with MDMA. As we predicted based on the result of MAMP, it is impossible for nonprofessionals to distinguish the SERS spectra of urine with or without MDMA even in a high level concentration of 50 ppm (see Figure S6 in the Supporting Information). Then the SERS spectra data were processed in the same steps by PCA and SVM. The result indicated that the average classification accuracy of 50, 2.5, and 0.1 ppm MDMA in urine were 95.9%, 95.3%, and 94.0%, respectively (see Table S4 in the Supporting Information). The space distribution of the SERS spectra processed by PCA was also getting closer to each other and showed the same tendency of MAMP (see Figure S7 in the F

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model analyzes the SERS spectra and if the accuracy is in the range of values we set, for example, when the classification accuracy is no less than 80%, we decided that there are drugs in the urine and the result is displayed on the screen. So when the real unknown samples are tested by this Raman spectrometer, there are two possible outcomes. In the first instance, the urine sample contains no drugs, then the test result “no drugs” is displayed on the screen visually. In the second, when there is drug like MAMP, the test result “The urine sample contains MAMP” is displayed. It just took no more than 2 min from taking the sample to obtaining an accurate result. Compared with other analytical technology, our method saves time and is easy to operate for a nonprofessional. With high accuracy and fast acquisition, it really is an ideal method which is appropriate to be applied on the drug scene.

Supporting Information). Therefore, it can be proved that our method can be generalized to other situations. Detection of Real Drug Abusers’ Urine Samples. To validate the applicability of the SVM model in practical detection, three real drug abusers’ urine samples and the urine of 50 human beings with and without 50, 2.5, 0.1 ppm MAMP were carried out to develop the final SVM classification model. Additionally, 100 SERS spectra of each drug abuser’s urine sample were obtained by D-SERS on assembled AuNRs and then were judged rapidly by the SVM model (Figure 6).

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CONCLUSIONS We developed a new and easy method that can detect a trace amount of drugs in human urine based on D-SERS with GNRs. Using the high performance and reliable GNRs as D-SERS substrates, we have achieved the spectra of urine and MAMP in urine without any sample pretreatment on a portable Raman spectrometer. For field test application and a visual result, we have successfully developed an accurate and rapid method to detect trace level MAMP using supporting vector machines, which can classify different samples perfectly. The result indicated that the average classification accuracy of 50, 2.5, and 1 ppm MAMP and MDMA in urine were 96.1% and 95.9%, 95.3% and 95.3%, 94.2% and 94.0%, respectively. We have also detected the MAMP in urine of real drugsters successfully with a high accuracy more than 90% and achieved the goal of the result’s visualization. Using our method, we found the detection time is about 2 min, which is much faster than the standard procedure regulated level of several hours. The demonstration of the advantages of combination D-SERS on assembled GNRs with SVM opens new opportunities for SERS detection that can provide simple, reliable, fast, and unique results.

Figure 6. Space distribution of SERS spectra of 3 real drug abusers’ urine samples containing 30 ppm (green), 3 ppm (blue), and 0.4 ppm (crimson) MAMP. The dotted line represents the boundary of urine with or without MAMP.

The SERS spectra of three real drug abusers were shown in Figure S8A−C in the Supporting Information. All the samples were judged rapidly after SERS signal was obtained. In Table 2,

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Table 2. Test Result of Real Drug Abusers’ Urine Samples sample

1

2

3

accuracy (%)

91.5

90.5

90.0

ASSOCIATED CONTENT

S Supporting Information *

Detailed experimental procedures; SERS spectra of volunteers and real drug abusers’ urine samples; detailed experimental data of urine sample containing MDMA. This material is available free of charge via the Internet at http://pubs.acs.org.

the accuracy of three samples judged by SVM were 91.5%, 90.5%, and 90.0%, respectively. The experimental results was in line with expectations, and even though the SERS signal obtained using D-SERS in different conditions compared with simulative samples, there was still high classification accuracy. It confirms that D-SERS has the capability to obtain reproducible signals and demonstrates that SVM model can distinguish and judge simulative and real samples effectively. In testing at the scene, considering saving time and the high classification accuracy of SVM, we just obtained 10 SERS spectra just in 50 s. Similarly, in order to testify the SVM model is still valid, the SERS spectra of different kinds of urine samples with or without MAMP were obtained in the same conditions. As a result, it still showed a high classification accuracy (see Table S5 in the Supporting Information), nearly 100%. In a word, in the practical test, there is no need to collect so many spectra and just 10 spectra can help us get the right result. After the SVM model was proved to be validated, it was embedded into the operating system of the portable Raman spectrometer to consist of a new Raman sensor system. When the SERS spectra have been obtained by D-SERS, the SVM

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AUTHOR INFORMATION

Corresponding Author

*Fax: (+86)551-65592420. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China (Grant 2011CB933700) and the National Instrumentation Program of China (Grants 2011YQ0301241001 and 2011YQ0301241101).

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DOI: 10.1021/acs.analchem.5b00137 Anal. Chem. XXXX, XXX, XXX−XXX