Rapid in situ repeatable analysis of drugs in powder form using reflectance near-infrared spectroscopy and multivariate calibration

This study takes the first step toward in situ analysis of powder drugs which does not require any alteration of the samples. A fast, inexpensive analytical method based on reflectance near-infrared (NIR) spectrometry and multivariate calibration was applied. A diode-array fiber-optic portable spectrometer in the 900-1700 nm range was employed. Samples were laboratory-prepared ternary powders (diacetylmorphine, caffeine, and paracetamol). Partial least squares regression was applied. The choice of the standard samples for calibration and validation was performed through a D-optimal experimental design. The explained variance was higher than 90%, and the relative root mean square errors were <2%. The number of principal components (6) was very low when compared with the number of raw variables (356 absorbance values). Response plots showed slopes and intercepts were very close to optimal values. Correlation coefficients ranged between 0.909 and 0.989. The method here proposed proved to be competitive with Fourier transform NIR spectrometry.     

Quantitative determination of sibutramine in adulterated herbal slimming formulations by TLC-image analysis method

A simple thin layer chromatographic (TLC)-image analysis method was developed for rapid determination and quantitation of sibutramine hydrochloride (SH) adulterated in herbal slimming products. Chromatographic separation of SH was achieved on a silica gel 60 F(254) TLC plate, using toluene-n-hexane-diethylamine (9:1:0.3, v/v/v) as the mobile phase and Dragendorff reagent as spot detection. Image analysis of the scanned TLC plate was performed to quantify the amount of SH. The polynomial regression data for the calibration plots showed good linear relationship in the concentration range of 1-6 μg/spot. The limits of detection and quantitation were 190 and 634 ng/spot, respectively. The method gave satisfactory specificity, precision, accuracy, robustness and was applied for determination of SH in herbal formulations. The contents of SH in adulterated samples determined by the TLC-image analysis and TLC-densitometry were also compared.     

Screening for Corticosteroid Adulterants in Korean Herbal Medicines

The objective of this study was to determine the presence of corticosteroids in illegal herbal medicines using ultra‐high‐performance liquid chromatography–tandem mass spectrometry. We collected 212 herbal medicine samples that were advertised as being effective for treatment of joint pain and bone aches. Samples were from the Korean commercial market during a span of four years (2010–2013), and the method was validated. The limits of quantification ranged from 0.47 to 15.0 ng/mL, and recoveries ranged from 80.6% to 119.5%. The intra‐ and interday precision ranged from 0.18% to 8.82% and from 0.09% to 8.96%, respectively. Among the samples, three samples (1.4%) were identified as adulterants. Dexamethasone was the only compound detected in the adulterated products. As the corticosteroid–adulteration of herbal medicines may become a major problem and lead to side effects, the continued development of screening procedures for herbal medicines is critical.     

Quantification of the amphetamine content in seized street samples by Raman spectroscopy

A Raman spectroscopy method for determining the drug content of street samples of amphetamine was developed by dissolving samples in an acidic solution containing an internal standard (sodium dihydrogen phosphate). The Raman spectra of the samples were measured with a CDD-Raman spectrometer. Two Raman quantification methods were used: (1) relative peak heights of characteristic signals of the amphetamine and the internal standard; and (2) multivariate calibration by partial least squares (PLS) based on second derivative of the spectra. For the determination of the peak height ratio, the spectra were baseline corrected and the peak height ratio (h(amphetamine at 994 cm(-1) )/h(internal standard at 880 cm(-1) )) was calculated. For the PLS analysis, the wave number interval of 1300-630 cm(-1) (348 data points) was chosen. No manual baseline correction was performed, but the spectra were differentiated twice to obtain their second derivatives, which were further analyzed. The Raman results were well in line with validated reference LC results when the Raman samples were analyzed within 2 h after dissolution. The present results clearly show that Raman spectroscopy is a good tool for rapid (acquisition time 1 min) and accurate quantitative analysis of street samples that contain illicit drugs and unknown adulterants and impurities.     

Anorectic sibutramine detected in a Chinese herbal drug for weight loss

In the presented case, a young healthy woman had ordered a Chinese herbal medicine, called "LiDa Dai Dai Hua Jiao Nang", via internet. She took this product for approximately 1 week, but on the second day she developed severe headache, vertigo and sensation of numbness. After discontinuing medication, symptoms disappeared within 2 days. For identification of the ingredients, a urine sample as well as a sample of the "LiDa" capsule were analysed by GC-MS and HPLC-DAD. One major ingredient was detected in urine as well as in the "LiDa" capsule and was identified as sibutramine. Quantification by HPLC-DAD yielded 27.4 mg sibutramine base, which is approximately two times the amount of the highest authorized single dose available on the pharmaceutical market in Germany. This case demonstrates a common problem with herbal medicines, where adulterations with synthetic therapeutic substances can lead to severe side effects and/or potentially fatal interactions with conventional medicines.     

Analysis of ecstasy tablets: comparison of reflectance and transmittance near infrared spectroscopy

Calibration models for the quantitation of commonly used ecstasy substances have been developed using near infrared spectroscopy (NIR) in diffuse reflectance and in transmission mode by applying seized ecstasy tablets for model building and validation. The samples contained amphetamine, N-methyl-3,4-methylenedioxy-amphetamine (MDMA) and N-ethyl-3,4-methylenedioxy-amphetamine (MDE) in different concentrations. All tablets were analyzed using high performance liquid chromatography (HPLC) with diode array detection as reference method. We evaluated the performance of each NIR measurement method with regard to its ability to predict the content of each tablet with a low root mean square error of prediction (RMSEP). Best calibration models could be generated by using NIR measurement in transmittance mode with wavelength selection and 1/x-transformation of the raw data. The models build in reflectance mode showed higher RMSEPs using as data pretreatment, wavelength selection, 1/x-transformation and a second order Savitzky-Golay derivative with five point smoothing was applied to obtain the best models. To estimate the influence of inhomogeneities in the illegal tablets, a calibration of the destroyed, i.e. triturated samples was build and compared to the corresponding data of the whole tablets. The calibrations using these homogenized tablets showed lower RMSEPs. We can conclude that NIR analysis of ecstasy tablets in transmission mode is more suitable than measurement in diffuse reflectance to obtain quantification models for their active ingredients with regard to low errors of prediction. Inhomogeneities in the samples are equalized when measuring the tablets as powdered samples.     

Rapid and nondestructive analysis of lipstick on different substrates using ATR-FTIR spectroscopy and chemometrics

Lipstick can be an important piece of evidence in crimes like murders, rapes, and suicides. Due to its prevalence, it can be an important corroborative evidence in crime reconstruction. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. We report the use of nondestructive ATR-FTIR spectroscopy combined with chemometrics for the classification of 10 brands of lipsticks with nine samples each. Chemometric method of partial least square-discriminant analysis (PLS-DA) has been employed to interpret the data and classify the samples into their respective classes. The PLS-DA model provides an AUC figure above 0.99 in all brands except one; for which it is slightly less at 0.94. We have also tested the traces of these lipstick samples on different substrates treating them as unknowns in the already trained PLS-DA model. 100% of the samples on nine substrates viz. a cotton, nylon, plastic, dry tissue, denim (blue jeans), wet tissue, nitrile gloves, white paper, and polyester were correctly attributed to their source brand. In conclusion, the results suggest that ATR-FTIR combined with the chemometrics is a rapid, nondestructive, and accurate method for the discrimination and source attribution of lipstick. This study has potential for use in actual forensic casework conditions.     

红外光谱法用于安钠咖中咖啡因和苯甲酸钠快速定性和定量分析

目的建立安钠咖样品中咖啡因和苯甲酸钠快速定性和定量分析的红外光谱方法。方法采用高纯度咖啡因和苯甲酸钠混合制样的方法制备定性和定量建模样品,通过分析混合样品的红外光谱图,确定安钠咖样品中咖啡因和苯甲酸钠的特征吸收峰。采用偏最小二乘法(partial least squares,PLS)建立红外光谱定量模型。结果通过分析17个咖啡因和苯甲酸钠混合样品(咖啡因纯度范围10%~80%)的红外光谱图,确定了咖啡因的特征吸收峰为1698、1650、1237、972、743、609cm-1;苯甲酸钠的特征吸收峰为1596、1548、1406、845、708、679cm-1。将所有特征吸收峰均检出作为阳性判断依据时,48个安钠咖缴获样品中咖啡因和苯甲酸钠的阳性检出率均为100%。咖啡因PLS定量模型的线性范围为10%~80%,决定系数(R2)为99.9%,交叉验证均方差(root mean square error of cross validation,RMSECV)为0.68%,预测均方差(root mean square error of prediction,RMSEP)为0.91%;苯甲酸钠PLS定量模型的线性范围为20%~90%,R2为99.9%,RMSECV为0.91%,RMSEP为1.11%。配对样本t检验结果显示,高效液相色谱法和红外光谱法的测定结果差异无统计学意义。采用所建立的红外定量方法分析48个安钠咖缴获样品,咖啡因的纯度为27.6%~63.1%,苯甲酸钠的纯度为36.9%~72.3%。结论采用红外光谱法对安钠咖样品中的咖啡因和苯甲酸钠进行快速定性和定量分析,可提高检验鉴定效率、降低检验成本。     

NIR analysis of cellulose and lactose--application to ecstasy tablet analysis

Cellulose and lactose are the most frequently used excipients in illicit ecstasy production. The aim of this project was to use near infrared reflectance spectroscopy (NIRS) for the determination of the different chemical forms of these two substances, as well as for the differentiation of their origin (producer). It was possible to distinguish between the different chemical forms of both compounds, as well as between their origins (producers), although within limits. Furthermore, the possibilities to apply NIR for the analysis of substances such as found in illicit tablets were studied. First, a few cellulose and lactose samples were chosen to make mixtures with amphetamine at three degrees of purity (5, 10 and 15%), in order to study the resulting changes in the spectra as well as to simultaneously quantify amphetamine and identify the excipient. A PLS2 model could be build to predict concentrations and excipient. Secondarily, the technique was to be applied to real ecstasy tablets. About 40 ecstasy seizures were analysed with the aim to determine the excipient and to check them against each other. Identification of the excipients was not always obvious, especially when more than one excipient were present. However, a comparison between tablets appeared to give groups of similar samples. NIR analysis results in spectra representing the tablet blend as a whole taking into account all absorbing compounds. Although NIRS seems to be an appropriate method for ecstasy profiling, little is known about intra- and intervariability of compression batches.     

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results. The present study offers a methodology allowing to provide more valuable information for organisations engaged in the fight against counterfeiting of medicines. A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers. The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.     

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.     

Rapid Identification of Synthetic Cannabinoids in Herbal Incenses with DART‐MS and NMR

The usage of herbal incenses containing synthetic cannabinoids has caused an increase in medical incidents and triggered legislations to ban these products throughout the world. Law enforcement agencies are experiencing sample backlogs due to the variety of the products and the addition of new and still‐legal compounds. In our study, proton nuclear magnetic resonance (NMR) spectroscopy was employed to promptly screen the synthetic cannabinoids after their rapid, direct detection on the herbs and in the powders by direct analysis in real time mass spectrometry (DART‐MS). A simple sample preparation protocol was employed on 50 mg of herbal sample matrices for quick NMR detection. Ten synthetic cannabinoids were discovered in fifteen herbal incenses. The combined DART‐MS and NMR methods can be used to quickly screen synthetic cannabinoids in powder and herbal samples, serving as a complementary approach to conventional GC‐MS or LC‐MS methods.     

Forensic Analysis of Cigarette Ash—Brand Determination Through Trace‐metal Analysis

The information inherent in cigarette ash in the form of trace‐metal concentrations may be of use in a forensic context as it can indicate the brand from which the ash originated. This knowledge might help place suspects at crime scenes or determine how many people may have been present. To develop and test statistical models capable of classifying ash samples according to brand, commercial cigarettes procured in the U.S. and overseas were “smoked” using a peristaltic pump, mimicking the range of human smoking habits. Ash samples were digested in a mixture of nitric and hydrochloric acid applying microwave digestion and analyzed using inductively coupled plasma‐mass spectrometry. Principal component analysis of the elemental data showed intrinsic differences between brands. Partial least squares‐discriminant analysis demonstrated that brand classification yields good sensitivity and specificity for a number of models tested. Varying smoking parameters did not impact the classification of ash samples.     

Secondary ion mass spectrometric characterization of nail polishes and paint surfaces

A variety of paint and fingernail polish samples, which were visually similar, but had different chemical compositions and formulations, was analyzed using quadrupole static secondary ion mass spectrometry (SIMS). Coating distinction was easily achieved in many cases because of the presence of dominant ions derived from the components of the coating, which could be observed in the SIMS spectra. In other instances, coating distinction was difficult within a product line because of spectral complexity; for this reason and because of the large numbers of spectra generated in this study, multivariate statistical techniques were employed, which allowed the meaningful classification and comparison of spectra. Partial Least Squares (PLS) and Principal Component Analysis (PCA) were applied to quadrupole SIMS data. PCA showed distinct spectral differences between most spectral groups, and also emphasized the reproducibility of the SIMS spectra. When using PLS analysis, reasonably accurate coating identification was achieved with the data. Overall, the PLS model is more than 90% effective in identifying the spectrum of a particular coating, and nearly 100% effective at telling which coating components represented in the PLS models are not present in a spectrum. The level of spectral variation caused by sample bombardment in the SIMS analysis was investigated using Fourier transform infrared spectroscopy (FT-IR) and quadrupole static SIMS. Changes in the FT-IR spectra were observed and were most likely a result of a number of factors involving the static SIMS analysis. However, the bulk of the sample is unaltered and may be used for further testing.     

A multivariate statistical approach to for the evaluation of the biogeographical ancestry information from traditional STRs

The capability to achieve biogeographic ancestry (BGA) information from DNA profiles have been largely explored in forensic genetics because of its potential usefulness in providing investigative clues. For law enforcement and security purposes, when genetic data have been obtained from unknown evidence, but no reference samples are available and no hints come out from DNA databases, it would be extremely useful at least to infer the ethno-geographic origin of the stain donor by just examining traditional STRs DNA profiles.Current protocols for ethnic origin estimation using STRs profiles are usually based on Principal Component Analysis approaches and Bayesian methods. The present study provides an alternative approach that involves the use of target multivariate data analysis strategies for estimation of the BGA information from unknown biological traces. A powerful multivariate technique such as Partial Least Squares-Discriminant Analysis (PLS-DA) has been applied on NIST U.S. population datasets containing, for instance, the allele frequencies of African-American, Asian, Caucasian and Hispanic individuals. PLS-DA approach provided robust classifications, yielding high sensitivity and specificity models capable of discriminating the populations on ethnic basis. Finally, a real casework has been examined by extending the developed model to smaller and more geographically-restricted populations involving, for instance, Albanian, Italian and Montenegrian individuals.     

Lifestyle illicit drug seizures: A routine ESI-LC-MS method for the identification of sildenafil and vardenafil

For street samples suspected of containing the phosphodiesterase-5 inhibitors sildenafil (Viagra(?)) and/or vardenafil (Levitra(?)), including powders or adulterated herbal supplements, a chemical analysis is needed to provide confirmatory identification of these illegally procured substances. Sildenafil and vardenafil are structurally similar and it is difficult to differentiate between them, as previous mass spectrometric studies have shown the two drugs to produce similar fragmentation patterns. The use of tandem mass spectrometry can produce confirmatory data, but the technique requires a high level of technical expertise. We have developed an electrospray ionization-liquid chromatography-mass spectrometry (ESI-LC-MS) method that allows differentiation between these two structurally similar molecules via in-source fragmentation in combination with an ion trap mass spectrometer. A very stable gas phase ion is formed during in-source fragmentation of vardenafil; the combination of the stability of this ion and the longer residence time for the ion in the ion trap results in a very strong signal. This feature results in a method that can provide clear differentiation between sildenafil and vardenafil while at the same time requiring less expertise from the routine analyst to confirm the presence or absence of the two compounds.     

Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham)

Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an active appearance model (AAM) to extract facial features from available images. An aging function is then modelled using sparse partial least squares regression (sPLS). Thereafter, the aging function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham's facial image that was taken when he was 21 months old to the ages of 6, 14, and 22 years. The algorithm presented in this study could potentially be used to enhance the search for missing people worldwide.     

Rapid separation and characterization of cocaine and cocaine cutting agents by differential mobility spectrometry-mass spectrometry

Forensic drug laboratories are inundated with cases requiring time-consuming GC- or LC-based chromatographic separations of submitted samples. High-throughput analytical methods would be of great practical utility within forensic drug analysis. Recently developed ion-mobility-based separation methods combined with mass spectrometry can often be used without chromatography, suppress chemical interferents of similar mass, and operate in seconds. We have evaluated differential mobility spectrometry-mass spectrometry (DMS-MS) for performance on adulterated cocaine mixtures. The DMS interface is only a few centimeters in length, operates in seconds, and can be adapted to any MS system using atmospheric pressure ionization. Drug cutting agents, typical targets such as cocaine, and drug metabolites are rapidly separated by the DMS ion prefilter. Tests demonstrated characterization of complex mixtures, such as isolation of levamisole, an adulterant with alarming side effects, from a 13-component mixture. DMS-MS holds great potential for the analysis of drug samples submitted for forensic analysis.     

Statistical discrimination of liquid gasoline samples from casework

The intention of this study was to differentiate liquid gasoline samples from casework by utilizing multivariate pattern recognition procedures on data from gas chromatography-mass spectrometry. A supervised learning approach was undertaken to achieve this goal employing the methods of principal component analysis (PCA), canonical variate analysis (CVA), orthogonal canonical variate analysis (OCVA), and linear discriminant analysis. The study revealed that the variability in the sample population was sufficient enough to distinguish all the samples from one another knowing their groups a priori. CVA was able to differentiate all samples in the population using only three dimensions, while OCVA required four dimensions. PCA required 10 dimensions of data in order to predict the correct groupings. These results were all cross-validated using the "jackknife" method to confirm the classification functions and compute estimates of error rates. The results of this initial study have helped to develop procedures for the application of multivariate analysis to fire debris casework.