Pressure sensitive adhesives and paper spray-mass spectrometry for the collection and analysis of fentanyl-related compounds from shipping materials

The rise of fentanyl and fentanyl analogs in the drug supply pose serious threats to public health. Much of these compounds enter the United States through shipping routes. Here we provide a method for fentanyl screening and analysis that utilizes pressure-sensitive adhesive (PSA) lined paper to recover drug residues from parcel-related surfaces. The paper used is commercially available repositionable notes (also called post-it or sticky notes). From this paper, mass spectra were obtained by paper spray-mass spectrometry (PS-MS), where PSA paper served as both a sampling and analysis substrate. Seven fentanyl-related compounds were analyzed: fentanyl, 4-anilino-N-phenethylpiperidine (4-ANPP), N,1-diphenethyl-N-phenylpiperidin-4-amine (phenethyl-4-ANPP), valerylfentanyl, 4-fluoroisobutyrylfentanyl (4-FIBF), carfentanil, and p-fluorofentanyl. These compounds were recovered by PSA paper and identified by PS-MS from packaging tape and plastic at 50 ng and from cardboard and shipping labels at 100 ng. The impact of cutting agents on PS-MS analysis of fentanyl analogs was explored. No trends of analyte suppression were found at high concentrations of the cutting agents caffeine, diphenhydramine, and lidocaine when recovered from surfaces. A cartridge that required no precise cutting of PSA paper prior to sampling or analysis was evaluated for use in PS-MS for fentanyl screening. Recovery and detection of fentanyl from plastic sheeting was demonstrated with this cut-free cartridge. The cut-free cartridge showed somewhat less consistency and lower analyte signal than the standard cartridge, but performance was suitable for potential screening applications. In combining PSA surface sampling with PS-MS for drug screening, both sampling and detection of fentanyl-related compounds is simple, rapid, and low-cost.     

NIST Mass Spectrometry Data Center standard reference libraries and software tools: Application to seized drug analysis

The standard reference libraries and associated custom software provided by the National Institute of Standards and Technology's Mass Spectrometry Data Center (NIST MSDC) are described with a focus on assisting the seized drug analyst with the identification of fentanyl-related substances (FRS). These tools are particularly useful when encountering novel substances when no certified sample is available. The MSDC provides three standard reference mass spectral libraries, as well as six software packages for mass spectral analysis, reference library searching, data interpretation, and measurement uncertainty estimation. Each of these libraries and software packages are described with references to the original publications provided. Examples of fentanyl identification by gas chromatography–mass spectrometry (GC–MS) and by direct analysis in real-time (DART) mass spectrometry are given. A link to online tutorials is provided.     

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.     

Quick Screening of Crystal Methamphetamine/Methyl Sulfone Exhibits by Raman Spectroscopy

Abstract: The analysis of mixtures of “crystal meth” (usually comprised of methyl sulfone [MS] and methamphetamine [MA]) by gas chromatography‐mass spectrometry (GCMS) is routine in many forensic drug laboratories. The utilization of Raman spectroscopy for the identification of such mixtures quickly and without the need for a separation technique is discussed. Samples were dissolved in water and Raman spectra of the resulting aqueous solutions were collected. By comparing these spectra to spectra of methylsulfone and MA mixtures of known composition, an indication of the composition of the sample can be obtained in only a few minutes. This spectral comparison also can be used as a semi‐quantitative analysis of MA concentrations in such exhibits.     

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*,†

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.     

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.     

Emergence of fentanyl-related deaths in Travis County,Texas and surrounding areas: A retrospective review of postmortem fentanyl-related drug toxicities from 2020 to 2022

The opioid epidemic has affected the United States (US) for decades with fentanyl and its analogs accounting for a recent surge in morbidity and mortality. Currently, there is a relative lack of information characterizing fentanyl-related fatalities specifically in the Southern US. A retrospective study was conducted to examine all postmortem fentanyl-related drug toxicities in Travis County, Texas, encompassing Austin (one of the fastest-growing cities in the US), from 2020 to 2022. Fentanyl contributed to 2.6% and 12.2% of deaths submitted for toxicology between 2020 and 2022, respectively, representing a 375% increase in fentanyl-related deaths over this 3-year period (n = 517). Fentanyl-related fatalities primarily occurred in males in their mid-30s. Fentanyl and norfentanyl concentrations ranged from 0.58 to 320 ng/mL and 0.53 to 140 ng/mL with mean (median) concentrations of 17.2 ± 25.0 (11.0) and 5.6 ± 10.9 (2.9) ng/mL, respectively. Polydrug use was present in 88% of cases, with methamphetamine (or other amphetamines) (25%), benzodiazepines (21%), and cocaine (17%) representing the most frequently identified concurrent substances. Co-positivity rates of various drugs and drug classes widely varied over time. Scene investigations reported illicit powder(s) (n = 141) and/or illicit pill(s) (n = 154) in 48% (n = 247) of fentanyl-related deaths. Illicit oxycodone (44%, n = 67) and illicit “Xanax” (38%, n = 59) pills were frequently reported on scene; however, toxicology only identified oxycodone and alprazolam in 2 and 24 of these cases, respectively. The results of this study provide a better understanding of the fentanyl epidemic in this region creating an opportunity to promote increased awareness, shift focus to harm reduction, and aid in minimizing public health risks.     

Detection of Anthrax and Other Pathogens Using a Unique Liquid Array Technology

A bead‐based liquid hybridization assay, Luminex^® 100?, was used to identify four pathogenic bacteria, Bacillus anthracis, Clostridium botulinum, Francisella tularensis subsp. tularensis, and Yersinia pestis, and several close relatives. Hybridization between PCR‐amplified target sequences and probe sequences (located within the 23S ribosomal RNA gene rrl and the genes related to the toxicity of each bacterium) was detected in single‐probe or multiple‐probe assays, depending on the organism. The lower limits of detection (LLDs) for the probes ranged from 0.1 to 10 ng. Sensitivity was improved using lambda exonuclease to digest the noncomplementary target strand. All contributors in 33 binary, ternary, and quaternary mixtures in which all components were present in a 1:1 ratio were identified with an 80% success rate. Twenty‐eight binary mixtures in which the two components were combined in various ratios were further studied. All target sequences were detected, even when the minor component was overshadowed by a tenfold excess of the major component.     

Control of a chemical precursor used in the illicit manufacture of fentanyl as a List I chemical. Interim rule with request for comments

This rulemaking controls the chemical N-phenethyl-4-piperidone (NPP) as a List I chemical under the Controlled Substances Act (CSA) (21 U.S.C. 801 et seq.). Clandestine laboratories are using this chemical to illicitly manufacture the schedule II controlled substance fentanyl. The recent distribution of illicitly manufactured fentanyl has caused an unprecedented outbreak of hundreds of suspected fentanyl-related overdoses, at least 972 confirmed fentanyl-related deaths, and 162 suspected fentanyl-related deaths occurring mostly in Delaware, Illinois, Maryland, Michigan, Missouri, New Jersey, and Pennsylvania. NPP has been identified as the starting material in several seized fentanyl clandestine laboratories. In addition to DEA's concern regarding the deaths associated with illicitly manufactured fentanyl, DEA is extremely concerned about the safety of law enforcement officers encountering these clandestine laboratories. Therefore, DEA is regulating NPP as a List I chemical through this Interim Rulemaking. DEA is soliciting comments on this Interim Rule. This rulemaking will subject handlers of NPP to the chemical regulatory provisions of the CSA and its implementing regulations, including 21 CFR Parts 1309, 1310, 1313, and 1316. This rulemaking does not establish a threshold for domestic and international transactions of NPP. As such, all transactions involving NPP, regardless of size, shall be regulated. This rulemaking also specifies that chemical mixtures containing NPP will not be exempt from regulatory requirements at any concentration. Therefore, all transactions of chemical mixtures containing any quantity of NPP will be regulated and will be subject to control under the CSA.     

GC–MS analysis of eight aminoindanes using three derivatization reagents

Aminoindanes are a class of novel psychoactive substances (NPSs) that have become more prevalent over the past decade. GC–MS is often utilized for identifying seized drugs and is well regarded for its ability to separate mixtures. However, certain aminoindanes have similar mass spectral data and require specific gas chromatographic stationary phases for separation. Derivatization is an alternative method that can be applied to GC–MS to enhance chromatographic results, providing more selective analysis in seized-drug identification. This study investigates derivatization techniques to provide options for forensic science laboratories in accurately identifying aminoindanes. Three derivatization reagents, N-methyl-bis(trifluoroacetamide) (MBTFA), heptafluorobutyric anhydride (HFBA), and ethyl chloroformate (ECF) were evaluated for the analysis of eight aminoindanes by GC–MS using two common gas chromatographic stationary phases, Rxi®-5Sil MS and Rxi®-1Sil MS. All three derivatization methods successfully isolated eight aminoindanes, including the isomers 4,5-methylenedioxy-2-aminoindane (4,5-MDAI), and 5,6-methylenedioxy-2-aminoindane (5,6-MDAI) that could not be differentiated prior to derivatization. Reduced peak tailing and increased abundance were observed after derivatization for all the compounds, and mass spectra of the derivatives contained individualizing fragment ions that allowed for further characterization of the aminoindanes. This excluded 4,5-MDAI and 5,6-MDAI as they shared the same characteristic ions and were only distinguishable by their retention times. All three derivatization techniques used in this study allow for successful characterization of the aminoindanes and give forensic science laboratories flexibility in their analysis approach when they encounter these compounds.     

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: Semen and blood mixtures

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids.     

Lipophilicity of fentalogs: Comparison of experimental and computationally derived data

Although fentanyl and a small number of derivatives used for medical or veterinary procedures are well characterized, physiochemical properties have not been determined for many of the newer fentanyl analogs. Partition coefficients (Log P) were determined for 19 fentalogs using the shake-flask method and liquid chromatography–tandem mass spectrometry (LC–MS/MS). Experimentally determined partition coefficients were compared with computationally derived data using six independent software sources (ACD/LogP, LogKOWWIN v 1.69, miLogP 2.2, OsirisP, XLOGP 3.0, ALogPS 2.1). Fentalogs with a wide variety of structural modifications were intentionally selected, yielding Log P values ranging from 1.21 to 4.90. Comparison of experimental and computationally derived Log P values were highly correlated (R² 0.854–0.967). Overall, substructure-based modeling using fragmental methods or property-based topological approaches aligned more closely with experimentally determined Log P values. LC–MS/MS was also used to estimate pK_a values for fentalogs with no previously reported data. Lipophilicity and pK_a are important considerations for analytical detection and toxicological interpretation. In silico methods allow the determination of physicochemical information prior to certified reference materials being readily available for in vitro or in vivo studies. Computationally derived data can provide insight regarding physiochemical characteristics of future fentalogs and other classes of synthetic analogs that have yet to emerge.     

How to Cautiously Uncover the “Black Box” of Machine Learning Models for Legislative Scholars

Machine learning models, especially ensemble and tree-based approaches, offer great promise to legislative scholars. However, they are heavily underutilized outside of narrow applications to text and networks. We believe this is because they are difficult to interpret: while the models are extremely flexible, they have been criticized as “black box” techniques due to their difficulty in visualizing the effect of predictors on the outcome of interest. In order to make these models more useful for legislative scholars, we introduce a framework integrating machine learning models with traditional parametric approaches. We then review three interpretative plotting strategies that scholars can use to bring a substantive interpretation to their machine learning models. For each, we explain the plotting strategy, when to use it, and how to interpret it. We then put these plots in action by revisiting two recent articles from Legislative Studies Quarterly.     

Forecasts of Violence to Inform Sentencing Decisions

Objectives

Recent legislation in Pennsylvania mandates that forecasts of "future dangerousness" be provided to judges when sentences are given. Similar requirements already exist in other jurisdictions. Research has shown that machine learning can lead to usefully accurate forecasts of criminal behavior in such setting. But there are settings in which there is insufficient IT infrastructure to support machine learning. The intent of this paper is provide a prototype procedure for making forecasts of future dangerousness that could be used to inform sentencing decisions when machine learning is not practical. We consider how classification trees can be improved so that they may provide an acceptable second choice.

Methods

We apply an version of classifications trees available in R, with some technical enhancements to improve tree stability. Our approach is illustrated with real data that could be used to inform sentencing decisions.

Results

Modest sized trees grown from large samples can forecast well and in a stable fashion, especially if the small fraction of indecisive classifications are found and accounted for in a systematic manner. But machine learning is still to be preferred when practical.

Conclusions

Our enhanced version of classifications trees may well provide a viable alternative to machine learning when machine learning is beyond local IT capabilities.     

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC x GC-TOFMS) for drug screening and confirmation

Comprehensive two-dimensional gas chromatography (GC x GC) is applied to analysis of drug standard mixtures containing 78 drugs of interest in forensic samples. For this study, underivatised drugs were employed. While several of the drugs were not detected at the low concentrations employed in the samples, most could be satisfactorily assigned their first and second dimension retentions in the GC x GC retention plane. For this study, time-of-flight mass spectrometry (TOFMS) detection was used. The enhanced separation possible in GC x GC is demonstrated, and typical linearity and apparatus precision are shown for tramadol, diazepam, olanzapine and desipramine using selected qualifier ions. Mass spectral library search quality for the detection of drugs in a selection of authentic forensic cases, along with retention position in the 2D retention plane, is used to support positive identification of the presence of the drugs. The analysis of 'difficult' drugs paracetamol and phenytoin is shown to produce anomalous chromatographic peak shape in the 2D plane, whereas most drugs gave acceptable peak shapes. The GC x GC technique was applied to screening drugs in forensic samples, with either flame ionisation (FID) or TOFMS detection, and compared favourably with conventional single column GC-MS analysis when tested for diazepam in an authentic forensic study.     

Field-portable detection of fentanyl and its analogs: A review

The need to detect fentanyl and its analogs in the field is an important capability to help prevent unintentional exposure or overdose on these substances, which may result in death. Many portable methods historically used in the field by first responders and other field users to detect and identify other chemical substances, such as hazardous materials, have been applied to the detection and identification of these synthetic opioids. This paper describes field portable spectroscopic methods used for the detection and identification of fentanyl and its analogs. The methods described are automated colorimetric tests including lateral flow assays; vibrational spectroscopy (mid-infrared and Raman); gas chromatography–mass spectrometry; ion mobility spectrometry, and high-pressure mass spectrometry. In each case the background and key details of these technologies are outlined, followed by a discussion of the application of the technology in the field. Attention is paid to the analysis of complex mixtures and limits of detection, including the required spectral databases and algorithms used to interrogate these types of samples. There is also an emphasis on providing actionable information to the (likely) non-scientist operators of these instruments in the field.     

Estimating the Temperature of Heat‐exposed Bone via Machine Learning Analysis of SCI Color Values: A Pilot Study

Determining maximum heating temperatures of burnt bones is a long‐standing problem in forensic science and archaeology. In this pilot study, controlled experiments were used to heat 14 fleshed and defleshed pig vertebrae (wet bones) and archaeological human vertebrae (dry bones) to temperatures of 400, 600, 800, and 1000°C. Specular component included (SCI) color values were recorded from the bone surfaces with a Konica‐Minolta cm‐2600d spectrophotometer. These color values were regressed onto heating temperature, using both a traditional linear model and the k‐nearest neighbor (k‐NN) machine‐learning algorithm. Mean absolute errors (MAE) were computed for 1000 rounds of temperature prediction. With the k‐NN approach, the median MAE prediction errors were 41.6°C for the entire sample, and 20.9°C for the subsample of wet bones. These results indicate that spectrophotometric color measurements combined with machine learning methods can be a viable tool for estimating bone heating temperature.     

Infrared Spectra of U.S. Automobile Original Finishes (Post – 1989). VIII: In Situ Identification of Bismuth Vanadate Using Extended Range FT‐IR Spectroscopy,Raman Spectroscopy,and X‐Ray Fluorescence Spectrometry

Chrome Yellow (PbCrO₄·xPbSO₄) was a common pigment in U.S. automobile OEM finishes for more than three decades, but in the early 1990s its use was discontinued. One of its main replacements was Bismuth Vanadate (BiVO₄·nBi₂MoO₆, n = 0–2), which was commercially introduced in 1985, as this inorganic pigment also produces a very bright hue and has excellent outdoor durability. This paper describes the in situ identification of Bismuth Vanadate in automotive finishes using FT‐IR and dispersive Raman spectroscopy and XRF spectrometry. Some differentiation of commercial formulations of this pigment is possible based on far‐infrared absorptions, Raman data, and elemental analysis. The spectral differences arise from the presence or absence of molybdenum, the use of two crystal polymorphs of BiVO₄, and differences in pigment stabilizers. Bismuth Vanadate is usually not used alone, and it is typically found with Isoindoline Yellow, hydrous ferric oxide, rutile, Isoindolinone Yellow 3R, or various combinations of these.     

Simultaneous Quantitative Determination of Amphetamines,Opiates, Ketamine,Cocaine and Metabolites in Human Hair: Application to Forensic Cases of Drug Abuse

A method using liquid chromatography–tandem mass spectrometry (LC–MS/MS) to simultaneously quantify amphetamines, opiates, ketamine, cocaine, and metabolites in human hair is described. Hair samples (50 mg) were extracted with methanol utilizing cryogenic grinding. Calibration curves for all the analytes were established in the concentration range 0.05–10 ng/mg. The recoveries were above 72%, except for AMP at the limit of quantification (LOQ), which was 48%. The accuracies were within ±20% at the LOQ (0.05 ng/mg) and between −11% and 13.3% at 0.3 and 9.5 ng/mg, respectively. The intraday and interday precisions were within 19.6% and 19.8%, respectively. A proficiency test was applied to the validated method with z-scores within ±2, demonstrating the accuracy of the method for the determination of drugs of abuse in the hair of individuals suspected of abusing drugs. The hair concentration ranges, means, and medians are summarized for abused drugs in 158 authentic cases.     

Colchicine Extract Suicidal Lethal Poisoning Confirmation Using High‐Resolution Accurate Mass Spectrometry: A Case Study

A case of suspected acute and lethal intoxication caused by colchicine has been reported. The woman was hospitalized after her suspicion of suicidal poisoning by a rare autumn crocus (Colchicum autumnale). Suspected colchicine poisoning was confirmed using a novel UHPLC method with a modern reversed‐phase stationary phase with a sub 2‐micron superficial porous particle size combined with a QTOF mass spectrometer. Sample preparation procedure included the addition of propiverine as internal standard, protein precipitation using methanol and solid phase extraction. High‐resolution MS only and targeted MS/MS modes are reported for the qualitative analysis and screening of other potential drugs of abuse in blood samples. All Ion MS mode was used for quantitative determination of colchicine afterward. The concentration of colchicine in the blood sample was approximately 41 ng/mL, and more than 200 μg/mL of the plant extract used for the suicide.