Trends of chemometrics in bloodstain investigations

Raman Spectroscopy for the Time since Deposition Estimation of a Menstrual Bloodstain

Forensic chemistry plays a crucial role in aiding law enforcement investigations by applying analytical techniques for the analysis of evidence. While bloodstains are frequently encountered at crime scenes, distinguishing between peripheral and menstrual bloodstains presents a challenge. This is due to their similar appearance post-drying. Raman spectroscopy has emerged as a promising technique capable of discriminating between the two types of bloodstains, offering invaluable probative information. Moreover, estimating the time since deposition (TSD) of bloodstains aids in crime scene reconstruction and prioritizing what evidence to collect. Despite extensive research focusing on TSD estimations, primarily in peripheral bloodstains, a crucial gap exists in determining the TSD of menstrual bloodstains. This study demonstrates how Raman spectroscopy effectively analyzes biological samples like menstrual blood, showing similar aging patterns to those of peripheral blood and provides proof-of-concept models for determining the TSD of menstrual blood. While this work shows promising results for creating a universal model for bloodstain age determination, further testing with more donors needs to be conducted before the implementation of this method into forensic practice.

Detection of fragrances on the skin and study of their interaction using infrared and Raman spectroscopy

Nowadays, fragrances belong to the widely used cosmetics. Their composition is designed in a way that it evolves and changes over time. In this work, the effect of fragrances on the skin was studied - the interactions between pig skin samples and fragrances and the possibility of their detection and mutual differentiation. Non-invasive techniques of vibrational spectroscopy were used to obtain the data, namely FT-IR spectroscopy with attenuated total reflection accessory and Raman microspectroscopy. Vibrational spectra were measured within 8 h with different time intervals and after 22 h from the application of fragrance for FT-IR and Raman measurements, respectively. The obtained spectra were pre‑processed and subsequently evaluated by multivariate statistical methods. The study showed that skin treated by fragrances is well distinguishable from untreated skin, even after 22 h. In addition, it is possible to differentiate individual fragrances from each other; therefore, the use of spectroscopical techniques could be a potential tool for forensic analysis of fragrances.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

Discrimination of human and animal bloodstains using hyperspectral imaging

Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.

Age estimation of bloodstains based on Raman spectroscopy and chemometrics

The accurate estimation of the bloodstain age, which is one of the important biological evidence of crime scene, can provide a lot of information related to crime. How to extract the information quickly and accurately from bloodstains without damage has been a focused problem. In this study, a bloodstains age estimation method based on Raman spectroscopy and chemometrics was developed. As many as 11 simulated environments based on different temperature and humidity were constructed in the method, and bloodstains of three species including human were studied. The influence of environmental factors such as temperature and humidity on the variation of Raman spectral peaks during the aging process of bloodstains was analyzed using the e-index fitting. When the humidity was kept constant, the increase of temperature generally promoted the changes of the spectral peaks. When the temperature was kept constant, the increase of humidity generally slowed the changes of spectral peaks. These works provide data support for the further development of Raman spectroscopy for bloodstain age estimation and could accelerate its application in actual scenes.

A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate

Traces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were "titrated" numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.

Hyperspectral imaging in forensic science: an overview of major application areas

Analysis of evidence is a challenge. Crime scene materials are complex, diverse, sometimes of an unknown nature. Forensic science provides the most critical applications for their examination. Chemical tests, analytical methods, and techniques to process the evidence must be carefully selected by the forensic scientist. Ideally, it may be interpreted, analyzed, and judged in the original context of the crime scene. In this sense, hyperspectral imaging (HSI) has been employed as an analytical tool that maintains the integrity of the samples/objects for multiple and sequential analysis and for counter-proof exams. This paper is an overview of forensic science trends for the application of HSI techniques in the last ten years (2011-2021). The examination of documents was the main area of exploration, followed by bloodstain analysis aging process; trace analysis of explosives and gunshot residue. Chemometric tools were also addressed since they are crucial to obtain the most important information from the samples. There are great challenges in applying HSI in forensic science, but there have been clear technological and scientific advances, and a solid foundation has been built for the use of HSI in real-life cases.

A Green-Emitting Luminol Analogue as the Next-Generation Chemiluminescent Substrate in Biochemical Analysis

Luminol and its derivatives are extensively used as chemiluminogenic substrates in bioimaging and biochemical analysis. Luminol reagents can typically emit blue chemiluminescence (CL), whose wavelength is normally outside the most sensitive detection range of human naked eyes and most CL analyzers with silicon-based charge-coupled device (CCD) detectors. Development of luminol analogues with longer wavelength emission is thus attractive. Herein, four new phthalhydrazide CL probes (GL-1/2/3/4) have been prepared through the derivatization of luminol. The most promising one, 5-(4-hydroxy-1,3-dioxoisoindolin-2-yl)-2,3-dihydrophthalazine-1,4-dione (GL-1), emits bright green CL upon oxidation and shows enhanced CL performance compared to its parent luminol. Bloodstain imaging, horseradish peroxidase (HRP)-based immunoassay, and the analysis of glucose/glucose oxidase reaction have been performed using the GL-1 reagent. These results indicate that GL-1 is a new chemiluminogenic luminol analogue with great potential in real analytical applications and will be an alternative to replace luminol in practical CL analysis.

Development of a rapid, in-situ analysis method using sheath-flow probe electrospray ionisation-mass spectrometry for the direct identification of cocaine metabolites in dried blood spots

RATIONALE

Small amounts of biofluid samples are frequently found at crime scenes; however, existing gold standard methods such as LC-MS frequently require destructive extraction of the sample before a time-consuming analysis which puts strain on forensic analysis providers and can preclude further sample analysis. This study presents the application of sheath-flow probe electrospray ionization-mass spectrometry (sfPESI-MS) to the direct analysis of drug metabolites in dried blood spots (DBS) as a high throughput, minimally destructive alternative.

METHODS

A rapid direct analysis method using a sfPESI ionisation source coupled to an Orbitrap Exactive mass spectrometer was applied to detect cocaine metabolites (benzoylecgonine, BZE, cocaethylene, CE, and ecgonine methyl ester, EME) from DBS. An optimisation study exploring the use of different chemical modifiers (formic acid and sodium acetate) in the sfPESI probe extraction solvent was conducted to enhance the sensitivity and reproducibility of the sfPESI-MS method.

RESULTS

Optimisation of the extraction solvent significantly enhanced the sensitivity and reproducibility of the sfPESI-MS method. A quantitative response over a five-point calibration range 0.5 to 10 μg/ml was obtained for BZE (R²  = 0.9979) and CE (R²  = 0.9948). Limits of detection (LOD) of 1.31, 0.29 and 0.15 μg/ml were achieved for EME, BZE and CE, respectively, from 48 h aged DBSs with % RSD (relative standard deviation) across the calibration range ranging between 19%-28% for [BZE + H]⁺ , 13%-21% for [CE + H]⁺ and 12%-29% for [EME + H]⁺ .

CONCLUSIONS

A rapid (< 20 s) quantitative method for the direct analysis of cocaine metabolites from DBS which requires no prior sample preparation was developed. Although the LOD achieved for BZE (LOD: 0.29 μg/ml) was above the UK threshold limit of exposure for drug driving (0.05 μg/ml), the method may be suitable for use in identifying overdose in forensic analysis.

Validation of the Metabolite Ergothioneine as a Forensic Marker in Bloodstains

Ergothioneine, which is a naturally occurring metabolite, generally accumulates in tissues and cells subjected to oxidative stress, owing to its structural stability at physiological pH; therefore, it has been attracting attention in various biomedical fields. Ergothioneine has also been suggested as a potential forensic marker, but its applicability has not yet been quantitatively validated. In this study, quantitative analysis of ergothioneine in bloodstains was conducted to estimate the age of bloodstains and that of bloodstain donors. Blood from youth and elderly participants was used to generate bloodstains. After extracting metabolites from the bloodstains under prevalent age conditions, ergothioneine levels were quantified by mass spectrometry via multiple reaction monitoring. The concentration of ergothioneine in day 0 bloodstains (fresh blood), was significantly higher in the elderly group than in the youth group, but it did not differ by sex. Statistically significant differences were observed between the samples from the two age groups on days 0, 5 and 7, and on days 2 and 3 compared with day 0. The findings suggest that ergothioneine can be used to estimate the age of bloodstains and of the donor; it could be useful as a potential marker in reconstructing crime scenes.

Determination of blood species using echelle Raman spectrometer and surface enhanced Raman spectroscopy

Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.

Pattern Recognition for Human Diseases Classification in Spectral Analysis

Pattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

A rapid and non-destructive ATR-FTIR spectroscopy method supported by chemometrics for discriminating between facial creams and the classification into herbal and non-herbal brands

Facial creams are considered to be essential beauty items and are used by both females and males on an everyday basis. These can be encountered as an evidentiary material in criminal investigations, particularly in cases related to sexual and physical assaults against women. These are found in trace amounts and therefore their analysis is difficult and also, it must be through non-destructive methods. In the present work ATR-FTIR spectroscopy was employed for the discrimination of 57 samples of face creams out of which 31 were non-herbal and 26 were from herbal category. Visual analysis of the obtained Spectra was done for discrimination purposes but the method was prone to human error and laborious too. The spectroscopic results were analyzed with PCA (Principal Component Analysis) and PLS-DA (Partial least square discriminant analysis) methods. A segregation of samples was seen in the PCA plots to some extent. The class separation and prediction of the samples was performed using PLS-DA method. A good classification was achieved between herbal and non-herbal samples using PLS-DA method. Further, validation of the model was also performed by testing 10 unknown samples.

ATR-FTIR spectroscopy probing of structural alterations in the cellular membrane of abscopal liver cells

In this study, we utilize ATR-FTIR spectroscopy to investigate the structural damages in the cell membrane lipids and proteins as a result of the oxidative stress in abscopal liver tissue of rats either whole-body, cranially or lower limb irradiated as compared with sham-irradiated group. We also question whether the original irradiation region would influence the induction of the abscopal effect. The data present compelling evidence that an abscopal effect was induced in the liver tissue following both cranial and lower limb irradiations, marked by damage in the membrane-associated lipids and proteins. Lipid damage manifestation is evident by; 1) decrease in the lipid/protein ratio. 2) Degradation of lipid as marked by the decrease in the area ratio CH ₂ asymmetric/CH ₃ asymmetric stretching bands. 3) Increase in the carbonyl content evident by the increase in the band area ratio of carbonyl ester/lipid. 4) Increase in the degree of methylation as indicated by the increase in the band area ratio of CH₃/lipid. 5) Disorder in the phospholipid acyl chains marked by the shift in the CH₂ asymmetric stretching and olefinic HCCH absorption bands. Protein damage was indicated by 1) Shifts in the position of amide I and amide II bands. 2) Decrease in the area ratio amide I/amide II. 3) Broadening in amide II band. Our data strongly suggest similar induction of the abscopal effect as a result of either cranial or lower limb irradiation, which means that the original irradiation region did not influence the induced abscopal effect in the examined system.

Analysis of the influence of EDTA-treated reference samples on forensic bloodstain age estimation

The age estimation of blood traces provides important leads for the chronological assessment of criminal events and their reconstruction. To determine bloodstain age, experimental comparative data from a laboratory environment are used. Under these conditions the utilization of anticoagulants such as EDTA helps to suppress the blood clotting mechanism to allow the examination over a longer time period. This unnatural prevention of blood coagulation is highly questionable when estimating bloodstain age, since the blood's physical and chemical properties are altered. For this reason, the authors determined actual influence of EDTA on blood spectra over time in order to formulate a statement as to whether this effect can be measured. Human and porcine blood samples were aged under controlled conditions. The resulting UV/VIS spectra were separated into their individual components using signal separation techniques, allowing the changes in the ratios of the individual hemoglobin derivatives to be observed over time. The results show a significant influence of EDTA on the conversion of oxyhemoglobin to methemoglobin and a minor influence on the conversion of methemoglobin to hemichrome within the relevant time range of 5-100 h. The use of EDTA thus slows down the aging process of blood spots. To illustrate the great influence of EDTA, spectra of untreated pig blood samples were included as comparison data. These show that the difference between EDTA-treated and untreated blood samples is as great as the difference between human blood and pig blood. As a consequence of our findings experimental comparative data for the age estimation of bloodstains should never result from EDTA-treated blood.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

A dataset for evaluating blood detection in hyperspectral images

The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published under an open access license, consists of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We have explored the dataset with blood detection experiments, for which we have used a hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.

On the discrimination of soil samples by derivative diffuse reflectance UV-vis-NIR spectroscopy and chemometric methods

The derivative diffuse reflectance UV-vis-NIR spectroscopy combined with the multivariate methods are utilized for the discrimination and classification of the soil samples collected from the north-western part of India. The acquired spectra reveal the presence of different organic and inorganic minerals such as humic acid, fulvic acid, hematite, etc. in varying amounts. The differentiation/segregation among soil samples is achieved by peak comparison and chemometric methods like clustering algorithm and principal component analysis (PCA). Among these, the PCA method gives clear discrimination of soil samples. The developed PCA model is further validated by analyzing unknown samples for the prediction to their respective clusters significantly. Principal component linear discriminant analysis (PC-LDA) based discriminant model is developed to classify the unknown soil samples to its respective groups. PC-LDA based model reveals 95 % accurate clustering of the soil by the leave-one-out cross-validation approach.

A low-cost LIBS detection system combined with chemometrics for rapid identification of plastic waste

We present, rapid and efficient identification of ten different types of post-consumer plastics obtained from a local recycling unit by deploying a low cost, compact CCD spectrometer in laser-induced breakdown spectroscopy (LIBS) technique. For this investigation, spectral emissions were collected by an Echelle spectrograph equipped with an intensified charge-coupled device (ES-ICCD) as well as a non-gated Czerny Turner CCD spectrometer (NCT-CCD). The performance is evaluated by interrogating the samples in a single-shot as well as accumulation mode (ten consecutive laser shots). The results from principal component analysis (PCA) have shown excellent discrimination. Further, the artificial neural network (ANN) analysis has demonstrated that individual identification accuracies/rates up to ~99 % can be achieved. The data acquired with ES-ICCD in the accumulation of ten shots have shown average identification accuracies ~97 %. Nevertheless, similar performance is achieved with the NCT-CCD spectrometer even in a single shot acquisition which reduces the overall analysis time by a factor of ~15 times compared to the ES-ICCD. Furthermore, the detector/collection system size, weight, and cost also can be reduced by ~10 times by employing a NCT-CCD spectrometer. The results have the potential in realizing a compact and low-cost LIBS system for the rapid identification of plastics with higher accuracies for the real-time application.

Feature Selection and Rapid Characterization of Bloodstains on Different Substrates

Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R² (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.

Chemical fingerprinting and quantitative monitoring of the doping drugs bambuterol and terbutaline in human urine samples using ATR-FTIR coupled with a PLSR chemometric tool

The use of performance-enhancing drugs is prohibited in sports competitions according to the World Anti-Doping Agency (WADA) regulations. Here, ATR-FTIR spectroscopy coupled with a partial least squares regression (PLSR) chemometric tool was used for the detection of the misuse of such substances. Bambuterol and its metabolite terbutaline have been included in the list of prohibited doping agents. Therefore, we used bambuterol and terbutaline as models for the accurate and simultaneous qualitative and quantitative analysis of bambuterol and terbutaline in human urine samples. The method was straightforward and once the urine samples were collected, they could be directly applied to the surface of the ZnSe prism (ATR unit) to get the results within one minute. A calibration set with a partial factorial design was used to develop the PLSR model that could be used to predict the concentration of unknown samples containing the two drugs. The developed method was carefully validated and successfully applied to the urine sample analysis of human volunteers. The drugs were quantified at nanogram level concentrations. A side-by-side comparison of the proposed method with the routine GC-MS method was performed to demonstrate the challenges and opportunities of each method.

ATR-FTIR spectroscopy coupled with chemometric tools could be a suitable alternative to the traditional techniques for quantification of the performance enhancing drugs such as bambuterol and terbutaline in urine samples in and out of competition.

Exploration of a Method of Distinguishing Different Nongxiang Tieguanyin Tea Grades Based on Aroma Determined by GC-MS Combined with Chemometrics

An aroma-based method for distinguishing different grades of Nongxiang Tieguanyin was explored by taking special grade (K110) and 1-4 grades (K101, K102, K103, and K104) of this tea as samples. Tea samples were analyzed by gas chromatography-mass spectrometry (GC-MS) combined with chemometrics. Results showed differences in the types and relative contents of aroma components among different grades of Nongxiang Tieguanyin tea. In the principal component analysis (PCA) scoring plot, except for K102 and K103, tea samples of different grades were distributed in different regions. Components satisfying variable important for the projection (VIP) > 1 and peak areas with significant differences (p < 0.05) among different tea grades were screened. Finally, 18 differential variables were screened out from 143 volatiles. The clustering results of these variables were consistent with those of PCA. K102 and K103 were initially clustered into one group and then clustered with K101, K110, and K104 in turn. The clear PCA separation of these samples and uniform hierarchical cluster analysis (HCA) clustering results suggests that GC-MS coupled with chemometrics analysis is a valid and accurate approach for discriminating different grades of Nongxiang Tieguanyin. The screened differential variables could represent a difference in aroma quality among five grades of Nongxiang Tieguanyin tea. Clear rules between peak area and the grade were also observed in some differential variables. 1-Ethylpyrrole and unknown-32 were positively correlated with grade. 2-Methylfuran, 2-ethylfuran, 2-methylidenecyclopentan-1-ol, mesityl oxide, 2-amylfuran, and D-limonene were negatively correlated with grade. The peak areas of methyl acetate, dimethyl sulfide, 6-methylhept-5-en-2-one, and (Z)-β-ocimene initially decreased but then increased with declining grade. The toluene content was especially high in K104 but only a negligible difference was observed among other grades. This study provides a potential method for differentiating Nongxiang Tieguanyin teas of different grades based on aroma. Unknown samples could be classified by comparison of their spatial distribution with those of known standard samples in PCA or HCA, as well as the peak area differences of differential variables between unknown samples and known standard samples.