Handheld Raman Spectroscopy Research Articles

Non-Invasive Detection of Nitrogen Deficiency in Cannabis sativa Using Hand-Held Raman Spectroscopy

Proper crop management requires rapid detection methods for abiotic and biotic stresses to ensure plant health and yield. Hemp (Cannabis sativa L.) is an emerging economically and environmentally sustainable crop capable of yielding high biomass. Nitrogen deficiency significantly reduces hemp plant growth, affecting photosynthetic capacity and ultimately decreasing yield. When symptoms of nitrogen deficiency are visible to humans, there is often already lost yield. A real-time, non-destructive detection method, such as Raman spectroscopy, is therefore critical to identify nitrogen deficiency in living hemp plant tissue for fast, precise crop remediation. A two-part experiment was conducted to investigate portable Raman spectroscopy as a viable hemp nitrogen deficiency detection method and to compare the technique’s predictive ability against a handheld SPAD (chlorophyll index) meter. Raman spectra and SPAD readings were used to train separate nitrogen deficiency discrimination models. Raman scans displayed characteristic spectral markers indicative of nitrogen deficiency corresponding to vibrational modes of carotenoids, essential pigments for photosynthesis. The Raman-based model consistently predicted nitrogen deficiency in hemp prior to the onset of visible stress symptoms across both experiments, while SPAD only differentiated nitrogen deficiency in the second experiment when the stress was more pronounced. Our findings add to the repertoire of plant stresses that hand-held Raman spectroscopy can detect by demonstrating the ability to provide assessments of nitrogen deficiency. This method can be implemented at the point of cultivation, allowing for timely interventions and efficient resource use.

Flow Injection Analysis With UV Detection Versus Raman Spectroscopy for the Quantitative Analysis of Remdesivir

ABSTRACTThe quality and the security of preparations in hospital centres are essential to guarantee the patient's safety. The development of fast and efficient analytical methods is required to control the finished product before use. In this context, the study aimed to compare the performance and interchangeability of two spectral analytical methods: the flow injection analysis (FIA) with UV detection and the Raman spectroscopy for the quality control of preparation before use to quantify the remdesivir as a SARS‐CoV‐2 drug. A quantitative study of remdesivir was performed using clinically relevant concentration solutions ranging from 0.25 to 1.625 mg.mL−1 in 0.9% NaCl. Samples were analysed by FIA‐UV at 245 nm and by a handheld Raman spectroscopy at 785 nm. Quantitative models were developed using a calibration set (n = 45 samples) and optimized using a validation set (n = 27). An external validation test set (n = 58) was used to compare the two methods by a Bland–Altman plot. Partial least square regression was used to analyse Raman spectra, while univariate analysis was performed at 245 nm for FIA‐UV. The regression coefficient was higher than 0.990 for both methods, and the root mean square error of prediction was 0.031 mg.mL−1 for Raman spectroscopy. The Bland–Altman plot confirmed the interchangeability of the two methods and the potential of Raman spectroscopy to control remdesivir during clinical preparation in the hospital.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Monitoring plastic pellet pollution in coastal environments through handheld Raman spectroscopy: Data from the Mediterranean coasts (Southern Italy)

This paper examines the distribution and chemical properties of beached plastic pellets along the Ionian and Tyrrhenian coasts of Southern Italy. Three locations have been sampled: Agnone Bagni (SR) and Paradiso (ME) on the Ionian coast of Sicily, Baia del Tono in Milazzo (ME) on the Sicilian Tyrrhenian coast, and Pizzo Calabro (VV) in Calabria on the Tyrrhenian coast. Variations in shape, size, compactness, color, and other physical features, correlated with residence times and transport, has been highlighted. Raman spectroscopy, used in a portable configuration, enabled rapid identification of polymer types, demonstrating its utility for on-site plastic pollutant monitoring. Polyethylene and polypropylene were the predominant polymers. Principal component analysis of the spectra determined the optimal chemometric classification of pellets by composition, avoiding interference or distortion. In conclusion, the study provided preliminary insights into pellet abundance, composition, weathering extent, and distribution across these shorelines, underscoring the importance of regular beach monitoring.

A combined approach to the vibrational characterization of medieval paints on parchment: Handheld Raman spectroscopy and micro‐SORS

AbstractA careful analytical study of the paint materials used in medieval illuminated manuscripts and their respective formulations is critical to their preservation. Due to their high value and fragile nature, access to these objects is often limited. Therefore, the development of ad‐hoc methodologies allowing researchers to collect data in the least invasive way possible is an essential task in conservation science. This article shows the potential of a combined approach that complements handheld Raman spectroscopy with micro‐SORS for the characterization of medieval paints. This methodology was tested on a reference collection of mock‐up samples prepared as pure, mixture, and layered paints on parchment, based on historical information regarding paint formulations from Iberian scriptoria. Conventional Raman analysis, carried out by means of two handheld spectrometers, was found particularly effective for materials identification in pure paints and multi‐component formulations of increasing complexity. Complementing this data, micro‐SORS proved decisive in differentiating between mixture and layered paints in most mock‐ups examined, yielding detailed information about the stratigraphy of reference samples produced through the overlaying of different paints. This combined methodology may be helpful to researchers who would like to approach the vibrational characterization of paints applied onto fragile artifacts and supports in a totally non‐invasive manner.

Investigating the Illicit Manufacture of Pharmaceutical Drugs in Sri Lanka-A Case Study on the Underworld of Counterfeit Drugs

The pharmaceutical drug industry is engaged in the research, development, manufacture and marketing of drugs and medications, which plays a major role in the healthcare industry. The global demand for the pharmaceutical drugs is on the rise, and unfortunately there is a concurrent increase in the illicit production of these drugs worldwide. This surge can be attributed to factors such as high demand, potential for profit, weak regulatory oversight and desire to exploit gaps in the legal market. Despite pharmaceutical drugs are intended for the treatment of medical conditions and pain relief, there is growing trend of illicit drug abusers to misusing these substances. Reports indicate a significant number of unlicensed pharmacies in Sri Lanka, but fewer accounts of illicit manufacturing of pharmaceutical drugs. In April 2023, the Police Narcotic Bureau officers arrested a building in a densely populated area in Colombo, Sri Lanka, suspecting it to be a clandestine laboratory for the production of illicit drugs. Subsequently, the Government Analyst of Sri Lanka was tasked with investigating the suspected clandestine laboratory. A team of forensic analysts from Government Analyst’s Department (GAD) visited the site, scrutinized the location, examined the equipment and chemicals and collected samples for further analysis. Although the clandestine laboratory was abandoned at the time of visit, evidence of machinery used for filling capsules, various chemicals, chemical glassware and other equipment were observed. Preliminary tests for illicit drugs and handheld Raman Spectroscopy testing were conducted at the scene. Gas Chromatography – Mass Spectroscopy (GCMS) was performed on the collected samples in the GAD laboratory. The analysis revealed the samples collected from the clandestine laboratory contained the substances considered pharmaceutical drugs namely, Tramadol, Lidocaine, Niacinamide, Menthol and Chlorpheniramine.

Identification of Four Similar Cell Culture Media According to their Glucose, Glutamine, and Pyruvate Content by Handheld Raman Spectroscopy.

This study describes the first efforts to build a spectral library to identify four cell culture media in powder form with spectra obtained with a handheld Raman spectrometer. These complex mixtures contain over 30 components and are among the most widely used cell culture media. A total of 32 spectra were collected for the four Dulbecco's Modified Eagle Medium cell culture media and pure materials (glucose and L-glutamine) in powder form. The spectra were preprocessed using standard normal variate with second derivative, and the barcode method before performing principal component analysis (PCA). The PCA model differentiated the pure glucose and the cell culture media according to the glucose concentration along the first principal component. The second principal component differentiated the three cell culture media with high glucose content according to the pyruvate concentration. The correlation coefficient showed that powdered cell culture media with high glucose concentration have a higher correlation with pure glucose, when compared with the cell culture media with low glucose. The Raman spectra made it possible to differentiate the four DMEM in the cell culture media from the majority of the external samples used in the method evaluation. However, sample heterogeneity affected the predictions. Additional studies are needed to improve the method's ability to differentiate the DMEM with high glucose.

Deep metric learning framework combined with Gramian angular difference field image generation for Raman spectra classification based on a handheld Raman spectrometer

Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification.

Handheld Raman Spectroscopy in the First UK Home Office Licensed Pharmacist-Led Community Drug Checking Service

Across the world, the interest in point-of-care drug checking as a harm-reduction intervention is growing. This is an attempt to improve intelligence about current drug trends and reduce drug-related morbidity and mortality. In the UK, drug-related harm is increasing exponentially year after year. As such, specialist community treatment services are exploring new methods to improve engagement with people who use drugs (PWUD), who may require support for their problematic drug use. This need has driven the requirement to pilot an on-site, time-responsive, readily available drug-checking service at point-of-support centres. In this study, we piloted the UK's first Home Office-licensed drug-checking service that was embedded into a community substance-misuse service and had all on-site analysis and harm-reduction interventions led and delivered by pharmacists. We report on the laboratory findings from the associated confirmatory analysis (UHPLC-MS, GC-MS, and 1H NMR) to assess the performance of the on-site hand-held Raman spectrometer and outline the challenges of providing real-time analysis of psychoactive substances in a clinical setting. Whilst acknowledging the limitation of the small sample size (n = 13), we demonstrate the potential suitability of using this technology for the purposes of screening substances in community-treatment services. Portability of equipment and timeliness of results are important and only very small samples may be provided by people who use the service. The challenges of accurately identifying substances from complex mixtures were equally found with both point-of-care Raman spectroscopy and laboratory confirmatory-analysis techniques. Further studies are required to confirm these findings.

Study of the Materials and Techniques of a Rare Papier-Mâché Mushroom Model Crafted in H. Arnoldi Factory.

The Natural History and Science Museum of the University of Porto houses a collection of 45 models of fungi in papier-mâché from the 19th-century, which were used at the university until 2015 as didactic models. For the first time, the materials and techniques used in the production of a Boletus edulis model were studied (vernacular name: cep, porcini). These sculptures, made to life-size scale, are painted in colors similar to those of the represented species (white, brown, and light brown). They are fixed to a rectangular base, which is painted black, and to which moss has been pasted. To fully characterize each color, at the molecular level, a multi-analytical approach was used, combining energy-dispersive x-ray fluorescence spectroscopy (micro-XRF) with fingerprinting techniques of Raman microscopy (microRaman and handheld Raman) spectroscopy and microFourier transform infrared spectroscopy (microFTIR). The papier-mâché was prepared with a groundwood paper to which kaolin and a quartz-based material have been added to reinforce the structure. Raman microscopy also identified carbon black in it, which is possibly responsible for its grey color. The white color was unequivocally identified as lithopone by microRaman. This white paint was prepared in a proteinaceous tempera, with calcium carbonate having been identified as filler (by microFTIR). In the brown color, iron was identified by microXRF, pointing to the use of ocher, which was not possible to identify by microRaman and microFTIR. Regarding the black rectangular base, the moss was fixed using a collagen-based glue. The binding medium in this black is possibly a mixture of drying oil and protein. Again, XRF detected iron as the main element, but it was not possible to acquire a Raman spectrum due to the high fluorescence of the binder/varnish. Others, such as the writing inks, will also be discussed. The colors identified are in line with the best materials available for use by artists of that time. This new knowledge is fundamental to informing the choice of the best conservation strategies for the preservation of these extraordinary models.

Quantifying PG : VG ratio and nicotine content in commercially available e-liquids using handheld Raman spectroscopy.

Electronic cigarettes are a popular nicotine consumption product that have risen in popularity as an alternative to cigarettes. However, their recent meteoric rise in market size and various controversies have resulted in the analyses of e-liquid ingredients to be focused on powerful laboratory-based slow methods such as chromatography and mass spectrometry. Here we present a complementary technology based on Raman spectroscopy combined with chemometrics as a fast, inexpensive, and highly portable screening tool to detect and quantify the propylene glycol : glycerol (PG : VG) ratio and nicotine content of e-cigarette liquids. Through this, the PG : VG ratio of 20 out of 23 commercial samples was quantified to within 3% of their stated value, while nicotine was successfully quantified to within 1 mg g-1 for 16 out of 23 samples without the need for accurate knowledge of flavonoid composition. High linearity was also achieved when flavours were kept constant. Finally, the limitations of Raman spectroscopy are discussed, and potential solutions are suggested.

Characterization of Street Drugs Using Handheld Fourier Transform Raman Spectroscopy

Handheld Raman spectroscopy offers a rapid and mobile technique for determining drugs nondestructively. The technique offers further advantage for analyzing drug products (DPs) thanks to its ability to measure samples through packaging, thus preserving evidence integrity and continuity. This research evaluated the use of handheld Fourier transform (FT)-Raman spectroscopy for characterization of street DPs (n = 254) of diverse formulations. Raman spectra of DPs inside their packaging were collected and matched against the instrumental in-built library, and then exported to Matlab 2020a for offline spectral interpretation. Reference analysis was confirmed using gas chromatography–mass spectrometry (GC–MS) and infrared (IR) spectroscopy. The main drugs present were 1,3-trifluoro- methylphenylpiperazine, cocaine, ketamine, 3,4-methylenedioxymethamfetamine, mephedrone, and 1-benzylpiprerazine. The main impurities present in DPs were benzocaine, caffeine, and lactose. The impurities affected the Raman signature (enhanced or inhibited) of DPs, enabling profiling of DPs and visualizing patterns among different products. These latter patterns were featured in the similarities highlighted by the correlation coefficient values between different DPs, and by adjacent clusters of different DPs envisaged in the principal component analysis scores plot.

Rapid Detection of Synthetic Cannabinoid Receptor Agonists Impregnated into Paper by Raman Spectroscopy

Abstract English The last decade has witnessed the emergence of new psychoactive substances that are analogues of classical drugs of abuse in order to escape the regulations surrounding the latter drugs. These drugs were of both herbal and synthetic origin and were advertised initially as ‘legal highs’; thus, they were perceived as safe by users. Hence, upon their emergence, they were not controlled by the Misuse of Drugs Act 1971, which contributed to their popularity and increased sales online and within street markets. In 2016, the Psychoactive Substance Act introduced a blanket ban on all new psychoactive substances except for caffeine, alcohol, and nicotine. This in turn, contributed to the change in the sale of new psychoactive substances products that have been sold as concealed in different matrices, including herbal products, papers, fabrics, and textiles. Concealing drugs in paper has been very popular, especially since the drug product is of lightweight and can be sent via postal services. However, new psychoactive concealed in papers are toxic not only to the users; but also, to the person handling them (i.e. mail employees). One of the classes of new psychoactive substances that have been commonly concealed in papers and that have been linked to toxicity and hospitalization cases is synthetic cannabinoids. Therefore, there is a need to identify new psychoactive substances concealed in papers non-destructively and rapidly to prevent toxicity linked to them. Handheld Raman spectroscopy offers this advantage as it is of lightweight and carries the sample to the matrix. Therefore, this work used handheld Raman spectroscopy for identifying synthetic cannabinoids concealed in papers using Raman spectroscopy combined with machine learning analytics. Synthetic cannabinoid and paper samples were measured non-destructively using a handheld Raman spectrometer equipped with a 1064 nm laser wavelength. Spectral data was exported into Matlab 2020b where machine learning analytics including identification and prediction was. The results showed that Raman spectroscopy could identify specific synthetic cannabinoids in papers that were either deposited on the surface of the paper or diffused inside the paper substrate. When machine learning analytics were applied to the Raman spectra of the papers, quantitative information was obtained regarding the amount of synthetic cannabinoid deposited on the paper surface. In summary, handheld Raman spectroscopy could identify and quantify synthetic cannabinoids on paper rapidly and non-destructively. Future work involves testing other classes of new psychoactive substance and applying deep learning analytics

Using Handheld Raman Spectroscopy Equipped with Orbital Raster Technology for Field Detection of Cocaine and its Impurities in Fingernails

Fingernails can accumulate drugs as a result of chronic exposure. This work employed Raman spectroscopy for detecting cocaine hydrochloride (HCl) and its impurities within fingernails, utilizing orbital raster scanning (ORS) technology, where the laser beam hits multiple positions within the sample. Doing so maintained sensitivity and ensured that more of each sample’s components were represented. Fingernails were spiked with powder and solution forms of cocaine HCl and its impurities, including benzocaine HCl, levamisole HCl, lidocaine HCl, and procaine HCl. The strong Raman scattering observed for these substances indicated a high drug accumulation in the fingernails. Key cocaine HCl bands were seen at 848, 874, and 898 cm-1 (C-C stretching-tropane ring), 1004 cm-1 (symmetric stretching-aromatic ring), 1278 cm-1 (C-N stretching), 1453 cm-1 (asymmetric CH3 deformation), and 1605 and 1712 cm-1 (C=C and C=O stretching). Principal components analysis (PCA) confirmed that 90% (nails spiked with drug powders) and 77.2% (nails spiked with drug solutions) were accounted for in the variance among the data. The findings showed that Raman spectroscopy identified the presence of cocaine HCl and its impurities within fingernails.

Rapid Identification of Foodborne Pathogens in Limited Resources Settings Using a Handheld Raman Spectroscopy Device

Rapid and precise methods to detect pathogens are paramount in ensuring food safety and selecting appropriate disinfection treatments. Raman spectrometry is a promising technology being investigated for detecting pathogens and achieving rapid, culture-free, and label-free methods. Nonetheless, previous Raman techniques require additional steps, including the preparation of slides that could introduce significant variability. In this study, we investigated the capability of a Raman handheld device for rapid identification of monocultures of Listeria monocytogenes, Salmonella Typhimurium, Escherichia coli O157:H7, and Staphylococcus aureus, and the combination of co-cultures in BHI broth suspension by utilising principal component analysis (PCA) and support vector machine (SVM) classification of Raman spectra. The detection method accurately identified monocultures (0.93 ± 0.20), achieving good discrimination after 24 h of bacterial growth. However, the PCA–SVM system was less accurate for classifying co-cultures (0.67 ± 0.35). These results show that this method requires an isolation step followed by biomass enrichment (>8 log10 CFU/mL) for accurate identification. The advantage of this technology is its simplicity and low-cost preparation, achieving high accuracy in monocultures in a shorter time than conventional culture-dependent methods.

Flipped detection of psychoactive substances in complex mixtures using handheld Raman spectroscopy coupled to chemometrics

AbstractNew psychoactive substance (NPS) misuse represents a critical social and health problem. Herein, a novel flipped approach is presented for the detection of psychoactive substances in complex mixtures using portable Raman spectroscopy. This consists firstly of evaluating the spectral dissimilarities of an NPS product to its constituent adulterants followed by detection of the NPS by means of key spectral signatures. To demonstrate it, three structurally diverse NPS and four commonly used adulterants were selected. A Design‐of‐Experiments guided approach was employed to determine the composition of simulate street samples, ranging from binary to quinary mixtures of varying concentrations. Spectra were acquired for all mixtures using a portable Raman spectrometer and examined using projection analysis on model systems, developed via principal component analysis using reference materials. For all 21 mixtures investigated, the innovative ‘flipped’ methodology resulted in isolated and unequivocal detection of the NPS. Interestingly, the NPS signatures were consistent across all mixtures investigated and were 1712, 1000, and 777/1022 cm−1 for 5F‐PB‐22, phenibut, and N‐Me‐2‐AI containing samples, respectively. Thus indicating that the developed model systems could be applicable to structural analogs. NPS were detected to concentrations as low as 6.0% w/w. This flipped methodology was benchmarked to the instrument's output algorithms and outperformed these in terms of NPS detection, particularly for low concentration ternary and quinary mixtures. As a result, this study represents a critical change in the conceptualization of novel approaches for the detection of psychoactive substances and further denotes a blueprint for the development of detection methodologies of target analytes in complex mixtures.

Raman spectroscopy enables highly accurate differentiation between young male and female hemp plants.

Hand-held Raman spectroscopy can be used for highly accurate differentiation between young male and female hemp plants. This differentiation is based on significantly different concentration of lutein in these plants. Last year, a global market of only industrial hemp attained the value of USD 4.7 billion. It is by far the fastest growing market with projected growth of 22.5% between 2021 and 2026. Hemp (Cannabis sativa L.) is a dioecious species that has separate male and female plants. In hemp farming, female plants are strongly preferred because male plants do not produce sufficient amount of cannabinoids. Male plants are also eliminated to minimize a possibility of uncontrolled cross-fertilization of plants. Silver treatments can induce development of male flowers on genetically female plants in order to produce feminized seed. Resulting cannabinoid hemp production fields should contain 100% female plants. However, any unintended pollination from male plants can produce unwanted males in production fields. Therefore, there is a growing demand for a label-free, non-invasive, and confirmatory approach that can be used to differentiate between male and female plants before flowering. In this study, we examined the extent to which Raman spectroscopy, an emerging optical technique, can be used for the accurate differentiation between young male and female hemp plants. Our findings show that Raman spectroscopy enables differentiation between male and female plants with 90% and 94% accuracy on the level of young and mature plants, respectively. Such analysis is entirely non-invasive and non-destructive to plants and can be performed in seconds using a hand-held spectrometer. High-performance liquid chromatography (HPLC) analysis and collected Raman spectra demonstrate that this spectroscopic differentiation is based on significantly different concentrations of carotenoids in male vs female plants. These findings open up a new avenue for quality control of plants grown in both field and a greenhouse.

Use of Handheld Raman Spectroscopy for Intraoperative Differentiation of Normal Brain Tissue From Intracranial Neoplasms in Dogs.

The aim of this study was to assess feasibility and accuracy of a hand-held, intraoperative Raman spectroscopy device as a neuronavigation aid to accurately detect neoplastic tissue from adjacent normal gray and white matter. Although Raman spectra are complicated fingerprints of cell signature, the relative shift corresponding to lipid and protein content (2,845 and 2,930 cm−1, respectively), can provide a rapid assessment of whether tissue is normal white or gray matter vs. neoplasia for real-time guidance of tumor resection. Thirteen client-owned dogs were initially enrolled in the study. Two were excluded from final analysis due to incomplete data acquisition or lack of neoplastic disease. The diagnoses of the remaining 11 dogs included six meningiomas, two histiocytic sarcomas, and three gliomas. Intraoperatively, interrogated tissues included normal gray and/or white matter and tumor. A total of five Raman spectra readings were recorded from the interrogated tissues, and samples were submitted for confirmation of Raman spectra by histopathology. A resultant total of 24 samples, 13 from neoplastic tissue and 11 from normal gray or white matter, were used to calculate sensitivity and specificity of Raman spectra compared to histopathology. The handheld Raman spectroscopy device had sensitivity of 85.7% and specificity of 90% with a positive predictive value of 92.3% and negative predictive value of 81.6%. The Raman device was feasible to use intraoperatively with rapid interpretation of spectra. Raman spectroscopy may be useful for intraoperative guidance of tumor resection.

Rapid detection and quantification of the adulteration of orange juice with grapefruit juice using handheld Raman spectroscopy and multivariate analysis.

Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination (Q2) of 0.81 and a root mean square error of prediction (RMSEP) of 12.5%. Such results were improved when the PLS-R model was trained and validated on the three separate mixture combinations, where the Q2 varied from 0.85 to 0.89 and RMSEP varied from 9.9% to 11.6%. Finally, we adopted a two step approach in which a partial least squares for discriminant analysis (PLS-DA) was trained first to classify the three sample sources and then three different PLS-R models were subsequently trained on samples from the same source. This resulted in a Q2 of 0.83 and RMSEP of 12.0%. In conclusion, we have demonstrated that Raman spectroscopy can be used as a portable and rapid analytical tool for detecting adulteration of grapefruit juice added to orange juice.

Investigations into Fentanyl Precursors Method Classification by Handheld Fourier Transform Infrared and Raman Spectroscopy Combined with Multivariate Statistical Analysis

Investigations into Fentanyl Precursors Method Classification by Handheld Fourier Transform Infrared and Raman Spectroscopy Combined with Multivariate Statistical Analysis