Rapid Discrimination Research Articles

Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning.

Matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry (MALDI‐TOF MS) has become a staple in clinical microbiology laboratories. Protein‐profiling of bacteria using this technique has accelerated the identification of pathogens in diagnostic workflows. Recently, lipid profiling has emerged as a way to complement bacterial identification where protein‐based methods fail to provide accurate results. This study aimed to address the challenge of rapid discrimination between Escherichia coli and Shigella spp. using MALDI‐TOF MS in the negative ion mode for lipid profiling coupled with machine learning. Both E. coli and Shigella species are closely related; they share high sequence homology, reported for 16S rRNA gene sequence similarities between E. coli and Shigella spp. exceeding 99%, and a similar protein expression pattern but are epidemiologically distinct. A bacterial collection of 45 E. coli, 48 Shigella flexneri, and 62 Shigella sonnei clinical isolates were submitted to lipid profiling in negative ion mode using the MALDI Biotyper Sirius® system after treatment with mild‐acid hydrolysis (acetic acid 1% v/v for 15 min at 98°C). Spectra were then analyzed using our in‐house machine learning algorithm and top‐ranked features used for the discrimination of the bacterial species. Here, as a proof‐of‐concept, we showed that lipid profiling might have the potential to differentiate E. coli from Shigella species using the analysis of the top five ranked features obtained by MALDI‐TOF MS in the negative ion mode of the MALDI Biotyper Sirius® system. Based on this new approach, MALDI‐TOF MS analysis of lipids might help pave the way toward these goals.

Cover Feature: Rapid Discrimination of Bacterial Drug Resistivity by Array‐Based Cross‐Validation Using 2D MoS 2 (Chem. Eur. J. 47/2022)

Sniff out the drug-resistant bacteria: Rapid screening of microbes based on family, class and drug resistivity is most important to combat bacterial infections. We have developed a rapid and highly sensitive sensor platform based on MoS2 as a 2D nanomaterial and green fluorescence protein as signal transducer. More information can be found in the Research Article by M. De and co-workers (DOI: 10.1002/chem.202201386).

Rapid discrimination of hepatic echinococcosis patients’ serum using vibrational spectroscopy combined with support vector machines

Echinococcosis is a severe zoonotic parasitic disease, and it is continuing to be a significant public health issue. The course of the disease is usually slow, and patients often remain asymptomatic for years. There is no standardized and widely accepted treatment, so early and accurate diagnosis is essential. Herein, this study utilized vibrational spectroscopic techniques, namely Raman and Fourier Transform Infrared (FTIR) spectroscopy, to quickly and accurately distinguish hepatic echinococcosis (HE) patients’ serum from the healthy group. Serum samples were collected from HE patients as well as healthy control subjects, and then the Raman and FTIR spectra of the two groups were recorded. After a series of pre-processing, support vector machines (SVMs) were then used to establish the classification models for the two spectral data sets. The performance of each diagnostic model was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation methods, respectively. For the distinction between HE and healthy groups, these two spectroscopic techniques had achieved satisfactory classification results, and the diagnostic capabilities of the Raman technique were comparable to that of the FTIR method. The results demonstrate that vibrational spectroscopy has great potential in the rapid and accurate detection of HE and is expected to make up for the shortcomings of the existing clinical diagnosis methods.

Rapid Discrimination of Bacterial Drug Resistivity by Array-Based Cross-Validation Using 2D MoS2.

The precise discrimination of microbes based on family, class and drug resistivity is essential for the early diagnosis of infectious diseases. Information about the type and strength of drug resistivity can help the analyst to prescribe a suitable antibiotic at the proper dosage to completely eradicate microbes without giving them a chance to gain further resistance. Herein, we propose a sensor array based on the use of cationic two-dimensional MoS2 units and green fluorescence protein as building blocks. Cationic surfaces of receptors with various functionality were suitable for tunable interaction with anionic surfaces of microbes. The array successfully discriminates six different bacterial strains. The versatile ability of the receptors to bind with the wild-type as well as the corresponding ampicillin-resistant strain contributed significantly to rapid detection with high sensitivity. The optimized array was able to classify five different types and three different extents of drug-resistant variants of Escherichia coli by using bacteria cells and lysates. Finally, we have introduced the cross identification method using both bacteria cells and lysates and we found a great enhancement of detection in sensitivity and accuracy. This is the first report of this approach, which can be extended to many other methods for better accuracy in array-based detection.

Construction of classification models for pathogenic bacteria based on LIBS combined with different machine learning algorithms.

Bacteria, especially foodborne pathogens, seriously threaten human life and health. Rapid discrimination techniques for foodborne pathogens are still urgently needed. At present, laser-induced breakdown spectroscopy (LIBS), combined with machine learning algorithms, is seen as fast recognition technology for pathogenic bacteria. However, there is still a lack of research on evaluating the differences between different bacterial classification models. In this work, five species of foodborne pathogens were analyzed via LIBS; then, the preprocessing effect of five filtering methods was compared to improve accuracy. The preprocessed spectral data were further analyzed with a support vector machine (SVM), a backpropagation neural network (BP), and k-nearest neighbor (KNN). Upon comparing the capacity of the three algorithms to classify pathogenic bacteria, the most suitable one was selected. The signal-to-noise ratio and mean square error of the spectral data after applying a Savitzky-Golay filter reached 17.4540 and 0.0020, respectively. The SVM algorithm, BP algorithm, and KNN algorithm attained the highest classification accuracy for pathogenic bacteria, reaching 98%, 97%, and 96%, respectively. The results indicate that, with the support of a machine learning algorithm, LIBS technology demonstrates superior performance, and the combination of the two is expected to be a powerful tool for pathogen classification.

A rapid classification method of tea products utilizing X-ray photoelectron spectroscopy: Relationship derived from correlation analysis, modeling, and quantum chemical calculation

The classification of tea products is nowadays mainly determined by sensory assessment and chemical analysis methods. These methods are subjective, time-consuming, and laborious. In this work, a rapid analytical method for tea classification was proposed on the basis of X-ray photoelectron spectroscopy (XPS) and quantum chemical calculation. A total of 56 kinds of tea products were studied. By utilizing the data fusion strategy, the correlation between XPS peak parameters and tea characteristics was established. The quantum chemical calculations of the core-level ionization potentials deepen the understanding of the XPS features. The binding energy of the main fitted peak for O 1s, BE1O1s, was found to have a good correlation with tea polyphenols contents, which can be used to classify tea products into six tea types (black, dark, green, oolong, white, and yellow tea) with an accuracy greater than 90 %. The results suggest that the proposed XPS method is suitable for the rapid discrimination of tea classification, which contributes to the efficient application of tea.

Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients

Rapid and accurate discrimination of sepsis and its potential severity currently require multiple assays with slow processing times that are often inconclusive in discerning sepsis from sterile inflammation. To analyze a whole-blood, multivalent, host-messenger RNA expression metric for estimating the likelihood of bacterial infection and 30-day mortality and compare performance of the metric with that of other diagnostic and prognostic biomarkers and clinical parameters. This prospective diagnostic and prognostic study was performed in the surgical intensive care unit (ICU) of a single, academic health science center. The analysis included 200 critically ill adult patients admitted with suspected sepsis (cohort A) or those at high risk for developing sepsis (cohort B) between July 1, 2020, and July 30, 2021. Whole-blood sample measurements of a custom 29-messenger RNA transcriptomic metric classifier for likelihood of bacterial infection (IMX-BVN-3) or 30-day mortality (severity) (IMX-SEV-3) in a clinical-diagnostic laboratory setting using an analysis platform (510[k]-cleared nCounter FLEX; NanoString, Inc), compared with measurement of procalcitonin and interleukin 6 (IL-6) plasma levels, and maximum 24-hour sequential organ failure assessment (SOFA) scores. Estimated sepsis and 30-day mortality performance. Among the 200 patients included (124 men [62.0%] and 76 women [38.0%]; median age, 62.5 [IQR, 47.0-72.0] years), the IMX-BVN-3 bacterial infection classifier had an area under the receiver operating characteristics curve (AUROC) of 0.84 (95% CI, 0.77-0.90) for discriminating bacterial infection at ICU admission, similar to procalcitonin (0.85 [95% CI, 0.79-0.90]; P = .79) and significantly better than IL-6 (0.67 [95% CI, 0.58-0.75]; P < .001). For estimating 30-day mortality, the IMX-SEV-3 metric had an AUROC of 0.81 (95% CI, 0.66-0.95), which was significantly better than IL-6 levels (0.57 [95% CI, 0.37-0.77]; P = .006), marginally better than procalcitonin levels (0.65 [95% CI, 0.50-0.79]; P = .06), and similar to the SOFA score (0.76 [95% CI, 0.62-0.91]; P = .48). Combining IMX-BVN-3 and IMX-SEV-3 with procalcitonin or IL-6 levels or SOFA scores did not significantly improve performance. Among patients with sepsis, IMX-BVN-3 scores decreased over time, reflecting the resolution of sepsis. In 11 individuals at high risk (cohort B) who subsequently developed sepsis during their hospital course, IMX-BVN-3 bacterial infection scores did not decline over time and peaked on the day of documented infection. In this diagnostic and prognostic study, a novel, multivalent, transcriptomic metric accurately estimated the presence of bacterial infection and risk for 30-day mortality in patients admitted to a surgical ICU. The performance of this single transcriptomic metric was equivalent to or better than multiple alternative diagnostic and prognostic metrics when measured at admission and provided additional information when measured over time.

A Combination of MALDI-TOF MS Proteomics and Species-Unique Biomarkers' Discovery for Rapid Screening of Brucellosis.

Brucellosis is considered to be a zoonotic infection with a predominant incidence in most parts of Iran that may even simply involve diagnostic laboratory personnel. In the present study, we apply matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) for rapid and reliable discrimination of Brucella abortus and Brucella melitensis, based on proteomic mass patterns from chemically treated whole-cell analyses. Biomarkers of the low molecular weight proteome in the MALDI-TOF MS spectra were assigned to conserved ribosomal and structural protein families that were found in genome assemblies of B. abortus and B. melitensis in the NCBI database. Significant protein mass signals successfully mapped to ribosomal proteins and structural proteins, such as integration host factor subunit alpha, cold-shock proteins, HU family DNA-binding protein, ATP synthase subunit C, and GNAT family N-acetyltransferase, with specific biomarker peaks that have been identified for each virulent and vaccine strain. Web-accessible bioinformatics algorithms, with a robust data analysis workflow, followed by ribosomal and structural protein mapping, significantly enhanced the reliable assignment of key proteins and accurate identification of Brucella species. Furthermore, clinical samples were analyzed to confirm the most dominant protein biomarker candidates and their relevance for the identifications of B. melitensis and B. abortus. With proper optimization, we envision that the presented MALDI-TOF MS proteomics analyses, coupled with special usage of bioinformatics, could be used as a cost-efficient strategy for the diagnostics of brucellosis and introduce a reliable identification protocol for species of dangerous bacteria.

Discrimination of raw and sulfur-fumigated ginseng based on Fourier transform infrared spectroscopy coupled with chemometrics

Ginseng (Panax ginseng), as a tonic and functional food in many countries and regions for thousands of years, is often sulfur-fumigated (SF) for storage and protection. However, our previous study indicated sulfur-fumigation could transform ginsenosides, the active components of ginseng, into sulfur-containing derivatives and thus affect the quality and safety of ginseng. In this study, a rapid and efficient method in discrimination of non-fumigated (NF) and SF ginseng was developed using Fourier transform infrared (FT-IR) spectroscopy coupled with multivariate statistical analysis. A total of 240 batches of raw spectra were obtained from NF and SF ginseng by FT-IR spectroscopy. After excluding the outliers, the different performance of 3 spectral signal enhancing methods, 3 modeling evaluation methods, and 4 model evaluation indexes were compared. The results demonstrated the feasibility of using FT-IR spectroscopy between 3650 and 3200 cm−1 for the detection of sulfur-fumigation in ginseng. After sulfur fumigation, the peak areas in fingerprint and functional group area varied significantly. In addition, the parameters of back propagation artificial neural network (BP-ANN) evaluation model are the highest, its accuracy = 91.67%, precision = 89.29%, recall = 96.15%, and F1 = 92.59%. The error rates of 3 models were k-nearest neighbor algorithm (KNN) (25.00%) > logistic regression (LR) (16.67%) > BP-ANN (8.33%). It can be concluded that FT-IR spectroscopy combined with multivariate statistical analysis has great potential in rapid discrimination of NF and SF ginseng, which can provide a valuable reference for the quality and effectiveness of edible and medicinal application of ginseng.

Phytochemical analysis and antioxidant activity of eight cultivars of tea (Camellia sinensis) and rapid discrimination with FTIR spectroscopy and pattern recognition techniques

Background: Tea (Camellia sinensis L.O. Kuntze) is one of the most commonly consumed beverages globally, with several beneficial health effects. The composition of leaves is affected by different factors such as climate and tea cultivar. Methods: In this study, the total phenolic, flavonoid, and tannin contents, and antioxidant activities of eight cultivars of tea growing in Iran were determined. The epigallocatechin gallate (EGCG) contents were measured by high-performance liquid chromatography (HPLC) analysis. The Fourier-transform infrared (FT-IR) spectra were used to construct a supervised pattern recognition model using a genetic algorithm-support vector machine (GA-SVM) for the classification of cultivars. Results: The results indicated a wide variation of total phenolic content (178.04 to 316.09 mg gallic acid equivalents (GAE)/g extract). Also, the flavonoid contents ranged from 25.54 to 41.1 mg quercetin equivalents )QE(/g extract. All the samples had close tannin amounts (ranging from 40.32 to 45.90 mg as tannic acid equivalent (TAE)/g extract). There was a significant linear relationship among total phenolic and flavonoid contents and antioxidant properties. The cultivars DN, PK2, and C.Y.9 had the highest content of phenolic and flavonoid content as well as the best antioxidant activity. The EGCG contents were from 2.66 to 4.12%. The highest amount of EGCG was found in cultivars 282, PK2, and C.Y.9. The discriminative region of FT-IR spectra (1350-1650 cm-1) were selected using a GA-SVM. This model showed 100% sensitivity and specificity for training and test sets. Conclusion: The leaves phytochemical compositions and antioxidant effect are deeply dependent on the type of tea cultivars. The cultivars PK2 and C.Y.9 can be considered richer sources of polyphenols, especially EGCG. The eight different cultivars can be classified based on chemical components using the recorded FT-IR spectra.

Rapid Discrimination and Prediction of Ginsengs from Three Origins Based on UHPLC-Q-TOF-MS Combined with SVM.

Ginseng, which contains abundant ginsenosides, grows mainly in the Jilin, Liaoning, and Heilongjiang in China. It has been reported that the quality and traits of ginsengs from different origins were greatly different. To date, the accurate prediction of the origins of ginseng samples is still a challenge. Here, we integrated ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) with a support vector machine (SVM) for rapid discrimination and prediction of ginseng from the three main regions where it is cultivated in China. Firstly, we develop a stable and reliable UHPLC-Q-TOF-MS method to obtain robust information for 31 batches of ginseng samples after reasonable optimization. Subsequently, a rapid pre-processing method was established for the rapid screening and identification of 69 characteristic ginsenosides in 31 batches ginseng samples from three different origins. The SVM model successfully distinguished ginseng origin, and the accuracy of SVM model was improved from 83% to 100% by optimizing the normalization method. Six crucial quality markers for different origins of ginseng were screened using a permutation importance algorithm in the SVM model. In addition, in order to validate the method, eight batches of test samples were used to predict the regions of cultivation of ginseng using the SVM model based on the six selected quality markers. As a result, the proposed strategy was suitable for the discrimination and prediction of the origin of ginseng samples.

A Conventional VOC-PID Sensor for a Rapid Discrimination among Aromatic Plant Varieties: Classification Models Fitted to a Rosemary Case-Study

This study explores the use of a photoionization detector (PID) to distinguish varieties of rosemary plant, based on their volatile organic compound (VOC) emissions. The aim was to be able to distinguish plant varieties using a simple, quick, and inexpensive method. Two varieties were studied, Rosmarinus officinalis L. “Prostratus” and “Erectus”. First, the PID was used to detect VOCs emitted by leaves from each variety, and subsequently essential oil was extracted from the same leaves. Then, the well-established GC-MS method was used to characterize and differentiate the oil from each of the two varieties. The PID was able to capture different signals, and a ‘fingerprint’ for each of the two varieties was obtained. To validate the PID performance, the data set obtained was analyzed by means of advanced statistical models (principal component analysis, cluster and support vector machine and artificial neural network) which were able to discriminate the two varieties with high accuracy (over 80%). Therefore, the results confirm that the PID was able to detect differences in VOC emissions. In conclusion, PID proved be an interesting instrument for the classification of rosemary plants, and in this sense could be applied to other aromatic plants.

Sensor Array Chip for Real‐Time Field Detection and Discrimination of Organophosphorus Neurotoxins

AbstractRapid and reliable detection of organophosphorus (OP) threats is essential for facilitating efficient and timely countermeasures. This work describes the design and operation of a miniaturized electrochemical sensor array chip, based on strategic coupling of diverse MOF‐based biomimetic and biocatalytic reagent layers (offering tunable OP target specificities) with different potentiometric, voltametric, and amperometric transducers to generate information‐rich analytical data and robust discrimination power towards rapid field identification of key classes of OP threats. This uniquely integrated 4‐electrode sensor array yields rich analytical data that enables rapid and efficient discrimination of P−F‐ and P−Cl‐type OP nerve agents (NAs) and OP pesticides from coexisting environmental pollutants. The sensor array outputs lead to distinct binary codes for the target OP threats that enable their rapid digital identification. This sensor array chip thus presents a significant advance towards reliable OP threat‐sensing hand‐held devices that meet the urgent demands of different defense surveillance applications.

In Vivo Bioelectronic Nose Based on a Bioengineered Rat Realizes the Detection and Classification of Multiodorants.

Inspired by the powerful capability of the biological olfactory system, we developed an in vivo bioelectronic nose based on a bioengineered rat by recording electrophysiological-responsive signals from the olfactory bulb with implanted multichannel microelectrodes. The bioengineered rat was prepared by overexpressing a selected olfactory receptor (OR3) on the rat olfactory epithelium, and multichannel electrophysiological signals were obtained from the mitral/tufted (M/T) cell population of the olfactory bulb. The classification of target multiodorants was realized by analyzing the redundant stimuli-responsive firing information. Ligand odorants induced significant firing changes with specific response patterns compared with nonligand odorants. The responsive curves were dependent on the concentration of target ligand odorants ranging from 10-6 to 10-3 M, and the detection limit was as low as 10-5 M. In addition, different ligand odorants were successfully discriminated via principal component analysis. This in vivo bioelectronic nose provides a novel approach for the detection of specific target odorants and has promising application potential in the field of rapid on-site odor discrimination.

Analysis of Transdermal Patch Layers via Confocal Raman Microscopy

A transdermal patch is a method of drug delivery through the skin into the bloodstream that is composed of layers that store and release the active pharmaceutical ingredient (API) over time. The ability to analyze these layers without damaging the patch allows for quality control and stability measurements. Raman spectroscopy has excellent chemical identification abilities, thanks to the fingerprint like spectrum it produces, that deliver rapid discrimination between components typically found in transdermal patches, such as polymers and APIs. Coupling this with a confocal microscope gives high spatial resolution on all three axes, with the pinhole providing otherwise unachievable axial resolution, permitting depth profile analysis. Using this technique, the patch can be analyzed from top to bottom without any risk of damage. This article highlights the use of confocal Raman microscopy to acquire a 3D Raman map of a transdermal patch for layer evaluation. The map revealed six layers which were composed of four polymers and the API layer at the center of the patch.

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage.In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances.Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

Discrimination of ginseng origin by using laser‐induced breakdown spectrum and machine learning algorithms

AbstractIn this paper, the ginsengs from five ginseng origins are discriminated by using laser‐ induced breakdown spectrum (LIBS) combined with random forest‐ support vector machine (RF‐ SVM) and random forest‐ multilayer perception (RF‐ MLP) machine learning algorithms. The raw LIBS of ginseng is pretreated by using the wavelet threshold method, denoise the background information and normalazation to improve the signal‐ to‐ background ratio and the experimental reliability. The RF algorithm is used to select 10 characteristic spectral lines as the input vectors of the MLP and the SVM models to identify the ginseng orgin. The experimental results show that the discrimination accuracy rates of RF‐ MLP and RF‐ SVM models are 99.75% and 99.5%, respectively. The disrimination accuracy of ginseng origin used in the RF‐MLP machine algorithm model is slightly higher than that of the RF‐ SVM model, and then calculated the speed of the RF‐ MLP model is faster than the RF‐ SVM model. The results show that LIBS combined with machine algorithms are both promising rapid discrimination methods for ginseng origin.

Species-level discrimination of microorganisms by high-resolution paper spray – Ion mobility – Mass spectrometry

There is a projection of 10 million deaths per year by 2050 due to antimicrobial resistance, highlighting the need for rapid detection and accurate identification of microorganisms. Herein, we used the paper spray technique coupled with ion mobility separation and tandem mass spectrometry (PS-IM-MS/MS) to rapidly discriminate and identify five Bacillus species in 2 min of analysis time after only 4 h of incubation time. Bacterial cells were harvested by filtering their liquid cultures and ionized directly by PS. Numerical multivariate statistics (principal component analysis, followed by linear discriminant analysis) allowed species-level discrimination with a prediction rate of 92.4% and 97.6% utilizing the negative and positive ion information of PS-MS/MS, respectively. However, upon including the biomarkers' corresponding drift times, i.e., PS-IM-MS/MS, prediction rates of ca. 99.7% and 100% were obtained utilizing the negative and positive ion information, respectively. We attributed the enhancement in the prediction rates to the capability of IM separations in resolving isomers. Surfactins and phospholipids were the major ion populations detected in our experiments. They were found to have various diagnostic isomers for various Bacillus species. Therefore, improved prediction rates could be obtained upon including high-resolution IM separations in the workflow. These proof-of-concept studies described here suggest that PS-IM-MS/MS workflow would enable rapid discrimination of closely related bacteria species and strains.

Rapid Discrimination of Aroma Components in Natural and Artificial Agarwood Incense by Static Headspace Sampling – Gas Chromatography – Mass Spectrometry (SHS-GC-MS) with Principal Component Analysis (PCA) and Partial Least Squares – Discrimination Analysis (PLS-DA)

Agarwood is a resinous, fragrant wood produced by tropical tree species of the genus Aquilaria, and is highly valued for its wide range of uses in medicine, incense, and perfume. In this study, the quality of agarwood and patterns of chemical constituents induced in natural and artificial agarwood by physical injury, chemical inducer, and chemical plus fungal infection were compared using static headspace sampling gas chromatography – mass spectrometry (SHS-GC-MS). The optimal agarwood heating temperature was determined to be 180 °C. The fingerprints of 40 batches of agarwood were established. Multivariate statistical analysis was utilized to identify 15 common and ten differentiating components, respectively. Agarwood induced by the physical method was more similar to natural agarwood. Exploration of the pyrolysis products of chromone derivatives and sesquiterpenes was undertaken to discover the causes of differences in chemical compositions between artificial and natural agarwood. Our findings revealed that benzylacetone, baimuxinal, and guaiol were significant for agarwood incense and these compounds may be used as markers for classification.

Absorbance-Transmittance Excitation Emission Matrix Method for Quantification of Major Cannabinoids and Corresponding Acids: A Rapid Alternative to Chromatography for Rapid Chemotype Discrimination of Cannabis sativa Varieties.

Background: Phytocannabinoids naturally occur in the cannabis plant (Cannabis sativa), and Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) predominate. There is a need for rapid inexpensive methods to quantify total THC (for statutory definition) and THC-CBD ratio (for classification into three chemotypes). This study explores the capabilities of a spectroscopic technique that combines ultraviolet-visible and fluorescence, absorbance-transmittance excitation emission matrix (A-TEEM). Methods: The A-TEEM technique classifies 49 dry flower extracts into three C. sativa chemotypes, and quantifies the total THC-CBD ratio, using validated gas chromatography (GC)-flame ionization (FID) and High-Performance Liquid Chromatography (HPLC) methods for reference. Multivariate methods used are principal components analysis for a chemotype classification, extreme gradient boost (XGB) discriminant analysis (DA) to classify unknown samples by chemotype, and XGB regression to quantify total THC and CBD content using GC-FID and HPLC data on the same samples. Results: The A-TEEM technique provides robust classification of C. sativa samples, predicting chemotype classification, defined by THC-CBD content, of unknown samples with 100% accuracy. In addition, A-TEEM can quantify total THC and CBD levels relevant to statutory determination, with limit of quantifications (LOQs) of 0.061% (THC) and 0.059% (CBD), and high cross-validation (>0.99) and prediction (>0.99), using a GC-FID method for reference data; and LOQs of 0.026% (THC) and 0.080% (CBD) with high cross-validation (>0.98) and prediction (>0.98), using an HPLC method for reference data. A-TEEM is highly predictive in separately quantifying acid and neutral forms of THC and CBD with HPLC reference data. Conclusions: The A-TEEM technique provides a sensitive method for the qualitative and quantitative characterization of the major cannabinoids in solution, with LOQs comparable with GC-FID and HPLC, and high values of cross-validation and prediction. As a spectroscopic technique, it is rapid, with data acquisition <45 sec per measurement; sample preparation is simple, requiring only solvent extraction. A-TEEM has the sensitivity to resolve and quantify cannabinoids in solution based on their unique spectral characteristics. Discrimination of legal and illegal chemotypes can be rapidly verified using XGB DA, and quantitation of statutory levels of total THC and total CBD comparable with GC-FID and HPLC can be obtained using XBD regression.