Identification Of Substances Research Articles

Detection of optical properties of chiral substances by a photoconductive THz polarization detector

Chiral enantiomers have significant differences in biochemical functions. The use of THz wave polarization detection to characterize the optical properties of chiral substances is of great significance to the development of life science and the identification and application of chiral substances. However, the traditional polarization detection procedures of THz waves are complex, which limits the study of chiral substances. Herein, we proposed a high-sensitivity THz polarization detector, which can simultaneously obtain the change information of amplitude, phase, and polarization state through a single measurement. The optical rotation and elliptical angle of solid and liquid D/L-Glutamic acid 5-methyl ester in the THz band are studied. Then it is verified that anisotropic interference may occur in the preparation of solid samples. Finally, the effects of sample content and thickness on polarization are obtained. The experimental results show that different chirality has the opposite effect on the state of polarization, and the difference between chiral enantiomers can be detected by this method. This work is of great significance for understanding the optical properties of chiral substances and promoting the development of chiral recognition.

Raman Match: Application for Automated Identification of Minerals from Raman Spectroscopy Data

Abstract Raman spectroscopy is a rapid, nondestructive analysis technique that is used in various scientific disciplines, including chemistry, materials science, and biology. The analysis of Raman spectra and the identification of specific substances in unknown samples can be complex and time-consuming tasks due to the large database of Raman spectra. The Raman Match application was developed to simplify and automate the sample identification process through a search and match method. The application integrates the well-established RRUFF Raman database with the Python programming language and provides a user-friendly graphical interface to load Raman spectra, identify and fit peaks, match peaks to the reference libraries, visualize the results, and generate publication-ready figures. The application offers a swift and automated method for mineral identification using Raman spectroscopy in both laboratory and field settings, as well as during planetary exploration missions to extraterrestrial environments with constraints on time and resources.

Broad evidence of xylazine in the UK illicit drug market beyond heroin supplies: Triangulating from toxicology, drug-testing and law enforcement.

Xylazine is a non-opioid sedative which has spread rapidly throughout the US illicit drug supply. This study aimed to describe the spread of xylazine throughout the UK illicit drug supply. Xylazine detections in human biological samples were collated from toxicology laboratories operating in the United Kingdom with the date, location, case type, xylazine concentration and co-detected drugs (with quantifications where performed) detailed, where permitted, by the corresponding coroner. Drug-testing cases positive for xylazine were collated from the Welsh Emerging Drugs and Identification of Novel Substances (WEDINOS) drug-testing postal service with the date, location, purchase intent and co-detected drugs detailed. Drug seizures made by UK law enforcement were communicated by the Office for Health Improvement and Disparities with the date and location detailed. By the end of August 2023, xylazine was detected in 35 cases from throughout toxicology, drug-testing and drug seizure sources covering England, Scotland and Wales. There were no cases reported from Northern Ireland. Xylazine was detected in biological samples from 16 people. In most cases where full toxicology results were provided, xylazine was detected with heroin and/or a strong opioid (n = nine of 11), but this polydrug use pattern was not evident in all cases (n = two of 11), suggesting a wider circulation of xylazine in the UK illicit drug market beyond heroin supplies. Evidence from WEDINOS supports this claim, as all 14 drug samples (100%) submitted from across the UK contained xylazine; however, in none of these cases was heroin the purchase intent but rather counterfeit prescription medication tablets (n = 11 of 14), tetrahydrocannabinol (THC) vapes (n = two of 14) or white powder (n = one of 14). Additional evidence for the spread of illicit xylazine comes from five drug seizures made by law enforcement. Xylazine has penetrated the UK illicit drug market and is not limited to heroin supplies.

Classification of Fats and Oils Based on Raman Spectroscopy and Deep Learning

Raman spectroscopy is a popular technology for material identification, but it encounters difficulties when distinguishing components with closely related substances by intuition and experience. The distinction of fats and oils is a typical example, which is significant in the food industry. In this work, the Raman spectroscopy and deep learning algorithm are combined to analyze closely related animal fats (lard, butter, mutton fat and chicken fat) and vegetable oils (soybean oil and peanut oil) in a dataset. A deep neural network founded on the VGG architecture with attention mechanism is developed, reaching an accuracy of 100% for fats and oils classification. By combining Raman spectroscopy with deep learning, this research provides a potent technique for tackling the identification of similar substances.

Enhanced degradation of tetracycline hydrochloride by microwave-activated sulfites: Influencing factors and mechanisms

Sulfite (SO32−) has garnered widespread attention for its ability to produce free radicals and environmentally friendly properties. In this work, the mechanism and feasibility of the microwave (MW) activated sulfite system for the removal of tetracycline hydrochloride (TCH) were investigated. The removal efficiency, reaction kinetics, and influencing factors of the MW/SO32− were explored. The identification of active substances and the reaction mechanism of the MW/SO32− system were further analyzed. The results showed that MW could activate sulfite effectively and the reaction process followed the pseudo-first-order kinetic. MW/SO32− achieved a 97.8 % degradation of TCH within 15 min. The influencing factors experiments showed that increasing the initial pH promoted the degradation of TCH. According to the results of EPR and free radical quenching experiments, the degradation of TCH by the MW/SO32− system had both free radical (SO3·−, SO5·−, SO4·−, and O2·−) and non-free radical pathway (1O2) reactions. The degradation pathways of TCH mainly included hydroxylation, removal reaction, ring-opening reaction, and oxidation reaction. Evaluation with the T.E.S.T system found that the ecological risk of TCH was reduced after MW/SO32− treatment. The use of microwaves as sulfite activation method was shown to be a new alternative to promote the rapid degradation of emerging organic pollutants.

Screening of volatile compounds of essential oils with acaricidal activity in Rhipicephalus microplus larvae using a new in vitro evaluation method

ABSTRACT The study assessed the acaricidal properties of 20 compounds derived from essential oils against the Rhipicephalus microplus tick, a significant menace to tropical livestock with considerable economic losses. A novel methodology was devised, involving the determination of mortality times for R. microplus larvae upon direct contact with these compounds. Mortality times were compared against a negative control (soybean oil) and two positive controls (commercial pour-on products). Tests were conducted in 48-well polyethylene plates, with around 100 larvae immersed in 200 µl of oil (at a 100% dose). The 10 compounds exhibiting the shortest mortality times were subsequently diluted in soybean oil at varying concentrations. Notably, thymol exhibited potent acaricidal activity, with the lowest effective dose (1.56%) resulting in larval mortality within 15 minutes, whereas positive controls necessitated over 66 minutes. A correlation was established between the molecular structure of the compounds and their acaricidal efficacy, indicating that cyclic compounds containing 10 carbon atoms demonstrated heightened lethality compared to acyclic counterparts. This innovative methodology enables the rapid identification of substances with substantial acaricidal potential in vitro, yielding results within minutes, in stark contrast to the traditional larval immersion test, which demands a minimum of 24 hours for conclusive outcomes.

Identification of Cellular Targets of Active Substances Using Microscopic Methods and Fluorescent Probes

Target identification of active substances is critical in optimizing drugs and minimizing side effects. The complex nature of biological systems presents challenges; to meet them, however, microscopic methods, particularly phenotypic screening, represented by "Cell Painting" method and fluorescent probes, can be used as valuable tools for understanding the impact of various substances at the cellular and organelle levels. These methods enable rapid testing of large libraries of compounds and offer unique insights into their mechanism of action by observing cell behavior, assessing cell morphology, motility, division, and migration. However, live cell microscopy faces challenges like phototoxicity, requiring a careful selection of fluorescent labels and optimized conditions. Among synthetic probes for live cell microscopy, BODIPY dyes stand out for their synthetic versatility and photophysical properties, providing minimal sample damage during bioimaging.

Metabolite markers for three synthetic tryptamines N-ethyl-N-propyltryptamine, 4-hydroxy-N-ethyl-N-propyltryptamine, and 5-methoxy-N-ethyl-N-propyltryptamine.

N-Ethyl-N-propyltryptamine (EPT), 4-hydroxy-N-ethyl-N-propyltryptamine (4-OH-EPT), and 5-methoxy-N-ethyl-N-propyltryptamine (5-MeO-EPT) are new psychoactive substances classified as tryptamines, sold online. Many tryptamines metabolize rapidly, and identifying the appropriate metabolites to reveal intake is essential. While the metabolism of 4-OH-EPT and 5-MeO-EPT are not previously described, EPT is known to form metabolites by indole ring hydroxylation among others. Based on general knowledge of metabolic patterns, 5-MeO-EPT is also expected to form ring hydroxylated EPT (5-OH-EPT). In the present study, the aim was to characterize the major metabolites of EPT, 4-OH-EPT, and 5-MeO-EPT, to provide markers for substance identification in forensic casework. The tryptamines were incubated with pooled human liver microsomes at 37°C for up to 4 h. The generated metabolites were separated and detected by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry analysis. The major in vitro EPT metabolites were formed by hydroxylation, N-dealkylation, and carbonylation. In comparison, 4-OH-EPT metabolism was dominated by double bond formation, N-dealkylation, hydroxylation, and carbonylation in vitro and hydroxylation or carbonylation combined with double bond loss, carbonylation, N-dealkylation, and hydroxylation in vivo. 5-MeO-EPT was metabolized by O-demethylation, hydroxylation, and N-dealkylation in vitro. The usefulness of the characterized metabolites in forensic casework was demonstrated by identification of unique metabolites for 4-OH-EPT in a human postmortem blood sample with suspected EPT or 4-OH-EPT intoxication.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification. However, most THz-based identification methods empirically intercept the low-frequency band of the THz absorption coefficients for analysis. In order to adaptively find out important intervals of the THz spectra, an interval-based sparse ensemble multi-class classifier (ISEMCC) for THz spectral data classification is proposed. In ISEMCC, the THz spectra are first divided into several small intervals through window sliding. Then the data of training samples in each interval are extracted to train some base classifiers. Finally, a final robust classifier is obtained through a nonnegative sparse combination of these trained base classifiers. With l1 -norm, two objective functions that based on Mean Square Error (MSE) and Cross Entropy (CE) are established. For these two objective functions, two iterative algorithms based on the Alternating Direction Method of Multipliers (ADMM) and Gradient Descent (GD) are built respectively. ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse constraint ensures only a few important spectral segments are selected. In order to verify the performance of the proposed algorithm, comparative experiments on identifying the origin of Bupleurum and the harvesting year of Tangerine peel are carried out. The base classifiers used by ISEMCC are Support Vector Machine (SVM) and Decision Tree (DT). The experimental results demonstrate that the proposed algorithm outperforms six typical classifiers, including Random Forest (RF), AdaBoost, RUSBoost, ExtraTree, and the two base classifiers, in terms of classification accuracy.

Ciprofloxacin (CIP)-polluted water treatment via a facile mechanochemical route: Influencing factors and mechanism insights

Degrading ciprofloxacin (CIP)-polluted water has recently emerged as an urgent environmental issue. This study introduced mechanochemical treatment (MCT) as an innovative and underexplored approach for the degradation of CIP in water. The influence of various additives (CaO, Fe2O3, SiO2, Al, and Fe) on CIP degradation efficiency was investigated. Additionally, six types of composite additives (Fe–CaO, Fe–Fe2O3, Fe–SiO2, Fe–Al, Al–SiO2, and Al–CaO) were explored, with the composite of 20% Fe and 80% SiO2 exhibiting notable performance. The impacts of additive content, pH value, and co-existing ions on CIP degradation efficiency were investigated. Furthermore, the effectiveness of MCT in degrading other medical pollutants (norfloxacin, ofloxacin, and enrofloxacin) was verified. The transformations and changes in the crystal structure, oxidation state, microstructure, and morphology of the Fe–SiO2 composite additive were characterized using X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy techniques. This study proposed a sigmoid trend kinetic model (the Delogu model) that better elucidates the MCT process. Three plausible degradation pathways were discussed based on intermediate substance identification and pertinent literature. This study not only establishes a pathway for the facile degradation of CIP pollutants through MCT but also contributes to advancements in wastewater treatment methodologies.

Identification of olfactory alarm substances in zebrafish

Escaping from danger is one of the most fundamental survival behaviors for animals. Most freshwater fishes display olfactory alarm reactions in which an injured fish releases putative alarm substances from the skin to notify its shoaling company about the presence of danger. Here, we identified two small compounds in zebrafish skin extract, designated as ostariopterin and daniol sulfate. Ostariopterin is a pterin derivative commonly produced in many freshwater fishes belonging to the Ostariophysi superorder. Daniol sulfate is a novel sulfated bile alcohol specifically present in the Danio species, including zebrafish. Ostariopterin and daniol sulfate activate distinct glomeruli in the olfactory bulb. Zebrafish display robust alarm reactions, composed of darting, freezing, and bottom dwelling, only when they are concomitantly stimulated with ostariopterin and daniol sulfate. These results demonstrate that the fish alarm reaction is driven through a coincidence detection mechanism of the two compounds along the olfactory neural circuitry.

Does Conformation Affect the Analytical Response? A Structural and Infrared Spectral Evaluation of Phenethylamines (2C-H, 25H-NBOH, and 25I-NBOMe) Using In Silico Methodology

The identification of new psychoactive substances (compounds that mimic the effects of outlawed substances) poses a significant challenge due to their rapid emergence and continuous modifications. This phenomenon results in these molecules escaping legal regulation, allowing them to circumvent legislation. The phenethylamine class has garnered attention because its molecules replicate the effects of LSD and are associated with numerous cases of intoxication. In this study, we focused on three phenethylamines—2C-H, 25H-NBOH, and 25I-NBOMe—with crystallographic structures available in the Cambridge Crystallographic Data Center (CCDC) database. We conducted a systematic conformational analysis and compared the structural information obtained. Subsequently, we compared the spectra derived from this analysis with experimental details from the ENFSI database. Structural comparisons were made based on the RMSDs between the lower energy conformations and experimental crystallographic structures. Additionally, structures obtained from direct optimization were compared. We then simulated the spectra based on the X-ray structures and compared them with those in the experimental database. Interpretation was carried out using heat maps and PCA in Pirouette software. Combining in silico methods with experimental approaches provides a more comprehensive understanding of the characterization process of new psychoactive substances (NPSs).

Infrared to terahertz identification of chemical substances used for the production of IEDs

In our modern times, improvised explosive devices (IEDs) have become more sophisticated than ever, capable of causing destruction and loss of life. The creative use of homemade substances for IEDs manufactures has led to efforts in developing sensitive detection methods that can anticipate, identify and protect against improvised attacks. Laser-based spectroscopic techniques provide rapid and accurate detection of chemicals in improvised explosives, but no single method can detect all components of all explosives. In this study, two spectroscopic methods are used for the sensitive identification of 8 explosive chemical substances in the form of powders and vapors. Absorption spectra of benzene, toluene, acetone and ethylene glycol were examined with CO2 laser photoacoustic spectroscopy. The photoacoustic signals of the samples were recorded in the CO2 laser emission range from 9.2 to 10.8 µm and a different spectral behavior was observed for each analyzed substance. Time-domain spectroscopy with THz radiation was used to analyze ammonium nitrate, potassium chlorate, dinitrobenzene, hexamethylenetetramine transmission spectra in the 0.1–3 THz range, and it was observed that they have characteristic THz fingerprint spectra. CO2 laser photoacoustic spectroscopy and THz time domain spectroscopy have met the criterion of proven effectiveness in identifying explosive components. The combination of these spectroscopic methods is innovative, giving a promising new approach for detection of a large number of IED components.

Isolation, Identification and Determination of Effective Substances of Three plants, Yarrow, False Chamomile and Fennel

This research was conducted in order to determine the amount of effective substances of yarrow, false chamomile and fennel in Hamadan region. First, the alcoholic extract of the three above-mentioned plants was obtained by maceration method. In this way, the body of the desired plant is dried and crushed and immersed in 70% alcohol and left in the same state for 14 days, and then the mixture is filtered and the dried extract was weighed and the ratio of the weight of the liquid extract to the weight of the dry plant was obtained. Also, the numerical amount of total ash and ash insoluble in acid was determined to determine the amount of solutes. Also, the amount of protein, calcium and phosphorus was obtained in three plants. By performing TLC chromatography (paper method), the type of active substances in three plants was determined and the pH of each of the manufactured products was also determined, and the results are as follows: The amount of insoluble ash in fennel acid was 1.6%, yarrow 2.8% and false chamomile 6.8%, and the amount of total ash was 12.9%, 13.7% and 7.4%, respectively. The percentage of calcium and phosphorus in fennel is 0.4% and 0.13%, in yarrow 0.98% and 0.5%, and in false chamomile is 0.74% and 0.39%. The amount of protein in fennel was 4.8%, in yarrow 24.5% and in false chamomile 19.2%. The pH of the products was as follows: the product prepared from fennel is 85/6, yarrow 80/4 and false chamomile 80/5. The results of paper chromatography of three plant samples are as follows: In fennel, in the vicinity of solvent 1 (Rf = 0.6 and 0.51), which is related to antole, and in the vicinity of solvent 2 (Rf = 0.84 and 0.76), which is related to fanchon. In yarrow plant, Rf = 0.86 is related to betapinene, Rf = 0.72 is related to sineon, and finally Rf = 0.1 is related to Borneon's special substance. In the false chamomile plant, Rf = 0.21 is related to gamma element, Rf = 0.30 to beta element, and Rf = 0.39 to the effective substance transmatricaria ester. By performing these steps and comparing the results obtained from previous researches on these plants, a step was taken towards the realization of the aforementioned goals, which is to determine the amount and type of effective substances of these three plants in Hamadan region.

Isolation and Identification of Allelopathic Substances from Forsythia suspensa Leaves, and Their Metabolism and Activity.

The fruit of Forsythia suspensa (Thunb.) Vahl has been used in traditional Chinese medicine as "Forsythiae fructus". The species is also grown in parks and gardens, and on streets and building lots, as an ornamental plant, but it requires pruning. In this study, the allelopathic activity and allelopathic substances in the leaves of pruned branches of F. suspensa were investigated to determine any potential application. The leaf extracts of F. suspensa showed growth inhibitory activity against three weed species; Echinochloa crus-galli, Lolium multiflorum, and Vulpia myuros. Two allelopathic substances in the extracts were isolated through the bioassay-guided purification process, and identified as (-)-matairesinol and (-)-arctigenin. (-)-Matairesinol and (-)-arctigenin, which showed significant growth inhibitory activity at concentrations greater than 0.3 mM in vitro. The inhibitory activity of (-)-arctigenin was greater than that of (-)-matairesinol. However, both compounds were more active than (+)-pinolesinol which is their precursor in the biosynthetic pathway. The investigation suggests that F. suspensa leaves are allelopathic, and (-)-matairesinol and (-)-arctigenin may contribute to the growth inhibitory activities. Therefore, the leaves of the pruned branches can be applied as a weed management strategy in some agricultural practices such as using the leaf extracts in a foliar spray and the leaves in a soil mixture, thereby reducing the dependency on synthetic herbicides in the crop cultivation and contributing to developing eco-friendly agriculture.

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper proposes a lightweight classification model, named RepDwNet, for identifying 28 different types of biological blood. The model integrates advanced techniques such as multi-scale convolutional kernels, depth-wise separable convolutions, and residual connections. These innovations enable the model to capture features at different scales while preserving the coherence of feature data to the maximum extent. The experimental results demonstrate that the average recognition accuracy of the model on the reflective Raman blood dataset and the transmissive Raman blood dataset are 97.31% and 97.10%, respectively. Furthermore, by applying structural reparameterization to compress the well-trained model, it maintains high classification accuracy while significantly reducing the parameter size, thereby enhancing the speed of classification inference. This makes the model more suitable for deployment in portable and mobile devices. Additionally, the proposed model can be extended to various Raman spectroscopy classification scenarios.

Physical Trace Gas Identification with the Photo Electron Ionization Spectrometer (PEIS).

Chemosensor technology for trace gases in the air always aims to identify these compounds and then measure their concentrations. For identification, traceable methods are sparse and relate to large appliances such as mass spectrometers. We present a new method that uses the alternative traceable measurement of the ionization energies of trace gases in a way that can be miniaturized and energetically tuned. We investigate the achievable performance. Since tunable UV sources are not available for photoionization, we take a detour via impact ionization with electrons, which we generate using the photoelectric effect and bring to sharp, defined energies on a nanoscale in the air. Electron impact ionization is thus possible at air pressures of up to 900 hPa. The sensitivity of the process reaches 1 ppm and is equivalent to that of classic PID. With sharpened energy settings, substance identification is currently possible with an accuracy of 30 meV. We can largely explain the experimental observations with the known quantum mechanical models.

Identification of key toxic substances in pyrolysis wastewater and proposal of improvement strategies for pretreatment

A comprehensive assessment of the biotoxicity changes of coal pyrolysis wastewater (CPW) during the treatment process is particularly important to alleviate the predicament of unstable biochemical treatment effects. However, in-depth research on reducing the biotoxicity of CPW is still limited. This study conducted an analysis of the composition of CPW, studied the corresponding relationship between pollutants and biotoxicity changes, clarified the key toxic substances (KTSs). And the effects of four traditional extraction systems and wet air oxidation (WAO) on reducing biotoxicity, chemical oxygen demand, total phenols and specific KTSs were evaluated and optimization strategies were proposed. The results showed that phenols contributed the most to biotoxicity. The biotoxicity reduction rates of each extraction system were as follows: butyl acetate (96.6 %) > methyl isobutyl ketone (95.1 %) > isopropyl ether (92.4 %) > trioctylamine (79.5 %). When using trioctylamine extraction, the phenol removal efficiency was the highest. However, due to the strong selectivity, it cannot remove nonphenolic pollutants in a broad-spectrum manner, which was not conducive to reducing overall biotoxicity. The removal rate of nitrogenous heterocyclic compounds (NHCs) was not ideal during extraction and biochemical treatment, and there was still a significant presence in the biochemical effluent. But after WAO treatment, NHCs can be completely removed, and the ratio of ammonia nitrogen to total nitrogen can be increased from less than 25 % to approximately 85 %. After WAO, the B/C ratio of the effluent increased from 0.24 to above 0.34 and the biotoxicity was significantly reduced. Although the biotoxicity was effectively reduced in the enhanced treatment process, the effluent still performed potential biotoxicity, which need to be further explored in order to reduce environmental risk.

High-precision sensor for glucose solution using active multidimensional feature THz spectroscopy.

Terahertz waves are known for their bio-safety and spectral fingerprinting features, and terahertz spectroscopy technology holds great potential for both qualitative and quantitative identification in the biomedical field. There has been a substantial amount of research utilizing this technology in conjunction with machine learning algorithms for substance identification. However, due to the strong absorption of water for terahertz waves, the single-dimensional features of the sample become indistinct, thereby diminishing the efficiency of the algorithmic recognition. Building upon this, we propose a method that employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with multidimensional feature spectrum identification for the detection of blood sugar and glucose mixtures. Our research indicates that combining THz-TDS with multidimensional feature spectrum and linear discriminant analysis (LDA) algorithms can effectively identify glucose concentrations and detect adulteration. By integrating the multidimensional feature spectrum, the identification success rate increased from 68.9% to 96.0%. This method offers an economical, rapid, and safe alternative to traditional methods and can be applied in blood sugar monitoring, sweetness assessment, and food safety.

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.