Profiling of Various Dry Cannabis Sativa From Aceh, Indonesia Based on Cannabinoids Compound Characteristics

Profiling of Various Dry Cannabis Sativa From Aceh, Indonesia Based on Cannabinoids Compound Characteristics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Profiling of Various Dry Cannabis Sativa From Aceh, Indonesia Based on Cannabinoids Compound Characteristics Supiyani supiyani, Sarah Salsabil, Alya Mumtazah, Alya Syakira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6186729/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2025 Read the published version in Egyptian Journal of Forensic Sciences → Version 1 posted 11 You are reading this latest preprint version Abstract Background Cannabis, which is a narcotic plant, refers to the leaves, flowers, stems, and seeds. Cannabis is used globally for its psychoactive properties with 2.5% of the world's population consuming it for. In Indonesia, the plant is classified as a Class 1 narcotic with a prevalence of use reaching 41.4%. Aceh is one of the largest cannabis producing regions in Indonesia, due to its favorable geographical and climatic conditions. Despite its illegal status, cannabis contains valuable phytocannabinoid compounds and is potentially important in medical applications. Previous studies have shown a correlation between the compound profile of cannabis and its geographical origin. This study aims to develop a classification method based on the cannabinoids compound profiles of dried cannabis samples taken from five regions in Aceh (Aceh Besar, Aceh Tengah, Bireuen, Lhokseumawe, and Pidie Jaya), by microscopy, raman spectrophotometry, GC-MS, and parametric statistical analysis to assist authorities in tracing the source of cannabis for law enforcement and forensic purposes. Results In this study, dried Cannabis sativa from five regions of Aceh, Indonesia, was tested with Raman spectroscopy and GC-MS to produce informative cannabinoid compound profiles as plant profiling. The results obtained 10 cannabinoids quantified in plant samples (Δ9-THC, CBD, THCV, CBL, CBTC, Methoxy-THC, CBC, CBG, Δ9-THCH, and CBN). The cannabinoids compound profile showed Δ9-THC had the highest overall content and was indicated as the most important compound in the cannabis plant clustering profile. Among the various regions, Aceh Besar had the highest cannabis content. Statistical analysis of Raman spectroscopy and GC-MS data found (1) revealed compounds responsible for clustering cultivars between clusters, (2) variation among cannabis chemical profiles as a result of growing environment, and (3) facilitated prediction of cannabis profiles in helping to categorize regions of unknown cannabis origin based on chemical profiles. Conclusion Raman spectroscopy and GC-MS have proven reliable and efficient methods for classifying Cannabis sativa based on its cannabinoid profile in Aceh, Indonesia. The findings help reveal the geographical origin of the growing location of cannabis plant specimens. All five cannabis samples contained a major Δ9-THC psychoactive constituent. The highest Δ9-THC content comes from AB due to the influence of environmental factors. Parametric test analysis concluded that there was no significant effect of geog raphical origin related to the relatively close distance range of samples. Additionally, comparing these methods with other analytical techniques will support defined classification models and improve their application in forensic science, particularly in drug enforcement and quality assessment. Cannabis sativa Cannabinoids GCMS Raman Spectroscopy parametric statistical analysis profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Background Cannabis, which is a narcotic plant, refers to the leaves, flowers, stems, and seeds (Fig. 1 ). Cannabis has only one genus with one species (sativa) that is highly variable. However, cannabis can also be classified into several subspecies or varieties of Cannabis sativa , such as C. sativa variety indica , C. sativa variety ruderalis , and C. sativa variety afghanica (Hourfane et al., 2023 ). Cannabis is a plant that is used globally mainly as a source of psychoactive substances, where 2.5% of the world's population use cannabis (Farrelly et al., 2023 ). In Indonesia, cannabis is a Class I narcotic with a prevalence of use reaching 41.4% and is the most widely used illicit drug (Indriana et al., 2024 ). Aceh is one of the main areas producing quality C. sativa in Indonesia, due to its geographical and climatic conditions that support the optimal growth of this plant (Sukri et al., 2020 ). Currently, most countries around the world consider cannabis to be an illegal drug that can be abused. Despite this, cannabis has the potential for abuse and its illegal status at the federal level in Indonesia. Research on the content of chemical compounds and pharmacological aspects has shown that cannabis also has medicinal properties. Over its long history, cannabis has been used by humans as a medicinal plant, intoxicant, and recreational substance (Hourfane et al., 2023 ; Sagar & Gruber, 2025 ). Cannabis can produce unique compounds, namely phytocannabinoids (Smith et al., 2022 ). When consumed, phytocannabinoids act on the Central Nervous System (CNS) and Peripheral Nervous System (PNS), causing changes in perception and producing euphoric, analgesic, and other effects (Barrales-Cureño et al., 2020 ). The impact of these key cannabinoid compounds explains why the cultivation and illegal consumption of cannabis-based products are widespread today. One of the commonly used cannabis profiling methods is classification based on physical appearance, external environmental aspects, and tetrahydrocannabinol (THC) content in the sample. However, other cannabinoid compounds such as cannabigerol (CBG), cannabinol (CBN), and tetrahydrocannabivarin (THCV) also play a role in cannabis differentiation, especially for medical and therapeutic applications (Blebea et al., 2024 ; Curran et al., 2020 ). The principle of classification refers to the existence of morphological and physiological differences depending on external factors and the origin of the cultivated plant (Hesami et al., 2023 ). Novotny et al. ( 1976 ) stated that different chromatograms were obtained from Cannabis samples with different regional origins, so there may be a correlation between changes in compound content and geographic origin. Tzimas et al. ( 2022 ) also reported the impact of changing environmental conditions on the chemical composition and variability of cannabinoids in C. sativa . A wide variety of analytical techniques have been used to perform cannabis profiling, including thin layer chromatography (Radosavljević-Stevanović et al., 2023 ), profiling with HPLC (Lynch et al., 2023 ), GC-MS (Arena et al., 2022 )d NMR to fingerprint aqueous extracts and cannabis tinctures (Politi et al., 2008 ). However, GC is the specific and most commonly used instrument to analyse cannabinoid content (Baron, 2015 ). Raman spectroscopy has also been successfully used to investigate THC content in samples with the advantage of its non-destructive nature (Sanchez et al., 2020 ). However, questions remain as to whether Raman spectroscopy combined with GC-MS and statistical analysis using parametric tests can differentiate cannabis species from different geographical origins to determine their origin. Moreover, there is no comprehensive classification available for the chemical profiles for samples in the Aceh region of Indonesia, which requires exploration of the similarities/differences that may exist among cannabis samples from the region. In this study, a comprehensive investigation was conducted to identify important compounds and determine the presence or absence of variations in the chemical profile of C. sativa as a result of cultivating the plants in different environments from five regions in Aceh: Aceh Besar, Aceh Tengah, Bireuen, Lhokseumawe, and Pidie Jaya. Therefore, this research aims to develop a classification method based on the chemical profile analysis of cannabis from various regions in Indonesia, particularly Aceh, by comparing the main content of A9-THC through a combination of tests using microscopy, Raman spectroscopy, GC-MS, and ANOVA analysis, with the hope of assisting authorities in tracing the source of cannabis plants in Aceh based on their chemical composition and gaining further insight into the regions producing high-quality cannabis plants in Aceh as a strategy for law enforcement and forensic purposes. Materials and Methods Materials and Samples Five samples of cannabis plants ( Cannabis sativa ) were confiscated from five different regions in Aceh Province, Indonesia, namely Aceh Besar (AB), Lhokseumawe (LS), Pidie Jaya (PJ), Bireuen (BR), and Aceh Tengah (AT). Samples were obtained from the Forensic Field Laboratory of the North Sumatra Regional Police, Indonesia. All samples collected were confirmed as C. sativa , with possible variations in phenotype and cannabinoid content due to differences in geographic origin as well as post-harvest morphological conditions. Samples were received in dry condition, consisting of buds, leaves, stems, seeds, and flowers. Standard used Δ9-THC Cerilliant Supelco 1.0 mg/L. Methanol were obtained from Merck (Darmstadt, Germany). Morphological analysis Morphological analysis of cannabis requires more precision due to its similarity to several other plant species. Cannabis plants have compound leaves similar to palm leaves that are divided into small units called “leaflets.” According to Barrales-Cureño et al. ( 2020 ), the shape of cannabis leaves is elongated with serrated edges. The leaf colour is dark green on the front and slightly lighter on the back. The leaf surface is covered by trichome hairs. The leaves are arranged in rows with 5–7 leaflets, with the longest leaf in the centre. Male plants have flowers at the tip (apex) that produce pollen. Female plants have flowers protected by protective leaves. According to Gjorgievska et al. ( 2024 ), the fruit of the cannabis plant is a type of achene fruit (dried fruit with one seed). Cannabis fruit contains one seed enclosed in a hard pericarp. The shape of the cannabis fruit is oval, slightly flattened, and has a smooth surface. Cannabis fruit has a brownish colour. The parameters observed in this analysis are leaf morphology, the presence of flowers, seeds, and leaf colour after drying. Anatomical Analysis Initial identification of the samples was done using PlantNet software to confirm the species under study was C. sativa . Once confirmed, the samples were observed under a light microscope to see glandular trichomes, which are known to act as centres for the production of secondary metabolites in C. sativa , including in the biosynthesis of THC (tetrahydrocannabinol) compounds (Conneely et al., 2021 ). Cannabis leaves and flowers are sliced as thin as possible, then scraped using a razor blade. The cut samples were placed on a glass slide that had been dripped with methanol solvent, then covered with a cover glass. The preparations were observed under a light microscope; the glandular trichomes found were documented using a camera. Preliminary Test The preliminary test was carried out by dripping the Fast Blue B (FBB) salt reagent on the sample and observing the colour reaction changes that occurred. The colour change is used as an indicator of the presence of cannabinoid compounds. Five drops of methanol solvent were dripped on the sample that had been placed on the Whatman filter paper, and we waited for the droplets to wet the area around the paper. The area that has been wetted is then given two drops of FBB Salt reagent while observing the colour transition formed. The sample was declared positive if a reddish-purple colour appeared. The process is then repeated according to the number of samples to be examined. Confirmation Test Raman Analysis Raman spectroscopic analysis was performed using a Rigaku CQL Max-ID handheld 1064 nm Raman analyzer. This tool allows the identification of chemical spectra with high accuracy and has a reference library of more than 13,000 compounds, including narcotics, explosives, toxic industrial chemicals, and Chemical Warfare Agents (CWAs). Standard used Δ9-THC Cerilliant Supelco 1.0 mg/L. This research focuses on the THC (tetrahydrocannabinol) content in samples from various regions in Aceh. Raman Shift intensity was analyzed to compare THC levels between regions. GC-MS Analysis The dried samples were pulverized using a pestle and mortar to form a fine powder and then sieved. A total of 0.2 grams of sample powder was put into a test tube, 4 mL of methanol was added, then it was homogenized using a vortex for 1 minute and extracted using an ultrasonic bath for 30 minutes to ensure optimal release of active compounds. After the extraction process, the filtrate was filtered and put into a chromatography vial before being analyzed using a GC-MS auto-injector for 28 minutes. Sample analysis with GC-MS Agilent Technologies GC system 7890B and MSD 5977A, column used HP-5MS (30 m x 0.250 mm x 0.25 µm), Helium carrier gas, flow rate 1 mL/min, methanol extract injected by 1 µL with splitless, initial temperature 45°C, 15°C/min increase to 290°C, 12 min hold to final temperature, and Velocity 34. Samples were analyzed using MassHunter software. Parametric Test This study used a random sampling method to obtain representative samples from five regions in Aceh Province. To test for significant differences in Δ9-Tetrahydrocannabinol (THC) levels between regions, parametric tests in the form of T-tests were used to read the results of the chromatogram test, and One-Way ANOVA was run on SPSS software (ver. 26.0, IBM, USA) to read the results of the spectrum test. The hypothesis tested in this study is H₀ (Null Hypothesis), which states that there is no significant difference in THC levels between regions, and H₁ (Alternative Hypothesis), which states that there is a significant difference in THC levels based on the geographical origin of the samples. The results of the analysis were considered significant when a p-value < 0.05 was obtained, indicating that there is a significant variation in THC levels between regions. Results and Discussion Morphology of C. sativa The morphology of dried samples of Cannabis sativa was studied on a macroscale. Apparently, a common leaf shape was found to prevail, an elongated lanceolate leaf with color variation being the main parameter for differentiation. Sample AB was found to maintain its dark green pigment color, whereas other samples exhibited varying colors such as dark brown to light brown. This change is directly correlated to chlorophyll degradation, which in turn is affected by several extrinsic factors such as storage duration and drying conditions. According to Benjamin et al. ( 2022 ), an increasing intensity of leaf darkening signifies longer storage duration or exposure to high temperatures, thereby accelerating the degradation of bioactive compounds. Morphological differences provide evidence in support of the distributions of flower and seed numbers among the samples. Sample AB exhibited no flowering and contained few seeds, while samples LS and BR contained higher flower and seed numbers. On the contrary, the PJ and AT samples displayed suppression in these two reproductive aspects. Environmental and genetic factors affect the development of plant reproductive organs in these variations. Such differences have repercussions on the variability in phytochemical content and bioactivity, which may finally affect the possible medical and industrial applications of C. sativa . The level of fragmentation of the morphological structures observed in the five samples showed two groups of results where AB, PJ, and AT samples tended to have intact structures with minimal fragmentation compared to LS and BR samples. This is positively correlated with the level of dryness; samples with significant levels of fragmentation tend to contain lower water content, potentially affecting the stability of the morphology and phytochemicals contained (Salgotra & Chauhan, 2023 ). Through this analysis, further correlations regarding morphology and environmental factors related to where each sample grows can be determined. The variability requires further tests using microscopes and instruments that can detect cannabinoid content so that knowledge of phenotypic aspects and potential can be deepened. In-depth research on the correlation between the environment and the content of active compounds and forms of C. sativa is needed for more optimal production and utilization. Anatomy of C. sativa Since cannabis glandular trichomes contain high levels of cannabinoid accumulation (Haiden et al., 2022 ; Tanney et al., 2021 ), they are the target structures to be found using microscopy as shown in Fig. 3 . Microscopic observations at 10x and 40x magnification showed that all C. sativa samples had a large number of trichomes. The type of trichomes found consistently in all samples was Glandular Trichomes (GT), which play a role in the synthesis and storage of secondary metabolites such as cannabinoids and terpenoids. The presence of abundant GTs may indicate the potential for high production of bioactive compounds, directly correlating with the pharmacological quality and economic value of these plants. Factors such as plant maturity level, abiotic stress conditions, i.e., photon radiation, nutrient deficiency, presence of heavy metals, drought, and temperature stress (Park et al., 2022 ). Trichomes are formed on the surface of various plant species with the main function as protection against environmental factors as well as secondary metabolite production centers. GTs are known as plant chemical factories because they play a role in the secretion and storage of bioactive compounds, including cannabinoids in C. sativa (Tanney et al., 2021 ). Cannabinoids are synthesized and accumulated in GTs, with content dependent on GT secretion, type, and density (Haiden et al., 2022 ). To date, the biosynthesis of cannabinoid compounds is explained through two main pathways. The biosynthesis of cannabinoid compounds in C. sativa involves a polyketide pathway that begins with tetraketide synthesis, where the enzyme Tetraketide Synthase (TKS) catalyzes the sequential condensation of hexanoyl-CoA with three molecules of malonyl-CoA to form 3,5,7-trioxododecane oil-CoA. Next, at the cyclization and aromatization stage, the Olivetolic Acid Cyclase (OAC) enzyme in collaboration with TKS removes CoA from the tetraketide product, which then undergoes cyclization and aromatization to form Olivetolic Acid (OLA). The process continues with a prenylation stage by the enzyme Aromatic Prenyltransferase, which adds a prenyl group from Geranyl Pyrophosphate (GPP) to OLA, producing Cannabigerolic Acid (CBGA). CBGA then acts as a major precursor and undergoes divergence through the action of specific enzymes, where Tetrahydrocannabinolic Acid Synthase (THCAS) converts CBGA to Tetrahydrocannabinolic Acid (THCA). Finally, THCA undergoes non-enzymatic decarboxylation involving the loss of the carboxylic acid group (CO₂H) to form THC as the final product (Filer, 2022 ). There is Methyl Erythritol 4 Phosphate (MEP) that serves as an alternative pathway in the biosynthesis of terpenoids including GPP, an important precursor in the biosynthesis of cannabinoids and some other basic terpenoids. This process starts with glyceraldehyde-3-phosphate and pyruvate in the presence of the enzyme 1-deoxy-D-xylulose 5-phosphate synthase (DXS). The product of this reaction is 1-Deoxy-D-Xylulose 5-Phosphate (DXP). DXP is converted to methyl erythritol 4 phosphates by the enzyme 1-Deoxy-D-Xylulose 5-Phosphate reductoisomerase (DXR) through a complex series of reduction and isomerization reactions. MEP is then converted to GPP in a series of steps. The MEP-specific enzymes responsible for this are MEP cytidyl transferase and geranyl pyrophosphate synthase. The end result is GPP, which is a key component in cannabinoid biosynthesis (Tahir et al., 2021 ). Trichomes are hair-like developments of epidermal cells that cover part of the plant surface. Based on their structure and function, trichomes are often classified as GT or Non-Glandular Trichome (NGT), which can be unicellular or multicellular (Feng et al., 2023 ). GTs are unique in that they have the ability to synthesize, secrete, and store large amounts of secondary metabolites. These specialized metabolites include terpenoids, phenylpropanoids, flavonoids, methyl ketones, acyl sugars, and some oils or resins (Chen et al., 2024 ). While NGT plays an important role in plant defense by reducing transpiration, increasing tolerance to cold, and deflecting high solar radiation, thereby reducing herbivory (Gostin, 2023 ). Cannabis female flowers have a large number of GTs on their surface. These defense structures develop mainly in conjunction with female flower tissue and provide protection by producing and storing a large number of secondary metabolites, which include cannabinoids (Dimopoulos et al., 2025 ). Preliminary Test A preliminary test with Fast Blue B (FBB) Salt reagent was conducted as an initial test of samples containing cannabinoid compounds by showing a colour change to purple-reddish, indicating a positive test result (Bhakti Nusantara & Aisyah Rahmania, 2024 ). Sample BR below represents the positive results for marijuana of the other five cannabis samples (Fig. 4 ). The formation of a reddish-purple colour with FBB reagent is based on the azo coupling reaction between diazonium compounds and phenolic groups on cannabinoids. The hydroxyl group (-OH) on the cannabinoid compound ionizes under alkaline conditions and produces a negative charge that is stabilized through a resonance system on its aromatic structure. The phenolic groups in this process act as electron donors that interact with the diazonium compound. This process also produces output in the form of aromatic compounds with electron systems that are able to absorb light waves and emit certain color indications that are characteristic. The stability of azo compounds in alkaline media allows for the estimation of cannabinoid content so that this method can be used as a quick, simple, and effective preliminary technique (Jornet-Martínez et al., 2023 ; Philp & Fu, 2018 ). Confirmation Test Raman Spectrum of Cannabis sativa Table 1 Raman Spectrum Band Assignments of Cannabis and Interpretation of Molecular Vibrations Vibration Mode Wave Number (cm-¹) Molecules C = C and C = O stretching 1747 − 1545 Cannabinoid CH₂ bending 1500 − 1100 Cannabinoid CH₂ twisting bending 1187 − 1138 Cannabinoid C-C stretching 1100 − 600 Cannabinoid Qualitative analysis for THC in the samples was confirmed by measurement of Raman spectra against the Δ9-THC standard by direct treatment of five samples (AB, LS, AT, BR, and PJ) at the inflorescence part, which is GT glass that shown in Fig. 3 . The main vibrational bands of THC-rich plants were observed at 1620 cm⁻¹. The determination of these bands is reported in Table 1 . The Raman spectra of the THC-rich plants showed a prominent vibrational band at 1620 cm⁻¹. From each plant, at least two different spots were analyzed, and the average of them was considered as representative. In this analysis, principal components allow us to examine the relationship of the data. Each principal component represents a linear combination of variables and gives the maximum variance. In this case, the variables considered are the maximum and minimum points of the spectrum or, in other words, the spectrum itself. Therefore, the principal components of the multivariate analysis represent the spectrum common to the various samples and represent how similar the spectra obtained from the various samples are (Table 3 ). Analysis of these spectra provides some parameter analysis for evaluation of the presence of THC content by analysis of THC functional groups with the main peak of C-C-H bending of aliphatic chain vibration at 1620 cm⁻¹. This can provide important insights into the presence of the compound. The bands of the cannabis spectrum were compared to the standard spectrum of THC, as the laser was focused on the main production site of the cannabinoid. Feature assignment of the Raman spectra of the cannabis samples was performed in the range 200–1800 cm⁻¹, as this is where the more typical vibrational modes of the molecules occur (molecular fingerprints) (Sanchez et al., 2020 ). Figure 5 shows the vibrational area of THC as the main constituent of cannabis was observed in the first zone at higher wave numbers related to the presence of C = C and C = O double bonds. The bands at 1747 cm⁻¹, 1620 cm⁻¹, and the shoulders at 1606 and 1545 cm⁻¹ are in the C = C stretching region the spectrum normalized to 1453 cm⁻¹. As can be seen, the overlapping spectra of the THC molecules indicate the presence of more molecules with related structures. It appears that the acidic form of the precursor may also be present and contributing to this broad band. Figure 5 shows the theoretically calculated Raman spectra of the acid molecule and the decarboxylated cannabinoid molecule. This allows visualization of the very similar position of the C = C stretching mode; therefore, it is appropriate to also assign the contribution of the acid form molecule to this spectral region. The second zone lies between 1500 and 1100 cm⁻¹ with bending of the CH ₂ signal. In conjunction with simulated THC vibrations, it is reasonable to expect a much higher frequency CH ₂ scissor bending spectrum around 1453 cm⁻¹. The bending at 1368 cm⁻¹ and nearby is related to the CH ₂ bending of other cannabinoids. The moderate bands recorded at 1187 and 1138 cm⁻¹ signify a retention of the higher frequency bands in the Raman spectrum. This results from the interplay of cannabinoids in the trichome acting on the CH twisting bending mode. The lower wavenumber region of the cannabis spectrum (1100 to 600 cm⁻¹) is less intense. However, several well-defined bands are observed around 962 cm⁻¹, which are associated with the C-C stretching vibrations of the alkyl groups included in the expected cannabinoids. Similarly, the bands at 796 and 756 cm⁻¹ correspond to the CH₃ and CH₂ vibrations of the same molecule in the cannabis trichomes. The average spectrum of each type of cannabis is visually very similar, with slight differences in the shift and intensity of the Raman bands, which will be discussed later in the statistical analysis. To observe the expansion of the number of cannabinoid molecules to be analyzed, as well as the fact that other forms of cannabinoids can be measured separately, this study uses advanced GC-MS testing to develop and validate the Raman method in measuring cannabis cannabinoids. GCMS of Cannabis sativa GC-MS analysis was conducted for the identification and quantification of cannabinoid compounds in C. sativa samples from five regions in Aceh, Indonesia, to compare the chemical profiles and variations in cannabinoid content between the samples. GC-MS analysis showed significant variations in cannabinoid compound content between the five samples. GC-MS can perform analysis in MRM mode based on specific collision-induced fragmentation of precursor ions, which significantly increases the signal-to-noise ratio as well as the sensitivity of the method. This GC-MS technique enables highly selective monitoring of ion transitions in MRM mode, thus improving the accuracy and precision of the analysis. In this method, the compound molecules undergo ionization and fragmentation, resulting in a distinctive mass spectrum pattern used for identification. The chromatogram results revealed ten peaks confirmed as cannabinoids, with the lead compound identified as Δ9-THC (Rontani, 2022 ). The results of the analysis using the SWGDRUG mass spectral library showed that there were ten components of cannabinoid compounds with specific retention times and similarity percentages with an average similarity of 94.3%. CBD was identified at a retention time of 15.022 minutes with 95% similarity. THCV was identified at a retention time of 15.225 minutes with 99% similarity. CBL was identified at a retention time of 15.308 minutes with 91% similarity. CBTC was identified at a retention time of 15.323 minutes with 91% similarity. CBC was identified at a retention time of 15.847 minutes with 95% similarity. Methoxy-THC was identified at a retention time of 15.996 minutes with 92% similarity. Δ9-THC was identified at a retention time of 16.418 minutes with 99% similarity. CBG was identified at a retention time of 16.579 minutes with 92% similarity. Δ9-THCH was identified at a retention time of 16.626 minutes with 92% similarity. CBN was identified at a retention time of 16.693 minutes with 97% similarity. The percentage area distribution of each compound in five samples with different regions (AB, LH, AT, BR, and PJ) showed variation in results as illustrated in Table 2 . Table 2 GC-MS analysis of cannabinoid content in C. sativa from five different regions in Aceh Province, Indonesia. No RT Library % Area AB LS AT BR PJ 1 15.022 Tetrahydrocannabivarin (THCV) 3.35 7.86 0.73 2.38 1.55 2 15.225 Cannabidiol (CBD) 5.26 2.42 2.53 3.88 4.23 3 15.308 Cannabicyclol (CBL) 2 - 1.29 - - 4 15.323 Cannabicitran (CBTC) - 5.33 - 2.39 0.99 5 15.996 Methoxy-THC - - 1.37 - 1.91 6 15.847 Cannabichromene (CBC) 16.07 13.92 4.88 8.49 6.06 7 16.418 Delta-9-Tetrahydrocannabinol (Δ9-THC) 57.48 45.12 28.9 31.56 45.4 8 16.579 Cannabigerol (CBG) 5.15 7.37 5.33 4.62 10.64 9 16.626 Δ9-THCH 2.71 - 0.79 - - 10 16.693 Cannabinol (CBN) 1.92 11.1 19.27 22.48 12.41 Δ9-tetrahydrocannabinol is an assumed major psychoactive substance of Cannabis sativa plants (Bhakti Nusantara & Aisyah Rahmania, 2024 ). It was indexed as the most abundant compound in all samples. Sample AB possessed the greatest area percentage for this compound (57.48%), followed by sample PJ (45.40%) and then by LS (45.12%). Comparatively, the other two samples, BR and AT, had much lower amounts of Δ9-THC: only 31.56 and 28.90%, respectively. A factor for producing higher Δ9-THC content in AB and less in AT is the morphological conditions of the sample plant, especially the life cycle stages (Fig. 2 ). Inflorescence tissues should be the richest in cannabinoids, but they may not necessarily relate to the flower yields. Predicting them becomes an even more complicated issue when people are after secondary metabolites. More than ever, the environment has to be quite influential here. There may be the other way around, with phytocannabinoids yield decreasing as the flower develops (Feder et al. , 2021). This will invariably introduce a dilution effect. According to Jurga et al. ( 2024 ), THC concentration in leaf tissues inversely relates to the plant height, while other flower characteristics need to be considered in addition to just inflorescence biomass, which is relevant to contributing to final productivity (Jurga et al., 2024 ). Alongside Δ9-THC, CBC registered significant percentages with the highest recorded in the sample AB as 16.07%, followed by LS at 13.92%. The remainder showed witnesses of lesser amounts of CBC, with the lowest being the AT sample at 4.88%. CBTC had a presence with samples AB, LS, and BR, giving percentage areas of 2%, 5.33%, and 2.39%, respectively. Another compound, CBN, which is the product of Δ9-THC degradation, was found in different quantities, with the highest value in BR at 22.48%; AT had 19.27% and a low of 1.92% in AB. Other compounds like CBD were found in all five samples, although that was in very low concentrations, but the highest was in LS with 7.86%. THCV reflected a slight stable reading with the highest sampling from AB showing 5.26%. CBG compounds appear in intermediate amounts, with the highest in the PJ sample at 10.64%. Very small and variable amounts were found in all samples of CBL and Methoxy-THC only in AT and PJ samples, suggesting that their biosynthesis might be affiliated with some conditions, particularly like maturity of plant or other growing conditions (Apicella et al., 2022 ). GC-MS analysis results showed variations in cannabinoid compound content between C. sativa samples from five regions in Aceh, where Δ9-THC was the dominant compound in all samples, with the highest content found in sample AB. Variations in the content of other compounds such as CBC, CBD, and CBN indicate the influence of environmental factors, especially temperature, storage, or post-harvest treatment (Desaulniers Brousseau et al., 2021 ; Hahm et al., 2024 ). Statistical Analysis Table 3 THC intensity of Raman spectrum on different regions at 1620 cm⁻¹ No Sample Intensity (a.u) 1 AB1U2 2.436.188 2 AB2U1 3.155.622 3 AB2U2 2.366.957 4 AB3U1 2.974.771 5 AB3U2 3.573.990 6 LS1U1 456.623 7 LS1U2 2.939.619 8 LS2U1 968.275 9 LS2U2 5.508.518 10 LS3U1 1.240.142 11 LS3U2 1.938.155 12 AT1U1 7.683.181 13 AT1U2 3.851.212 14 AT2U1 2.449.104 15 AT2U2 2.406.167 16 AT3U1 3.281.505 17 AT3U2 5.305.152 18 BR1U1 6.207.261 19 BR1U2 3.307.412 20 BR2U1 1.627.322 21 BR2U2 8.505.115 22 BR3U1 1.782.435 23 BR3U2 3.115.554 24 PJ1U1 3.515.122 25 PJ1U2 1.136.306 26 PJ2U1 4.680.964 27 PJ2U2 1.651.093 28 PJ3U1 2.055.480 29 PJ3U2 6.244.719 The correlation between geographical origin and Δ9-THC levels in each sample using T-test and One-Way ANOVA is illustrated in Table 4 . Table 4 Effect of Geographical Origin on THC Content Using Raman Spectroscopy and GC-MS Comparison r p -value Significance THC Intensity by Raman 0.63 0.356 ns % Area THC by GC-MS 0.03 0.956 ns significance: ns (not significant) The correlation between regional origin and percentage THC area via Raman spectroscopy was at r = 0.63, p = 0.356, while via GC-MS r = 0.03, p = 0.956. Based on these results, the null hypothesis failed to be rejected, concluding that there is no significant influence of the geographical origin of the five samples on the THC intensity detected through Raman spectroscopy. The high content of Δ9-THC should be positively correlated with geographical conditions, especially temperature and altitude (Trancoso et al., 2022 ; Younas et al., 2024 ). A more specific analysis of external effects on cannabinoids is mandatory to confirm the hypothesis and to generate new evidence on Raman spectra. The reason for the absence of significant differences is related to the relatively close distance range and still being in the same archipelago. Although there are slight differences in the environmental parameters of the five samples, especially temperature and altitude, where sample AB grows in warmer conditions compared to AT, where the climate tends to be cooler due to being in the highlands (Azizs & Asnawi, 2024 ), the temperature difference between the two is not too extreme to significantly affect cannabinoid accumulation. Thus, the homogeneity of environmental conditions at the sampling sites was the main factor that led to the absence of significant differences in THC content between the analyzed regions. Conclusion The GC-MS used recorded the chemical profiles of 10 cannabinoids in five Aceh Indonesian cannabis samples (Δ9-THC, CBD, THCV, CBL, CBTC, Methoxy-THC, CBC, CBG, Δ9-THCH, and CBN). The profiles obtained can then be used to categorize cannabis. This grouping of cannabis helps reveal the geographical origin of the growing location of cannabis plant specimens. All five cannabis samples contained the most abundant major psychoactive constituent, Δ9-THC. The highest Δ9-THC content in AB is due to the influence of environmental factors. AB has a warm climate while AT has a cold climate. Cannabinol and Cannabidiol were also present in all five samples. Parametric T-test and one-way ANOVA analysis showed the presence of multiple absorption band spectra; Raman and GC-MS concluded no significant effect of geographical origin. This study successfully characterized dried Cannabis sativa samples from different regions in Aceh, Indonesia, based on their cannabinoid compound fingerprint characteristics. Abbreviations AB Aceh Besar ANOVA Analysis of Variance AT Aceh Tengah BR Bireueun CBC Cannabichromene CBD Cannabidiol CBG Cannabigerol CBGA Cannabigerolic Acid CBL Cannabicylol CBN Cannabinol CBTC Cannabicitran CNS Central Nervous System CWAs Chemical Warfare Agents CQL Chemical Threat Analyzer DXP 1-deoxy-d-xylulose 5-phosphate synthase DXS 1-Deoxy-D-xylulose 5-phosphate synthase FBB Fast Blue B GC-MS Gas Chromatography Mass Spectrometry GPP Geranyl Pyrophosphate GT Glandular Trichomes H-NMR Hydrogen Nuclear Magnetic Resonance HP-5MS High Performance-(5%-phenyl)-methylpolysiloxane HPLC High-performance liquid chromatography IBM International Business Machines LS Lhokseumawe MEP Methyl Erythritol 4 Phosphate MRM Multiple Reaction Monitoring MSD Mass Selective Detector NGT Non-Glandular Trichome OAC Olivetolic Acid Cyclase OLA Olivetolic Acid PJ Pidie Jaya PNS Peripheral Nervous System RT Retention Time SPSS Statistical software package for social sciences THCA Tetrahydrocannabinolic Acid THCAS Tetrahydrocannabinolic Acid Synthase THCV Tetrahydrocannabivarin TKS Tetraketide Synthase Δ9-THC delta 9- tetrahydrocannabinol Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical guidelines of the institution where it is carried out. 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Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2025 Read the published version in Egyptian Journal of Forensic Sciences → Version 1 posted Editorial decision: Revision requested 06 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviews received at journal 28 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 08 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6186729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435530951,"identity":"bf59dd36-d723-4643-b7da-c9536d33a40b","order_by":0,"name":"Supiyani supiyani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCA0DMw4/gMzZIEKVFsoEULWBgcACJg1eL/Izcg4d5au7IGN/ITnvws41Bnr+BufEGXsNv5CUc5jn2jMfsRu52w942BsMZBxibLfBqkcgxOMzDdhikZZsE7zYGxg0MjG0EHAbS8u8wj/GM3G2Sf7cx2BPUwnADqIW37TCPgUTuNmmgLYkEtRiceZdwcG7fYR6JM2+3Scv+k0iecZiAX+Tbcw9/ePPtsD1/O9Bhb87Y2Pa3tz/EG2LASGRg4kHwgE5ixq8erIXxB0FFo2AUjIJRMKIBADk/Sj67YKefAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Supiyani","middleName":"","lastName":"supiyani","suffix":""},{"id":435530952,"identity":"9854ba6f-5375-4d65-8768-c1d76662e0e2","order_by":1,"name":"Sarah Salsabil","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Salsabil","suffix":""},{"id":435530953,"identity":"75b38efd-b6d1-4ae7-a129-83eea0f2ba71","order_by":2,"name":"Alya Mumtazah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alya","middleName":"","lastName":"Mumtazah","suffix":""},{"id":435530954,"identity":"ce769d6a-f024-4642-8128-0b12ff563f61","order_by":3,"name":"Alya Syakira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alya","middleName":"","lastName":"Syakira","suffix":""}],"badges":[],"createdAt":"2025-03-09 04:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6186729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6186729/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41935-025-00480-y","type":"published","date":"2025-09-19T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79741023,"identity":"b79b481d-6b2b-405c-9efc-91a160b6bba4","added_by":"auto","created_at":"2025-04-02 07:55:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eC. sativa\u003c/em\u003e at developmental progression from seed to mature cultivation in Aceh, Indonesia. \u003cstrong\u003ea\u003c/strong\u003e \u003cem\u003eC. sativa\u003c/em\u003e seeds displaying variations in size and color. \u003cstrong\u003eb\u003c/strong\u003e Juvenile \u003cem\u003eC. sativa\u003c/em\u003e plant with serrated leaves growing in soil. \u003cstrong\u003ec\u003c/strong\u003e Leaves, stems and roots of \u003cem\u003eC. sativa\u003c/em\u003e at different age growth stages. \u003cstrong\u003ed\u003c/strong\u003e Cannabis cultivation vegetation in nature.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/f8aa488d91e4b5e3f3a15dab.jpg"},{"id":79741025,"identity":"bdf3ff02-9e17-4b16-9a62-132bf6f68bab","added_by":"auto","created_at":"2025-04-02 07:55:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164406,"visible":true,"origin":"","legend":"\u003cp\u003eDried marijuana from: \u003cstrong\u003ea\u003c/strong\u003e. AB, \u003cstrong\u003eb\u003c/strong\u003e. LS, \u003cstrong\u003ec\u003c/strong\u003e. PJ, \u003cstrong\u003ed\u003c/strong\u003e. BR, \u003cstrong\u003ee\u003c/strong\u003e. AT.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/2391a597e07819113afa3924.jpg"},{"id":79741026,"identity":"93b53629-4348-4e63-9737-323b6433e215","added_by":"auto","created_at":"2025-04-02 07:55:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50445,"visible":true,"origin":"","legend":"\u003cp\u003eTrichomes of \u003cem\u003eC. sativa\u003c/em\u003e observed under optic microscope. A: Images at 10x magnification showing different trichome types and morphologies across various: \u003cstrong\u003ea\u003c/strong\u003e Glandular trichome with prominent head and stalk (AB sample), b Glandular and non-glandular trichome with head and stalk (LS sample) \u003cstrong\u003ec\u003c/strong\u003e Glandular and non-glandular trichome with smaller head and shorter stalk (AT sample) \u003cstrong\u003ed \u003c/strong\u003eSome glandular trichomes and non-glandular trichomes with longer head and stalk (PJ sample) e Abundant glandular trichomes and non-glandular trichomes with longer head and stalk (PJ sample). B: The same samples at 40x magnification revealing detailed structures of the glandular trichomes, which are the primary sites of cannabinoid accumulation.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/92065761ead8efb3ca69eb7c.jpg"},{"id":79741451,"identity":"b2259e6e-b3a4-4609-9273-72567725d1a4","added_by":"auto","created_at":"2025-04-02 08:03:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35140,"visible":true,"origin":"","legend":"\u003cp\u003eFBB test of sample BR results before (left) and after (right) reagents was applied.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/35e97e7bff4a5e644df74860.jpg"},{"id":79741032,"identity":"b560a1ba-c6db-411e-ad72-d57591ba5dcc","added_by":"auto","created_at":"2025-04-02 07:55:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55049,"visible":true,"origin":"","legend":"\u003cp\u003eRaman spectrum of five cannabis samples (PD, LS, AB, AT, BR) and THC standard.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/407d39e3a0eabb50bc3d6716.jpg"},{"id":91889913,"identity":"ab351345-ed3e-4500-bd32-60b999ca3df5","added_by":"auto","created_at":"2025-09-22 16:03:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6186729/v1/9db7c061-b956-4d3f-8063-abe0c2263876.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProfiling of Various Dry Cannabis Sativa From Aceh, Indonesia Based on Cannabinoids Compound Characteristics\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\n\u003ch3\u003eBackground\u003c/h3\u003e\n\u003cp\u003eCannabis, which is a narcotic plant, refers to the leaves, flowers, stems, and seeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cannabis has only one genus with one species (sativa) that is highly variable. However, cannabis can also be classified into several subspecies or varieties of \u003cem\u003eCannabis sativa\u003c/em\u003e, such as \u003cem\u003eC. sativa\u003c/em\u003e variety \u003cem\u003eindica\u003c/em\u003e, \u003cem\u003eC. sativa\u003c/em\u003e variety \u003cem\u003eruderalis\u003c/em\u003e, and \u003cem\u003eC. sativa\u003c/em\u003e variety \u003cem\u003eafghanica\u003c/em\u003e (Hourfane et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cannabis is a plant that is used globally mainly as a source of psychoactive substances, where 2.5% of the world's population use cannabis (Farrelly et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Indonesia, cannabis is a Class I narcotic with a prevalence of use reaching 41.4% and is the most widely used illicit drug (Indriana et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Aceh is one of the main areas producing quality \u003cem\u003eC. sativa\u003c/em\u003e in Indonesia, due to its geographical and climatic conditions that support the optimal growth of this plant (Sukri et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCurrently, most countries around the world consider cannabis to be an illegal drug that can be abused. Despite this, cannabis has the potential for abuse and its illegal status at the federal level in Indonesia. Research on the content of chemical compounds and pharmacological aspects has shown that cannabis also has medicinal properties. Over its long history, cannabis has been used by humans as a medicinal plant, intoxicant, and recreational substance (Hourfane et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sagar \u0026amp; Gruber, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Cannabis can produce unique compounds, namely phytocannabinoids (Smith et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When consumed, phytocannabinoids act on the Central Nervous System (CNS) and Peripheral Nervous System (PNS), causing changes in perception and producing euphoric, analgesic, and other effects (Barrales-Cure\u0026ntilde;o et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The impact of these key cannabinoid compounds explains why the cultivation and illegal consumption of cannabis-based products are widespread today.\u003c/p\u003e \u003cp\u003eOne of the commonly used cannabis profiling methods is classification based on physical appearance, external environmental aspects, and tetrahydrocannabinol (THC) content in the sample. However, other cannabinoid compounds such as cannabigerol (CBG), cannabinol (CBN), and tetrahydrocannabivarin (THCV) also play a role in cannabis differentiation, especially for medical and therapeutic applications (Blebea et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Curran et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The principle of classification refers to the existence of morphological and physiological differences depending on external factors and the origin of the cultivated plant (Hesami et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Novotny et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) stated that different chromatograms were obtained from Cannabis samples with different regional origins, so there may be a correlation between changes in compound content and geographic origin. Tzimas et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also reported the impact of changing environmental conditions on the chemical composition and variability of cannabinoids in \u003cem\u003eC. sativa\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA wide variety of analytical techniques have been used to perform cannabis profiling, including thin layer chromatography (Radosavljević-Stevanović et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), profiling with HPLC (Lynch et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), GC-MS (Arena et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)d NMR to fingerprint aqueous extracts and cannabis tinctures (Politi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, GC is the specific and most commonly used instrument to analyse cannabinoid content (Baron, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Raman spectroscopy has also been successfully used to investigate THC content in samples with the advantage of its non-destructive nature (Sanchez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, questions remain as to whether Raman spectroscopy combined with GC-MS and statistical analysis using parametric tests can differentiate cannabis species from different geographical origins to determine their origin. Moreover, there is no comprehensive classification available for the chemical profiles for samples in the Aceh region of Indonesia, which requires exploration of the similarities/differences that may exist among cannabis samples from the region.\u003c/p\u003e \u003cp\u003eIn this study, a comprehensive investigation was conducted to identify important compounds and determine the presence or absence of variations in the chemical profile of \u003cem\u003eC. sativa\u003c/em\u003e as a result of cultivating the plants in different environments from five regions in Aceh: Aceh Besar, Aceh Tengah, Bireuen, Lhokseumawe, and Pidie Jaya. Therefore, this research aims to develop a classification method based on the chemical profile analysis of cannabis from various regions in Indonesia, particularly Aceh, by comparing the main content of A9-THC through a combination of tests using microscopy, Raman spectroscopy, GC-MS, and ANOVA analysis, with the hope of assisting authorities in tracing the source of cannabis plants in Aceh based on their chemical composition and gaining further insight into the regions producing high-quality cannabis plants in Aceh as a strategy for law enforcement and forensic purposes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterials and Samples\u003c/h2\u003e \u003cp\u003eFive samples of cannabis plants (\u003cem\u003eCannabis sativa\u003c/em\u003e) were confiscated from five different regions in Aceh Province, Indonesia, namely Aceh Besar (AB), Lhokseumawe (LS), Pidie Jaya (PJ), Bireuen (BR), and Aceh Tengah (AT). Samples were obtained from the Forensic Field Laboratory of the North Sumatra Regional Police, Indonesia. All samples collected were confirmed as \u003cem\u003eC. sativa\u003c/em\u003e, with possible variations in phenotype and cannabinoid content due to differences in geographic origin as well as post-harvest morphological conditions. Samples were received in dry condition, consisting of buds, leaves, stems, seeds, and flowers. Standard used Δ9-THC Cerilliant Supelco 1.0 mg/L. Methanol were obtained from Merck (Darmstadt, Germany).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMorphological analysis\u003c/h3\u003e\n\u003cp\u003eMorphological analysis of cannabis requires more precision due to its similarity to several other plant species. Cannabis plants have compound leaves similar to palm leaves that are divided into small units called \u0026ldquo;leaflets.\u0026rdquo; According to Barrales-Cure\u0026ntilde;o et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the shape of cannabis leaves is elongated with serrated edges. The leaf colour is dark green on the front and slightly lighter on the back. The leaf surface is covered by trichome hairs. The leaves are arranged in rows with 5\u0026ndash;7 leaflets, with the longest leaf in the centre. Male plants have flowers at the tip (apex) that produce pollen. Female plants have flowers protected by protective leaves. According to Gjorgievska et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the fruit of the cannabis plant is a type of achene fruit (dried fruit with one seed). Cannabis fruit contains one seed enclosed in a hard pericarp. The shape of the cannabis fruit is oval, slightly flattened, and has a smooth surface. Cannabis fruit has a brownish colour. The parameters observed in this analysis are leaf morphology, the presence of flowers, seeds, and leaf colour after drying.\u003c/p\u003e\n\u003ch3\u003eAnatomical Analysis\u003c/h3\u003e\n\u003cp\u003eInitial identification of the samples was done using PlantNet software to confirm the species under study was \u003cem\u003eC. sativa\u003c/em\u003e. Once confirmed, the samples were observed under a light microscope to see glandular trichomes, which are known to act as centres for the production of secondary metabolites in \u003cem\u003eC. sativa\u003c/em\u003e, including in the biosynthesis of THC (tetrahydrocannabinol) compounds (Conneely et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cannabis leaves and flowers are sliced as thin as possible, then scraped using a razor blade. The cut samples were placed on a glass slide that had been dripped with methanol solvent, then covered with a cover glass. The preparations were observed under a light microscope; the glandular trichomes found were documented using a camera.\u003c/p\u003e\n\u003ch3\u003ePreliminary Test\u003c/h3\u003e\n\u003cp\u003eThe preliminary test was carried out by dripping the Fast Blue B (FBB) salt reagent on the sample and observing the colour reaction changes that occurred. The colour change is used as an indicator of the presence of cannabinoid compounds. Five drops of methanol solvent were dripped on the sample that had been placed on the Whatman filter paper, and we waited for the droplets to wet the area around the paper. The area that has been wetted is then given two drops of FBB Salt reagent while observing the colour transition formed. The sample was declared positive if a reddish-purple colour appeared. The process is then repeated according to the number of samples to be examined.\u003c/p\u003e\n\u003ch3\u003eConfirmation Test\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRaman Analysis\u003c/h2\u003e \u003cp\u003eRaman spectroscopic analysis was performed using a Rigaku CQL Max-ID handheld 1064 nm Raman analyzer. This tool allows the identification of chemical spectra with high accuracy and has a reference library of more than 13,000 compounds, including narcotics, explosives, toxic industrial chemicals, and Chemical Warfare Agents (CWAs). Standard used Δ9-THC Cerilliant Supelco 1.0 mg/L. This research focuses on the THC (tetrahydrocannabinol) content in samples from various regions in Aceh. Raman Shift intensity was analyzed to compare THC levels between regions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGC-MS Analysis\u003c/h3\u003e\n\u003cp\u003eThe dried samples were pulverized using a pestle and mortar to form a fine powder and then sieved. A total of 0.2 grams of sample powder was put into a test tube, 4 mL of methanol was added, then it was homogenized using a vortex for 1 minute and extracted using an ultrasonic bath for 30 minutes to ensure optimal release of active compounds. After the extraction process, the filtrate was filtered and put into a chromatography vial before being analyzed using a GC-MS auto-injector for 28 minutes.\u003c/p\u003e \u003cp\u003eSample analysis with GC-MS Agilent Technologies GC system 7890B and MSD 5977A, column used HP-5MS (30 m x 0.250 mm x 0.25 \u0026micro;m), Helium carrier gas, flow rate 1 mL/min, methanol extract injected by 1 \u0026micro;L with splitless, initial temperature 45\u0026deg;C, 15\u0026deg;C/min increase to 290\u0026deg;C, 12 min hold to final temperature, and Velocity 34. Samples were analyzed using MassHunter software.\u003c/p\u003e\n\u003ch3\u003eParametric Test\u003c/h3\u003e\n\u003cp\u003eThis study used a random sampling method to obtain representative samples from five regions in Aceh Province. To test for significant differences in Δ9-Tetrahydrocannabinol (THC) levels between regions, parametric tests in the form of T-tests were used to read the results of the chromatogram test, and One-Way ANOVA was run on SPSS software (ver. 26.0, IBM, USA) to read the results of the spectrum test. The hypothesis tested in this study is H₀ (Null Hypothesis), which states that there is no significant difference in THC levels between regions, and H₁ (Alternative Hypothesis), which states that there is a significant difference in THC levels based on the geographical origin of the samples. The results of the analysis were considered significant when a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was obtained, indicating that there is a significant variation in THC levels between regions.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eMorphology of\u003c/strong\u003e \u003cstrong\u003eC. sativa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe morphology of dried samples of \u003cem\u003eCannabis sativa\u003c/em\u003e was studied on a macroscale. Apparently, a common leaf shape was found to prevail, an elongated lanceolate leaf with color variation being the main parameter for differentiation. Sample AB was found to maintain its dark green pigment color, whereas other samples exhibited varying colors such as dark brown to light brown. This change is directly correlated to chlorophyll degradation, which in turn is affected by several extrinsic factors such as storage duration and drying conditions. According to Benjamin et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), an increasing intensity of leaf darkening signifies longer storage duration or exposure to high temperatures, thereby accelerating the degradation of bioactive compounds.\u003c/p\u003e\n\u003cp\u003eMorphological differences provide evidence in support of the distributions of flower and seed numbers among the samples. Sample AB exhibited no flowering and contained few seeds, while samples LS and BR contained higher flower and seed numbers. On the contrary, the PJ and AT samples displayed suppression in these two reproductive aspects. Environmental and genetic factors affect the development of plant reproductive organs in these variations. Such differences have repercussions on the variability in phytochemical content and bioactivity, which may finally affect the possible medical and industrial applications of \u003cem\u003eC. sativa\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe level of fragmentation of the morphological structures observed in the five samples showed two groups of results where AB, PJ, and AT samples tended to have intact structures with minimal fragmentation compared to LS and BR samples. This is positively correlated with the level of dryness; samples with significant levels of fragmentation tend to contain lower water content, potentially affecting the stability of the morphology and phytochemicals contained (Salgotra \u0026amp; Chauhan, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThrough this analysis, further correlations regarding morphology and environmental factors related to where each sample grows can be determined. The variability requires further tests using microscopes and instruments that can detect cannabinoid content so that knowledge of phenotypic aspects and potential can be deepened. In-depth research on the correlation between the environment and the content of active compounds and forms of \u003cem\u003eC. sativa\u003c/em\u003e is needed for more optimal production and utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnatomy of\u003c/strong\u003e \u003cstrong\u003eC. sativa\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e\n \u003cp\u003eSince cannabis glandular trichomes contain high levels of cannabinoid accumulation (Haiden et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tanney et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), they are the target structures to be found using microscopy as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Microscopic observations at 10x and 40x magnification showed that all \u003cem\u003eC. sativa\u003c/em\u003e samples had a large number of trichomes. The type of trichomes found consistently in all samples was Glandular Trichomes (GT), which play a role in the synthesis and storage of secondary metabolites such as cannabinoids and terpenoids. The presence of abundant GTs may indicate the potential for high production of bioactive compounds, directly correlating with the pharmacological quality and economic value of these plants. Factors such as plant maturity level, abiotic stress conditions, i.e., photon radiation, nutrient deficiency, presence of heavy metals, drought, and temperature stress (Park et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTrichomes are formed on the surface of various plant species with the main function as protection against environmental factors as well as secondary metabolite production centers. GTs are known as plant chemical factories because they play a role in the secretion and storage of bioactive compounds, including cannabinoids in \u003cem\u003eC. sativa\u003c/em\u003e (Tanney et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cannabinoids are synthesized and accumulated in GTs, with content dependent on GT secretion, type, and density (Haiden et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). To date, the biosynthesis of cannabinoid compounds is explained through two main pathways.\u003c/p\u003e\n \u003cp\u003eThe biosynthesis of cannabinoid compounds in \u003cem\u003eC. sativa\u003c/em\u003e involves a polyketide pathway that begins with tetraketide synthesis, where the enzyme Tetraketide Synthase (TKS) catalyzes the sequential condensation of hexanoyl-CoA with three molecules of malonyl-CoA to form 3,5,7-trioxododecane oil-CoA. Next, at the cyclization and aromatization stage, the Olivetolic Acid Cyclase (OAC) enzyme in collaboration with TKS removes CoA from the tetraketide product, which then undergoes cyclization and aromatization to form Olivetolic Acid (OLA). The process continues with a prenylation stage by the enzyme Aromatic Prenyltransferase, which adds a prenyl group from Geranyl Pyrophosphate (GPP) to OLA, producing Cannabigerolic Acid (CBGA). CBGA then acts as a major precursor and undergoes divergence through the action of specific enzymes, where Tetrahydrocannabinolic Acid Synthase (THCAS) converts CBGA to Tetrahydrocannabinolic Acid (THCA). Finally, THCA undergoes non-enzymatic decarboxylation involving the loss of the carboxylic acid group (CO₂H) to form THC as the final product (Filer, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThere is Methyl Erythritol 4 Phosphate (MEP) that serves as an alternative pathway in the biosynthesis of terpenoids including GPP, an important precursor in the biosynthesis of cannabinoids and some other basic terpenoids. This process starts with glyceraldehyde-3-phosphate and pyruvate in the presence of the enzyme 1-deoxy-D-xylulose 5-phosphate synthase (DXS). The product of this reaction is 1-Deoxy-D-Xylulose 5-Phosphate (DXP). DXP is converted to methyl erythritol 4 phosphates by the enzyme 1-Deoxy-D-Xylulose 5-Phosphate reductoisomerase (DXR) through a complex series of reduction and isomerization reactions. MEP is then converted to GPP in a series of steps. The MEP-specific enzymes responsible for this are MEP cytidyl transferase and geranyl pyrophosphate synthase. The end result is GPP, which is a key component in cannabinoid biosynthesis (Tahir et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTrichomes are hair-like developments of epidermal cells that cover part of the plant surface. Based on their structure and function, trichomes are often classified as GT or Non-Glandular Trichome (NGT), which can be unicellular or multicellular (Feng et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). GTs are unique in that they have the ability to synthesize, secrete, and store large amounts of secondary metabolites. These specialized metabolites include terpenoids, phenylpropanoids, flavonoids, methyl ketones, acyl sugars, and some oils or resins (Chen et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). While NGT plays an important role in plant defense by reducing transpiration, increasing tolerance to cold, and deflecting high solar radiation, thereby reducing herbivory (Gostin, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cannabis female flowers have a large number of GTs on their surface. These defense structures develop mainly in conjunction with female flower tissue and provide protection by producing and storing a large number of secondary metabolites, which include cannabinoids (Dimopoulos et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePreliminary Test\u003c/h2\u003e\n \u003cp\u003eA preliminary test with Fast Blue B (FBB) Salt reagent was conducted as an initial test of samples containing cannabinoid compounds by showing a colour change to purple-reddish, indicating a positive test result (Bhakti Nusantara \u0026amp; Aisyah Rahmania, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sample BR below represents the positive results for marijuana of the other five cannabis samples (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe formation of a reddish-purple colour with FBB reagent is based on the azo coupling reaction between diazonium compounds and phenolic groups on cannabinoids. The hydroxyl group (-OH) on the cannabinoid compound ionizes under alkaline conditions and produces a negative charge that is stabilized through a resonance system on its aromatic structure. The phenolic groups in this process act as electron donors that interact with the diazonium compound. This process also produces output in the form of aromatic compounds with electron systems that are able to absorb light waves and emit certain color indications that are characteristic. The stability of azo compounds in alkaline media allows for the estimation of cannabinoid content so that this method can be used as a quick, simple, and effective preliminary technique (Jornet-Mart\u0026iacute;nez et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Philp \u0026amp; Fu, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eConfirmation Test\u003c/h2\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003eRaman Spectrum of Cannabis sativa\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRaman Spectrum Band Assignments of Cannabis and Interpretation of Molecular Vibrations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVibration Mode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWave Number (cm-\u0026sup1;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMolecules\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u0026thinsp;=\u0026thinsp;C and C\u0026thinsp;=\u0026thinsp;O stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1747\u0026thinsp;\u0026minus;\u0026thinsp;1545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCH₂ bending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1500\u0026thinsp;\u0026minus;\u0026thinsp;1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCH₂ twisting bending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1187\u0026thinsp;\u0026minus;\u0026thinsp;1138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-C stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1100\u0026thinsp;\u0026minus;\u0026thinsp;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eQualitative analysis for THC in the samples was confirmed by measurement of Raman spectra against the \u0026Delta;9-THC standard by direct treatment of five samples (AB, LS, AT, BR, and PJ) at the inflorescence part, which is GT glass that shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The main vibrational bands of THC-rich plants were observed at 1620 cm⁻\u0026sup1;. The determination of these bands is reported in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The Raman spectra of the THC-rich plants showed a prominent vibrational band at 1620 cm⁻\u0026sup1;. From each plant, at least two different spots were analyzed, and the average of them was considered as representative. In this analysis, principal components allow us to examine the relationship of the data. Each principal component represents a linear combination of variables and gives the maximum variance. In this case, the variables considered are the maximum and minimum points of the spectrum or, in other words, the spectrum itself. Therefore, the principal components of the multivariate analysis represent the spectrum common to the various samples and represent how similar the spectra obtained from the various samples are (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Analysis of these spectra provides some parameter analysis for evaluation of the presence of THC content by analysis of THC functional groups with the main peak of C-C-H bending of aliphatic chain vibration at 1620 cm⁻\u0026sup1;. This can provide important insights into the presence of the compound.\u003c/p\u003e\n \u003cp\u003eThe bands of the cannabis spectrum were compared to the standard spectrum of THC, as the laser was focused on the main production site of the cannabinoid. Feature assignment of the Raman spectra of the cannabis samples was performed in the range 200\u0026ndash;1800 cm⁻\u0026sup1;, as this is where the more typical vibrational modes of the molecules occur (molecular fingerprints) (Sanchez et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the vibrational area of THC as the main constituent of cannabis was observed in the first zone at higher wave numbers related to the presence of C\u0026thinsp;=\u0026thinsp;C and C\u0026thinsp;=\u0026thinsp;O double bonds. The bands at 1747 cm⁻\u0026sup1;, 1620 cm⁻\u0026sup1;, and the shoulders at 1606 and 1545 cm⁻\u0026sup1; are in the C\u0026thinsp;=\u0026thinsp;C stretching region the spectrum normalized to 1453 cm⁻\u0026sup1;.\u003c/p\u003e\n \u003cp\u003eAs can be seen, the overlapping spectra of the THC molecules indicate the presence of more molecules with related structures. It appears that the acidic form of the precursor may also be present and contributing to this broad band. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the theoretically calculated Raman spectra of the acid molecule and the decarboxylated cannabinoid molecule. This allows visualization of the very similar position of the C\u0026thinsp;=\u0026thinsp;C stretching mode; therefore, it is appropriate to also assign the contribution of the acid form molecule to this spectral region.\u003c/p\u003e\n \u003cp\u003eThe second zone lies between 1500 and 1100 cm⁻\u0026sup1; with bending of the CH\u003csub\u003e₂\u003c/sub\u003e signal. In conjunction with simulated THC vibrations, it is reasonable to expect a much higher frequency CH\u003csub\u003e₂\u003c/sub\u003e scissor bending spectrum around 1453 cm⁻\u0026sup1;. The bending at 1368 cm⁻\u0026sup1; and nearby is related to the CH\u003csub\u003e₂\u003c/sub\u003e bending of other cannabinoids. The moderate bands recorded at 1187 and 1138 cm⁻\u0026sup1; signify a retention of the higher frequency bands in the Raman spectrum. This results from the interplay of cannabinoids in the trichome acting on the CH twisting bending mode.\u003c/p\u003e\n \u003cp\u003eThe lower wavenumber region of the cannabis spectrum (1100 to 600 cm⁻\u0026sup1;) is less intense. However, several well-defined bands are observed around 962 cm⁻\u0026sup1;, which are associated with the C-C stretching vibrations of the alkyl groups included in the expected cannabinoids. Similarly, the bands at 796 and 756 cm⁻\u0026sup1; correspond to the CH₃ and CH₂ vibrations of the same molecule in the cannabis trichomes. The average spectrum of each type of cannabis is visually very similar, with slight differences in the shift and intensity of the Raman bands, which will be discussed later in the statistical analysis. To observe the expansion of the number of cannabinoid molecules to be analyzed, as well as the fact that other forms of cannabinoids can be measured separately, this study uses advanced GC-MS testing to develop and validate the Raman method in measuring cannabis cannabinoids.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eGCMS of Cannabis sativa\u003c/h2\u003e\n \u003cp\u003eGC-MS analysis was conducted for the identification and quantification of cannabinoid compounds in \u003cem\u003eC. sativa\u003c/em\u003e samples from five regions in Aceh, Indonesia, to compare the chemical profiles and variations in cannabinoid content between the samples. GC-MS analysis showed significant variations in cannabinoid compound content between the five samples. GC-MS can perform analysis in MRM mode based on specific collision-induced fragmentation of precursor ions, which significantly increases the signal-to-noise ratio as well as the sensitivity of the method. This GC-MS technique enables highly selective monitoring of ion transitions in MRM mode, thus improving the accuracy and precision of the analysis. In this method, the compound molecules undergo ionization and fragmentation, resulting in a distinctive mass spectrum pattern used for identification. The chromatogram results revealed ten peaks confirmed as cannabinoids, with the lead compound identified as \u0026Delta;9-THC (Rontani, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe results of the analysis using the SWGDRUG mass spectral library showed that there were ten components of cannabinoid compounds with specific retention times and similarity percentages with an average similarity of 94.3%. CBD was identified at a retention time of 15.022 minutes with 95% similarity. THCV was identified at a retention time of 15.225 minutes with 99% similarity. CBL was identified at a retention time of 15.308 minutes with 91% similarity. CBTC was identified at a retention time of 15.323 minutes with 91% similarity. CBC was identified at a retention time of 15.847 minutes with 95% similarity. Methoxy-THC was identified at a retention time of 15.996 minutes with 92% similarity. \u0026Delta;9-THC was identified at a retention time of 16.418 minutes with 99% similarity. CBG was identified at a retention time of 16.579 minutes with 92% similarity. \u0026Delta;9-THCH was identified at a retention time of 16.626 minutes with 92% similarity. CBN was identified at a retention time of 16.693 minutes with 97% similarity. The percentage area distribution of each compound in five samples with different regions (AB, LH, AT, BR, and PJ) showed variation in results as illustrated in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGC-MS analysis of cannabinoid content in \u003cem\u003eC. sativa\u003c/em\u003e from five different regions in Aceh Province, Indonesia.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLibrary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e% Area\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTetrahydrocannabivarin (THCV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabidiol (CBD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabicyclol (CBL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabicitran (CBTC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethoxy-THC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabichromene (CBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelta-9-Tetrahydrocannabinol (\u0026Delta;9-THC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabigerol (CBG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;9-THCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannabinol (CBN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026Delta;9-tetrahydrocannabinol is an assumed major psychoactive substance of \u003cem\u003eCannabis sativa\u003c/em\u003e plants (Bhakti Nusantara \u0026amp; Aisyah Rahmania, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). It was indexed as the most abundant compound in all samples. Sample AB possessed the greatest area percentage for this compound (57.48%), followed by sample PJ (45.40%) and then by LS (45.12%). Comparatively, the other two samples, BR and AT, had much lower amounts of \u0026Delta;9-THC: only 31.56 and 28.90%, respectively. A factor for producing higher \u0026Delta;9-THC content in AB and less in AT is the morphological conditions of the sample plant, especially the life cycle stages (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Inflorescence tissues should be the richest in cannabinoids, but they may not necessarily relate to the flower yields. Predicting them becomes an even more complicated issue when people are after secondary metabolites. More than ever, the environment has to be quite influential here. There may be the other way around, with phytocannabinoids yield decreasing as the flower develops (Feder \u003cem\u003eet al.\u003c/em\u003e, 2021). This will invariably introduce a dilution effect. According to Jurga et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), THC concentration in leaf tissues inversely relates to the plant height, while other flower characteristics need to be considered in addition to just inflorescence biomass, which is relevant to contributing to final productivity (Jurga et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAlongside \u0026Delta;9-THC, CBC registered significant percentages with the highest recorded in the sample AB as 16.07%, followed by LS at 13.92%. The remainder showed witnesses of lesser amounts of CBC, with the lowest being the AT sample at 4.88%. CBTC had a presence with samples AB, LS, and BR, giving percentage areas of 2%, 5.33%, and 2.39%, respectively. Another compound, CBN, which is the product of \u0026Delta;9-THC degradation, was found in different quantities, with the highest value in BR at 22.48%; AT had 19.27% and a low of 1.92% in AB. Other compounds like CBD were found in all five samples, although that was in very low concentrations, but the highest was in LS with 7.86%. THCV reflected a slight stable reading with the highest sampling from AB showing 5.26%. CBG compounds appear in intermediate amounts, with the highest in the PJ sample at 10.64%. Very small and variable amounts were found in all samples of CBL and Methoxy-THC only in AT and PJ samples, suggesting that their biosynthesis might be affiliated with some conditions, particularly like maturity of plant or other growing conditions (Apicella et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eGC-MS analysis results showed variations in cannabinoid compound content between \u003cem\u003eC. sativa\u003c/em\u003e samples from five regions in Aceh, where \u0026Delta;9-THC was the dominant compound in all samples, with the highest content found in sample AB. Variations in the content of other compounds such as CBC, CBD, and CBN indicate the influence of environmental factors, especially temperature, storage, or post-harvest treatment (Desaulniers Brousseau et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hahm et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTHC intensity of Raman spectrum on different regions at 1620 cm⁻\u0026sup1;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntensity (a.u)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAB1U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.436.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAB2U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.155.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAB2U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.366.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAB3U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.974.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAB3U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.573.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS1U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS1U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.939.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS2U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e968.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS2U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.508.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS3U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.240.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS3U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.938.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT1U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.683.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT1U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.851.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT2U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.449.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT2U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.406.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT3U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.281.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT3U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.305.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR1U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.207.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR1U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.307.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR2U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.627.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR2U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.505.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR3U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.782.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBR3U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.115.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ1U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.515.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ1U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.136.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ2U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.680.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ2U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.651.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ3U1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.055.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ3U2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.244.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe correlation between geographical origin and \u0026Delta;9-THC levels in each sample using T-test and One-Way ANOVA is illustrated in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of Geographical Origin on THC Content Using Raman Spectroscopy and GC-MS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHC Intensity by Raman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Area THC by GC-MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003esignificance: ns (not significant)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe correlation between regional origin and percentage THC area via Raman spectroscopy was at r\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;=\u0026thinsp;0.356, while via GC-MS r\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.956. Based on these results, the null hypothesis failed to be rejected, concluding that there is no significant influence of the geographical origin of the five samples on the THC intensity detected through Raman spectroscopy.\u003c/p\u003e\n \u003cp\u003eThe high content of \u0026Delta;9-THC should be positively correlated with geographical conditions, especially temperature and altitude (Trancoso et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Younas et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). A more specific analysis of external effects on cannabinoids is mandatory to confirm the hypothesis and to generate new evidence on Raman spectra. The reason for the absence of significant differences is related to the relatively close distance range and still being in the same archipelago. Although there are slight differences in the environmental parameters of the five samples, especially temperature and altitude, where sample AB grows in warmer conditions compared to AT, where the climate tends to be cooler due to being in the highlands (Azizs \u0026amp; Asnawi, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the temperature difference between the two is not too extreme to significantly affect cannabinoid accumulation. Thus, the homogeneity of environmental conditions at the sampling sites was the main factor that led to the absence of significant differences in THC content between the analyzed regions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe GC-MS used recorded the chemical profiles of 10 cannabinoids in five Aceh Indonesian cannabis samples (Δ9-THC, CBD, THCV, CBL, CBTC, Methoxy-THC, CBC, CBG, Δ9-THCH, and CBN). The profiles obtained can then be used to categorize cannabis. This grouping of cannabis helps reveal the geographical origin of the growing location of cannabis plant specimens. All five cannabis samples contained the most abundant major psychoactive constituent, Δ9-THC. The highest Δ9-THC content in AB is due to the influence of environmental factors. AB has a warm climate while AT has a cold climate. Cannabinol and Cannabidiol were also present in all five samples. Parametric T-test and one-way ANOVA analysis showed the presence of multiple absorption band spectra; Raman and GC-MS concluded no significant effect of geographical origin. This study successfully characterized dried \u003cem\u003eCannabis sativa\u003c/em\u003e samples from different regions in Aceh, Indonesia, based on their cannabinoid compound fingerprint characteristics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Aceh Besar\u003c/p\u003e\n\u003cp\u003eANOVA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;Analysis of Variance\u003c/p\u003e\n\u003cp\u003eAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Aceh Tengah\u003c/p\u003e\n\u003cp\u003eBR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Bireueun\u003c/p\u003e\n\u003cp\u003eCBC \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Cannabichromene\u003c/p\u003e\n\u003cp\u003eCBD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Cannabidiol\u003c/p\u003e\n\u003cp\u003eCBG \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Cannabigerol\u003c/p\u003e\n\u003cp\u003eCBGA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Cannabigerolic Acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCBL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Cannabicylol\u003c/p\u003e\n\u003cp\u003eCBN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Cannabinol\u003c/p\u003e\n\u003cp\u003eCBTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;Cannabicitran\u003c/p\u003e\n\u003cp\u003eCNS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Central Nervous System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCWAs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chemical Warfare Agents\u003c/p\u003e\n\u003cp\u003eCQL\u003cstrong\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eChemical Threat Analyzer\u003c/p\u003e\n\u003cp\u003eDXP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1-deoxy-d-xylulose 5-phosphate synthase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDXS\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1-Deoxy-D-xylulose 5-phosphate synthase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFBB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fast Blue B\u003c/p\u003e\n\u003cp\u003eGC-MS \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Gas Chromatography Mass Spectrometry\u003c/p\u003e\n\u003cp\u003eGPP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Geranyl Pyrophosphate\u003c/p\u003e\n\u003cp\u003eGT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glandular Trichomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH-NMR \u0026nbsp; Hydrogen Nuclear Magnetic Resonance \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHP-5MS\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High Performance-(5%-phenyl)-methylpolysiloxane\u003c/p\u003e\n\u003cp\u003eHPLC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High-performance liquid chromatography\u003c/p\u003e\n\u003cp\u003eIBM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International Business Machines\u003c/p\u003e\n\u003cp\u003eLS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Lhokseumawe\u003c/p\u003e\n\u003cp\u003eMEP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Methyl Erythritol 4 Phosphate\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRM \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Multiple Reaction Monitoring\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mass Selective Detector\u003c/p\u003e\n\u003cp\u003eNGT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Non-Glandular Trichome\u003c/p\u003e\n\u003cp\u003eOAC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Olivetolic Acid Cyclase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOLA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Olivetolic Acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePJ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Pidie Jaya\u003c/p\u003e\n\u003cp\u003ePNS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;Peripheral Nervous System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Retention Time\u003c/p\u003e\n\u003cp\u003eSPSS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Statistical software package for social sciences\u003c/p\u003e\n\u003cp\u003eTHCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tetrahydrocannabinolic Acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTHCAS \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tetrahydrocannabinolic Acid Synthase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTHCV \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tetrahydrocannabivarin\u003c/p\u003e\n\u003cp\u003eTKS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tetraketide Synthase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026Delta;9-THC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; delta 9- tetrahydrocannabinol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical guidelines of the institution where it is carried out. I, as first author, hereby give my informed consent for the collection, use, and publication of my own fingerprint data in the present research study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented to publish this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMarco bonete as a JEO Assistant said remove the author's contribution\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eApicella PV, Sands LB, Ma Y, Berkowitz GA (2022) Delineating genetic regulation of cannabinoid biosynthesis during female flower development in \u003cem\u003eCannabis sativa\u003c/em\u003e. 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BMC Plant Biol 24(1):1031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12870-024-05730-0/FIGURES/7\u003c/span\u003e\u003cspan address=\"10.1186/S12870-024-05730-0/FIGURES/7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"egyptian-journal-of-forensic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejfs","sideBox":"Learn more about [Egyptian Journal of Forensic Sciences](http://ejfs.springeropen.com)","snPcode":"41935","submissionUrl":"https://submission.springernature.com/new-submission/41935/3?","title":"Egyptian Journal of Forensic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cannabis sativa, Cannabinoids, GCMS, Raman Spectroscopy, parametric statistical analysis, profiling","lastPublishedDoi":"10.21203/rs.3.rs-6186729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6186729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eCannabis, which is a narcotic plant, refers to the leaves, flowers, stems, and seeds. Cannabis is used globally for its psychoactive properties with 2.5% of the world's population consuming it for. In Indonesia, the plant is classified as a Class 1 narcotic with a prevalence of use reaching 41.4%. Aceh is one of the largest cannabis producing regions in Indonesia, due to its favorable geographical and climatic conditions. Despite its illegal status, cannabis contains valuable phytocannabinoid compounds and is potentially important in medical applications. Previous studies have shown a correlation between the compound profile of cannabis and its geographical origin. This study aims to develop a classification method based on the cannabinoids compound profiles of dried cannabis samples taken from five regions in Aceh (Aceh Besar, Aceh Tengah, Bireuen, Lhokseumawe, and Pidie Jaya), by microscopy, raman spectrophotometry, GC-MS, and parametric statistical analysis to assist authorities in tracing the source of cannabis for law enforcement and forensic purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIn this study, dried Cannabis sativa from five regions of Aceh, Indonesia, was tested with Raman spectroscopy and GC-MS to produce informative cannabinoid compound profiles as plant profiling. The results obtained 10 cannabinoids quantified in plant samples (Δ9-THC, CBD, THCV, CBL, CBTC, Methoxy-THC, CBC, CBG, Δ9-THCH, and CBN). The cannabinoids compound profile showed Δ9-THC had the highest overall content and was indicated as the most important compound in the cannabis plant clustering profile. Among the various regions, Aceh Besar had the highest cannabis content. Statistical analysis of Raman spectroscopy and GC-MS data found (1) revealed compounds responsible for clustering cultivars between clusters, (2) variation among cannabis chemical profiles as a result of growing environment, and (3) facilitated prediction of cannabis profiles in helping to categorize regions of unknown cannabis origin based on chemical profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eRaman spectroscopy and GC-MS have proven reliable and efficient methods for classifying Cannabis sativa based on its cannabinoid profile in Aceh, Indonesia. The findings help reveal the geographical origin of the growing location of cannabis plant specimens. All five cannabis samples contained a major Δ9-THC psychoactive constituent. The highest Δ9-THC content comes from AB due to the influence of environmental factors. Parametric test analysis concluded that there was no significant effect of geog raphical origin related to the relatively close distance range of samples. Additionally, comparing these methods with other analytical techniques will support defined classification models and improve their application in forensic science, particularly in drug enforcement and quality assessment.\u003c/p\u003e","manuscriptTitle":"Profiling of Various Dry Cannabis Sativa From Aceh, Indonesia Based on Cannabinoids Compound Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 07:55:52","doi":"10.21203/rs.3.rs-6186729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-06T09:41:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T15:10:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-28T20:28:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314404349426082234595683713939271052532","date":"2025-03-27T02:03:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196833840318177731257656020028546249193","date":"2025-03-26T22:40:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74081269353874103316303327724066755332","date":"2025-03-25T21:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86980502798951333956299704109240995137","date":"2025-03-25T21:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T21:23:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T06:46:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T06:43:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Egyptian Journal of Forensic Sciences","date":"2025-03-09T04:36:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"egyptian-journal-of-forensic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejfs","sideBox":"Learn more about [Egyptian Journal of Forensic Sciences](http://ejfs.springeropen.com)","snPcode":"41935","submissionUrl":"https://submission.springernature.com/new-submission/41935/3?","title":"Egyptian Journal of Forensic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0891bb03-f6d1-4f4c-9d38-0422baae3a49","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T16:02:07+00:00","versionOfRecord":{"articleIdentity":"rs-6186729","link":"https://doi.org/10.1186/s41935-025-00480-y","journal":{"identity":"egyptian-journal-of-forensic-sciences","isVorOnly":false,"title":"Egyptian Journal of Forensic Sciences"},"publishedOn":"2025-09-19 15:57:28","publishedOnDateReadable":"September 19th, 2025"},"versionCreatedAt":"2025-04-02 07:55:52","video":"","vorDoi":"10.1186/s41935-025-00480-y","vorDoiUrl":"https://doi.org/10.1186/s41935-025-00480-y","workflowStages":[]},"version":"v1","identity":"rs-6186729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6186729","identity":"rs-6186729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}