This study develops a volatile fingerprinting approach for authenticating traditional sesame oil using HS-SPME-GC-MS with GC×GC-MS-assisted compound identification. Conventional GC-MS fingerprints were evaluated by similarity analysis, while GC×GC-MS improved identification of volatile markers in authentic and adulterated samples.The method detected maize oil dilution at levels as low as 5% and sesame oil essence adulteration from 0.2%. Principal component analysis and sensory evaluation supported the fingerprint-based classification, demonstrating a reproducible strategy for detecting different types of sesame oil adulteration.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#sec0010">Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification&nbsp;</a></h4>Ruibing Jin, Fuyun Kuang, Qi Meng, Lijin Wang, Huanlu Song, Xueping Feng, Mingshan ZouJournal of Food Composition and Analysis, 2026, 155:109277<div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.jfca.2026.109277">https://doi.org/10.1016/j.jfca.2026.109277</a></div><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div><div><div><div>Sesame is cultivated worldwide for its nutritional value and flavor, especially in India, China, Myanmar, and Tanzania (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib23">Yin et al., 2021</a>). Sesame oil (SO) is an edible oil made from sesame seeds roasted at high temperatures to produce the distinct aroma and then processed using pressing, leaching, or other methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib5">Huang et al., 2024</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib11">Ma et al., 2022</a>). This traditional Chinese characteristic edible oil is extremely popular among consumers because of its high nutritional value and unique flavor. The unique aroma is an important indicator of quality and the main feature that distinguishes SO from other vegetable oils. SO contains fatty acids, vitamin E, and phytosterols that support cardiovascular health and anticarcinogenic processes (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib21">Xu et al., 2025</a>), along with sesamin and other lignans that impart strong antioxidant and anti-corticosteroid properties (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib24">Yin et al., 2024</a>). Owing to its nutritional and sensory values, the high retail price of SO has motivated adulteration with cheaper edible oils or SO essences (SOE). Therefore, the development of SO adulteration detection technologies such as fingerprinting is essential.Fingerprinting technology typically involves using analytical detection and data processing methods to generate chromatograms/spectra of the separate sample components simultaneously. In China, fingerprinting is primarily used in the fields of traditional Chinese medicine, biology, and food production. To ensure the quality of traditional Chinese medicines, the Chinese Pharmacopoeia Commission developed the “Chinese Medicine Chromatographic Fingerprint Spectra Similarity Evaluation System” (Fingerprint Similarity System). After importing sample chromatograms, the software generates a reference fingerprint spectrum based on the retention times and peak heights, from which the similarity between the individual samples and reference spectrum is calculated. Samples with similarities &gt; 0.900 are considered highly compatible with the reference fingerprint (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib13">National Medical Products Administration, 2000</a>). Chromatographic fingerprinting is particularly valuable for traditional SOs and reveals how variations in raw material origin or production processes influence the overall similarity of the volatile component profiles across samples. This enables more effective adulteration detection, origin discrimination, varietal differentiation, and characteristic substance analysis (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib3">Fu et al., 2023</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib14">Shi et al., 2025</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib15">Sun et al., 2023</a>).Volatile compound fingerprinting has been employed to characterize the volatile profiles of food products, enabling product discrimination and quality assessment (including identification and grading). <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib16">Sun et al. (2020)</a> analyzed volatile flavor compounds in SO and SO adulterated with soybean oil and identified characteristic compounds that can serve as indicators for adulteration. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18">Wang et al. (2024a)</a> used UV spectral fingerprints in combination with chemometric analytical methods to assess the quality of pressed SO with different levels of adulteration with refined SO. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1">Aghili et al. (2023)</a> analyzed the fatty acid compositions of diluted SO samples using an electronic nose (e-nose) and gas chromatography-mass spectrometry (GC-MS), revealing differences in the volatile profiles between the original and adulterated SO samples, which could aid in distinguishing adulterations. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20">Xing et al. (2019)</a> established fatty acid fingerprints capable of identifying sesame oil adulterated with rapeseed oil at levels as low as 5%. However, the sensitivity decreased for adulteration with corn oil, where only levels above 10% were detectable. Although <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26">Zhang et al. (2016)</a> established a simple and rapid ion mobility spectrometry method to identify sesame oil adulterated with flavoring substances, its detection capability is limited to adulteration levels above 10%. However, few studies have assessed the performance of GC-MS-based fingerprinting in the concurrent detection of SO dilution and essence adulteration from volatile profiles.Although the chromatographic fingerprint similarity evaluation system was originally developed for traditional Chinese medicines, it has recently been successfully applied to edible oils. For example, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib7">Li et al. (2025)</a> established volatile fingerprints for fried pepper oils using the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" and adopted a similarity threshold of 0.900 for quality discrimination. Therefore, we characterized the volatile fingerprint of authentic traditional SO using headspace solid-phase microextraction (HS-SPME) coupled with two-dimensional GC (GC×GC)-MS; co-elution issues were resolved by parallel GC×GC-MS analysis, while fingerprint similarity was evaluated using the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" based on GC-MS data. By comparing this fingerprint with those of diluted SO by maize oil (MO) and sesame essence-adulterated oil, we investigated the accuracy of the fingerprint model. This study provides a scientific basis for quality control and authentication of traditional SO.<h4>2. Materials and methods</h4><h5>2.4. GC×GC-MS</h5>GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=8890">8890</a>–<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=5977B">5977B</a>, Agilent Technologies, Santa Clara, CA, USA) with a solid-state modulator (SSM) 1800 (J&amp;X Technologies, Shanghai, China) was used to qualitatively analyze the volatile compounds of SO (B. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib17">Wang et al., 2023</a>).Volatile compounds were detected as previously described (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib2">Duan et al., 2025</a>), with some modifications. Mass spectra were acquired over the range of 40–450 m/z. Ultra-purity helium (99.999% purity) served as the carrier gas, flowing at a rate of 1.2 mL/min. A splitless injection mode was used. The oven temperature program was set to 40 °C and held this temperature for 2 min, slowly increased to 230 °C at 4 °C per min, and held at 230 °C for 5 min. The detector temperature was set to 250 °C. The quadrupole temperature and transfer line temperature were 150 °C and 250 °C, respectively. An electron impact ion source with an electron energy of 70 eV was used. The ion source temperature was set to 230 °C. A full scan mode was applied for acquisition. Two columns were used inside the GC oven: the first was a polar column, DB-wax (30 m × 0.25 mm × 0.25 μm; Agilent Technologies); the second was a mid-polar column, DB−17 (mid-polar, 1.85 m × 0.18 mm × 0.18 μm; J&amp;X Technologies). SSM1800 was placed between them for the heating and cooling stages. The SSM modulation period was 4 s. GC×GC-MS was used solely for qualitative confirmation of compounds that co-eluted in GC-MS.<h5>2.5. GC-MS</h5>Volatile compounds were analyzed semi-quantitatively by GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7890A">7890A</a>−<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7000B">7000B</a>; Agilent Technologies), and 2-methyl−3-heptanone was used as an internal standard. GC-MS analysis was performed using a DB-wax column (polar, 30 m × 0.25 mm, 0.25 μm; Agilent Technologies, Folsom, CA, USA) to distinguish the volatile compounds. The carrier gas (ultra-pure helium, 99.999%) was delivered at a flow rate of 1.2 mL/min. The method, heating, and mass spectrometry conditions are consistent with those described in Section 2.4. All quantitative and fingerprint similarity analyses were based on the GC-MS data obtained from this section.<h4>3. Results and discussion</h4><h5>3.2. Analysis of volatile compounds</h5>The GC×GC-MS result of JH and the GC-MS results of JH, MSO 5 (50% MO), and MSE 3 (0.6% SOE) are shown in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#fig0005">Fig. 1</a>. A total of 85 volatile compounds were identified in the SO, MSO, and MSE samples, and 33 common peaks were identified, including ketone (1), acid (1), nitrogen-containing compounds (16), sulfur-containing compounds (7), furans (3), aldehyde (1), and phenolic compounds (4, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#tbl0015">Table 3</a>).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_1_A_and_B_respectively_represent_the_three_dimensional_chromatogram_and_two_dimensional_chromatogram_336c1e9b86.jpg" alt="Journal of Food Composition and Analysis, 2026, 155:109277: Fig. 1. (A) and (B) respectively represent the three-dimensional chromatogram and two-dimensional chromatogram of volatile compounds within the JH−1 sesame oil sample; (C)-(E) respectively represent the GC-MS results of volatile compounds within the JH−1, MSO 6 (50% MO), and MSE 3 (0.6% MSE)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_1_A_and_B_respectively_represent_the_three_dimensional_chromatogram_and_two_dimensional_chromatogram_336c1e9b86.jpg 119w," sizes="100vw" width="1600" height="2107"><h5>3.3. Establishment of GC-MS fingerprints and similarity analysis of volatile substances</h5>The GC-MS volatile compound data from the test SO samples were imported into the Fingerprint Similarity System to generate chromatographic fingerprints. The reference fingerprint profile was constructed as the arithmetic mean of all profiles (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#fig0015">Fig. 3</a>). Similarity values between the sample fingerprints and reference were calculated to evaluate compositional consistency. The SO samples showed a reference fingerprint similarity of 0.910–0.993 (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#tbl0020">Table 4</a>), surpassing the 0.900 benchmark (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib10">Ma et al., 2024</a>). This demonstrates a consistent composition of key volatile components across samples with minimal quality variation, satisfying the fundamental fingerprinting and quality assessment requirements for traditional SO.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_3_Reference_fingerprint_of_volatile_compounds_b17f79ad1a.jpg" alt="Journal of Food Composition and Analysis, 2026, 155:109277: Fig. 3. Reference fingerprint of volatile compounds in traditional SO established by GC-MS. Different numbers are represented the different compounds in Table 2." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Food_Composition_and_Analysis_2026_155_109277_Fig_3_Reference_fingerprint_of_volatile_compounds_b17f79ad1a.jpg 245w," sizes="100vw" width="1600" height="584"><h4>4. Conclusion</h4>This study established a volatile compound fingerprinting method using HS‑SPME‑GC‑MS with GC×GC‑MS‑assisted identification for the authentication of traditional sesame oil. A total of 33 common volatile peaks were identified, with pyrazines and guaiacol as major contributors. OPLS‑DA models (R²Y &gt; 0.98, Q² &gt; 0.97) demonstrated robust discrimination between authentic and adulterated oils, as well as between the two adulteration types. Compounds with VIP &gt; 1 and p &lt; 0.05 were identified as potential key markers. Selected key markers were then subjected to external standard quantification, and the obtained absolute concentrations were used to perform PCA. All calibration curves showed excellent linearity (R² &gt; 0.997), and the external standard PCA clearly separated sesame oil essence from all other samples. Fingerprint similarity analysis, validated by ROC (AUC = 1.000, optimal cutoff 0.8935), showed that adulterated samples had similarity values below 0.9, clearly distinguishing them from authentic oils. The method successfully detected maize oil dilution adulteration as low as 5% and sesame oil essence adulteration as low as 0.2%.Compared with previous methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1">Aghili et al., 2023</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18">Wang et al., 2024a</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20">Xing et al., 2019</a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26">Zhang et al., 2016</a>), our approach uniquely detects both dilution and essence adulteration in a single workflow, achieves lower detection limits (5% for MO, 0.2% for SOE), and provides an intuitive 0.900 similarity threshold without complex chemometric modeling. While non‑destructive methods (e‑nose, IMS) are more suitable for rapid screening, our approach offers higher chemical specificity, the ability to distinguish between dilution and essence adulteration, and an intuitive similarity threshold. Current limitations include testing only maize oil and one essence type, and validation on laboratory‑prepared samples. Future work will expand to other adulterants (soybean, rapeseed oil) and blind commercial samples. Overall, this method provides a reproducible, empirically validated tool for traditional sesame oil authentication. Furthermore, external standard quantification in the present study was performed only for a selected set of key volatile markers (VIP&nbsp;&gt;&nbsp;1 and p&nbsp;&lt;&nbsp;0.05). The same approach can be extended to other compounds in future studies, enabling odor activity value (OAV) calculations for deeper insights into sensory contributions, more precise adulteration level prediction, and improved model robustness across different production batches.</div></div></div></div></div>

Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification

Comprehensive investigation of matrix effect evaluation approaches for reliable quantification in bioanalysis using UHPLC-MS/MS

This study compares loss-on-ignition, acid decarbonation, and combustion elemental analysis for measuring organic carbon in sediments using certified reference materials and real samples. Loss-on-ignition conversions were inaccurate, while acid washing before elemental analysis caused significant organic carbon loss and poor precision.Combustion pretreatment followed by elemental analysis provided accurate and precise organic and inorganic carbon measurements, confirmed by temperature-dependent combustion. Across more than 150 intertidal sediment samples, acid decarbonation underestimated organic carbon by 37% ± 8%, supporting combustion-based elemental analysis as the preferred approach for reliable sediment carbon determination.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#s0055">Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice</a></h4>Robert B. Sparkes, Stuart A. Rae, Lucy McMahon, Hannah L. Mossman, David McKendry, Clare E. Dean, Graham Tinsley, Frances Fowden, Connor J. Grisdale, Hafiza Begum, Holly Hope, Charlotte McEnhill, Samuel O’Boyle, Nawal Sohail, Marvyn Wachira, Rachel M. DunkOrganic Geochemistry, Volume 218, 2026, 105215<div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.orggeochem.2026.105215">10.1016/j.orggeochem.2026.105215</a></div><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div><div><div><div>Environmental and Earth scientists frequently require measurements of carbon contents, including both the total carbon concentration (TC) and the fractions contributed by carbon from organic matter (organic carbon; OC) and carbonate minerals (inorganic carbon; IC; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0025">Bendle et al., 2009</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0220">Radabaugh et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0230">Saderne et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0240">Scheingross et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0260">Smeaton et al., 2021</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0295">Vonk et al., 2012</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0315">Wood et al., 2021</a>).Accurate, precise, and affordable carbon concentration measurements are critical to the drive towards net-zero emissions, which requires monitoring, reporting and verification of carbon burial and release from an array of global environments (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0210">Post et al., 2001</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0265">Smith, 2004</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0270">Smith et al., 2020</a>). Carbon concentration measurements also underpin further analyses (e.g. normalising the concentration of individual biomarker compounds against the total organic carbon concentration, calculating elemental carbon to nitrogen ratios (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0290">van Dongen et al., 2008</a>), determining the correct sample load for isotope ratio measurements). Despite carbon concentration measurements being ubiquitous throughout multiple disciplines there is no standard approach for making these, especially determining the organic component of the sedimentary carbon (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0080">Fest et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0320">Young et al., 2018</a>). Different disciplines have often generated their own consensus approach, which may not align with studies from another field, even those working on similar samples and asking similar questions (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0070">Farmer et al., 2014</a>). Use of an agreed standard measurement protocol for carbon concentrations, or understanding the agreement/variability between protocols, is vital for regulatory monitoring, reporting and verification (MRV), such as the ongoing development of carbon offsetting standards that, if implemented robustly, could provide real-world benefits to climate mitigation (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0035">Bonn et al., 2014</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0045">Burden et al., 2023</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0105">Guizar-Coutiño et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0190">Needelman et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0205">Pan et al., 2022</a>). Determining a standardised, verified approach to carbon measurement is important for those performing MRV, who may not have access to advanced and/or expensive analytical instrumentation.This study compares three common methods for quantifying the total and organic carbon concentrations in sedimentary samples: i) loss-on-ignition, ii) elemental analysis combined with acid decarbonation, and iii) elemental analysis combined with organic matter combustion. The comparison focuses on assessing the accuracy, precision, and repeatability of each method, identifying their respective strengths and weaknesses, and independently confirming findings via a fourth method (temperature dependent combustion and infrared CO2 measurement) to provide recommendations for best practice. A summary review of these methods is provided below.<h4>2. Materials and methods</h4><h5>2.3. Independent testing of reference materials via TDC</h5>Standards were analysed by the supplier (Elemental Microanalysis Ltd, UK) as part of the certification process: 15/30 elemental analysis measurements gave expected values of TC, OC and IC and the uncertainty for TC, following ISO/IEC17025. These values were independently checked using an <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=16&amp;search=Soli+TOC">Elementar SoliTOC cube</a> instrument at the instrument manufacturer’s headquarters (Elementar Analysensysteme, Hanau, Germany), which used the TDC method. Two combustion ramps were applied, first a two-step OC and IC determination at 500 °C and 900 °C, and then a three-step programme at 400 °C and 600 °C for easily-combustible and residual OC fractions and 900 °C for IC, with three repeat measurements of each. The SoliTOC cube instrument also analysed the agricultural soil sample, again making three repeat measurements of both the two-step and three-step temperature dependent combustion protocols (see Supplementary Table 3).<h5>2.5. Elemental analysis</h5>Total carbon concentrations were measured following the protocol described in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0180">Mossman et al. (2022)</a>. 20&nbsp;mg of either untreated or pre-treated sample was weighed into tin capsules (Elemental Microanalysis Ltd.) and analysed for carbon concentrations using a Vario EL cube elemental analyser (Elementar Ltd.). Before each analytical run, the instrument was cleaned using three blanks without oxygen and seven blanks with oxygen, and calibrated using five analyses of up to 14&nbsp;mg ethylenediaminetetraacetic acid (EDTA) standard (Elemental Microanalysis Ltd.) and three analyses of an independent Soil Standard (Clay) CRM, acquired separately to the Soil Standard (Clay) CRM used to create analytical samples for this study (Elemental Microanalysis Ltd.). Three analyses of the EDTA calibration standard were repeated after every twenty samples. Long-term drift was monitored by repeated measurements of an internal laboratory standard sample, which has been analysed since 2015. Over the course of 65 measurements across 10 instrument runs for this study the laboratory standard showed no systematic drift.<h4>3. Results and discussion</h4><h5>3.1. Testing of certified reference materials (CRMs) using temperature dependent combustion (TDC)</h5>Both two-step and three-step TDC protocols consistently measured carbon contents close to but slightly below the supplier-certified values (see <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0005">Fig. 1</a>). For Soil Standard (Clay), the TDC instrument under-estimated OC by 0.10&nbsp;wt% (two-step) and 0.14&nbsp;wt% (three-step) and IC by 0.15&nbsp;wt% (two-step) and 0.087&nbsp;wt% (three-step). For Soil Standard (Peaty) the TDC instrument underestimated OC by 0.26&nbsp;wt% (two-step) and 0.34&nbsp;wt% (three-step), and IC by 0.021&nbsp;wt% (two-step) and 0.090&nbsp;wt% (three-step). Relative differences for OC are small, 5.1 – 7.0% for Clay and just 1.7 – 2.2% for Peaty, but higher for IC due to the small amount of IC present in the standards (25 – 44% for Clay, 7.0 – 30% for Peaty).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 1. Comparison of organic carbon and inorganic carbon measurements on two certified reference materials using four methods. Upper panel is Soil Standard (Clay) and lower panel Soil Standard (Peaty). Yellow diamonds show the certified value (Elemental Microanalysis Ltd) with manufacturer stated uncertainty in total carbon used as error bars for OC and IC (n = 30). White and grey symbols represent Loss-on-Ignition results using four different organic matter to organic carbon conversion equations (grey sqaures use the “van Bemmelen” scaling factor (Pribyl, 2010), grey circles the Craft et al. (1991) factor, white diamonds the Ouyang and Lee (2020) equation and white circles the Smeaton et al. (2022) equation. IC values for all LoI results use the C/CO2 mass fraction (Wang et al., 2011). Blue circles show the acid decarbonation and elemental analysis protocol, orange circles the combustion and elemental analysis protocol. Green and purple circles show mean results from temperature dependent combustion method (2-step and 3-step protocols respectively). Error bars represent one standard deviation after multiple measurements (n = 2 to n = 4). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg 116w," sizes="100vw" width="2756" height="3698"><h5>3.4. Reproducibility tests</h5>Having shown that combustion coupled with elemental analysis was the most robust method for measuring OC in pure and diluted standards, the next experiment tested the reproducibility of these protocols over multiple implementations. A dried, ground agricultural soil sample was repeatedly analysed using each method (separate aliquots were measured using LoI at 500 °C and 800 °C, separate untreated aliquots were analysed via EA, separate aliquots were combusted at 500 °C and analysed via EA, and separate acid decarbonations were carried out prior to EA measurements). This required a large amount of starting material, more than was available in the CRMs. Independent external measurement of the soil sample using temperature dependent combustion (Elementar, Germany) gave mean OC&nbsp;=&nbsp;2.50&nbsp;±&nbsp;0.002&nbsp;wt% and IC&nbsp;=&nbsp;1.29&nbsp;±&nbsp;0.008&nbsp;wt% for combustion to 500 °C (n&nbsp;=&nbsp;3), whereas combustion to 600 °C gave OC&nbsp;=&nbsp;2.52&nbsp;±&nbsp;0.075 and IC&nbsp;=&nbsp;1.12&nbsp;±&nbsp;0.010&nbsp;wt% (n&nbsp;=&nbsp;3).<div>LoI showed differences in mean OC depending on the equation chosen (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010">Fig. 2</a>). The conversions of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0050">Craft et al. (1991)</a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0200">Ouyang and Lee (2020)</a> were similar to the independent measurements (2.90&nbsp;±&nbsp;0.05&nbsp;wt% and 2.60&nbsp;±&nbsp;0.07&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each), but the equations of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0255">Smeaton et al. (2022)</a> and van Bemmelen over-estimated OC (4.19&nbsp;±&nbsp;0.05 and 4.21&nbsp;±&nbsp;0.08&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each). IC estimates using the C/CO2 mass fraction conversion were higher than the expected value (1.91&nbsp;±&nbsp;0.06&nbsp;wt%, n&nbsp;=&nbsp;9). Note that the relative standard deviation for OCLoI was&nbsp;∼&nbsp;1.8%, and 3.2% for ICLoI, showing acceptable run-to-run precision.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 2. Organic and inorganic carbon measurements following repeated analysis of a homogenised soil sample via multiple methods. Markers for each method are shown in the legend and are the same as Fig. 1. Small black circles and error bars show the mean and one standard deviation after multiple repetitions of each protocol (n = 9 for LoI, C-EA and AD-EA, n = 3 for TDC)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg 204w," sizes="100vw" width="2363" height="1815">Since OCC-EA and ICAD-EA were calculated by subtraction from mean TCEA, all data from the C-EA and AD-EA methods plotted on a straight line in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010">Fig. 2</a>, representing OC&nbsp;+&nbsp;IC&nbsp;=&nbsp;TC. However, position and scatter along that line varied between the two pre-treatment techniques. OCC-EA clustered just higher than the independent measurements using TDC (2.60&nbsp;±&nbsp;0.03&nbsp;wt%, n&nbsp;=&nbsp;9). Propagating the errors in TC and IC gave a combined error estimate of&nbsp;±&nbsp;0.1&nbsp;wt% for the repeated OCC-EA measurements. ICC-EA was also close to but slightly higher than the TCD results, 1.35&nbsp;±&nbsp;0.03&nbsp;wt%. AD-EA, however, gave mean OC values of 2.00&nbsp;±&nbsp;0.12&nbsp;wt% (n&nbsp;=&nbsp;9) which was more variable run-to-run than the other methods and gave a significantly (p&nbsp;&lt;&nbsp;0.0001) lower mean OC value than the TCD results. Once again, acid decarbonation results suggested that a large amount of organic matter was lost during the acid treatment, tested further in the next section.</div><h4>4.3. Conclusions</h4>An accurate, precise, efficient, and standardised method for measuring total, organic and inorganic carbon contents of sediment samples is critical for both scientific research and environmental monitoring and reporting and verification. We rigorously tested three methods for measuring carbon contents in soils and intertidal sediments, using certified reference materials and&nbsp;&gt;&nbsp;150 sediment samples. Loss-on-ignition (LoI) is cheap and easy but showed moderate run-to-run variability and, without careful calibration, it is not accurate.Elemental analysis (EA) is an accurate and precise method for total carbon (TC) analysis. Measuring organic and inorganic carbon via EA requires pre-treatment of the sample, and this study tested both acid decarbonation and combustion preparation techniques. Acid decarbonation led to significant and variable organic carbon loss, meaning that true organic carbon concentrations are likely higher than those measured following acidification. Combustion pre-treatment was shown to efficiently remove organic matter, leading to accurate and precise results, and we would recommend making organic carbon measurements in this way.</div></div></div></div></div>

Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice

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Determination of nitrosamine content (Bioanalytic)

Our offer includes comprehensive development and validation of methods for determination of nitrosamines in pharmaceutical products. We perform the work according to the criteria and needs of the client based on the EMA note.

Determination of nitrosamines is one of the pharmaceutical industry's top priorities. After the presence of N-nitrosodimethylamine (NDMA) was first reported in an active pharmaceutical ingredient (API), the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) mandated the identification and control of all APIs and finished products in which there is a risk of nitrosamines.The content of nitrosamines in drug products can be controlled primarily by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis. More than that, a reliable analysis result in our offer consists of a well-chosen sample preparation procedure, a selective and sensitive analytical method, and the experience of the analyst, who selects the best method parameters at the optimization stage.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Bioanalytic_Determination_of_nitrosamine_content_Bioanalytic_2_ce4f440837.jpg" alt="Bioanalytic: Determination of nitrosamine content." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Bioanalytic_Determination_of_nitrosamine_content_Bioanalytic_2_ce4f440837.jpg 234w," sizes="100vw" width="1536" height="1024">We have gained experience working with leading pharmaceutical companies. As part of our ongoing work, we have developed analytical methods for small-molecule nitrosamines of 70-150 g/mol. These compounds are mainly formed during manufacturing processes and depend on the presence and type of excipients (e.g. NDBA - N-nitroso-dibutylamine). We have also developed methods for determining molecules with higher nitrosamine drug substance related impurities (NDSRIs). In our work, we focus on achieving the highest possible sensitivity, selectivity and precision. To this end, we individually select the parameters of the methods depending on the composition of the drug and the expected nitrosamines. Due to the above criteria and the required detection limits, we perform nitrosamine impurity determinations using LC-MS/MS kits based on <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=52&amp;search=QTRAP+5500%2B">QTRAP 5500+</a> and <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=52&amp;search=QTRAP+6500%2B">QTRAP 6500+</a> mass spectrometers (<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=55">SCIEX</a>).<blockquote>Our offerOur offer includes comprehensive development and validation of methods for determination of nitrosamines in pharmaceutical products. We perform the work according to the criteria and needs of the client based on the EMA note.At the stage of sample preparation, we focus on:<ul><li>obtaining the highest possible recovery of nitrosamines (matrix purification);</li><li>limiting additional synthesis of impurities (selection of appropriate equipment and reagents);</li><li>ensuring the stability of the sample prepared for LC-MS/MS analysis.</li></ul>During the optimization of the LC-MS/MS method, we solve such problems as:<ul><li>selection of an appropriate internal standard;</li><li>separation of the impurity from the API;</li><li>obtaining the expected sensitivity of the analytical method.</li></ul>Quantitative analysis of nitrosamines requires sensitivity, precision and accuracy, which we provide to our customers.</blockquote><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Bioanalytic_Determination_of_nitrosamine_content_Bioanalytic_1_d629e00e62.jpg" alt="Bioanalytic: Determination of nitrosamine content." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Bioanalytic_Determination_of_nitrosamine_content_Bioanalytic_1_d629e00e62.jpg 103w," sizes="100vw" width="1130" height="1710">

Identification of the proteins (Bioanalytic)

We perform proteomic analyses on a state-of-the-art high-resolution ZenoTOF 7600 (SCIEX), in which the Zeno ion trap and EAD (Electron-Activated Dissociation) technology together make a powerful combination.

Protein identification is based on experimental measurement of peptide molecular weights and matching the amino acid sequence of the protein under study with protein sequences deposited in libraries (databases). Two approaches are most commonly used in proteomic studies: top-down and bottom-up. The top-down method involves analyzing full protein sequences. Bottom-up analyses, on the other hand, use digestion of proteins with proteolytic enzymes. The peptides obtained by digestion are analyzed using a mass spectrometer. Each analysis leads to a comparison of experimental data with data obtained by theoretical digestion of proteins described in the database.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Bioanalytic_Identification_of_the_proteins_Bioanalytic_2_6231b73f0d.jpg" alt="Bioanalytic: Identification of the proteins." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Bioanalytic_Identification_of_the_proteins_Bioanalytic_2_6231b73f0d.jpg 245w," sizes="100vw" width="1536" height="766"><h4>LC-MS/MS</h4>Tandem mass spectrometers are commonly used to determine protein sequences. They allow ion selection of pseudomolecular peptides and then fragmentation and measurement of the masses of the resulting fragments. Fragmentation using collision induced dissociation(CID ) is an invaluable tool in targeted and untargeted MS/MS analysis of small molecules. It is important to note that in proteomic analysis, data obtained using the aforementioned fragmentation method may leave gaps. For example, spectra obtained by CID fragmentation do not allow unambiguous differentiation of some amino acids: leucine from isoleucine (identical molar masses and CID fragmentation) and lysine from glutamine (very similar masses). For this reason, we perform proteomic analyses on a state-of-the-art high-resolution <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=52&amp;search=+ZenoTOF+7600">ZenoTOF 7600</a> (SCIEX), in which the Zeno ion trap and EAD (Electron-Activated Dissociation) technology together make a powerful combination. It enables the creation of unique fragments to differentiate problematic amino acids and increase the sensitivity and specificity of the analysis.<blockquote>We perform analyses of:<ul><li>samples from solution;</li><li>samples from a gel;</li><li>samples from a complex matrix (by prior arrangement);</li><li>samples from lyophilizer;</li><li>samples previously prepared by the client.</li></ul>Qualitative analysis of proteins includes:<ul><li>To confirm the presence of a protein, the sample is subjected to digestion and the result obtained from experimental data is compared with theoretical data;</li><li>Comparison of experimental data on the masses of peptides included in the analyzed protein with the protein sequences described in the database;</li><li>Analysis and identification of the protein mixture;</li><li>Identification of post translational modifications (PTMs) (we use the tools: Skyline, Spectronaut).</li><li>Reporting of the result including, but not limited to, confirmation of the coverage of the protein sequence obtained in the test sample with the theoretical protein and the list of modified peptides, location and % of modifications.</li></ul>Comparative analyses include:<ul><li>Determining the composition of mixtures;</li><li>Comparison and profiling of sample series - protein identification and trend analysis.</li></ul>Our offer also includes:<ul><li>Support in the planning and execution of studies (the scope is determined individually);</li><li>Assistance in the development of extraction methods from complex matrices - biological fluids, plant tissue, animal tissues (the scope is determined individually);</li><li>Statistical analysis using tools: MarkerView (PCA), GraphPad Prism, Trends, Sciex OS;</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1294">Quantitative analysis of proteins</a>;</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1296">Analysis and characterization of monoclonal antibodies</a>.</li></ul></blockquote><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Bioanalytic_Identification_of_the_proteins_Bioanalytic_1_97cf8c94b3.jpg" alt="Bioanalytic: Identification of the proteins." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Bioanalytic_Identification_of_the_proteins_Bioanalytic_1_97cf8c94b3.jpg 117w," sizes="100vw" width="1500" height="2000">

Blind common anabolic steroid and aromatase inhibitors screening – raws

Blind HPLC screening of common anabolic steroids and aromatase inhibitors in raw API powders. The analysis identifies the target compound and determines its percentage content.

The Blind Common Anabolic Steroid and Aromatase Inhibitor Screening is a comprehensive laboratory analysis designed to identify and quantify commonly encountered anabolic-androgenic steroids and aromatase inhibitors in raw active pharmaceutical ingredient (API) powders.Because this is a blind analysis, there is no need to specify which substance is expected in the sample. The analysis screens for all target compounds listed below and identifies and quantifies any detected substance.<h4>About the Target Compounds</h4>Anabolic-androgenic steroids are synthetic derivatives of testosterone or structurally related steroid hormones. They interact with androgen receptors and produce both anabolic effects, associated with tissue and muscle growth, and androgenic effects. The screening covers testosterone and nandrolone esters, trenbolone derivatives, drostanolone esters, boldenone undecylenate, trestolone acetate, dihydroboldenone cypionate, and several other commonly encountered anabolic steroids.Esterification modifies properties such as solubility and the rate at which a steroid is released after administration. For this reason, the panel includes multiple ester forms of testosterone, trenbolone, nandrolone, drostanolone, methenolone, and other steroid compounds.Letrozole, anastrozole, and exemestane are aromatase inhibitors. They inhibit aromatase, the enzyme involved in estrogen biosynthesis, thereby reducing estrogen production. Letrozole and anastrozole are non-steroidal aromatase inhibitors, whereas exemestane is a steroidal inhibitor.<h4>Key Features</h4><ul><li>Blind screening with no need to declare the expected compound</li><li>Identification of common anabolic steroids and aromatase inhibitors in raw API powders</li><li>Broad panel covering steroid bases, esters, and aromatase inhibitors</li><li>Quantification of the detected target compound</li><li>Analysis by HPLC</li><li>Results reported as percentage content</li><li>Average turnaround time of 7 business days</li><li>Minimum sample amount of 200 mg of raw powder</li></ul><h4>Analysis Parameters</h4><ul><li>Target compounds: testosterone propionate, testosterone phenylpropionate, testosterone isocaproate, testosterone enanthate, testosterone cypionate, testosterone decanoate, trenbolone base, trenbolone acetate, trenbolone enanthate, trenbolone hexahydrobenzylcarbonate, trestolone acetate, nandrolone phenylpropionate, nandrolone decanoate, drostanolone propionate, drostanolone enanthate, dihydroboldenone cypionate, boldenone undecylenate, letrozole, anastrozole, dihydrotestosterone (DHT), exemestane, fluoxymesterone, methandrostenolone, oxandrolone, stanozolol, 4-chlorodehydromethyltestosterone, mesterolone, methasterone, oxymetholone, methenolone acetate, methenolone enanthate, and testosterone base</li><li>Other common names and brand names: Andriolos, Parabolan, MENT, NPP, Deca, BU, Equipoise, EQ, Parenabol, Femara, Arimidex, Aromasin, Halotestin, Halo, Anavar, Var, Winstrol, Turinabol, Proviron, Anadrol, Anapolon, Primobolan, Test E, Test C, Methandienone, Dianabol, and Dbol</li><li>Testing method: HPLC</li><li>Detection limit cutoff: 50%</li><li>Result format: % [98%+, 96%+ or % value such as 94.15%]</li><li>Minimum sample amount: 200 mg of raw powder</li><li>Average turnaround time: 7 business days</li></ul><blockquote>Important Notice: The detection cutoff is 50%. Samples containing a target compound below this level may therefore fall outside the standard scope of the screening.</blockquote><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Complete Raw Analytical Data</li><li>Fentanyl Screening</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2524">GC–MS Contamination Screening</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis (As, Cd, Pb, Hg)</a></li><li>Additional Laboratory Report</li></ul>

Blind common anabolic steroid screening – ID only

Blind HPLC identification of common anabolic steroids and selected aromatase inhibitors in oils, oral products, and raw API powders. The analysis reports the detected compound without quantification.

The Blind Common Anabolic Steroid Screening – ID Only is a laboratory analysis designed to identify commonly encountered anabolic-androgenic steroids and selected aromatase inhibitors in an unknown sample. The test is suitable for oil-based samples, oral dosage forms such as tablets and capsules, and raw active pharmaceutical ingredient (API) powders.Because this is a blind analysis, there is no need to specify which substance is expected in the sample. The analysis screens for all target compounds listed below and reports the identity of the detected compound.<h4>About the Target Compounds</h4>Anabolic-androgenic steroids are synthetic derivatives of testosterone or structurally related steroid hormones. They interact with androgen receptors and produce both anabolic effects, associated with tissue and muscle growth, and androgenic effects.The analysis covers testosterone, trenbolone, nandrolone, drostanolone, methenolone, boldenone, and other commonly encountered anabolic steroids. It includes both non-esterified steroid bases and esterified forms, which differ in properties such as solubility and release rate after administration.The screening also includes letrozole, anastrozole, and exemestane. These compounds are aromatase inhibitors that reduce estrogen production by inhibiting the aromatase enzyme. Letrozole and anastrozole are non-steroidal inhibitors, whereas exemestane is a steroidal aromatase inhibitor.<h4>Key Features</h4><ul><li>Blind identification with no need to declare the expected compound</li><li>Identification of common anabolic steroids and selected aromatase inhibitors</li><li>Suitable for oil-based samples, tablets, capsules, and raw API powders</li><li>Broad selection of steroid bases and esterified compounds</li><li>Analysis by HPLC</li><li>Results reported as the name of the detected compound</li><li>Average turnaround time of 7 business days</li><li>Minimum sample requirements adapted to the submitted sample type</li></ul><h4>Analysis Parameters</h4><ul><li>Target compounds: testosterone propionate, testosterone phenylpropionate, testosterone isocaproate, testosterone enanthate, testosterone cypionate, testosterone decanoate, trenbolone base, trenbolone acetate, trenbolone hexahydrobenzylcarbonate, trestolone acetate, nandrolone phenylpropionate, nandrolone decanoate, drostanolone propionate, drostanolone enanthate, dihydroboldenone cypionate, boldenone undecylenate, letrozole, anastrozole, exemestane, fluoxymesterone, methandrostenolone, oxandrolone, stanozolol, 4-chlorodehydromethyltestosterone, mesterolone, methasterone, oxymetholone, methenolone acetate, methenolone enanthate, and testosterone base</li><li>Testing method: HPLC</li><li>Result format: Name of the AAS found in the sample</li><li>Minimum sample amount&nbsp;<ul><li>oils: 0.5–1 mL</li><li>oral products: One tablet or capsule</li><li>raw powders: 200 mg</li></ul></li><li>Average turnaround time: 7 business days</li></ul><blockquote>Important Notice: This analysis provides compound identification only. The amount, concentration, or percentage content of the detected substance is not measured.</blockquote><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Fentanyl Screening</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2524">GC–MS Contamination Screening</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis (As, Cd, Pb, Hg)</a></li><li>Additional Laboratory Report</li></ul>

Blind common anabolic steroid screening – oils

Blind HPLC screening of common anabolic steroids in oil-based samples. The analysis identifies and quantifies all detected target compounds, including mixtures.

The Blind Common Anabolic Steroid Screening – Oils is a comprehensive laboratory analysis designed to identify and quantify commonly encountered anabolic-androgenic steroids in oil-based samples.Because this is a blind analysis, there is no need to specify which substance is expected in the sample. The analysis screens for all target compounds listed below and identifies and quantifies every detected substance, including samples containing mixtures of multiple compounds.<h4>About the Target Compounds</h4>Anabolic-androgenic steroids are synthetic derivatives of testosterone or structurally related steroid hormones. They interact with androgen receptors and produce both anabolic effects, associated with tissue and muscle growth, and androgenic effects.The analysis covers several esterified forms of testosterone, trenbolone, nandrolone, drostanolone, methenolone, and boldenone, as well as testosterone undecanoate, methyltestosterone, trestolone acetate, and dihydrotestosterone (DHT).Esterification influences properties such as solubility and the rate at which a steroid is released after administration. Different ester forms of the same steroid may therefore differ in their physicochemical and pharmacokinetic characteristics.<h4>Key Features</h4><ul><li>Blind screening with no need to declare the expected compound</li><li>Identification and quantification of common anabolic steroids in oil-based samples</li><li>Detection of mixtures containing multiple target compounds</li><li>Broad range of steroid bases and esterified compounds</li><li>Analysis by HPLC</li><li>Results reported in mg/mL</li><li>Average turnaround time of 7 business days</li><li>Minimum sample amount of 0.5–1 mL of oil</li></ul><h4>Analysis Parameters</h4><ul><li>Target compounds: testosterone propionate, testosterone phenylpropionate, testosterone isocaproate, testosterone enanthate, testosterone cypionate, testosterone decanoate, testosterone undecanoate, methyltestosterone, trenbolone acetate, trenbolone enanthate, trenbolone hexahydrobenzylcarbonate, trestolone acetate, dihydrotestosterone (DHT), nandrolone phenylpropionate, nandrolone cypionate, nandrolone decanoate, drostanolone propionate, drostanolone enanthate, dihydroboldenone cypionate, methenolone enanthate, boldenone acetate, boldenone cypionate, and boldenone undecylenate</li><li>Other common names and brand names: Sustanon, Test E, Test C, Deca, Tren, Parabolan, MENT, NPP, Masteron, Masteron Propionate, Mast P, Masteron Enanthate, Mast E, Primobolan, Primo E, BU, Equipoise, EQ, and Parenabol</li><li>Testing method: HPLC</li><li>Detection limit cutoff:&nbsp;<ul><li>15 mg/mL</li><li>drostanolone propionate and drostanolone enanthate: 25 mg/mL</li></ul></li><li>Result format: mg/mL</li><li>Minimum sample amount: 0.5–1 mL of oil</li><li>Average turnaround time: 7 business days</li></ul><blockquote>Important Notice: The standard detection cutoff is 15 mg/mL. For drostanolone propionate and drostanolone enanthate, the cutoff is 25 mg/mL. Target compounds present below these levels may not be reliably detected or quantified within the standard scope of the analysis.</blockquote><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Benzyl Alcohol and Benzyl Benzoate Testing</li><li>Complete Raw Analytical Data</li><li>Fentanyl Screening</li><li>pH Measurement</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2524">GC–MS Contamination Screening</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2527">Sterility Testing (TAMC and TYMC)</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis (As, Cd, Pb, Hg)</a></li><li>Additional Laboratory Report</li></ul>

DHT enanthate analysis

Targeted HPLC analysis of DHT enanthate in oil-based samples and raw API powders. The test confirms compound identity and determines its content in mg/mL or percentage.

The DHT Enanthate Analysis is a targeted laboratory test designed to confirm the identity and determine the amount of dihydrotestosterone enanthate in oil-based samples and raw active pharmaceutical ingredient (API) powders.The analysis specifically targets DHT enanthate. Samples expected to contain non-esterified DHT should instead be submitted for the appropriate blind common anabolic steroid screening for <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2478">oils </a>or<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2476"> raw API powders</a>.<h4>About DHT Enanthate</h4>DHT enanthate, also known as dihydrotestosterone heptanoate, is an esterified form of DHT designed for slower release and potentially prolonged activity. The enanthate group increases the compound’s lipophilicity. After release, DHT activates androgen receptors and, unlike testosterone, cannot be converted into estradiol by aromatase.<h4>Key Features</h4><ul><li>Targeted identification and quantification of DHT enanthate</li><li>Suitable for oil-based samples and raw API powders</li><li>Analysis by HPLC</li><li>Results for oil samples reported in mg/mL</li><li>Results for raw API powders reported as percentage content</li><li>Optional mass-by-mass purity reporting for raw API powders</li><li>Minimum sample amount of 0.5–1 mL for oil samples</li><li>Minimum sample amount of 200 mg for raw API powders</li><li>Average turnaround time of 7 business days</li></ul><h4>Analysis Parameters</h4><ul><li>Target compound: DHT enanthate</li><li>Testing method: HPLC</li><li>Detection limit cutoff: 15 mg/mL</li><li>Result format for oil samples: mg/mL</li><li>Minimum sample amount for oil samples: 0.5–1 mL</li><li>Result format for raw API powders: % [98%+, 96%+ or % value such as 94.15%]</li><li>Minimum sample amount for raw API powders: 200 mg</li><li>Average turnaround time: 7 business days</li></ul><blockquote>Important Notice: This analysis is intended specifically for DHT enanthate. It is not designed for the analysis of non-esterified DHT.</blockquote><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Benzyl Alcohol and Benzyl Benzoate Testing</li><li>Complete Raw Analytical Data</li><li>Alternative Result Format: Mass-by-Mass Purity - only for raw API powders</li><li>pH Measurement</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2524">GC–MS Contamination Screening</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2527">Sterility Testing (TAMC and TYMC)</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis (As, Cd, Pb, Hg)</a></li><li>Additional Laboratory Report</li></ul>

Epistane analysis

Targeted HPLC analysis of epistane in tablets, capsules, and raw API powders. The test confirms compound identity and determines its content in mg per unit or as a percentage.

The Epistane Analysis is a targeted laboratory test designed to confirm the identity and determine the amount of epistane in oral dosage forms and raw active pharmaceutical ingredient (API) powders.&nbsp;The analysis is suitable for tablets and capsules, with results reported in milligrams per individual dosage unit. Raw API powders can be evaluated by percentage content, with optional purity reporting in % (m/m).<h4>About Epistane</h4>Epistane, also known as methylepitiostanol or 2,3-thioepoxy madol, is a synthetic, orally active anabolic-androgenic steroid structurally related to dihydrotestosterone (DHT). It is a 17α-methylated derivative of epitiostanol and interacts with androgen receptors, producing anabolic and androgenic effects.Epistane has been identified in products marketed as dietary or performance-enhancing supplements. Scientific studies have also associated its use with a risk of liver injury, including cholestasis.<h4>Key Features</h4><ul><li>Targeted identification and quantification of epistane</li><li>Suitable for tablets, capsules, and raw API powders</li><li>Analysis by HPLC</li><li>Results for oral products reported in mg per tablet or capsule</li><li>Results for raw API powders reported as percentage content</li><li>Optional purity reporting in % (m/m) for raw API powders</li><li>Minimum sample amount of three tablets or capsules</li><li>Minimum sample amount of 200 mg for raw API powders</li><li>Average turnaround time of 7 business days</li></ul><h4>Analysis Parameters</h4><ul><li>Target compound: Epistane</li><li>Other names: Hemapolin, 2,3-thioepoxy madol</li><li>CAS number: 4267-80-5</li><li>Testing method: HPLC</li><li>Minimum amount of target compound for evaluation in oral products: 4 mg per tablet / capsule</li><li>Minimum content of target compound for evaluation in raw API powders: 10%</li><li>Result format for oral products: mg per tablet or capsule</li><li>Result format for raw API powders: % [98%+, 96%+ or % value such as 94.15%]</li><li>Minimum sample amount for oral products: 3 tablets / capsules</li><li>Minimum sample amount for raw API powders: 200 mg</li><li>Average turnaround time: 7 business days</li></ul><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Complete Raw Analytical Data</li><li>Alternative Result Format: Purity in % (m/m) - only for raw API powders</li><li>Batch Variance Testing</li><li>Fentanyl Screening</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2524">GC–MS Contamination Screening</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis (As, Cd, Pb, Hg)</a></li><li>Additional Laboratory Report</li></ul>

Products from category Pharmaceutical analyses - page 13

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