<p><strong>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.</strong></p><p>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.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#sec0010"><strong>Volatile compound fingerprinting for traditional sesame oil authentication using GC-MS with GC×GC-MS-assisted identification&nbsp;</strong></a></h4><p>Ruibing Jin, Fuyun Kuang, Qi Meng, Lijin Wang, Huanlu Song, Xueping Feng, Mingshan Zou</p><p>Journal of Food Composition and Analysis, 2026, 155:109277</p><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/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><div><div><div><div><p>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"><span>Yin et al., 2021</span></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"><span>Huang et al., 2024</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib11"><span>Ma et al., 2022</span></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"><span>Xu et al., 2025</span></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"><span>Yin et al., 2024</span></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.</p><p>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"><span>National Medical Products Administration, 2000</span></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"><span>Fu et al., 2023</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib14"><span>Shi et al., 2025</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib15"><span>Sun et al., 2023</span></a>).</p><p>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"><span>Sun et al. (2020)</span></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"><span>Wang et al. (2024a)</span></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"><span>Aghili et al. (2023)</span></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"><span>Xing et al. (2019)</span></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"><span>Zhang et al. (2016)</span></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.</p><p>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"><span>Li et al. (2025)</span></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.</p><h4><strong>2. Materials and methods</strong></h4><h5><strong>2.4. GC×GC-MS</strong></h5><p>GC-MS (<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=8890"><strong>8890</strong></a>–<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=5977B"><strong>5977B</strong></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"><span>Wang et al., 2023</span></a>).</p><p>Volatile compounds were detected as previously described (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib2"><span>Duan et al., 2025</span></a>), with some modifications. Mass spectra were acquired over the range of 40–450 <i>m/z</i>. 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.</p><h5><strong>2.5. GC-MS</strong></h5><p>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"><strong>7890A</strong></a>−<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/gcms/library?mainauthorItems=10&amp;search=7000B"><strong>7000B</strong></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.</p><h4><strong>3. Results and discussion</strong></h4><h5><strong>3.2. Analysis of volatile compounds</strong></h5><p>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"><span>Fig. 1</span></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"><span>Table 3</span></a>).</p><p><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"></p><h5><strong>3.3. Establishment of GC-MS fingerprints and similarity analysis of volatile substances</strong></h5><p>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"><span>Fig. 3</span></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"><span>Table 4</span></a>), surpassing the 0.900 benchmark (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib10"><span>Ma et al., 2024</span></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.</p><p><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"></p><h4><strong>4. Conclusion</strong></h4><p>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%.</p><p>Compared with previous methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib1"><span>Aghili et al., 2023</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib18"><span>Wang et al., 2024a</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib20"><span>Xing et al., 2019</span></a>; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0889157526004205#bib26"><span>Zhang et al., 2016</span></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.</p></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

<p><span><strong>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.</strong></span></p><p><span>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.</span></p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#s0055"><strong>Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice</strong></a></h4><p>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. Dunk</p><p>Organic Geochemistry, Volume 218, 2026, 105215</p><div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.orggeochem.2026.105215"><span>10.1016/j.orggeochem.2026.105215</span></a></div><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><div><div><div><div><p>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"><span>Bendle et al., 2009</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0220"><span>Radabaugh et al., 2018</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0230"><span>Saderne et al., 2019</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0240"><span>Scheingross et al., 2019</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0260"><span>Smeaton et al., 2021</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0295"><span>Vonk et al., 2012</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0315"><span>Wood et al., 2021</span></a>).</p><p>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"><span>Post et al., 2001</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0265"><span>Smith, 2004</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0270"><span>Smith et al., 2020</span></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"><span>van Dongen et al., 2008</span></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"><span>Fest et al., 2022</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0320"><span>Young et al., 2018</span></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"><span>Farmer et al., 2014</span></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"><span>Bonn et al., 2014</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0045"><span>Burden et al., 2023</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0105"><span>Guizar-Coutiño et al., 2022</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0190"><span>Needelman et al., 2018</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0205"><span>Pan et al., 2022</span></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.</p><p>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 CO<sub>2</sub> measurement) to provide recommendations for best practice. A summary review of these methods is provided below.</p><h4><strong>2. Materials and methods</strong></h4><h5><strong>2.3. Independent testing of reference materials via TDC</strong></h5><p>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"><strong>Elementar SoliTOC cube</strong></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).</p><h5><strong>2.5. Elemental analysis</strong></h5><p>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"><span>Mossman et al. (2022)</span></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.</p><h4><strong>3. Results and discussion</strong></h4><h5><strong>3.1. Testing of certified reference materials (CRMs) using temperature dependent combustion (TDC)</strong></h5><p>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"><span>Fig. 1</span></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).</p><p><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"></p><h5><strong>3.4. Reproducibility tests</strong></h5><p>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).</p><div><p>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"><span>Fig. 2</span></a>). The conversions of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0050"><span>Craft et al. (1991)</span></a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0200"><span>Ouyang and Lee (2020)</span></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"><span>Smeaton et al. (2022)</span></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/CO<sub>2</sub> 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 OC<sub>LoI</sub> was&nbsp;∼&nbsp;1.8%, and 3.2% for IC<sub>LoI</sub>, showing acceptable run-to-run precision.</p><p><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"></p><p>Since OC<sub>C-EA</sub> and IC<sub>AD-EA</sub> were calculated by subtraction from mean TC<sub>EA</sub>, 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"><span>Fig. 2</span></a>, representing OC&nbsp;+&nbsp;IC&nbsp;=&nbsp;TC. However, position and scatter along that line varied between the two pre-treatment techniques. OC<sub>C-EA</sub> 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 OC<sub>C-EA</sub> measurements. IC<sub>C-EA</sub> 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.</p></div><h4><strong>4.3. Conclusions</strong></h4><p>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.</p><p>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.</p></div></div></div></div></div>

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

Scientific article

Science and research

Everything from the world of analytical chemistry in one place. We connect people in solving their problems. At Labrulez you will find all the necessary information easily, quickly and clearly. Stop searching and start finding.

LabRulez

Wiley KnowItAll Analytical Edition Software

KnowItAll by Wiley offers a comprehensive solution for identifying, analyzing, and managing analytical data, enhancing spectral analysis speed and overall lab efficiency.

<p class="cke-user_style-important"><strong>Solutions for IR, MS, NMR, Raman, &amp; UV-Vis</strong></p><blockquote><p><strong>More information on the Wiley Science Solutions website: </strong><a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/knowitall-analytical-edition-software/"><strong>KnowItAll Analytical Edition Software</strong></a></p></blockquote><h5><strong data-start="4" data-end="56">Accelerate Spectral Analysis &amp; Boost Efficiency!</strong></h5><p data-start="60" data-end="206"><strong data-start="60" data-end="73">KnowItAll</strong> by <strong data-start="77" data-end="86">Wiley</strong> is an <strong data-start="93" data-end="107" data-is-only-node="">all-in-one</strong> solution for <strong data-start="121" data-end="161">identifying, analyzing, and managing</strong> spectral data, streamlining lab workflows.</p><ul data-start="208" data-end="581" data-is-only-node="" data-is-last-node=""><li data-start="208" data-end="328"><strong data-start="210" data-end="237">User-friendly interface</strong> with tools for <strong data-start="253" data-end="326">processing, searching, mixture analysis, deconvolution, and reporting</strong></li><li data-start="329" data-end="406"><strong data-start="331" data-end="365">Patented spectral intelligence</strong> for automated, expert-driven workflows</li><li data-start="407" data-end="481"><strong data-start="409" data-end="458">Supports multiple techniques &amp; vendor formats</strong> for full flexibility</li><li data-start="482" data-end="581" data-is-last-node=""><strong data-start="484" data-end="543">Integrated with Wiley’s high-quality spectral databases</strong> for <strong data-start="548" data-end="581" data-is-last-node="">faster, more accurate results</strong></li></ul><figure class="image"><img style="aspect-ratio:1922/1036;" src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_Wiley_Know_It_All_Analytical_Edition_Software_cover_a703599ad2/Wiley-Wiley-KnowItAll-Analytical-Edition-Software-cover.jpg" alt="Wiley: Wiley KnowItAll Analytical Edition Software" width="1922" height="1036"></figure><h4><strong>Streamline Your Analysis with KnowItAll</strong></h4><p data-start="51" data-end="223"><strong data-start="51" data-end="64">KnowItAll</strong> integrates all essential tools and data into a <strong data-start="112" data-end="146">single, user-friendly platform</strong>, saving you time, optimizing workflows, and enhancing data interpretation.</p><h4><strong data-start="229" data-end="254">Compatible Data Types</strong></h4><p data-start="257" data-end="406">A <strong data-start="259" data-end="286">vendor-neutral solution</strong>, KnowItAll supports <strong data-start="307" data-end="334">130+ instrument formats</strong>, ensuring seamless workflow continuity even as lab equipment evolves.</p><ul data-start="408" data-end="518"><li data-start="408" data-end="473"><strong data-start="410" data-end="439">Spectroscopic Techniques:</strong> IR, MS, NIR, NMR, Raman, UV-Vis</li><li data-start="474" data-end="518"><strong data-start="476" data-end="516">Chromatograms &amp; Molecular Structures</strong></li></ul><h3 data-start="520" data-end="542"><strong data-start="524" data-end="540">Key Features</strong></h3><p data-start="543" data-end="699">Eliminate the need for multiple software packages—KnowItAll <strong data-start="603" data-end="657">combines powerful tools in one intuitive interface</strong> for seamless transitions between tasks.</p><ul data-start="701" data-end="998"><li data-start="701" data-end="750"><strong data-start="703" data-end="748">Comprehensive Spectrum &amp; Mixture Analysis</strong></li><li data-start="751" data-end="799"><strong data-start="753" data-end="797">Database Management &amp; Advanced Searching</strong></li><li data-start="800" data-end="863"><strong data-start="802" data-end="848">Structure Drawing &amp; Professional Reporting</strong> (ChemWindow)</li><li data-start="864" data-end="907"><strong data-start="866" data-end="905">Spectrum Processing &amp; Deconvolution</strong></li><li data-start="908" data-end="956"><strong data-start="910" data-end="954">Extensive Spectral Library Subscriptions</strong></li><li data-start="957" data-end="998"><strong data-start="959" data-end="996">Quantitative Analysis Module</strong></li><li data-start="1030" data-end="1072"><strong data-start="1032" data-end="1070">IR/Raman functional group analysis</strong></li><li data-start="1073" data-end="1128"><strong data-start="1075" data-end="1107">MS reverse &amp; adaptive search</strong> <em data-start="1108" data-end="1126"><i>(patente)</i></em></li><li data-start="1129" data-end="1174"><strong data-start="1131" data-end="1172">Automated MS deconvolution &amp; analysis</strong></li><li data-start="1175" data-end="1212"><strong data-start="1177" data-end="1210">NMR chemical shift prediction</strong></li></ul><h4><strong>What’s Included?</strong></h4><h5><strong data-start="1245" data-end="1261">Data Toolbox</strong></h5><ul data-start="1264" data-end="1540"><li data-start="1264" data-end="1318"><strong data-start="1266" data-end="1280">ID Expert™</strong> – One-click spectral identification</li><li data-start="1319" data-end="1411"><strong data-start="1321" data-end="1334">SearchIt™</strong> – Advanced database search with mixture analysis &amp; multi-technique support</li><li data-start="1412" data-end="1470"><strong data-start="1414" data-end="1425">MineIt™</strong> – Spectral database creation &amp; data mining</li><li data-start="1471" data-end="1540"><strong data-start="1473" data-end="1487">QC Expert™</strong> – Quality control for IR, Raman, and Chromatograms</li></ul><h5><strong data-start="1547" data-end="1578">Spectral Processing Toolbox</strong></h5><ul data-start="1581" data-end="1716"><li data-start="1581" data-end="1652"><strong data-start="1583" data-end="1597">MS Expert™</strong> – GC-MS deconvolution with automated database search</li><li data-start="1653" data-end="1716"><strong data-start="1655" data-end="1669">ProcessIt™</strong> – Spectrum processing for IR, MS, NMR, Raman</li></ul><h5><strong data-start="1723" data-end="1752">Spectral Analysis Toolbox</strong></h5><ul data-start="1755" data-end="1882"><li data-start="1755" data-end="1827"><strong data-start="1757" data-end="1771">AnalyzeIt™</strong> – Functional group analysis for IR, Raman, &amp; polymers</li><li data-start="1828" data-end="1882"><strong data-start="1830" data-end="1848">PredictIt™ NMR</strong> – NMR chemical shift prediction</li></ul><h5><strong data-start="1889" data-end="1907">Basics Toolbox</strong></h5><ul data-start="1910" data-end="2115"><li data-start="1910" data-end="1965"><strong data-start="1912" data-end="1926">ChemWindow</strong> – Full-featured 2D structure drawing</li><li data-start="1966" data-end="2049"><strong data-start="1968" data-end="1981">ReportIt™</strong> – Professional reporting for structures, spectra, &amp; chromatograms</li><li data-start="2050" data-end="2115"><strong data-start="2052" data-end="2065">BrowseIt™</strong> – Web portal with KnowItAll resources &amp; updates</li><li data-start="2050" data-end="2115"><strong data-start="2121" data-end="2153">Optional Quantitation Module - </strong>Enhance <strong data-start="2164" data-end="2200">GC-MS, IR, Raman, UV-Vis, and GC</strong> workflows with integrated quantification.</li></ul><h5><strong data-start="2250" data-end="2292">Access High-Quality Spectral Libraries</strong></h5><ul><li><p data-start="2295" data-end="2423" data-is-last-node="" data-is-only-node="">Subscribe to <strong data-start="2308" data-end="2386">Wiley’s industry-leading IR, MS, NMR, Raman, and UV-Vis spectral databases</strong> for <strong data-start="2391" data-end="2402">optimal</strong> analytical accuracy.</p></li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_Wiley_Know_It_All_Analytical_Edition_Software_Know_It_All_Spectral_Libraries_2fdbf59fd0/Wiley-Wiley-KnowItAll-Analytical-Edition-Software-KnowItAll-Spectral-Libraries.jpg" alt="Wiley: Wiley KnowItAll Analytical Edition Software - KnowItAll Spectral Libraries" width="474" height="343"></p><h4><strong>System Recommendations &amp; Compatibility</strong></h4><ul><li><a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/system-recommendations/">See system recommendations here.</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/knowitall-supported-file-formats/">View file formats supported.</a></li></ul><p>*Subscription required to KnowItAll Spectral Libraries.</p>

Wiley SmartSpectra Raman Database Collection

Unlock a broader spectrum of possibilities with Wiley SmartSpectra Databases, enhancing your chemical compound coverage to ensure more accurate and comprehensive unknown identification.

<blockquote><p>Unlock a broader spectrum of possibilities with Wiley SmartSpectra Databases, enhancing your chemical compound coverage to ensure more accurate and comprehensive unknown identification.</p><p><strong>More information on the Wiley Science Solutions website: </strong><a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/solutions/technique/raman/wiley-smartspectra-raman-database-collection/"><strong>Wiley SmartSpectra Raman Database Collection</strong></a></p></blockquote><p data-start="0" data-end="254">The <strong data-start="4" data-end="52">Wiley SmartSpectra Raman Database Collection</strong> offers access to <strong data-start="70" data-end="101">33,163 high-quality spectra</strong> for identifying unknown compounds and enhancing analytical workflows. Built on <strong data-start="181" data-end="210">Wiley’s trusted databases</strong>, it ensures scientifically sound results.</p><h5><strong>Key Benefits:</strong></h5><ul data-start="276" data-end="738" data-is-only-node="" data-is-last-node=""><li data-start="276" data-end="352">Aids in identifying rare compounds when empirical databases lack matches</li><li data-start="353" data-end="438">Supports <strong data-start="364" data-end="399">functional group classification</strong>, excelling with <strong data-start="416" data-end="436">rigid structures</strong></li><li data-start="439" data-end="530">Derived from <strong data-start="454" data-end="497">Wiley’s high-quality empirical datasets</strong>, including <strong data-start="509" data-end="528">Sadtler spectra</strong></li><li data-start="531" data-end="599">Covers a wide range of compound classes for diverse applications</li><li data-start="600" data-end="689">Includes <strong data-start="611" data-end="623">metadata</strong> (physical properties, chemical structures) for precise searches</li><li data-start="690" data-end="738" data-is-last-node=""><strong data-start="692" data-end="738" data-is-last-node="">Validated by Subject Matter Experts (SMEs)</strong></li></ul><h4><strong>So, just how much can the SmartSpectra database expand your compound coverage?</strong></h4><p>The <strong data-start="4" data-end="20">SmartSpectra</strong> database accelerates <strong data-start="42" data-end="69">spectral identification</strong> by expanding <strong data-start="83" data-end="113">chemical compound coverage</strong>. As shown, <strong data-start="125" data-end="152">SmartSpectra data (red)</strong> extends the <strong data-start="165" data-end="183">chemical space</strong> of the <strong data-start="191" data-end="219">empirical dataset (blue)</strong>, significantly increasing compound coverage.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_Wiley_Smart_Spectra_Raman_Database_Collection_Wiley_Raman_empirical_data_only_blue_Smart_Spectra_Raman_data_included_red_da42d540b6.jpg" alt="Wiley: Wiley SmartSpectra Raman Database Collection - Wiley Raman empirical data only (blue), SmartSpectra Raman data included (red)."></p><h4><strong>Applications</strong></h4><p data-start="19" data-end="229">Use the <strong data-start="27" data-end="43">SmartSpectra</strong> collection with <strong data-start="60" data-end="83">empirical databases</strong> for <strong data-start="88" data-end="110">Raman spectroscopy</strong>.</p><h4><strong>Trusted Data</strong></h4><p data-start="250" data-end="471" data-is-last-node="" data-is-only-node=""><strong data-start="250" data-end="259">Wiley</strong> is a leading source for spectral data. Its <strong data-start="303" data-end="324">Sadtler databases</strong> undergo <strong data-start="333" data-end="363">rigorous quality protocols</strong>, from <strong data-start="370" data-end="414" data-is-only-node="">data acquisition to database development</strong>. All partner data is thoroughly vetted before inclusion.</p><blockquote><p><i>KnowItAll is a trademark of John Wiley &amp; Sons, Inc. in certain jurisdictions.</i></p><p><i>Subscription content is subject to change as data may be added or deleted from collections periodically.</i></p></blockquote><h3><strong>Databases Included</strong></h3><p><strong>Subscription includes access to 11 databases including:</strong></p><ul><li>Wiley SmartSpectra Raman Database of General Compounds (24,585 spectra)</li><li>Wiley SmartSpectra Raman Database of Biochemicals (59 spectra)</li><li>Wiley SmartSpectra Raman Database of Chemical Standards (6,458 spectra)</li><li>Wiley SmartSpectra Raman Database of Drugs (130 spectra)</li><li>Wiley SmartSpectra Raman Database of Dyes (36 spectra)</li><li>Wiley SmartSpectra Raman Database of Explosives and Hazmat Compounds (51 spectra)</li><li>Wiley SmartSpectra Raman Database of Food Related Compounds (277 spectra)</li><li>Wiley SmartSpectra Raman Database of Hydrocarbons (527 spectra)</li><li>Wiley SmartSpectra Raman Database of Industrial Compounds (439 spectra)</li><li>Wiley SmartSpectra Raman Database of Pollutants (449 spectra)</li><li>Wiley SmartSpectra Raman Database of Monomers (152 spectra)</li></ul><h3><strong>Additional information</strong></h3><p>This SmartSpectra library contains the following valuable metadata to help you narrow and refine your search results:</p><ul><li>Chemical Structure</li><li>Chemical Name</li><li>Exact Mass</li><li>Formula</li><li>IUPAC InChI/InChIKey</li><li>Molecular Weight</li><li>Nominal mass</li><li>Spectrum/Structure Validation Score</li></ul><h3><strong>Compatibility</strong></h3><ul><li>Recommended as a supplement to the <i>KnowItAll IR Spectral Library</i> collection of curated empirical data.</li><li>For use with <i>KnowItAll 2024</i> software.</li><li>KnowItAll software is compatible with most major instrument formats. For full <i>KnowItAll</i> file format compatibility information, please visit <a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/compatibility/">sciencesolutions.wiley.com/compatibility</a>.</li></ul>

Mestrelab Mnova Gears - Chrom Cal software

Chrom Cal is an advanced software solution developed by Mestrelab Research to automate and streamline the creation of calibration curves for accurate chromatographic quantitation

<h4><strong>Automated evaluation and generation of calibration curves for multiple compounds</strong></h4><p data-start="0" data-end="639"><strong data-start="0" data-end="13">Chrom Cal</strong> is an advanced software solution developed by Mestrelab Research to <strong>automate</strong> and <strong>streamline the creation of calibration curves for accurate chromatographic quantitation</strong>. Fully integrated within the Mnova suite, Chrom Cal efficiently processes spectral datasets, plots integration areas against known analyte concentrations, performs statistical analyses to fit calibration lines, validates results based on pre-set criteria, and generates automatic reports and result files for downstream quantitation. This comprehensive approach enhances both efficiency and accuracy, effectively handling multiple compounds simultaneously.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>Easy generation of calibration curves </strong>for all the compounds in one run.</li><li><strong>Huge time saving</strong> <strong>on result reviewing</strong>, enabling a review-by-exception strategy.</li><li><strong>Better understanding and revision of the results</strong> taking into account the spectral context.</li><li><strong>Seamless transfer of calibration results to downstream analyses</strong>, avoiding transcription errors.</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">Automatic pick-up and grouping of spectral datasets for analysis</strong></h5><p data-start="1039" data-end="1542">Chrom Cal is fully integrated in Mnova and can handle spectral datasets, extracting the information needed for calculating calibration curves without the need for data transcription into third-party software or spreadsheets. After data ingestion, Chrom Cal can detect and group samples from different compounds automatically based on an input CSV file, allowing calibration curves for those compounds to be generated in a single run.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Automatic_pick_up_and_grouping_of_spectral_datasets_for_analysis_f4dab8cce6.webp" alt="Mestrelab Research: Mestrelab Mnova Gears - Chrom Cal - Automatic pick-up and grouping of spectral datasets for analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Automatic_pick_up_and_grouping_of_spectral_datasets_for_analysis_f4dab8cce6.webp 182w," sizes="100vw" width="1282" height="1100"></p><h5><strong data-start="1546" data-end="1659">Powerful algorithms to fit calibration curves and evaluate results based on pre-defined quality requirements</strong></h5><p data-start="1546" data-end="2109">Chrom Cal processes spectral data, integrates compound peaks, and fits calibration curves to data points in a fully automated fashion. Acceptance criteria, such as R² value thresholds or slope ranges, and cross-validation rules can be pre-defined in the method and evaluated at the end of the analysis to approve or reject calibration results. This allows control over the quality and linearity of results and speeds up the result reviewing process.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Powerful_algorithms_to_fit_calibration_curves_and_evaluate_results_based_on_pre_defined_quality_requirements_65569ca27a.webp" alt="Mestrelab Research: Mestrelab Mnova Gears - Chrom Cal - Powerful algorithms to fit calibration curves and evaluate results based on pre-defined quality requirements" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Powerful_algorithms_to_fit_calibration_curves_and_evaluate_results_based_on_pre_defined_quality_requirements_65569ca27a.webp 245w," sizes="100vw" width="1448" height="910"></p><h5><strong data-start="2113" data-end="2150">Live results in tables and plots</strong></h5><p data-start="2113" data-end="2622">Calibration and validation results are displayed in a result viewer that allows both global and detailed review of the data. Users can navigate between compounds to check standard curve plots and parameters. Each data point on the graph is dynamically linked to results tables and spectra. Users can zoom in or out on the plot, include or exclude samples from the analysis, or correct peak assignments or integrations when needed, with results automatically recalculated.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Live_results_in_tables_and_plots_a3b64eaabb.webp" alt="Mestrelab Research: Mestrelab Mnova Gears - Chrom Cal - Live results in tables and plots" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Live_results_in_tables_and_plots_a3b64eaabb.webp 245w," sizes="100vw" width="1328" height="827"></p><h5><strong data-start="2626" data-end="2687">Multiple analysis outputs nicely generated and organized</strong></h5><p data-start="2626" data-end="3043">Chrom Cal generates automatic CSV, Mnova, PDF, and HTML reports with calibration and validation results, saved in chosen locations along with metadata, logs, and settings, improving data management. Calibration results in JSON files feed downstream quantitative analysis, allowing extraction without manual transcription, reducing errors, and saving time.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Multiple_analysis_outputs_nicely_generated_and_organized_86d47119e2.webp" alt="Mestrelab Research: Mestrelab Mnova Gears - Chrom Cal - Multiple analysis outputs nicely generated and organized" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Gears_Chrom_Cal_Multiple_analysis_outputs_nicely_generated_and_organized_86d47119e2.webp 245w," sizes="100vw" width="1153" height="709"></p>

Mestrelab Mbook 4.0 

Mbook, a powerful Electronic Laboratory Notebook (ELN), is designed specifically for chemists, providing a streamlined solution for managing experimental data.

<p class="cke-user_style-important"><strong>Designed by chemists, for chemists</strong></p><p>In today's chemistry labs, efficiency, data security, and seamless collaboration are essential for success. <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/main-product/mbook-chemistry?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=mar&amp;utm_term=mbook&amp;utm_content=blogpost"><strong>Mbook</strong></a>, a powerful Electronic Laboratory Notebook (ELN), is designed specifically for chemists, providing a streamlined solution for managing experimental data, analytical results, team coordination, stockroom inventory, and overall workflow efficiency.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mbook_Header_Image_e5fa76cd18.jpg" alt="Mestrelab Research: Mbook 4.0" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mbook_Header_Image_e5fa76cd18.jpg 242w," sizes="100vw" width="1406" height="906"></p><h4><strong>Why choose Mbook?</strong></h4><p>Mbook 4.0 is more than just an ELN for recording your reactions, projects, or experiments—it offers advanced capabilities to optimize your laboratory management, including:</p><ul><li>Experiment witnessing for enhanced supervision and confidence</li><li>COSHH assessments to simplify chemical safety management</li><li>Comprehensive inventory management with a Compound DB of over 2,500 preloaded compounds</li><li>Customizable Stockroom with PDF exportable options</li><li>Bottle consumption and tracking for accurate inventory control</li><li>Customizable Reports for actionable insights</li><li>Integrated raw data processing and analysis powered by Mnova 15.1</li><li>In-app messaging system for seamless communication</li><li>Advanced experiment write-up section for detailed documentation<br>…and much more!</li></ul><h4><strong>What’s New in Mbook 4.0?</strong></h4><p>Mbook continues to evolve with cutting-edge features and improvements. <strong>Mbook 4.0 is now available</strong>, bringing enhanced usability, performance upgrades, and deeper integration with advanced analytical tools. Explore its powerful features today!<br>Start optimizing your lab workflow today! <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/mbook-trial"><strong>Click here </strong></a>to begin your free trial of Mbook.</p>

Mestrelab Mnova Hub software

Mnova Hub is a data browser that connects to various data providers and allows you to seamlessly retrieve, modify, and store data from within Mnova.

<p><strong>Streamlining Data Access and Exploration through Seamless Integration</strong></p><p>Spectral data is often scattered across diverse file systems and cloud-based repositories and is difficult to access. Mnova Hub is a data browser that connects to various data providers and allows you to seamlessly retrieve, modify, and store data from within Mnova, offering a centralized platform for exploration and analysis.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>Seamlessly push and pull data from various providers using our unique data browser tool.</li><li>Cut out the middleman! Forget about manually downloading data; open and view it directly within Mnova.</li><li>Do not compromise security for easy data browsing; enjoy the ‘best of both worlds’ by effortlessly navigating secure data platforms.</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">Connection to various data providers</strong></h5><p>Mnova Hub provides easy and safe connections to various data sources, including databases (SQL and MnDB), Scientific Data Management Systems and data hubs (LOGS, ZONTAL), TopSpin (part of the Bruker Chemist Suite), and file systems. Each provider has a unique setup dialog that allows for customized configuration. Its flexible design ensures that integrating new providers is easily achievable.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Hub_Connection_to_various_data_providers_ecc1b930ce.webp" alt="Mestrelab Research: Mestrelab Mnova Hub - Connection to various data providers." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Hub_Connection_to_various_data_providers_ecc1b930ce.webp 245w," sizes="100vw" width="1104" height="641"></p><h5><strong data-start="1546" data-end="1659">Data searches and filtering</strong></h5><p>Mnova Hub has a configurable results display table that adapts to your preferences, allowing you to preview data, sort and filter results using available metadata filters, and fine-tune your search. This enables you to quickly and efficiently identify relevant datasets.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Hub_Data_searches_and_filtering_fd7c371f66.webp" alt="Mestrelab Research: Mestrelab Mnova Hub - Data searches and filtering" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Hub_Data_searches_and_filtering_fd7c371f66.webp 245w," sizes="100vw" width="1196" height="694"></p><h5><strong data-start="2113" data-end="2150">Seamless Data Management with Cloud Advantages</strong></h5><p>Mnova Hub offers a seamless experience for opening and saving data with just one step, akin to managing files on a local file system. With Mnova Hub, you no longer need to compromise between the convenience of local file handling and the advanced security, accessibility, and searchability features of cloud storage.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mestrelab_Mnova_Hub_Seamless_Data_Management_with_Cloud_Advantages_8b8effce32.webp" alt="Mestrelab Research: Mestrelab Mnova Hub - Seamless Data Management with Cloud Advantages." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mestrelab_Mnova_Hub_Seamless_Data_Management_with_Cloud_Advantages_8b8effce32.webp 245w," sizes="100vw" width="1104" height="640"></p>

Mestrelab Mnova ElViS software

By integrating Mnova ElViS into your analytical workflows, you can enhance the efficiency and accuracy of your spectroscopic data analysis across a wide range of techniques.

<h4><strong>Visualize, process, analyze and report various electronic and vibrational spectroscopic techniques</strong></h4><p>Carry out the analysis of various optical spectroscopy data including ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence, and other spectroscopic methods operating in the whole region of wavelength from 100 nm to 100 µm.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>Spectroscopic processing and analysis of ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence methods.</li><li>Vendor and Operating System agnostic, supports most data file formats.</li><li>Wide range of processing functions, including a dynamic processing template.</li><li>Manual and automatic peak picking analysis.</li><li>Data Analysis of stacked/arrayed spectra.&nbsp;</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">PLS-Based Baseline Correction Tool (IarPLS)</strong></h5><p>To improve the processing of electronic and vibrational spectroscopy data, Mnova 15 introduces an enhanced PLS-based baseline correction tool, IarPLS (penalized-least-squares based). It now features an automatic button to compute the near-optimal asymmetry parameter.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp" alt="Mestrelab Research: Mnova EIVis - PLS-Based Baseline Correction Tool (IarPLS)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp 245w," sizes="100vw" width="1500" height="845"></p><h5><strong>Normalization Processing Modes</strong></h5><p>Three Normalization Processing modes, namely Largest Peak, Integral and Vector Length are available in Mnova 15. These three features aim to simplify the baseline correction process, streamline your workflow, and provide a rapid method for normalizing data to address quantitative issues.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp" alt="Mestrelab Research: Mnova EIVis - Normalization Processing Modes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp 245w," sizes="100vw" width="1500" height="844"></p><h5><strong>Electronic and Vibrational Spectroscopies supported file formats</strong></h5><p>Mnova ElViS supports the most frequently used data file formats for these analytical techniques, including import and export of single and multi-column matrix-like spectra (i.e., ASCII XY and XYY, as .txt or .csv files). Supported formats include SPC (Galactic), JCAMP-DX, OPUS (Bruker), SPA and SPG (Omnic), Perkin Elmer's .sp files, and ASCII (XY and XYY).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp" alt="Mestrelab Research: Mnova EIVis - Electronic and Vibrational Spectroscopies supported file formats" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp 245w," sizes="100vw" width="1500" height="910"></p><h5><strong>Manual &amp; automatic spectral analysis</strong></h5><p>Mnova ElViS provides both manual and automatic peak picking, offering flexibility and convenience in your spectral analysis. It also features advanced tools for the analysis of stacked or arrayed spectra, ensuring thorough data evaluation and interpretation for all your spectral needs.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp" alt="Mestrelab Research: Mnova EIVis - Manual &amp; automatic spectral analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp 240w," sizes="100vw" width="940" height="611"></p><h5><strong>Main processing features</strong></h5><p>Mnova ElViS comes with a range of useful processing features, including Multiplicative Scatter Correction, Probabilistic Quotient Normalization, Baseline correction (both automatic IarPLS and tailored Multipoint Baseline Correction), several smoothing techniques (such as Savitzky-Golay, Nonlocal Means, Exponential), normalization and Standard Normal Variate (SNV), derivatives, and units conversion between absorbance and transmittance.<br><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp" alt="Mestrelab Research: Mnova EIVis - Main processing features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp 245w," sizes="100vw" width="1074" height="581"></p>

Mestrelab Mnova IUPAC Name software

Mnova IUPAC Name quickly generates IUPAC names for molecular structures within Mnova documents

<h4><strong>Generate the IUPAC name for your molecular structures</strong></h4><p><strong>Mnova IUPAC</strong> Name quickly generates IUPAC names for molecular structures within Mnova documents, making it an essential tool for organic chemists. With just one click, you can name any molecular structure. The tool also supports full automation, allowing entire compound libraries to be named in minutes. For more details, explore the Mnova Gears IUPAC Name solution.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_IUPAC_8e9ddfe661.webp" alt="Mestrelab Research: Mnova IUPAC Name" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_IUPAC_8e9ddfe661.webp 228w," sizes="100vw" width="1244" height="853"></p><p><strong>What’s IUPAC Name?</strong></p><p>The International Union of Pure and Applied Chemistry (<strong>IUPAC</strong>) is the world authority on chemical nomenclature and terminology, including the naming of new elements in the periodic table; on standardized methods for measurement; and on atomic weights, and many other critically-evaluated data.</p><p>IUPAC is the universally-recognized authority on chemical nomenclature and terminology and two IUPAC bodies take leading roles in this activity: Division VIII – Chemical Nomenclature and Structure Representation and the Inter-divisional Committee on Terminology, Nomenclature, and Symbols.</p><p><a target="_blank" rel="noopener noreferrer" href="https://iupac.org/"><strong>Read more about IUPAC here.</strong></a></p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>One-click generation of IUPAC names within Mnova software.</li><li>Phane nomenclature for precise naming of complex molecular structures.</li><li>Generates Preferred IUPAC Names (PIN) for all classes of parent hydrides, including acyclic, monocyclic, polyalicyclic, spiro, fused, and bridged ring systems.</li><li>Works with a smaller library of functional groups and advanced algorithms for correct IUPAC combination.&nbsp;</li></ul>

Mestrelab Mnova Advanced Chemometrics software

Leverage sophisticated multivariate analysis tools such as PCA, PLS, and SIMCA for in-depth data exploration, regression, and classification.

<h4><strong>Advanced Chemometrics for Enhanced Data Analysis</strong></h4><p>Leverage sophisticated multivariate analysis tools such as <strong>PCA, PLS, and SIMCA</strong> for in-depth data exploration, regression, and classification. The user-friendly interface ensures seamless integration of spectral data with analysis results, making it ideal for advanced users seeking powerful chemometric techniques for comprehensive data analysis.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>All-in-One Solution</strong>: Offers seamless spectral processing, binning, and analysis with just a few clicks, simplifying workflows and managing complex data efficiently in a single platform.</li><li><strong>Incorporates Recognized Methods:</strong> Includes PCA, PLS, and SIMCA for advanced data analysis and unsupervised chemometrics.</li><li><strong>Powerful and Intuitive Interface:</strong> Provides a user-friendly design for a streamlined analysis experience.</li><li><strong>Improved Classification and Prediction</strong>: Classifies samples with SIMCA and builds predictive models using PLS.</li><li><strong>Noise and Outlier Reduction</strong>: Effectively reduces noise and detects outliers, ensuring accurate and reliable results.</li><li><strong>Expandable Plugin:</strong> Features Higher Order Structure (HOS) capabilities, powered by Mnova BioHOS.&nbsp;</li></ul><h4><strong>Features:</strong></h4><h5><strong>New MCR Tool for Peak Purity Assessment in LCMS Data</strong></h5><p>Introduces an MCR tool based on the MCR-ALS algorithm for peak purity assessment, providing comprehensive tables and plots to streamline LCMS data analysis and enhance decision-making. This tool is available with the Advanced Chemometrics or BioHOS plugins and requires an MSChrom plugin license.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_New_MCR_Tool_for_Peak_Purity_Assessment_in_LCMS_Data_94993e71c3.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - New MCR Tool for Peak Purity Assessment in LCMS Data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_New_MCR_Tool_for_Peak_Purity_Assessment_in_LCMS_Data_94993e71c3.webp 245w," sizes="100vw" width="1920" height="1200"></p><h5><strong>Enhanced Data Analysis with Partial Least Squares (PLS) Regression</strong></h5><p>Facilitates comprehensive analysis and accurate prediction of dependent variables from independent variables (such as binned spectra), while also providing intuitive graphical evaluation tools for deeper insights.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Enhanced_Data_Analysis_with_Partial_Least_Squares_PLS_Regression_7363eca35a.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Enhanced Data Analysis with Partial Least Squares (PLS) Regression" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Enhanced_Data_Analysis_with_Partial_Least_Squares_PLS_Regression_7363eca35a.webp 211w," sizes="100vw" width="1175" height="870"></p><h5><strong>Advanced Data Classification with SIMCA Modeling</strong></h5><p>Offers a powerful statistical method for supervised classification of data, allowing researchers to categorize and analyze complex datasets with precision.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_Data_Classification_with_SIMCA_Modeling_830f63d8a0.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Advanced Data Classification with SIMCA Modeling" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_Data_Classification_with_SIMCA_Modeling_830f63d8a0.webp 195w," sizes="100vw" width="1026" height="820"></p><h5><strong>Advanced PCA Analysis for In-Depth Data Insights</strong></h5><p>Provides features for comprehensive data exploration and visualization, including quantile plots (1D NMR), comparison tools, plot labels display, enhanced selection and interactivity in scores and loadings plots, and additional distance. calculation options in scores plots. These enhancements enable precise, interactive analysis for deeper data insights.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_PCA_Analysis_for_In_Depth_Data_Insights_32b2644333.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Advanced PCA Analysis for In-Depth Data Insights" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_PCA_Analysis_for_In_Depth_Data_Insights_32b2644333.webp 245w," sizes="100vw" width="1026" height="647"></p>

Mestrelab Mnova Verify software

Designed for synthetic and medicinal chemists, which automatically checks a proposed structure against a set of analytical data, LC-MS or GC-MS and/or 1D NMR and/or 2D NMR.

<h4><strong>Automatic confirmation of structure identity based on NMR and/or LC/GC-MS data</strong></h4><p>Designed for synthetic and medicinal chemists, which automatically checks a proposed structure against a set of analytical data, LC-MS or GC-MS and/or 1D NMR and/or 2D NMR.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>Solution coupling NMR </strong>(1H, 13C and HSQC) and <strong>MS techniques</strong> in a commercial off-the-shelf software.</li><li><strong>Helps non-expert users </strong>to take decisions while reducing the workload of the experts.</li><li><strong>Fully automatic analysis</strong> for singletons or batch data.</li><li><strong>Mnova Verify checks</strong> whether the proposed compound is correct!</li><li><strong>Flexibly accepts a variety of inputs</strong> and generate automatically fully customized reports.</li><li><strong>Straightforward results</strong> check that warns about inconsistencies.</li></ul><h4><strong>Features:</strong></h4><h5><strong>Powerful algorithm running behind the scenes</strong></h5><p><strong>This powerful algorithm integrates multiple modules to deliver:</strong></p><ul><li>Automated processing and analysis of 1D &amp; 2D NMR (¹H NMR, HSQC) and LC/GC-MS data.</li><li>Generation of theoretical data for proposed structures.</li><li>Prediction of NMR spectra (¹H, ¹³C, HSQC) and MS data (mass ion, isotopic cluster, fragmentation, MS/MS).</li><li>Comparison of experimental and theoretical data.</li><li>Results presentation using an innovative scoring system.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_Powerful_algorithm_running_behind_the_scenes_be1dfb5e2f.webp" alt="Mestrelab Research: Mnova Verify - Powerful algorithm running behind the scenes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_Powerful_algorithm_running_behind_the_scenes_be1dfb5e2f.webp 245w," sizes="100vw" width="936" height="427"></p><h5><strong>An easy and flexible scoring system</strong></h5><p>Evaluates analytical data to judge the compatibility of proposed structures:</p><ul><li>Provides a quality score (-1.0 to +1.0) for each structure, combining score and significance.</li><li>Displays detailed results for each test applied.</li><li>Ranks structures to quickly identify the best fit with experimental data.</li><li>Allows easy re-working of unclear results in Mnova.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_An_easy_and_flexible_scoring_system_da1c07eb98.webp" alt="Mestrelab Research: Mnova Verify - An easy and flexible scoring system" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_An_easy_and_flexible_scoring_system_da1c07eb98.webp 245w," sizes="100vw" width="936" height="427"></p><h5><strong>Automated batch &amp; real-time operations</strong></h5><p>Provides QA for large data volumes like libraries or registration QC samples:</p><ul><li><strong>Batch Mode: </strong>Verifies libraries and identifies unsuitable samples for assay submission, generating an HTML report.</li><li><strong>Real-Time Analysis Mode: </strong>Performs unassisted verification of individual samples, providing immediate feedback on structure-data compatibility.</li><li>Requires Mnova v10 or higher (NMR, NMRPredict, and/or MS modules). Additional modules like Mnova DB or qNMR can also be used.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_Automated_batch_and_real_time_operations_6fa9241b15.webp" alt="Mestrelab Research: Mnova Verify - Automated batch &amp; real-time operations" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_Automated_batch_and_real_time_operations_6fa9241b15.webp 245w," sizes="100vw" width="936" height="427"></p>

Mestrelab Mnova DB software

A professional database integrated within Mnova for efficiently shared storage of molecular structures, NMR, LC-MS, GC-MS, and other Mnova objects.

<h4><strong>Store, share and search your chemical and analytical data efficiently</strong></h4><p>A professional database integrated within Mnova for efficiently shared storage of molecular structures, NMR, LC-MS, GC-MS, and other Mnova objects.</p><p>Create new databases, save records with a couple of clicks, and run searches by structure, substructure, specific peak positions, multiplets, or mass peaks.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>Database enviroment for storing analytical data</strong> (NMR, LC/GC-MS, UV-Vis, IR, etc.) and chemical structures.</li><li><strong>Ranked queries by chemical struture and/or analytical data,</strong> enabling faster and more efficient data retrieval.</li><li><strong>Tools for managing and controlling your databases</strong>, with password protection for security.</li><li><strong>Quickly create new databases</strong> and save your analytical records in seconds, enhancing efficiency and productivity.</li><li><strong>Access and view reports in a variety of formats</strong>, including tables, records, well plates, and HTML views, for versatile data presentation.</li><li><strong>Manage user accounts with different privilege levels-administrator</strong>, power user, and standard user.</li></ul><h4><strong>Features:</strong></h4><h5><strong>Create a database and save your legacy data</strong></h5><ul><li>Companies and research institutes often have large volumes of analytical data spread across various sources, needing better management to make it shareable and searchable.</li><li>Once stored in the DB, chemists can easily access this data using Sample ID, Compound ID, peak, or Retention Time searches, especially for unknowns.</li><li>Mnova provides scripts to upload multiple NMR and/or LC/GC/MS datasets into a database.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Create_a_database_and_save_your_legacy_data_7e9ee2e6f7.webp" alt="Mestrelab Research: Mnova DB - Create a database and save your legacy data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Create_a_database_and_save_your_legacy_data_7e9ee2e6f7.webp 245w," sizes="100vw" width="1380" height="801"></p><h5><strong>Save your data during the workflow</strong></h5><ul><li>Chemists can save their work to the Database, making it easily accessible to others later.</li><li>Mnova DB integrates with Mnova Suite, allowing you to save or search data seamlessly during your workflow.</li><li>When working with NMR or LC/GC/MS datasets in Mnova, you can save raw data, parameters, analysis results, annotations, and molecular structures to the database with a couple of clicks.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Save_your_data_during_the_workflow_169e99f805.webp" alt="Mestrelab Research: Mnova DB - Save your data during the workflow." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Save_your_data_during_the_workflow_169e99f805.webp 245w," sizes="100vw" width="1472" height="854"></p><h5><strong>Efficient sharing and searching of your analytical data</strong></h5><p>The basic search query can be the following:</p><ul><li>Structure or substructure</li><li>1D or 2D peak locations</li><li>1D multiplets</li><li>m/z values</li><li>Texts or numerics</li><li>Mnova DB allows you to combine any of the queries listed above, using logic and or operators, this is useful when you need to refine your search results iteratively. Mnova DB also save the results into a temporary database and continue to search to refine it.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Efficient_sharing_and_searching_of_your_analytical_data_8bde34ce47.webp" alt="Mestrelab Research: Mnova DB - Efficient sharing and searching of your analytical data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Efficient_sharing_and_searching_of_your_analytical_data_8bde34ce47.webp 245w," sizes="100vw" width="1472" height="854"></p><h5><strong>Seamlessly deliver data to your organization</strong></h5><ul><li>We can automate the DB setup so data is automatically imported when acquired by your instruments.</li><li>Chemists will no longer need to browse through file folders to review or analyze data. They can retrieve it directly in Mnova using Sample ID, Sample Number, Compound ID, or structure searches.</li><li>This will save your team time and significantly improve productivity.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Seamlessly_deliver_data_to_your_organization_b4c354eeb3.webp" alt="Mestrelab Research: Mnova DB - Seamlessly deliver data to your organization" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Seamlessly_deliver_data_to_your_organization_b4c354eeb3.webp 245w," sizes="100vw" width="1472" height="854"></p>

Products focused on Software - page 6

Related content

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

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

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