<p><strong>In the week from September 14, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>6 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Technology Networks: Automating Microplastic Analysis To Support Water Quality Research</strong></h5><ul><li>Tu, 15.9.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6471">Registration</a></li></ul><p>Join this webinar on automated microplastic analysis and learn how automated sample preparation can improve workflow efficiency and support water quality research.</p><h5><strong>2. C&amp;EN: Fast Track Closed-Loop Experimentation with AI-Driven Automation on Stuntman</strong></h5><ul><li>Tu, 15.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6472">Registration</a></li></ul><p>Discover how the Stuntman platform integrates AI and laboratory automation to enable closed-loop experimentation and accelerate research and discovery workflows.</p><h5><strong>3. Agilent Technologies: ICP-OES Preventive Maintenance: What to Clean, How, and When</strong></h5><ul><li>Tu, 15.9.2026 21:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6413">Registration</a></li></ul><p>Learn key ICP-OES cleaning procedures and maintenance schedules to prevent signal drift, instability, and unnecessary instrument downtime.</p><h5><strong>4. Bruker: From Pit to Port: XRF Solutions for Base Metal Mining Operations</strong></h5><ul><li>We, 16.9.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6502">Registration</a></li></ul><p>Explore how EDXRF and WDXRF support base metal mining operations, from grade control and process monitoring to quality assurance across the production chain.</p><h5><strong>5. Bruker: AI-Assisted Analysis of Viscoelasticity with AFM</strong></h5><ul><li>We, 16.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6503">Registration</a></li></ul><p>Learn how machine learning and the ViscoDirect module accelerate AFM-based viscoelasticity analysis and cell nanorheology workflows.</p><h5><strong>6. Anton Paar: Extreme-Temperature Stability of MXenes Revealed by In-Situ High-Temperature XRD</strong></h5><ul><li>We, 16.9.2026 18:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6429">Registration</a></li></ul><p>Discover how non-ambient XRD chambers enable in-situ characterization of MXene structural dynamics at temperatures up to 1200 °C.</p><h5><strong>7. Agilent Technologies: Accelerating Biopharmaceutical Development with the Agilent Cary 3500 UV-Vis Spectrophotometer</strong></h5><ul><li>Th, 17.9.2026 07:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6507">Registration</a></li></ul><p>Explore how the Agilent Cary 3500 UV-Vis spectrophotometer supports biopharmaceutical characterization from development through routine quality control.</p><h5><strong>8. Agilent Technologies: Landscape of Standardization and Overview of Analytical Techniques Used for Microplastics Analysis</strong></h5><ul><li>Th, 17.9.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6539">Registration</a></li></ul><p>Explore current trends in microplastics analysis, including analytical standards, molecular spectroscopy, Py-GC/MS, TD-GC/MS, and future developments for reliable characterization.</p><h5><strong>9. SelectScience: Master EV profiling with Leprechaun – A case study from North Texas Eye Research Institute</strong></h5><ul><li>Th, 17.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6506">Registration</a></li></ul><p>Explore advanced extracellular vesicle profiling and discover how the Leprechaun platform streamlines EV characterization for research applications.</p><h5><strong>10. Agilent Technologies: Analysis of High Purity Titanium Using an Agilent 9500 ICP-QQQ</strong></h5><ul><li>Th, 17.9.2026 20:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6414">Registration</a></li></ul><p>Learn how the Agilent 9500 ICP-QQQ overcomes titanium matrix effects to enable trace impurity analysis in high-purity titanium.</p>

Webinars LabRulezICPMS Week 38/2026

ISC 2026 - Day 4

<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

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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>

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