<p><strong>The goal of this study is to develop an efficient and low-cost method for recognizing fluorite ore grade using smartphone images and spectral clustering. By photographing pellet samples derived from drilling chips and correcting image colors with a ColorChecker, the method ensures consistency under variable lighting conditions. Principal component analysis of color and texture data is used to build clustering models that can discriminate between ore qualities.</strong></p><p>This approach provides a reliable screening tool, especially effective in identifying waste material with up to 94.74% accuracy using spectral clustering. Unlike previous methods, it does not require expensive cameras or skilled operators, making it particularly beneficial for small- and medium-sized mining companies. The method supports more accessible ore classification and opens the door to broader applications in mine planning, processing, and quality assessment.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425"><strong>Fluorite ore recognition using spectral clustering and smartphone digital images calibrated with a ColorChecker: A case study at the Lújar underground mine, Spain</strong></a></h4><p>Enming Li, Pablo Segarra, José A. Sanchidrián, Santiago Gómez, Luis Iglesias, Alberto Fernández, Rafael Navarro</p><p>Minerals Engineering, Volume 232, 2025, 109514</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.mineng.2025.109514">https://doi.org/10.1016/j.mineng.2025.109514</a></p><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><p>With the advancement of portability, connectivity and camera resolution of smartphones, they have been widely applied across numerous image analysis domains, such as the segmentation of images (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0025"><span>Campos-Taberner et al., 2016</span></a>), digital image colorimetry (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0065"><span>Fan et al., 2021</span></a>) and photomicrograph acquisition (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0155"><span>Roy et al., 2014</span></a>); the smartphone seems to be a suitable “low cost” replacement for a professional photography device to procure the color information from the tasks of ore grade estimation. This study proposes a smartphone as photography device to procure fluorite-based pellet images, to be analyzed for fluorite grade recognition and prediction. In order to overcome the variations in lighting conditions which may critically introduce noise or inconsistencies in image colors, this study proposes to employ a ColorChecker (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0140"><span>Pascale, 2005</span></a>) that allows correcting the colors of pellet images to obtain the genuine color of each pixel. Significant color characteristics are extracted from the corrected pellet images and used for predicting the fluorite grade by a simple unsupervised machine learning technique. As a background for this approach, the authors demonstrate that color information of images of in-borehole walls drilled in the same mining site are sensitive to the fluorite content that it is predicted with reasonable accuracy (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0100"><span>Li et al., 2023</span></a>).</p><h4><strong>2. Data overview</strong></h4><p>Drilling chips of production blastholes and exploration boreholes from Lújar mine (F-Pb-Zn) were collected while drilling for grade control purposes. The Lújar mine is an underground mine, located in Granada (southeast of Spain). It mainly extracts fluorite, often accompanied by lead (in the form of galena) or zinc (in form of sphalerite or smithsonite). It is a Mississippi Valley Type (MVT) stratabound deposit hosted in the carbonate sequences of the Ladinian (Upper Triassic), mainly composed of dolomites but also limestones as gangue. Geologically, it belongs to the Alpujarride Complex (Internal Zones of the Betic Chain) (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0015"><span>Amor et al., 2019</span></a>). The samples are quartered, dried, grinded to a size below 80&nbsp;μm, and further quartered to obtain a 10&nbsp;g sample from which pressed powder pellets of 32&nbsp;mm of diameter and 5&nbsp;mm height with particles than 80&nbsp;μm are prepared. The pellets were analyzed in the mine laboratory with an X-Ray Fluorescence (XRF) spectrometer by Thermo Fisher Scientific, model <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1212"><strong>ARL OPTIM'X WDXRF</strong></a> with a Rhodium anode X-ray tube (50&nbsp;kV, 150&nbsp;mA) and LiF200, InSb and AX06 analyzer crystals, using the Oxsas 2.2 software. That device provides an elemental composition from which the amount of calcium fluoride, calcium carbonate, calcium magnesium carbonate (dolomite), silica, ferric oxide, aluminum oxide, zinc and lead are obtained by stoichiometric calculations.</p><div><p>A dataset composed by 494 pellets from Lújar underground mine is considered in this work. 198 and 296 samples were collected from levels 70 and 345, respectively, during 2020 and 2021. The distributions of the main chemical compounds of the pellets are shown in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#f0005"><span>Fig. 1</span></a>; the kernel density estimate (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0075"><span>Holger, 2015</span></a>) of the probability density (grey patch) is overlapped with the box-and-whisker diagram. Fluorite (CaF<sub>2</sub>), limestone (L) and dolomite (D) are predominant (left graph), while the oxides are less abundant (right graph, note the different Y-axis scale). The pellets are dolomite-rich in general, with a fluorite content below 20&nbsp;% in most of the cases. Pellets with fluorite as predominant compound are scarce (see blue dots in the fluorite distribution, left graph, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#f0005"><span>Fig. 1</span></a>), while fluorite is less than 3.6&nbsp;% in 124 pellets (a 25&nbsp;% of the total) that are rich in either dolomite or limestone. This reflects the chemical composition of the deposit, with typical grades in the order of 15&nbsp;% in fluorite, complicating the in-situ ore recognition (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0010"><span>Amor and Navarro, 2016</span></a>). Silica is the predominant oxide in the pellets (see right graph), with a 75 percentile of 6.5&nbsp;%. The amount of the other oxides is in general smaller, though in some pellets they are atypically high which could affect to the color. There are also traces of zinc and lead, lower than 1.5&nbsp;% in 95&nbsp;% of the pellets; for pellets with a large lead (&gt;30&nbsp;%) amount, the color becomes dark (i.e. greyish-black).</p><p>The pellets were photographed with a smartphone with a resolution up to 50 Mpx following the layout in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#f0010"><span>Fig. 2</span></a>. Sunlight was used as the light source. The pellets were placed with the clean side (i.e. opposite to the damaged one by the X-Ray beam) towards the camera. Taking pictures can be voice-activated to avoid the movement of camera. Some camera settings like the shutter speed value (1/30&nbsp;s), the aperture (f/1.85), and the focal length (6.67&nbsp;mm) are constant, while the exposure time or the ISO are automatically tuned by the smart phone. They are in the range 1/451 – 1/98&nbsp;s for exposure time and 50 – 160 for ISO, with mean values of 1/141&nbsp;s and 50 respectively. The images are recorded in “jpg” format. Variations in the camera settings and in the light intensity distort the pellet colors in the images. To assess these errors and correct them, a low-cost 24 patch ColorChecker Classic Mini target (63.5&nbsp;×&nbsp;109.0&nbsp;mm) manufactured by “X-Rite” was placed on the top part of the image in the same relative position with respect to the pellet. The ground sampling distance is 0.0687&nbsp;mm. A standardized television test card, i.e., PM5544-PAL, is used to ensure diverse background colors and facilitate the automatic detection of the ColorChecker. This background has the advantage that it is easily reproducible, but since was in-house printed still makes necessary a color reference card (e.g. ColorChecker) to control the image colors under any illumination. Similar test cards than PM5544-PAL, will likely have a similar performance, while other tested backgrounds, like a black cardboard, hinder the detection of the patches of the color reference card.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Minerals_Engineering_Volume_232_2025_109514_Fig_2_Experimental_layout_of_taking_the_fluorite_based_pellets_and_color_correction_by_a_Colorchecker_831a5a7398.jpg" alt="Minerals Engineering, Volume 232, 2025, 109514: Fig. 2. Experimental layout of taking the fluorite-based pellets and color correction by a Colorchecker." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Minerals_Engineering_Volume_232_2025_109514_Fig_2_Experimental_layout_of_taking_the_fluorite_based_pellets_and_color_correction_by_a_Colorchecker_831a5a7398.jpg 160w," sizes="100vw" width="1200" height="1173"></p><h5><strong>2.2. Features extraction</strong></h5><p>Various color parameters are calculated, however, only a few parameters have stronger correlation with the fluorite grade and thus selected to develop the model. The pixel color intensities (PCIs) of the corrected photos of the ROI of each pellet are described with the mean of the red, green and blue intensities of the pixels, that are abbreviated as avR, avG and avB, respectively. The resulting distributions are shown in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#f0030"><span>Fig. 6</span></a>; mean red intensities range from 0.85 to 0.55 while blue intensities are the smaller ones, down to 0.3. A comparison of the median of each color channel before and after correction (green versus red horizonal lines, respectively), indicates the color shift towards slightly darker colors. The Blue Flower patch (P5) stays now inside the 99&nbsp;% coverage of the mean intensities together with the Light Skin (P2), Neutral 8 (P20), and Neutral 6.5 (P21) patches. Mean green intensities are strongly correlated with the mean of the red and blue intensities with Spearman coefficients of 0.88 and 0.98, respectively and are discarded as an input for predicting fluorite grade.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Minerals_Engineering_Volume_232_2025_109514_Fig_6_Violin_and_box_plots_of_the_descriptor_parameters_of_the_images_of_the_ROI_of_the_pellets_126aad2a57.jpg" alt="Minerals Engineering, Volume 232, 2025, 109514: Fig. 6. Violin and box plots of the descriptor parameters of the images of the ROI of the pellets: mean of red, green and blue intensities (avR, avG, avB, respectively), uncertainty (U) and texture-correlation (Tc); note that all the parameters are non-dimensional. The circular markers show the colors of the patches lying within the 99 % coverage of the mean color intensities. The green horizontal lines are the median of the pixel intensities of ROI in raw pellet images. (For interpretation of the references to color 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_Minerals_Engineering_Volume_232_2025_109514_Fig_6_Violin_and_box_plots_of_the_descriptor_parameters_of_the_images_of_the_ROI_of_the_pellets_126aad2a57.jpg 196w," sizes="100vw" width="1600" height="1276"></p><p>The dispersion in the color clouds for each pellet in the RGB space is described with the standard deviational ellipsoid (SDE), which is extensively applied to investigate uncertainty of datasets in 2D and 3D spaces (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0225"><span>Yang et al., 2020</span></a>). The SDE is calculated with the covariance matrix of the PCIs under the assumption that the data comes from a normal distribution using the MATLAB function <i>error_ellipse</i> defined by Johnson (2023) (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#b0005"><span>A.J., 2023</span></a>). Its major axis indicates the direction of largest variability, and the scatter in PCIs is taken as the length of the major semi axis. This single parameter describes the uncertainty (U) in the PCIs and is used instead of the standard deviation, or other dispersion measurements, of the color pixel intensities.</p><div><p><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0892687525003425#f0035"><span>Fig. 7</span></a> shows, as an example, the color intensities of a pellet in the RGB space and the SDE at a 95&nbsp;% confidence level. This ellipsoid is representative of the uncertainty observed in the rest of the pellets; it is elongated with the major axis significantly longer than the other two having an angle with the green axis of 46.3° sd 1.0°, and an inclination with respect to the blue axis of 54.2° sd 1.6 (mean and standard deviation from all the pellets). The major semi-axis with a length of 0.0895 is close to the median uncertainty of all pellets, and it lumps mainly dispersion in the red and blue colors. The minor semi axes are both about 0.0168.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Minerals_Engineering_Volume_232_2025_109514_Fig_7_Color_pixel_intensities_in_the_normalized_RGB_space_of_the_ROI_of_the_corrected_image_of_pellet_2e9ee5763b.jpg" alt="Minerals Engineering, Volume 232, 2025, 109514: Fig. 7. Color pixel intensities in the normalized RGB space of the ROI of the corrected image of pellet 37,711 and its 95 % confidence SDE; the size of the points is proportional to their relative probability." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Minerals_Engineering_Volume_232_2025_109514_Fig_7_Color_pixel_intensities_in_the_normalized_RGB_space_of_the_ROI_of_the_corrected_image_of_pellet_2e9ee5763b.jpg 245w," sizes="100vw" width="1600" height="947"></p></div><h4><strong>5. Conclusions</strong></h4><p>In this study, a smartphone with a high-resolution camera is employed to procure photographs of pellets made from finely ground (below 80&nbsp;μm) and compacted drilling chips samples. As a novelty, a standard color reference card is placed next to the pellets to obtain the non-lighting-interfered ore colors independently of the light conditions. Image-based analysis is integrated with clustering algorithms. Three clusters are generated according to the three principal components obtained from the color properties, namely mean of red and blue pixel color intensities (PCIs), uncertainty in PCIs, and texture-correlation. The precision for each cluster is calculated from the chemical analysis of the pellets and cluster C2 could be assigned to waste with high occurrence probability and cluster C1 to ore. The lower precision from this cluster makes it necessary to consider chemical analysis if the pellets are clustered in this cluster. However, if the color attributes of the pellets correspond to cluster C3, the pellets could be deemed as waste with an excellent precision, close to 95&nbsp;%. Such results enable a fast and cheap screening criterion of pellets. Hence lowering grading costs, increasing equipment availability, and reducing maintenance costs by extending the average lifespan of the X-ray tube.</p><p>Compared with previous studies, the image-based recognition procedure proposed here is a low-cost technique that does not requires a rigid operational environment, nor high-professional skills. Neither the image processing nor the spectral clustering add computational difficulties, and the entire process can be easily applied to other ores that exhibit distinct colors corresponding to variation in their mineral composition and concentrations. Though not complete substituting geochemistry analysis, these research opens the way to use smartphone images to assess ore grading in the different stages of the mining cycle, from modeling of deposits to mineral processing. This will contribute to decrease resources and cost dedicated to these tasks, enabling ore-grading for cases with resources limitations.</p></div>

Fluorite ore recognition using spectral clustering and smartphone digital images calibrated with a ColorChecker: A case study at the Lújar underground mine, Spain

<p><strong>In the week of June 30, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><p><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></p><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>4 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: From Insights to Outcomes: Real-World Optimization of Lab Operations Using CrossLab Connect Digital Services</strong></h5><ul><li>Already taken place Monday, June 30, 2025</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5256">Record</a></li></ul><p>Discover how labs capitalize on utilization and service data with CrossLab Connect. Case studies show how informed decisions drive cost savings, productivity gains, and sustainability progress.</p><h5><strong>2. Agilent Technologies: Optimizing Laboratory Procurement with Digital Services: Take Control of Your Laboratory Spend with Agilent CrossLab Connect</strong></h5><ul><li>Tuesday, July 1, 2025, 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5257">Registration</a></li></ul><p>Learn how labs use CrossLab Connect to optimize CapEx &amp; OpEx, avoid unnecessary instrument purchases, and fine-tune service coverage using risk-based, data-driven insights and real case studies.</p><h5><strong>3. Bruker: Single-Cell Explorations on the timsTOF Ultra 2</strong></h5><ul><li>Tuesday, July 1, 2025, 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5254">Registration</a></li></ul><p>Discover how the timsTOF Ultra 2 is transforming routine single cell research. Learn how researchers are leveraging its powerful capabilities to tackle high-impact biological questions.</p><h5><strong>4. CAMAG: Detection of DEG and EG as impurities in syrup products</strong></h5><ul><li>Tuesday, July 1, 2025, 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5282">Registration</a></li></ul><p>Join us for a focused session on the detection of toxic impurities — diethylene glycol (DEG) and ethylene glycol (EG) — in oral liquid pharmaceuticals using High-Performance Thin-Layer Chromatography (HPTLC).</p><h5><strong>5. Agilent Technologies: From Detection to Documentation: Your Compliance Ready Analytical Workflow</strong></h5><ul><li>Wednesday, July 2, 2025, 07:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5241">Registration</a></li></ul><p>Ensure regulatory compliance from detection to documentation. Learn how Agilent’s LC/TQ software secures electronic records, tracks actions, and meets 21 CFR Part 11 and Annex 11—while boosting lab efficiency.</p><h5><strong>6. SCIEX: Roundtable discussion: Breaking through the bottleneck in metabolomics</strong></h5><ul><li>Wednesday, July 2, 2025, 15:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5266">Registration</a></li></ul><p>Join this expert panel discussion addressing the major bottlenecks in metabolite identification.</p>

Webinars LabRulezLCMS Week 27/2025

<p><strong>In the week of June 30, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><p><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></p><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>4 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: From Insights to Outcomes: Real-World Optimization of Lab Operations Using CrossLab Connect Digital Services</strong></h5><ul><li>Already taken place Monday, June 30, 2025</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5256">Registration</a></li></ul><p>Discover how labs capitalize on utilization and service data with CrossLab Connect. Case studies show how informed decisions drive cost savings, productivity gains, and sustainability progress.</p><h5><strong>2. Agilent Technologies: Go Beyond Limits in Pesticide Analysis: Watch, Ask, Connect with Agilent</strong></h5><ul><li>Already taken place Monday, June 30, 2025</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5262">Registration</a></li></ul><p>Watch the webinars, submit your question and join us for live Q&amp;A session on June 30 to consult directly with Agilent experts and gain practical insights on pesticide analysis.</p><h5><strong>3. Agilent Technologies: Optimizing Laboratory Procurement with Digital Services: Take Control of Your Laboratory Spend with Agilent CrossLab Connect</strong></h5><ul><li>Tuesday, July 1, 2025, 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5257">Registration</a></li></ul><p>Learn how labs use CrossLab Connect to optimize CapEx &amp; OpEx, avoid unnecessary instrument purchases, and fine-tune service coverage using risk-based, data-driven insights and real case studies.</p><h5><strong>4. Wasson-ECE Instrumentation: Data Handling Software: OpenLab Chemstation vs OpenLab CDS</strong></h5><ul><li>Tuesday, July 1, 2025, 21:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5276">Registration</a></li></ul><p>Explore how OpenLab CDS enhances chromatography workflows by integrating features from ChemStation, EZChrom, and MassHunter to boost usability, productivity, and user experience.</p><h5><strong>5. Agilent Technologies: Biopharmaceutical Raw Material Verification with Handheld Vaya Raman Spectrometer</strong></h5><ul><li>Thursday, July 3, 2025, 20:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/2770">Registration</a></li></ul><p>The Agilent Vaya handheld Raman spectrometer increases raw material identification throughput without increasing costs by reducing the need for sampling.</p>

Webinars LabRulezGCMS Week 27/2025

<p><strong>In the week from June 30, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><p><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></p><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>4 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: From Insights to Outcomes: Real-World Optimization of Lab Operations Using CrossLab Connect Digital Services</strong></h5><ul><li>Already taken place Monday, June 30, 2025</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5256">Record</a></li></ul><p>Discover how labs capitalize on utilization and service data with CrossLab Connect. Case studies show how informed decisions drive cost savings, productivity gains, and sustainability progress.</p><h5><strong>2. Bruker: Beyond the Surface: Micro-XRF and the Hidden Complexity of Porcelain</strong></h5><ul><li>Tuesday, July 1, 2025, 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5273">Registration</a></li></ul><p>Discover how micro-XRF enables non-invasive analysis of porcelain’s glaze, pigments, and provenance. Learn how Bruker tools uncover its history through elemental insights in art, archaeology, and science.</p><h5><strong>3. Agilent Technologies: Optimizing Laboratory Procurement with Digital Services: Take Control of Your Laboratory Spend with Agilent CrossLab Connect</strong></h5><ul><li>Tuesday, July 1, 2025, 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5257">Registration</a></li></ul><p>Learn how labs use CrossLab Connect to optimize CapEx &amp; OpEx, avoid unnecessary instrument purchases, and fine-tune service coverage using risk-based, data-driven insights and real case studies.</p><h5><strong>4. Agilent Technologies: Biopharmaceutical Raw Material Verification with Handheld Vaya Raman Spectrometer</strong></h5><ul><li>Thursday, July 3, 2025, 20:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2770">Registration</a></li></ul><p>The Agilent Vaya handheld Raman spectrometer increases raw material identification throughput without increasing costs by reducing the need for sampling.</p>

Webinars LabRulezICPMS Week 27/2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 30th June 2025? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you other document by <strong>Agilent Technologies </strong>and<strong> </strong>posters by<strong> Shimadzu / ASMS, Thermo Fisher Scientific / ASMS </strong>and <strong>Waters / ASMS</strong>!</p><h3><strong>1. Agilent Technologies: Understanding Solvent Focusing Gas Chromatography and How it can be Optimized for Splitless Injections&nbsp;</strong></h3><ul><li>Other document (white paper)</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/wp_solvent_focusing_gc_5994_8387en_agilent_7911ade217.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Discussions of the basic mechanism of gas chromatography (GC), samples can start as a liquid, solid, or a gas. These samples, when not in gas phase, are converted into a vapor made up of solvent and analytes that are then separated using a GC column. The type of film or stationary phase used in the column helps to separate the compounds of a sample mixture. Meanwhile, the role of the gaseous mobile phase is to move compounds through the column without chemically interacting with them.1 With splitless injections, almost the entire sample is transferred onto the column head, making it ideal for trace level analysis, but more susceptible to solvent interactions due to the large volume of solvent also introduced to the column. It is important to understand all the variables and how to optimize them to successfully perform splitless injections, including the role of the sample solvent and related solvent effects.</p><h4><strong>Sample preparation and solvent selection</strong></h4><p>Prior to efficient sample preparation methods for the analysis of pesticides, a guard column was used to help protect the analytical column from heavy matrix or nonvolatile analytes. The primary method of sample preparation used was solvent dilution in hexane or dichloromethane, which potentially passed through a filter, and then injected directly into the inlet and analyzed on a 5% phenyl phase type column. This can be referred to as the dilute and shoot method. As dichloromethane and hexane are both compatible solvents with a 5% phenyl phase, the guard column was only needed to protect the analytical column from matrix, and not from adverse&nbsp;wettability.&nbsp;</p><p>With the adoption of more involved sample preparation techniques, traditionally used in liquid chromatography, samples typically finish their preparation in the polar solvent acetonitrile. For GC analysis solvent exchange is employed, which is the process of drying down a sample in one solvent under nitrogen gas and reconstituting it in another solvent such as hexane. This procedure is performed to ensure proper interactions between solvent and column phase. Since these cleaned up samples contained less matrix, the need for a guard column was decreased.6 Gradually the requirement for solvent exchanges was decreased to decrease the overall sample preparation time, but this left the prepared samples in a final solvent of acetonitrile.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>For a gas chromatography analysis to be successful there are many factors to be considered. These factors include injection speed, solvent choice, installation of the column, and the column phase selection. Solvent selection is important in more decisions than determining which solvent will dissolve analytes. Solvent selection can impact vaporization volume in the liner, as well as impact reconcentration at the head of the GC column.&nbsp;</p><p>Splitless&nbsp;injections will be more affected by solvent selection, as there will be a greater amount of solvent introduced to the column and greater interactions between the solvent and column phase. If the solvent and column phase are mismatched, there will be an increase in the flooded zone, and a subsequent impact to the peak shape of analytes. To mitigate the problem of using a polar solvent with a nonpolar column, a guard column can be used to help refocus the solvent and analytes at the head of the analytical column and improve analyte peak shape.</p><h3><strong>2. Shimadzu / ASMS: PFAS in Bottled Water: A Simple Approach Using HS-SPME GC/MS/MS for Volatile Contaminant Analysis</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ASMS_25_TP_312_a610778a69.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Concerns about per- and polyfluoroalkyl substances (PFAS) contamination in bottled water, linked to significant health risks, are growing. The Food and Drug Administration (FDA) has published plans surveying bottled water for PFAS in 2024-2025. While Liquid Chromatography/Mass Spectrometry (LC/MS) is the primary analytical method for PFAS, it struggles to analyze certain PFAS related compounds like fluorotelomer alcohols (FTOHs), perfluoroalkyl iodides (PFIs), and (n:2) fluorotelomer iodides (FTIs). Gas Chromatography/Mass Spectrometry (GC/MS) offers a complementary approach for volatile PFAS. This study utilizes Head-Space Solid Phase Microextraction Triple Quadrupole Gas Chromatography/Mass Spectrometry (HS-SPME GC/MS/MS) to analyze PFAS in bottled water. This technique offers several advantages, including minimal sample preparation, making it a valuable tool for comprehensive PFAS analysis.</p><h4><strong>Methods</strong></h4><p>A volatile PFAS analysis method was developed on a Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMS-TQ8040+NX"><strong>GCMS-TQ8040 NX</strong></a> with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=AOC-6000+Plus"><strong>AOC-6000 Plus</strong></a> multifunctional autosampler equipped with a solid phase microextraction (SPME) module (Figure 1).&nbsp;</p><p>Thirteen PFAS target compounds were included in the Multiple Reaction Monitoring (MRM) method. The PFAS chemical classes were perfluoroalkyl iodides (PFIs), (n:2) fluorotelomer iodides (FTIs), (n:2) fluorotelomer acrylates (FTACs), (n:2) fluorotelomer methacrylates (FTMACs), (n:2) fluorotelomer alcohols (FTOHs) and perfluoroalkane sulfonamides (FASAs). Internal standards (IS) FTOHs, FASAs and FTAC mass-labelled compounds were added to each vial prior to extraction. Concentrations of the target compounds were calculated using isotope dilution.&nbsp;</p><p>An internal calibration curve was prepared in 10 mL of reagent water at concentrations of 2000, 1000, 500, 100, 50, 10, 2.5 and 1 ng/L. The IS were spiked at 100 ng/L to each calibrator. Sodium Chloride (NaCl) was added to each vial to achieve a final salinity concentration of 2% NaCl (w/v). These calibrators were vortex for 30 seconds and then placed on the AOC-6000 Plus autosampler rack for analysis.&nbsp;</p><p>The optimized parameters of the HS-SPME GC/MS method for the targeted PFAS are listed in table 1. Quantitative and reference ions for each PFAS target are listed in table 2. The associated internal standard used for each compound is also listed in table 2.&nbsp;</p><p>A laboratory control sample (LCS) was analyzed to determine the general performance of the method in a clean matrix. The bottled water samples were analyzed to determine the effect of matrix on method performance. The type of bottled water analyzed was purified water.</p><h4><strong>Conclusion</strong></h4><p>The PFAS family includes thousands of compounds across diverse chemical classes, making comprehensive analysis challenging and often necessitating multiple analytical techniques. LC-MS is widely used and well-established for PFAS analysis but is less effective for detecting certain compounds - particularly volatile PFAS. In contrast, GC-MS excels at analyzing volatile PFAS and serves as a valuable complement to LC-MS, offering a more complete and balanced approach to PFAS detection. By extending the range of detectable compounds, GC-MS helps overcome PFAS analysis limitations previously imposed by instrumentation constraints. This study demonstrates the strong performance of an HS-SPME GC/MS/MS method for measuring volatile PFAS in bottled water. The application presents a simple, rapid, robust, precise, and accurate workflow that significantly improves the ability to detect a broader spectrum of PFAS compounds in bottled water.</p><h3><strong>3. Thermo Fisher Scientific / ASMS: Large-scale targeted biomarker analysis of volatile organic compounds in breath by TD-GC-MS&nbsp;</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/PO_456_ASMS_25_large_scale_targeted_biomarker_analysis_po456_na_en_910bf58c57.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Breath analysis of volatile organic compounds (VOCs) is emerging as a promising tool in non-invasive disease diagnostics. While untargeted methods dominate current workflows, they often lack reproducibility and specificity. This study introduces a fully integrated, large-scale targeted workflow for VOC biomarker quantification using TD-GC-MS, aiming to support the development of Owlstone Medical’s Breath Biopsy VOC Atlas® and broader breathomics applications.</p><h4><strong>Materials and Methods</strong></h4><p>Breath samples were collected using the ReCIVA® Breath Sampler and CASPER® air supply to reduce ambient contamination. Samples were analyzed with a <strong>Markes </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=7&amp;search=TD100xr+thermal+desorber"><strong>TD100xr thermal desorber</strong></a><strong> </strong>coupled to a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/18"><strong>Thermo Scientific Orbitrap Q Exactive GC-MS system</strong></a>, known as the Breath Biopsy OMNI® method. A total of 200 VOC targets were monitored, using 36 deuterated internal standards. All data acquisition and analysis were performed in <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76"><strong>Chromeleon CDS software</strong></a> in a secure, cloud-based enterprise environment.</p><h4><strong>Results</strong></h4><p>The workflow includes automated system suitability and intelligent run controls (SST/IRC) to avoid the loss of valuable patient samples. High-confidence compound identification was achieved using composite scoring, ion ratio matching, and HRAM spectral libraries. Method validation across two independent breath sample cohorts demonstrated strong precision, with over 70% of VOCs meeting the &lt;20% RSD threshold across studies and sequences.</p><h4><strong>Conclusion</strong></h4><p>This targeted TD-GC-MS workflow provides a robust, compliant, and scalable solution for high-confidence breath biomarker analysis. The integration of cloud-based Chromeleon CDS, automated QC protocols, and HRAM detection supports reproducible, high-throughput studies that are essential for advancing clinical breathomics and precision medicine.</p><h3><strong>4. Waters / ASMS: GC-APCI-MS/MS Analysis of Polychlorinated Dibenzo-p-dioxins and Furans to Revised US EPA1613 Guidelines</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008849_81ffe9aec9.pdf"><strong>Full PDF for download</strong></a></li></ul><p>In response to the U.S. EPA’s 2024 update of Method 1613 for the analysis of PCDD/Fs in wastewater, this study evaluates the performance of an alternative workflow using tandem quadrupole mass spectrometry (MS/MS) with APCI instead of traditional high-resolution mass spectrometry (HRMS). The goal was to demonstrate that such a system can meet all critical quality control (QC) metrics required by the revised method.</p><h4><strong>Materials and Methods</strong></h4><p>Analysis was carried out using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/55"><strong>Waters Xevo TQ-XS tandem quadrupole MS</strong></a><strong> </strong>with an APGC™ source, coupled to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=8890+GC"><strong>Agilent 8890 GC</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=8&amp;search=CIS-4+PTV"><strong>Gerstel CIS-4 PTV injector</strong></a>, with <strong>autosampling via </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=PAL+3+RSI"><strong>PAL 3 RSI</strong></a>. Chromatographic separation used a Restek Rtx-Dioxin2 column. Calibration standards from Wellington Laboratories were diluted to test both linearity and detection limits, and internal QC metrics were adapted to accommodate the MS/MS platform.</p><h4><strong>Results</strong></h4><p>The adapted workflow successfully replicated key EPA1613 QC checks, including relative response factors, isotopic ratios (±15%), and detection limits. The system showed excellent mass accuracy and resolution, verified through an automated Python-based script. Ionization suppression events were detected and visualized, aiding sample quality assessment. Signal-to-noise ratio evaluation, even in low-noise MS/MS traces, was possible after modifying detector settings.</p><h4><strong>Conclusion</strong></h4><p>This work proves that a GC-APCI-MS/MS system can fully comply with revised EPA1613 guidelines. The platform demonstrated robust quantitative performance, reproducibility, and reliable QC monitoring. It represents a valid and cost-effective alternative to HRMS for regulatory dioxin and furan analysis in environmental samples.</p>

News from LabRulezGCMS Library - Week 27, 2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 30th June 2025? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you other document by <strong>Agilent Technologies </strong>and posters by<strong> Shimadzu / ASMS, Thermo Fisher Scientific / ASMS </strong>and<strong> Waters Corporation / ASMS!</strong></p><h3><strong>1. Agilent Technologies: Agilent InfinityLab Assist Hub: Representing Advancements in Cybersecurity</strong></h3><ul><li>Other document (white paper)</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/wp_infinitylab_assist_cybersecurity_5994_8261en_agilent_310c5c9a6c.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>The laboratory as an operational technology (OT) environment: bridging the gap between OT and IT&nbsp;</strong></h4><p>Compared to connected industrial production environments, the connected laboratory environment closely aligns with an operational technology (OT) environment. OT is defined as "the practice of using hardware and software to control industrial equipment, and it primarily interacts with the physical world" (RedHAT, 2024).&nbsp;</p><p>In the classical Purdue reference model, established in the early 1990s to protect identities, information, and assets, the OT environment is clearly separated and secured from the information technology (IT)/enterprise environment.&nbsp;</p><p>The model only allows direct communication between adjacent levels. OT environments (lab environments) are necessary due to the long life cycles of the systems typically exposed to cybersecurity threats, with the mitigations listed in Table 1.&nbsp;</p><p>Addressing cybersecurity challenges in OT environments requires customized security strategies that incorporate both traditional IT security principles and specific measures for the physical operations they control.&nbsp;</p><p>Unlike some IT system components and devices where complete infrastructure replacement is common, lab devices are typically replaced gradually over years, sometimes even over&nbsp;decades.</p><p>The clear separation between OT and IT is becoming increasingly blurred due to new connectivity requirements, cloud functionality, and Industrial Internet of Things integration. This process, known as IT/OT convergence, is essential for achieving the next level of automation, such as Industry 4.0/5.0 or its counterparts, such as Pharma 4.0. The effectiveness of any new development will be evaluated based on how well it addresses cybersecurity within a converging IT/OT environment.</p><h4><strong>Standards and organizations&nbsp;</strong></h4><p>The diverse standards and organizations can be exemplified by the approaches taken in the U.S., Europe, and China. This summary aims to showcase the complexity of regulatory environments, and is not comprehensive. For specific situations, consulting with dedicated field experts is crucial.</p><h5><strong>Europe&nbsp;</strong></h5><p>European cybersecurity regulations prioritize privacy and data protection, with an emphasis on harmonizing standards across member states.&nbsp;</p><ul><li><strong>General Data Protection Regulation (GDPR): </strong>GDPR is the cornerstone of data protection in the European Union (EU). It sets strict rules on handling personal data, including requirements for data breach notifications, data minimization, and individual rights over personal data. Noncompliance can result in severe fines.</li><li><strong>Network and Information Security Directive (NIS&nbsp;Directive): </strong>Adopted in 2016, the NIS Directive focuses on the cybersecurity of essential services (such as energy, transportation, and finance) and digital service providers. It requires member states to strengthen their national cybersecurity capabilities and cooperation.&nbsp;</li><li><strong>European Union Agency for Cybersecurity (ENISA):</strong> ENISA is the EU's dedicated cybersecurity agency, helping to develop and promote cybersecurity standards across Europe, including guidelines for incident reporting and cybersecurity&nbsp;certifications.</li><li><strong>ISO/IEC 27001: </strong>While ISO/IEC 27001 is a global standard for information security management systems, it is heavily adopted in Europe. This standard outlines processes for systematically managing sensitive company information, ensuring its security.&nbsp;</li><li><strong>Cybersecurity Act (EU): </strong>The Cybersecurity Act establishes a framework for cybersecurity certification for products, services, and processes, ensuring they meet recognized EU-wide standards.</li></ul><h4><strong>Conclusion&nbsp;</strong></h4><p>With the initial release of the InfinityLab Assist Control Software, the InfinityLab Assist Hub offers plenty of cybersecurity features that can be leveraged for a secure lab network environment founded on a customer-specific OT/IT security strategy.</p><h3><strong>2. Shimadzu / ASMS: Single Quadrupole Mass Spectrometry Quick Workflows Specified to Oligonucleotides Screening&nbsp;</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ASMS_25_TP_605_fa8deb906e.pdf"><strong>Full PDF for download</strong></a></li></ul><p>A workflow has been developed to deliver molecular mass information on the full-length product and related impurities for chemically synthesized oligonucleotides by applying a deconvolution algorithm to all detected components within a targeted data stream (UV/PDA, TIC or BPC). It has been designed to simplify the data processing experience without the need for a sequence input delivering results quickly by automating mass assignment of all detected components in a data stream.</p><h4><strong>2. Materials and Methods&nbsp;</strong></h4><h5><strong>2.1 LC-MS/MS method&nbsp;</strong></h5><p><strong>20-mer sample.</strong> A phosphorothioate-modified oligonucleotide purified was used as a model sample (20-mer anti-sense oligo; C229 H316 N69 O121 P19 S19; average mass 7168.05, Te-smCe-sTe-sTe-sGe-sGd-sTd-sTd-sAd-sCd-sAd-sTd-sGd-sAd-sAd-sAe-sTe-smCe-smCesmCe) spiked with 10% impurities (N-1(5’) and N-3(5’) shortmers) to the full-length product.&nbsp;</p><p><strong>80-mer antisense DNA sample</strong>. 80-mer PolyT oligonucleotide sample (C800 H1041 N160 O558 P79; average mass 24273.62) was also analyzed was analyzed by ion-pair reversedphase chromatography mode using HFIP (hexafluoroisopropanol) and 10 mM TEA (triethylamine).</p><p><strong>LC Separation</strong>. Components were separated using the Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=3&amp;search=Nexera+XS"><strong>Nexera XS</strong></a> inert LC and Shim-pack Scepter Claris column using HFIP (hexafluoroisopropanol) and 10 mM TEA (triethylamine) as ion pair additives.&nbsp;</p><p><strong>Mass Spectrometry Detection. </strong>Shimadzu Single Quadrupole<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=3&amp;search=+LCMS-2050"><strong> LCMS-2050</strong></a>. Data acquired in profile mode. MS mass scan m/z 600-2000; 1 secs; negative ion mode.</p><h4><strong>3.Results&nbsp;</strong></h4><h5><strong>3.1 Workflows and Methods&nbsp;</strong></h5><p>Insight Biologics software application supports two workflows to help accelerate oligonucleotide characterization. The default data processing behavior was driven by a defined FLP sequence to confirm sequence composition, identify product related impurities and assess impurity profiles. Data processing has been further augmented by enabling automated deconvolution routines to be applied without a defined FLP sequence input to help workflows that need to quickly check on molecular mass information in a synthesis (Figure 2).</p><h4><strong>4. Conclusions&nbsp;</strong></h4><ul><li>LabSolutions Insight Biologics offers flexibility by supporting both user-defined and automated deconvolution workflows for comprehensive oligonucleotide analysis.&nbsp;</li><li>A Quick Picking algorithm has been developed and applied to the analysis of 20-mer and 80-mer antisense DNA. In the case of a 20-mer sample, the FLP and associated impurities were also automatically detected and molecular weight information reported. The analysis showed high correspondence with data processing results using a defined FLP sequence input.&nbsp;</li><li>The Quick Picking algorithm enables a molecular weight confirmation of any detected peak and can be a powerful tool for synthesis confirmation in cases where the product is not as intended.&nbsp;</li><li>This LC-MS analysis platform provides a rapid and cost-effective solution for purity analysis and molecular weight confirmation, making it ideal for quality control environments.</li></ul><h3><strong>3. Thermo Fisher Scientific / ASMS: Comprehensive data acquisition workflow on a modified Orbitrap Astral MS to achieve deep lipidome coverage with high confidence annotations</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/PO_003964_ms_ASMS_25_Astral_Zoom_Lipidomics_po003964_na_en_0ccd4bd053.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Lipid identification is often considered the most challenging and bottleneck of the lipidomics workflow. This is due to the vast diversity of lipid structures, isomers and varying concentrations. Fragmentation of lipids generate characteristic fragments that help in lipid annotation, structural elucidation, isomer differentiation, and improved confidence in lipid identification. Having fragmentation for most of the lipids detected in a sample hence is highly desirable. This requires fast and sensitive instrumentation. Here we evaluated the new Orbitrap Astral Zoom mass spectrometer (Figure 1) for deeper lipidome coverage and confident lipid identification from 73 mice plasma extracts.</p><h4><strong>Materials and methods&nbsp;</strong></h4><h5><strong>Sample Preparation&nbsp;</strong></h5><p>Lipids were extracted from plasma samples obtained from 73 mice fed with a high fat diet and a normal diet. Lipid standards and bovine liver lipid extracts were purchased from Avanti Polar Lipids.&nbsp;</p><h5><strong>LCMS Method&nbsp;</strong></h5><p>Plasma lipid extract was separated using Thermo Scientific nanoLC PepMap (C18, 75µmx150mm, 2µm) column connected to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/398"><strong>Vanquish Neo UHPLC system</strong></a>. Data acquisition was carried out on the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1959"><strong>Orbitrap Astral Zoom mass spectrometer</strong></a> using full-scan Orbitrap MS1 in parallel with data-dependent Astral MS2.</p><h5><strong>Data Analysis&nbsp;</strong></h5><p>Data processing was performed using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1759"><strong>Thermo Scientific Compound Discoverer 3.4 software</strong></a>. Compound Discoverer 3.4 software includes new features such as Thermo Scientific LipidSearch software node for simplified lipid identification, QC for small molecule experiments using internal standards, automatically detects and groups MS1 fragments and other performance improvements.</p><h4><strong>Results&nbsp;</strong></h4><h5><strong>Orbitrap Astral Zoom mass spectrometer DDA MS2 scan rate&nbsp;</strong></h5><p>The Orbitrap Astral Zoom mass spectrometer offers an enhanced MS2 scan rate compared to the already high-speed Orbitrap Astral MS system, enabling DDA with Top 30, Top 40, or even Top 50 MS2 scans (Figure 2) without compromising data quality. This advancement delivers unprecedented speed, achieving faster DDA MS2 scan rates than ever before.</p><h5><strong>Unprecedented speed and depth: dual analyzer power of Orbitrap Astral Zoom MS</strong></h5><p>The parallel data acquisition on Orbitrap Astral Zoom MS using both the Thermo Scientific Orbitrap analyzer and Thermo Scientific Astral analyzer, enables a higher number of Orbitrap HRAM MS1 full scans with sub-ppm mass accuracy. Simultaneously, the fast Astral analyzer delivers a significantly greater number of highquality MS2 scans at 80k resolution (77% more) compared to Orbitrap-Orbitrap configurations, which is essential for deep coverage and confident lipid identification (Figure 3).</p><h4><strong>Conclusions&nbsp;</strong></h4><ul><li>The novel Orbitrap Astral Zoom mass spectrometer provides deeper lipidome coverage and confident identification, utilizing the parallel operation of Orbitrap and Astral analyzers.&nbsp;</li><li>The enhanced MS2 speed in Orbitrap Astral Zoom mode, coupled with Stepped NCE, enables efficient data acquisition and comprehensive lipidome coverage with a 15% increase with grade A and B lipid annotation.&nbsp;</li><li>The use of nano-flow increased the sensitivity and the number of detected lipids while achieving a significant reduction in the use of sample and solvent.&nbsp;</li><li>A significant difference was observed between control and high fat diet mice samples with potential biomarker upregulated neutral lipids (triglycerides and cholesterol esters) in the high fat diet.</li></ul><h3><strong>4. Waters Corporation / ASMS: Routine EPA 1633 PFAS Analysis with a Novel Slotted Bandpass Ion Guide to Improve Signal Response Robustness</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008888_65b495feb8.pdf"><strong>Full PDF for download</strong></a></li></ul><p>With increasing regulatory pressure around PFAS monitoring, this study evaluates a robust and long-term solution for EPA 1633A-compliant PFAS analysis. It focuses on how the StepWave XR ion guide, integrated into the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=5&amp;search=Xevo+TQ+Absolute+XR"><strong>Waters Xevo TQ Absolute XR tandem quadrupole mass spectrometer,</strong></a> improves signal robustness and method stability across demanding environmental matrices such as river water, biosolids, and landfill leachate.</p><h4><strong>Materials and Methods</strong></h4><p>Samples included NIST-certified biosolids, landfill leachate, and spiked river water. Solid-phase extraction (SPE) was followed by LC-MS/MS analysis on an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/191"><strong>ACQUITY Premier System</strong></a> with BEH C18 columns and detection via the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=5&amp;search=Xevo+TQ+Absolute+XR"><strong>Xevo TQ Absolute XR</strong></a> using negative electrospray ionization and MRM. The robustness of the method was assessed over 12 weeks with over 9,000 injections and weekly calibrations, using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1544"><strong>waters_connect for Quantitation Software</strong></a> for data processing and review.</p><h4><strong>Results</strong></h4><p>Throughout the 12-week study, all sample types showed high consistency and low variability. For example, L-PFHxS in biosolids averaged 8.78 µg/kg (%RSD 7.2%) compared to the certified 9.39 µg/kg, while PFOA in landfill leachate showed a mean of 2.52 ng/mL (%RSD 2.7%). QC plots confirmed that all monitored PFAS met trueness and ion ratio targets across thousands of injections with no unscheduled downtime, highlighting the system’s long-term reliability.</p><h4><strong>Conclusion</strong></h4><p>The StepWave XR ion guide enabled reliable, high-throughput PFAS analysis with minimal maintenance and excellent reproducibility. The Xevo TQ Absolute XR maintained calibration stability, accurate quantitation, and strong matrix resilience across diverse sample types. Combined with streamlined software workflows, this setup is well-suited for routine EPA 1633A PFAS analysis in environmental labs.</p>

News from LabRulezLCMS Library - Week 27, 2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 30th June 2025? Check out new documents from the field of spectroscopy/spectrometry and related techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> libraries.</p></blockquote><p>This week we bring you posters by <strong>Agilent Technologies / ASMS </strong>and<strong> Thermo Fisher Scientific / ASMS</strong>!</p><h3><strong>1. Agilent Technologies / ASMS: Measurement of Metals in Tissue Samples Using Laser Ablation-ICP-MS and an Organic Matrix-Based Standard</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ps_8890_la_icp_ms_metals_tissue_asms_2025_thp_067_en_agilent_ee9bc7a285.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Laser Ablation-Inductively Coupled Plasma-Mass Spectroscopy (LA-ICP-MS) can provide localization information of elements on solid samples. Therefore, bioimaging techniques using LA-ICP-MS have been developed to investigate the distribution of metals on thin sections.&nbsp;</p><p>Primary biliary cholangitis (PBC) is an autoimmune disorder that primarily targets the interlobular bile ducts. The resulting bile duct damage causes chronic cholestasis in the liver, which can eventually progress to cirrhosis. It is well-established that individuals with PBC exhibit impaired copper (Cu) excretion, leading to Cu accumulation within the liver.&nbsp;</p><p>Pathologists have employed orcein or Victoria blue staining to visualize Cu deposition in pathological specimens. 1 Orcein staining is commonly used to assess the stage of PBC. However, both staining methods measure Cu levels indirectly by detecting Cu-binding proteins, which introduces significant variability in measurement accuracy across different facilities and yields relatively low detection sensitivity.&nbsp;</p><p>The aim of the study was to directly quantify Cu in PBC specimen using LA-ICP-MS, a highly sensitive technique, and compare the results with that obtained from normal-looking tissue in liver. The elemental concentrations were determined from external calibration curves generated using an organic matrix-based standard (Solid scaleTM, FUJIFILM Wako Chemicals).&nbsp;</p><h4><strong>Conclusions</strong></h4><p>This study demonstrates the high performance of the Agilent <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=8900+ICP-QQQ"><strong>8900 ICP-QQQ</strong></a> equipped with an ESL 213 laser ablation system for bioimaging human liver specimens.&nbsp;</p><ul><li>An organic matrix-based standard (Solid scaleTM ) provided quantitative results of metals in human liver specimens.&nbsp;</li><li>Patterns of metal distribution (24Mg, 31P, 44Ca, 56Fe, 63Cu, 95Mo) in human liver showed accumulation of Cu in a PBC specimen and its unique localization pattern along the regenerative nodule.&nbsp;</li></ul><p>These results demonstrate that determining metal concentrations in thin sections using LA-ICP-MS provides valuable insights into the mechanisms of PBC and other metal-related diseases.</p><h3><strong>2. Thermo Fisher Scientific / ASMS: Trace metals analysis in baby food using triple quadrupole inductively coupled plasma mass spectrometry (TQ-ICP-MS)</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/PO_296_ASMS_25_metals_baby_food_TQ_ICP_MS_na_en_3c869a51aa.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Many countries have established their own regulations and guidelines to ensure the safety and quality of baby food products. These regulations are generally based on scientific research and risk assessments conducted by health and food safety authorities. Among a range of other contaminants, toxic heavy metals, such as lead, arsenic, cadmium, and mercury, are subject to screening.&nbsp;</p><p>The Baby Food Safety Act of 2021 requires manufacturers and the FDA to take long overdue action by setting maximum levels of inorganic arsenic (10 μg∙kg-1, 15 μg∙kg-1 for cereal), lead (5 μg∙kg-1, 10 μg∙kg-1 for cereal), cadmium (5 μg∙kg-1, 10 μg∙kg-1 for cereal), and mercury (2 μg∙kg-1) allowed in baby food.&nbsp;</p><p>Since January 2022, baby food manufacturers need to meet these maximum levels for toxic elements by testing their final products (because only ingredient testing significantly underestimates toxic heavy metal levels in the final product), and to post the results of their product testing online twice per year. Similarly, as the FDA is working on a “closer to zero plan”, the European Union has established regulations and maximum limits for various contaminants, including toxic elements, in baby food. ICP-MS (inductively coupled plasma mass spectrometry) is the commonly used analytical technique to measure trace elements in various samples, including baby food. The specific trace elements to be checked by ICP-MS in baby food may vary depending on the purpose of the analysis and the specific concerns related to the safety and quality of the product.</p><h4><strong>Materials and methods&nbsp;</strong></h4><h5><strong>Instrumentation&nbsp;</strong></h5><p>A <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1488"><strong>Thermo Scientific iCAP MTX ICP-MS</strong></a> and a Thermo Scientific <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1715"><strong>iSC-65 Autosampler</strong></a> were used for analysis. The ICP-MS was operated using the parameters highlighted in Table 1. To facilitate the robustness to analyze a high number of potentially very different sample types, the instrument was operated using argon gas dilution (AGD), applying a mild (approximately 5 times) dilution to all samples. This ensures the productivity and uptime requirements in a high productivity laboratory environment can be met. To meet the stringent detection limit requirements put in place by regulatory authorities, the iCAP MTX ICP-MS was operated in SQ-KED and TQ-O2 modes to confidently eliminate all polyatomic and isobaric interferences but also deliver the sensitivity needed to reach the requested quantification limits for the critical elements.</p><h4><strong>Results&nbsp;</strong></h4><p>Table 3 summarizes the analytical figures of merit for all analytes including the correlation coefficients, the limits of quantification, and the quantification limits of the method (MLOQ). Whereas the limit of quantification (LOQ) only considers what can be quantified by the instrument, the method quantification limit (MLOQ) also accounts for the complete sample preparation, i.e., the sample weight and the dilution factor. Specifically highlighted in the table are the results obtained for As, Cd, Hg, and Pb, elements which are of specific regulatory concern due to their toxicity and risk for bioaccumulation. As can be seen, the achieved MLOQs are at least a factor two lower compared to the nominal limits specified in EAM 4.7.</p><h4><strong>Conclusions&nbsp;</strong></h4><p>This workflow, combining sample preparation using microwave assisted digestion and analysis using the iCAP MTX ICP-MS, allows accurate measurement of the concentration of toxic elements, dangerous for children, as well as the essential elements, necessary for the good growth and health of children, in various baby food products.&nbsp;</p><ul><li>The automatic and constant 5-times dilution allows robust analysis of different types of baby food products, whether dry, liquid, or fatty, without any compromise on sensitivity to reach the regulated limits.&nbsp;</li><li>The stability of the iCAP MTX ICP-MS allows a large variety of different food samples can be analyzed in the same run, ensuring productivity in an applied testing laboratory.&nbsp;</li><li>All types of interferences (polyatomic as well as isobaric interference, including doubly charged ions) are effectively suppressed with either helium collision gas or by using oxygen as a reactive gas in triple quadrupole mode.&nbsp;</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1489"><strong>Qtegra ISDS Software</strong></a> with the Reaction Finder functionality, allows simplified handling for the operator by automatically defining the isotopes and the acquisition modes to obtain reliable results.&nbsp;</li><li>The maintenance assistant also ensures alerts to the user when actions need to be carried out by the operator to guarantee an instrument with best performance throughout instrument lifetime.</li></ul>

News from LabRulezICPMS Library - Week 27, 2025

NDMA in macrolides: GC-MS/MS method for its detection and study of its formation

<p><strong>The goal of the study is to investigate the presence of pharmaceuticals, personal care products, and industrial chemicals in treated wastewater discharged from expedition cruise ships operating in polar regions. As cruise traffic increases in these sensitive environments, there is a growing concern over the release of contaminants of emerging concern.</strong></p><p>By analyzing samples from three ships using LC-HRMS and data-dependent acquisition, the study identified over 160 compounds, including multiple antibiotics and cardiovascular drugs. The findings highlight the potential for cruise ship effluent to contribute to environmental contamination and the spread of antibiotic resistance, emphasizing the need for further research and regulatory attention.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209"><strong>Occurrence of Pharmaceuticals and Other Anthropogenic Compounds in the Wastewater Effluent of Arctic Expedition Cruise Ships</strong></a></h4><p>Veronica van der Schyff*, Marek Stiborek, Zdeněk Šimek, Branislav Vrana, Verena Meraldi, Andrew Luke King, Lisa Melymuk</p><p>Environ. Sci. Technol. Lett. 2025, 12, 5, 648–654</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.estlett.5c00209">https://doi.org/10.1021/acs.estlett.5c00209</a></p><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><p>Cruise ships discharge wastewater generated from onboard activities. Many ships have advanced wastewater treatment systems (WWTS) onboard. Passenger ships produce graywater (drainage from dishwashers, galley sinks, showers, laundry, baths, and washbasins) and blackwater (from toilets, sanitary areas, and areas with animals). The volume of graywater discharged by cruise ships in 2023 was estimated to be 14 billion L, a 40% increase from 2014 to 2023, largely driven by the growing number of cruise ships. <a href="javascript:void(0);">(11)</a></p><p>According to the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex IV, <a href="javascript:void(0);">(12)</a> treated blackwater may be discharged more than 3 nautical miles (NM) from the nearest land. Untreated blackwater may be discharged more than 12 NM from land, as the high seas are considered to be capable of assimilating blackwater through natural bacterial action. <a href="javascript:void(0);">(12)</a> Similarly, the Polar Code states that untreated blackwater should be discharged more than 12 NM from land, fast ice, or ice shelf, and treated wastewater &gt;3 NM. <a href="javascript:void(0);">(13)</a> There are currently no global regulations on the emission of graywater. <a href="javascript:void(0);">(14)</a> Depending on the onboard WWTS, gray- and blackwater can be discharged separately or together. The Baltic Sea is currently the only “special area” under MARPOL Annex IV. There, the discharge of untreated blackwater is strictly prohibited by passenger ships, and where possible, ships are expected to discharge blackwater at ports with reception facilities. <a href="javascript:void(0);">(15)</a></p><p>Conventional terrestrial-based WWTS do not eliminate all chemical compounds, and treated effluent contains a complex mix of organic and inorganic micropollutants that are then released into the environment. <a href="javascript:void(0);">(16−18)</a> This limitation likely extends to ship-based WWTS, particularly given their faster processing times and space constraints compared to terrestrial systems. As with the general population, cruise passengers and crew take medication and excrete metabolites into wastewater, with a potential impact on marine environments. <a href="javascript:void(0);">(19)</a> A particular concern is the release of antibiotics, which can promote the development of antibiotic-resistant microbes. <a href="javascript:void(0);">(4,5)</a> In addition, ship wastewater may contain other compounds like surfactants, personal care products, plasticizers, and flame retardants, which are often persistent, bioaccumulative, and toxic to biota. <a href="javascript:void(0);">(4)</a> Ocean currents can transport these contaminants from their point of release to remote areas, potentially affecting sensitive regions and ecosystems. Arctic regions are especially vulnerable to pollution since the efficiency of the naturally occurring bacterial decay of organic contaminants in seawater is slowed by low temperatures. Other environmental factors such as sea ice and fluctuating ultraviolet light exposure due to large seasonal variation also impact pollutant accumulation patterns in the Arctic. <a href="javascript:void(0);">(20,21)</a></p><p>With the number and passenger capacity of cruises increasing, it is unclear whether dilution remains sufficient to mitigate the impact of contaminants released from wastewater, particularly in regions with a high density of cruise ships. Pharmaceuticals and other chemicals have been detected in seawater in remote Arctic regions. <a href="javascript:void(0);">(20,22)</a> While substandard WWTS in remote Arctic regions are considered the main source of contaminants, <a href="javascript:void(0);">(23)</a> other studies <a href="javascript:void(0);">(24,25)</a> suggest that the effects of ship effluent should also be studied as a source of pollution.</p><p>Limited attention has been paid to wastewater effluent discharges of cruise ships. To the best of our knowledge, only two studies have quantified synthetic compounds in treated wastewater, <a href="javascript:void(0);">(4,26)</a> and no chemical screening has been conducted on treated wastewater intended for offshore discharge from cruise ships while in operation. This study aims to identify organic contaminants associated with wastewater discharge from cruise ships as a first step to better evaluate the impact of contaminant emissions from cruise ships on sensitive marine environments.</p><h4><strong>Methods and Materials</strong></h4><h5><strong>LC-HRMS Analyses and Data Processing</strong></h5><p>In brief, high-resolution mass spectrometry was used for qualitative analyses of the wastewater extracts. Two aliquots per ship were analyzed. Chromatographic separation was performed using an Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=1290+Infinity+LC"><strong>1290 Infinity LC </strong></a>System (Agilent Technologies, Santa Clara, CA) equipped with a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1514"><strong>Kinetex Core–Shell Biphenyl column</strong></a> (150 mm × 2.1 mm, particle size of 1.7 μm) and a precolumn (Phenomenex, Torrance, CA). Mass spectrometry detection was performed by using an Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=6550+Q-TOF+iFunnel"><strong>6550 Q-TOF iFunnel</strong></a> System operating in positive and negative electrospray ionization (ESI) modes. Iterative data-dependent acquisition (DDA) of MS/MS spectra was applied, with a maximum of four precursor ions fragmented per cycle at collision energies of 10, 20, and 40 eV. A detailed description of the analytical method is presented in the <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.estlett.5c00209/suppl_file/ez5c00209_si_001.pdf">Supporting Information</a>.</p><p>Combined full-scan and MS/MS raw data files were processed with Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=MassHunter+Qualitative+Analysis+Software"><strong>MassHunter Qualitative Analysis Software</strong></a> 10.0 (Agilent Technologies). Data processing included peak picking, molecular formula assignment, isotope pattern scoring, and identification of the found features based on compliance with the predicted molecular formula and MS/MS spectra present in mass spectrum libraries. A full list of databases used and quality assurance is included in the <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#notes1">Supporting Information</a>. All compounds reported here were identified at confidence level 1 or 2 according to the Schymanski identification confidence levels (SL1, confirmed a with reference standard, or SL2, probable structure; library match or diagnostic evidence) in high-resolution mass spectrometric analysis. <a href="javascript:void(0);">(28)</a></p><h4><strong>Results and Discussion</strong></h4><p>Wastewater samples from the older ships built in the 2000s (ships 1 and 2) had &gt;11 000 found features, while the newest ship, ship 3, built 2020, had 7571 (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.estlett.5c00209/suppl_file/ez5c00209_si_001.pdf">Table S1</a>). From these features, a total of 168 compounds were confirmed at SL1 or the chemical structure was proposed at SL2 in treated wastewater across all three ships: 86, 99, and 78 compounds from ships 1–3, respectively (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#fig1">Figure 1</a> and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.estlett.5c00209/suppl_file/ez5c00209_si_001.pdf">Tables S2–S4</a>). Twenty-seven compounds were identified at SL1 or SL2 across all ships (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#tbl1">Table 1</a>), with pharmaceuticals and industrial chemicals (10 each) constituting the majority, followed by five food/natural compounds, one pesticide, and one UV blocker. Eight of the 27 compounds common to all ships were identified at SL1 based on external standards (EnviMix, Helmholtz-Centre for Environmental Research <a href="javascript:void(0);">(29)</a>), and 19 identified at SL2.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_Lett_2025_12_5_648_654_Figure_1_A_Number_of_compounds_identified_at_SL_1_or_SL_2_in_three_expedition_ships_8915e4f530.jpg" alt="Environ. Sci. Technol. Lett. 2025, 12, 5, 648–654: Figure 1. (A) Number of compounds identified at SL1 or SL2, in three expedition ships showing overlap in the compounds found in wastewater among the three ships and (B–D) distribution and number of confirmed or probable compounds according to use category in wastewater effluent from three expedition ships." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_Lett_2025_12_5_648_654_Figure_1_A_Number_of_compounds_identified_at_SL_1_or_SL_2_in_three_expedition_ships_8915e4f530.jpg 245w," sizes="100vw" width="1624" height="917"></p><p>For all ships, pharmaceuticals, primarily cardiovascular medication, dominated in terms of the number of compounds identified at SL1 or SL2 (43–59%), followed by industrial chemicals (21–31%) and natural/food compounds (12–17%) (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#fig1">Figure 1</a>).</p><p>Industrial chemicals such as surfactants, lubricants, and cleaning products can originate from onboard operations, or from being incorporated into onboard products, like in the case of flame retardants and plasticizers, with release to dust and transfer to wastewater during cleaning or laundering. <a href="javascript:void(0);">(36)</a> Many natural compounds can originate from food preparation and consumption, including some with potential environmental impacts, such as caffeine and aromatic amines, the latter being mutagenic. <a href="javascript:void(0);">(37)</a></p><p>The compound group of the highest concern consists of pharmaceuticals due to their potential biological activity at low concentrations. <a href="javascript:void(0);">(38)</a> The pharmaceuticals were primarily cardiovascular medications, with the sartan group (losartan, irbesartan, candesartan, and telmisartan) identified at SL1 or SL2 (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#tbl1">Table 1</a>) in all three ship wastewater profiles. Other cardiovascular medications, flecainide, and bisoprolol, were also identified in wastewater from all three ships (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/full/10.1021/acs.estlett.5c00209#tbl1">Table 1</a>). <a href="javascript:void(0);">(25)</a> Since the aforementioned medications are persistent and known for incomplete removal, even in typical WWTS, it stands to reason that they should be prominent in wastewater from ships too. <a href="javascript:void(0);">(39,40)</a></p><p>There is a significant gap in our understanding of the types of compounds present in ship wastewater discharges. This issue is becoming increasingly urgent as the cruise industry expands, especially in sensitive polar regions, and as sustainable development and tourism become more important priorities. Identification and quantification of pharmaceuticals and industrial compounds can support better environmental risk assessment. Water in shipping lanes and cruising routes should be studied to evaluate the effectiveness of dilution and degradability in marine water, particularly in areas where ships are permitted to discharge wastewater. If needed, sensitive regions should be denoted (in addition to the 3 and 12 NM limits) where wastewater discharges should be avoided. Special emphasis should be placed on compounds consistently identified across multiple vessels, including SSRIs, antibiotics, and other substances that could pose environmental concerns at increased concentrations. Moreover, the mixture effects of the various chemicals found in wastewater should be thoroughly examined, as the combined presence of different pharmaceuticals and industrial chemicals may result in additive or synergistic effects that can intensify their harmful impact on marine ecosystems. <a href="javascript:void(0);">(55,56)</a> Current regulations and treatment standards of graywater may need reevaluation as we gain an understanding of the impact of trace organic contaminants on marine systems.</p>

Occurrence of Pharmaceuticals and Other Anthropogenic Compounds in the Wastewater Effluent of Arctic Expedition Cruise Ships

Comprehensive IC-ICP-MS analysis of polyphosphonates and their transformation products

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