<p><strong>LA-ICP-MS imaging, particularly in TOF mode, provides rapid, high-resolution elemental maps of complex geological materials, but quantitative accuracy is often limited by heterogeneous mineralogy. This study introduces a workflow using UMAP for unsupervised dimensionality reduction combined with k-means clustering to segment mineral phases directly from per-pixel elemental data.</strong></p><p>Cluster compositions are matched to known minerals based on stoichiometry, allowing phase-specific normalization and reducing quantification errors by up to 60%. Validated on dawsonite-bearing sandstones, the approach enables consistent phase-resolved geochemical comparisons across depth profiles and establishes UMAP as a practical tool for accurate, interpretable multielement imaging.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398"><strong>Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging</strong></a></h4><p>Barbara Umfahrer, Jakub Buday, Pavel Pořízka, Jozef Kaiser, Paolo S. Garofalo*, and Detlef Günther*</p><p>Anal. Chem. 2026, 98, 1, 581–589</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05398">https://doi.org/10.1021/acs.analchem.5c05398</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>Since its introduction in 1985, <a href="javascript:void(0);">(1)</a> Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) has matured into a routine technique for both qualitative and quantitative elemental analysis, finding use in diverse fields such as geology, <a href="javascript:void(0);">(2−4)</a> biology, <a href="javascript:void(0);">(5,6)</a> materials science, <a href="javascript:void(0);">(7)</a> and cultural heritage. <a href="javascript:void(0);">(8,9)</a> Especially in comparison to liquid analysis techniques, the method excels through minimal sample preparation, negligible sample destruction, and low limits of detection, which enable trace element analysis. However, besides the availability of standard reference materials, a longstanding challenge of quantitative LA-ICP-MS is the need for a suitable internal standard to account for variations in ablation rate and transport efficiency, as well as instrumental drift. <a href="javascript:void(0);">(10)</a> Developed for applications in geological matrices, this requirement was elegantly circumvented by Liu et al. <a href="javascript:void(0);">(11)</a> through 100 wt % normalization, which assumes that all detected elements form oxides summing up to 100 wt %. This approach was conceptually inspired by the summed-spectrum normalization calibration procedure introduced by Leach and Hieftje in 2000, which emphasized the use of total signal intensities for robust internal standardization. <a href="javascript:void(0);">(12)</a> The 100 wt % normalization strategy was subsequently extended to the bulk analysis of carbonates. <a href="javascript:void(0);">(13)</a></p><p>Concurrently, the advent of time-of-flight (TOF) mass analyzers and the development of fast-washout laser ablation cells <a href="javascript:void(0);">(14−16)</a> have transformed LA-ICP-MS into a powerful imaging tool, capable of generating spatial maps across a variety of sample matrices at high data acquisition rates. <a href="javascript:void(0);">(17−19)</a> Furthermore, in comparison to, e.g., X-ray fluorescence spectroscopy (XRF) or Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), LA-ICP-MS offers a high depth resolution of approximately 0.2 μm on glass, enabling precise measurement of fine-scale structures not only laterally but also in depth. <a href="javascript:void(0);">(18)</a> However, as the volume and complexity of these elemental images have grown according to the progressive advancement of the method itself, now consisting of gigabytes of data produced within minutes, manual data interpretation has become increasingly impractical. To uncover meaningful details and patterns, researchers have applied multivariate methods such as Principal Component Analysis (PCA) and k-means clustering to reduce dimensionality and delineate distinct regions within the images. <a href="javascript:void(0);">(20−23)</a> The nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP), <a href="javascript:void(0);">(24)</a> which supports data interpretation with its capability to capture and separate subtle local structures, has first been employed to LA-ICP-MS bulk analysis data in 2024, where it was used to classify pyrite samples and discriminate between Pb and Zn deposit types. <a href="javascript:void(0);">(25,26)</a> In combination with elemental imaging, UMAP has recently been applied to biological tissue data sets as an exploratory analysis tool, <a href="javascript:void(0);">(27)</a> further enhancing the ability to visualize and interpret complex elemental distributions.</p><p>Despite these advances, no existing approach combines the unsupervised segmentation of mineral phases by UMAP with phase-specific normalization. In this study, we introduce a UMAP-driven workflow that (1) automatically segments distinct mineralogical phases within fine-grained geological matrices and (2) applies phase-specific 100 wt % normalization (e.g., oxide <a href="javascript:void(0);">(11)</a> or carbonate <a href="javascript:void(0);">(13)</a>), thereby improving quantification accuracy and enabling phase-specific quantitative interpretation of mineral distributions in complex matrices.</p><h4><strong>Materials and Methods</strong></h4><h5><strong>LA-ICP-TOFMS Imaging</strong></h5><p>LA-ICP-TOFMS analyses were performed using a 193 nm ArF excimer laser (GeoLas C, Lambda Physik, Göttingen, Germany) coupled to an ICP-TOFMS instrument (icpTOF 2R, TOFWERK AG, Thun, Switzerland). Ablation was conducted in the modified parallel flow ablation cell (MPFAC) <a href="javascript:void(0);">(16,34)</a> under a He atmosphere (99.999%, Linde Gas Schweiz AG, Dagmersellen, Switzerland) with Ar (99.996%, Linde Gas Schweiz AG) used as the carrier gas. A piezoelectrically driven xyz translational stage (1 nm resolution, SmarAct GmbH, Oldenburg, Germany) enabled precise sample positioning. Laser, stage movement, and ICP-TOFMS data acquisition were synchronized using a custom-built imaging control system. <a href="javascript:void(0);">(19)</a> The system was optimized each day based on maximum sensitivity while ensuring a <sup>238</sup>U<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> intensity ratio of 1.0 to 1.1 on NIST SRM 610 and a <sup>232</sup>Th<sup>16</sup>O<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> rate of below 0.5%.</p><h5><strong>LIBS Imaging</strong></h5><p>The LIBS analyses were performed on a Firefly system (Lightigo, Czech Republic). The laser (1064 nm, 5 mJ, pulse duration ∼10 ns, diameter 6 mm, operating at 50 Hz) was focused on the sample with a singlet lens (focal length 50 mm) to a 30 μm spot. The spatial resolution of the acquired elemental maps, with a size of about 16 mm × 26 mm, was 20 μm. Plasma radiation is collected by a multifiber configuration with a collimator and an optical fiber (400 μm core diameter), which delivered the light to several Czerny–Turner spectrometers (resolution 0.25–0.4 nm). The spectral range of the spectrometers is 180–850 nm. The radiation is collected 1.5 μs after ablation, with a gate width of 15 μs.</p><p>Data processing was done with Python 3.11.8 in the Spyder environment and included background correction and per-pixel z-score normalization.</p><h4><strong>Results and Discussion</strong></h4><h5><strong>LIBS and XRD Measurements</strong></h5><p>To assess whether carbonate-based normalization <a href="javascript:void(0);">(13)</a> is warranted, first, large-scale LIBS elemental intensity maps of the samples across about a third of the thin sections were acquired. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig1">Figure 1</a> reveals the results for “Layer 2” of the Poggio del Gatto sandstone for selected elements. A complete mapping of all four thin sections can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Figures S3 and S6</a> in the Supporting Information. The maps reveal that C is strongly enriched in fracture veins as well as in finer-grained domains within the matrix and correlates with Ca, indicating carbonate minerals rather than oxides. Bulk XRD analysis identifies calcite alongside quartz, plagioclase, white mica, and kaolinite. These complementary observations confirm calcite as a major phase and thus justify the need for performing the 100 wt % normalization on a carbonate basis for pixels representing calcite.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 1. LIBS elemental intensity maps of the data set from “Layer 2” of the Poggio del Gatto sandstone mounted on a microscope slide, depicting calcite veins and fine-grained structures within the matrix, underlining the need for carbonate-based normalization for specific pixels. Emission lines for evaluation are shown in the corresponding subfigure title. The epoxy used to fix the thin section on the slide is visible along the margin." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg 245w," sizes="100vw" width="2088" height="414"></p><h5><strong>Renormalization Based on Carbonates</strong></h5><p>Applying an oxide-based normalization to carbonate samples will introduce composition-dependent biases that cannot immediately be generalized as a singular numerical value. The difference between the molecular masses of carbonate CO<sub>3</sub><sup>2–</sup> and its oxide equivalent O<sup>2–</sup> yields an intrinsic bias on the order of a factor of about two, depending on the stoichiometry. Once this data is renormalized to a total of 100 wt %, the magnitude of the error becomes highly dependent on the specific elemental composition of the sample. An exemplary calculation illustrating this effect for a representative calcite pixel is provided in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Table S2</a>, yielding a relative error of 79%. In practice, this error varies across the image according to the local phase composition. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig6">Figure 6</a> illustrates the impact of normalization approaches for layer 2 of the samples as well as the spatial distribution of the resulting relative error. The figure presents Ca and Si elemental maps in two rows, while the first three columns compare different normalization strategies: normalization based on all oxides, normalization based on all carbonates, and a pixel-wise normalization guided by mineral phase identification, as outlined in the workflow proposed in this study. The final column displays the relative error between the bulk oxide- or carbonate-based normalization and the pixel-wise, phase-informed normalization.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 6. Comparison of normalization strategies for Ca and Si LA-ICP-TOFMS elemental maps in “Layer 2” of the Poggio del Gatto sandstone. Rows show Ca (top) and Si (bottom); columns compare (1) oxide-based normalization, (2) carbonate-based normalization, (3) pixel-wise, phase-informed normalization (this study), and (4) relative error between the first two and the phase-informed approach." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg 202w," sizes="100vw" width="2088" height="1622"></p><p>For Ca (in this matrix primarily associated with calcite, a dominant carbonate phase in our samples), the relative error depicted quantifies the effect of applying oxide-based normalization across the entire image. This results in a systematic overestimation of the Ca concentration by approximately 60%. Conversely, for Si─in this matrix found mainly in quartz and various plagioclase phases─the error is shown for applying carbonate-based normalization across an oxidic matrix. In such cases, the Si concentrations are underestimated by roughly 10% to 40%, depending on the mineral phase.</p><p>These findings highlight that using a single normalization scheme across heterogeneous samples introduces substantial quantification errors. To mitigate this, the pixel-wise phase segmentation enabled by the proposed UMAP-based workflow offers a significant advantage, especially in fine-grained matrices. In short, phase-aware, pixel-wise normalization combines the strengths of both normalization approaches and proves to be highly beneficial.</p><h4><strong>Conclusion</strong></h4><p>This study demonstrates that phase-specific normalization, guided by unsupervised phase segmentation of LA-ICP-MS images, can substantially improve the accuracy of elemental quantification in fine-grained geological matrices. Rather than relying on a single global assumption (e.g., all oxides or all carbonates), the workflow adapts normalization to the actual mineralogy, minimizing systematic errors and enabling phase-resolved geochemical interpretation.</p><p>The segmentation step, based on UMAP embedding and <i>k</i>-means clustering, is fully unsupervised. However, the subsequent matching of clusters to specific minerals still relies on a limited set of preidentified stoichiometries and is therefore not yet fully unsupervised. Expansion to larger stoichiometric databases could help overcome this limitation and move toward automated mineral classification. A key challenge remains the similarity of cation ratios in certain mineral groups, which may still require complementary data to ensure robust assignments.</p><p>Overall, beyond the specific case of the Poggio del Gatto sandstone presented in this study, this work provides a framework for quantitative elemental imaging of complex geological samples and is readily transferable to other contexts that require spatially resolved composition-aware normalization. This contribution adds a practical implementation to a growing literature that already uses multivariate statistics to unlock the immense information depth of LA-ICP-TOFMS.</p>

Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging

<p><strong>Eurachem and CITAC welcome analysts interested in the evaluation of chemical analysis uncertainty and in how to use this information in results interpretation. The Eurachem Week 2026 will begin with a training course on “Evaluation of the measurement uncertainty from in-house precision and recovery data” scheduled for Sunday, 10 May 2026. A two-days workshop on “Quality in Analytical Measurements: Uncertainty Evaluation and Results Interpretation” open to all interested participants will take place from 11 to 12 May 2026. On 13 May 2026, the Eurachem Working Groups will convene, followed by the Eurachem General Assembly, which will bring together the Executive and national delegates on 14 and 15 May. Both Working Group Meetings and the General Assembly are restricted to their members.</strong></p><p>The results of qualitative and quantitative chemical analyses should have an acceptable uncertainty, which should ideally be reported and considered in results interpretation.</p><p>For many analytical applications, simplified uncertainty evaluations can determine uncertainty values that meet the defined requirements for this parameter. The workshop will focus on the key technical challenges involved in practical evaluations of measurement uncertainty based on precision and recovery data gathered during in-house method validation and quality control. There will be a discussion of how to model the variation of the measurement uncertainty within the analytical range and how to handle divergent recovery values from the analysis of various reference materials. A session on the use of measurement uncertainty in conformity assessment will explore how the risk of false conformity decisions can be managed.</p><blockquote><h5><strong>New Eurachem guidance on using in-house precision and recovery data will be discussed at the workshop.</strong></h5></blockquote><p>Topics will also include the validation and uncertainty evaluation for qualitative analyses with particular attention to current accreditation requirements for these types of analysis. The last session of the workshop will be dedicated to the evaluation of measurement uncertainty arising from sampling and using it to judge the fitness for purpose, and hence validation, of measurement procedures that include sampling. Four sessions of the workshop will include engaging breakout sessions where participants will have the chance to share their views about challenging aspects of the analytical work. Participants are invited to propose posters and oral communications on session topics or any other relevant topic for analytical chemists or stakeholders of the analytical work.</p><h3><strong>Registration</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://conferences.chemistry.pt/register/eurachem2026"><strong>Register here for the workshop</strong></a></p></blockquote><h4><strong>Registration Fees</strong></h4><h5><strong>Early bird (EUR) - Until April 15th</strong></h5><ul><li><strong>Workshop: Regular participant (11 &amp; 12 May 2026): </strong>400 EUR</li><li><strong>Workshop: Student (11 &amp; 12 May 2026): </strong>250 EUR</li><li><strong>Eurachem Working Group<sup>1</sup> (13 May 2026):</strong> 75</li><li><strong>General Assembly<sup>2</sup> (14 &amp; 15 May 2026): </strong>250</li><li><strong>Training On Uncertainty Evaluation<sup>3</sup> (10 May 2026):</strong> 150</li></ul><h5><strong>After April 15th</strong></h5><ul><li><strong>Workshop: Regular participant (11 &amp; 12 May 2026): </strong>450 EUR</li><li><strong>Workshop: Student (11 &amp; 12 May 2026):</strong> 300 EUR</li><li><strong>Eurachem Working Group<sup>1</sup> (13 May 2026): </strong>100</li><li><strong>General Assembly<sup>2</sup> (14 &amp; 15 May 2026):</strong> 300</li><li><strong>Training On Uncertainty Evaluation<sup>3</sup> (10 May 2026): </strong>200</li></ul><blockquote><p>§ - <strong>50 EUR reduction for members of Eurachem</strong>, CITAC, SPQ, RELACRE, ForMEQ and SPMet at the workshop.</p><p>10% reduction for organisations that registered more than one participant.</p><p><sup>1 </sup>- Working group members</p><p><sup>2</sup> - Eurachem members</p><p><sup>3</sup> - 50% reduction for workshop participants</p></blockquote><h3><strong>Workshop</strong></h3><h4><strong>Eurachem/CITAC Workshop on Quality in Analytical Measurements:</strong></h4><h5><strong>Uncertainty Evaluation and Results Interpretation</strong></h5><p>The results of qualitative and quantitative chemical analyses should have an acceptable uncertainty, which should ideally be reported and considered in results interpretation. For many analytical applications, simplified uncertainty evaluations can determine uncertainty values meeting defined requirements for this parameter. The workshop will focus on the key technical challenges involved in practical evaluations of measurement uncertainty based on precision and recovery data gathered during in-house method validation. It will be discussed how to model the variation of the measurement uncertainty within the analytical range and how to handle divergent recovery values from the analysis of various reference materials. A session on the use of measurement uncertainty in conformity assessment will explore how the risk of false conformity decisions can be managed.</p><p>Topics will also include the validation and uncertainty evaluation of qualitative analyses with particular attention to current accreditation requirements for these types of analysis. The last session of the workshop will be dedicated to the evaluation of measurement uncertainty arising from sampling and using it to judge the fitness for purpose, and hence validation, of measurement procedures that include sampling.</p><p>Four sessions of the workshop will include engaging breakout sessions where participants will have the chance to share their views about challenging aspects of the analytical work. Participants are invited to propose posters and oral communications on session topics or any other relevant topic for analytical chemists or stakeholders of the analytical work.</p><h3><strong>Training Course</strong></h3><h4><strong>Evaluation of the measurement uncertainty from in-house precision and recovery data</strong></h4><ul><li>10 May 2026</li></ul><p>A course on the fundamentals of the evaluation of the measurement uncertainty from in-house precision and recovery data will be held on Sunday, 10 May 2026. This training is targeted for analysts with a basic understanding of the measurement uncertainty concept, how uncertainty components propagate, and how the combined uncertainty is expanded. Following the morning presentation of the theory of uncertainty evaluation, the afternoon will be dedicated to applying the theory to real-world cases. Trainees should have basic skills in using Microsoft Excel spreadsheets and bring a laptop with Microsoft Excel to complete the exercises. The interaction between trainers and trainees will be promoted to guarantee learning efficiency.</p><h5><strong>Trainers:</strong></h5><ul><li>Ricardo Bettencourt da Silva, Faculdade de Ciências da Universidade de Lisboa, Portugal (rjsilva@ciencias.ulisboa.pt)</li><li>Stephen L. R. Ellison - UK National Measurement Laboratory (NML) hosted at LGC, United Kingdom</li><li>Vicki Barwick - UK National Measurement Laboratory (NML) hosted at LGC, United Kingdom</li></ul><h4><strong>Agenda</strong></h4><h5><strong>8:00 Registration</strong></h5><h5><strong>9:00 Opening</strong></h5><h5><strong>9:05 Identifying, combining, and expanding uncertainty components: Theory and examples</strong></h5><h5><strong>9:45 Precision uncertainty: Theory and examples</strong></h5><h5><strong>10:30 </strong>Coffee break</h5><h5><strong>10:50 Trueness-related uncertainty: Theory and examples</strong></h5><h5><strong>12:00 Additional questions and discussion</strong></h5><h5><strong>12:30 </strong>Lunch</h5><h5><strong>13:30 Application example 1</strong></h5><h5><strong>15:30 </strong>Coffee break</h5><h5><strong>15:50 Application example 2</strong></h5><h5><strong>17:00 Additional questions and discussion</strong></h5><h5><strong>17:30 Closing</strong></h5><h3><strong>Venue</strong></h3><p>The series of Eurachem events will be held in:</p><ul><li>Meliã Lisboa Aeroporto ****</li><li>Rua C,2 1749-125 Lisboa, Portugal&nbsp;</li></ul>

Eurachem/CITAC Workshop on Quality in Analytical Measurements 2026

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 9th February 2026? 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 application notes by <strong>Agilent Technologies</strong>, <strong>Metrohm,</strong> <strong>Shimadzu </strong>and<strong> Thermo Fisher Scientific!</strong></p><h3><strong>1. Agilent Technologies: Black Pepper Origin Differentiation Using Large ICP-MS Datasets and Chemometric Tools</strong></h3><p><strong>Elemental profiling using an </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=7850+ICP-MS"><strong>Agilent 7850 ICP-MS</strong></a><strong> and Agilent </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=Mass+Profiler+Professional+software"><strong>Mass Profiler Professional (MPP) software</strong></a></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_black_pepper_origin_7850_icp_ms_5994_8741en_agilent_7c190968ef.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Black pepper’s high value and global demand are drivers for economically motivated adulteration, a fraudulent practice that can pose public health risks. Because black pepper is most often sold in ground, crushed, or mixed forms, its supply chain is difficult to monitor and manage. Also, consumer campaigns against deforestation and the European Union Regulation on Deforestation-free (EUDR) products have increased calls for greater sourcing transparency. Products with a known geographical indication can demand a higher price. Companies do not want to become implicated in false claims about the sourcing of products, and many are keen to have full visibility of their products’ origin. To address this need, the European Commission has established the Protected Geographical Indication (PGI) system to protect regional specific products, thereby promoting their unique characteristics based on geographical origin and traditional competence of production. The PGI scheme has increased the demand for detailed information regarding the geographical source of food products, a trend that is likely to continue as EUDR is implemented across the EU and associated markets. Verifying the geographical origin of black pepper using elemental fingerprints is highly relevant to protecting value chain stakeholders, ensuring both quality and traceability while upholding the principles of food integrity.&nbsp;</p><p>Several analytical methods can be used for profiling certain elements in foods. However, inductively coupled plasma mass spectrometry (ICP-MS) is unique because of its rapid throughput, multi-element capability, ultratrace sensitivity, isotopic analysis, versatility across sample types, low sample volume requirements, compliance with regulatory requirements, and low cost.<sup>3</sup>&nbsp;</p><p>The Agilent 7850 ICP-MS is equipped with an Octopole Reaction System (ORS4 ), a collision/reaction cell (CRC) that uses helium (He) gas and Kinetic Energy Discrimination (KED) to remove polyatomic interferences on key analytes.<sup>4</sup> If left unchecked, these interferences can affect data accuracy in complex food products. The integration of the 7850 with Agilent ICP-MS MassHunter software streamlines data analysis. When integrated with chemometric analysis, ICP-MS demonstrates high precision in multi-elemental profiling of diverse food matrices, effectively distinguishing their geographical origin.<sup>5</sup>&nbsp;</p><p>In this study, 150 black pepper samples from five countries (Brazil, Cambodia, India, Indonesia, Vietnam) were analyzed using the 7850 ICP-MS to obtain elemental fingerprints of the samples. Agilent Mass Profiler Professional (MPP) software was used to process the ICP-MS elemental data and to construct chemometric models for distinguishing the geographical origin of the samples, as adapted from Chilaka et al.<sup>5</sup></p><h4><strong>Experimental&nbsp;</strong></h4><h5><strong>Instrumentation&nbsp;</strong></h5><p>An <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=7850+ICP-MS"><strong>Agilent 7850 single-quadrupole ICP-MS</strong></a> was used for the analysis of 38 elements in the black pepper samples. Sample introduction was performed via a peristaltic pump connected to an Agilent MicroMist nebulizer and an Agilent SPS 4 autosampler. All elements were measured using helium collision (He KED) mode, except for boron (B), which was analyzed in no gas mode. Instrument control was performed using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=MassHunter"><strong>Agilent ICP-MS MassHunter 5.1 software</strong></a>, and data processing was conducted using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=Mass+Profiler+Professional+software"><strong>Agilent MPP software</strong></a>, version 15.1. Instrument operating conditions as shown in Table 1.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This study investigated the potential of ICP-MS elemental profiling coupled with chemometric analysis for geographical origin discrimination of black pepper samples from five countries, including Brazil, Cambodia, India, Indonesia, and Vietnam. A total of 38 elements was measured in 150 black pepper samples using the Agilent 7850 ICP-MS. Chemometric analysis was performed on the ICP-MS elemental data using Agilent Mass Profiler Professional (MPP) software. The PCA analysis exhibited satisfactory reliability and classification rates and demonstrated potential for the authentication of the geographical origin of black pepper. However, the separation of the Cambodian and Vietnamese samples was less distinct than the other groups, revealing potential similarities in elemental profiling between the bordering countries, which share similar climatic, geological, and agricultural conditions. Using LDA and RF classification models on the black pepper test set, we achieved a high overall accuracy in determining the geographical origin of the samples. These findings provide empirical support for the combined use of elementomics and chemometric techniques in black pepper authentication, contributing to fraud prevention. The study also demonstrates the potential of the method for application to other food types.</p><h3><strong>2. Metrohm: Milk powder analysis by near-infrared spectroscopy</strong></h3><p><strong>Determination of moisture, fat, lactose, and protein content</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_nir_128_download_867321cfb0.pdf"><strong>Full PDF for download</strong></a></li></ul><p>For milk powder producers, final product control is essential to meet strict regulatory standards, guarantee comprehensive quality assurance, ensure consistent nutritional quality, and extend shelf life. These are all especially important for infant formula and dairy ingredients used in sensitive applications. Near-infrared spectroscopy (NIRS) is a fast, reagentfree method for measuring key quality parameters such as moisture, protein, lactose, and fat content directly in milk powder. The NIRS solution requires no sample preparation, enabling real-time monitoring either in the lab or directly on the production line. This allows producers to react quickly to process variations, minimize waste, and maintain product integrity batch after batch.</p><h4><strong>EXPERIMENTAL EQUIPMENT&nbsp;</strong></h4><p>More than 600 samples of powdered milk from different suppliers were analyzed on an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=56&amp;search=OMNIS+NIR"><strong>OMNIS NIR Analyzer</strong></a> (Figure 1). The different milk powders were placed into an OMNIS sample cup and analyzed in diffuse reflection mode. To include sample variety, the sample rotated during measurement to collect spectra from different locations. The automatically averaged spectra were used for model development. Reference values were obtained by official methods, e.g., AOAC 927.05 (moisture), AOAC 939.02 (protein), and AOAC 932.06 (fat). For the lactose content determination, a phenol-sulfuric acid method was used.</p><h4><strong>CONCLUSION</strong></h4><p>This Application Note presented the analysis of milk powder using NIR spectroscopy. Models for several quality parameters (fat, protein, lactose, and moisture content) were created. Independent validation samples confirmed the robustness and reliability of the models, with high correlation coefficients and low prediction errors achieved across all parameters. Notably, the dataset included samples from diverse global origins, capturing a broad range of product variability. This study shows that NIRS can be successfully integrated into the quality control workflow for dairy powder analysis.</p><h3><strong>3. Shimadzu: Analysis of Electrolyte and Electrode in LIB Degraded by Overcharge and High Temperature</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01064_en_b4f9f4330a.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>User Benefits</strong></h4><ul><li>ICP-AES enables accurate quantification of cathode material dissolved in electrolytes.</li><li>GC-MS assesses degradation from components in electrolytes with unknown usage.</li><li>EDXRF easily evaluates transition metals, phosphorus, fluorine, and other elements deposited on electrodes</li></ul><p>When lithium-ion batteries are used in EVs or ESS applications, numerous cells are connected in series and parallel to form modules, and multiple modules are further connected to construct a pack. In such configurations—where many cells are connected in series—the state of charge of individual cells varies, as shown in Figure 1. Moreover, because the cells are densely packed to reduce volume, the ambient temperature experienced by each cell also varies. In this application, we conducted cycle tests under overcharged or high-temperature conditions, disassembled the degraded pouch cells, and analyzed both the electrolytes and the electrodes.</p><h4><strong>Analysis of electrolyte and electrode</strong></h4><p>Electrolytes and electrodes were extracted from the batteries degraded through the cycle tests (hereafter referred to as “degraded cells”) and analyzed using the instruments shown in Figures 3–5. It is known that electrolytesin degraded cells contain transition metals (Ni, Co, Mn) dissolved from the cathode active material1). In addition, electrolyte solvents are known to generate characteristic degradation products as deterioration progresses2). By analyzing these degradation-related elements and compounds, the degradation state of the battery can be assessed. In this article, we propose a multifaceted evaluation of electrolytes using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=3&amp;search=ICPE-9820"><strong>ICP-AES</strong></a> and GC-MS. On both the cathode and anode, insulating layers known as CEI/SEI form on the electrode surfaces. Under improper charging conditions, excessive formation of these layers can lead to capacity loss. Furthermore, the dissolved metals from the cathode deposit and precipitate on the anode. These deposits can cause internal short circuits, posing risks such as fire hazards. While such electrode conditions are typically evaluated by ICP-AES or XPS to examine elemental content or surface chemical states, this article introduces a simpler evaluation method using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=3&amp;search=EDX-8100"><strong>EDXRF</strong></a>.</p><h4><strong>Summary</strong></h4><p>Analysis of the electrolytesfrom degraded batteries using ICP-AES and GC-MS revealed dissolution of transition metals as well as the formation of degradation products. Furthermore, EDXRF analysis of the electrodes enabled straightforward detection of changesin elemental composition, including transition metals, fluorine, and phosphorus. These analytical techniques allow for a multifaceted evaluation of battery degradation. In real-world battery operation, fluctuations in charging conditions and ambient temperature introduce variability in degradation states. By assessing degradation mechanisms individually, as demonstrated in thisreport, it is possible to obtain insights that contribute not only to improving intrinsic battery performance but also to optimizing implementation methods and control conditions.</p><h3><strong>4. Thermo Fisher Scientific: Analyzing Spinel refractory MgO-Al2O3 premix using </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=2&amp;search=ARL%E2%84%A2+X%E2%80%99TRA+Companion"><strong>ARL X’TRA Companion XRD</strong></a></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/spinel_refractory_xrd_an41525_en_75f2527e78.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Spinel refractories—primarily magnesia–alumina (MgAl₂O₄)— are the workhorses of high-temperature linings in steel ladles, cement rotary kilns, glass tanks, and non-ferrous smelters. Their appeal lies in a combination of shock resistance, chemical stability against basic slags, and an expansion behavior that mitigates cracking. Depending on the duty, they are used as bricks, castables, or gunned repairs, sometimes with engineered microstructures that blend spinel with periclase, corundum, or zirconia to tune creep, corrosion, and spalling performance.&nbsp;</p><p>X-ray diffraction (XRD) underpins manufacturing control from raw mix to fired product. During synthesis, XRD tracks the solid-state reaction of MgO and Al₂O₃ into spinel, verifying the disappearance of periclase and alumina and quantifying the spinel fraction by Rietveld analysis. Lattice parameters reveal cation stoichiometry and solid-solution effects; peak breadth trends indicate crystallite growth and microstrain, proxies for sintering progress. In finished components, XRD detects secondary phases such as calcium aluminates introduced by binders, slags, impurities, and it provides fingerprinting for batch-to-batch consistency after thermal exposure. Embedded into routine QA/QC, XRD shortens feedback loops, stabilizes recipes, and links processing conditions to the microstructure that governs refractory life.</p><h4><strong>Instrument &amp; software&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=2&amp;search=ARL%E2%84%A2+X%E2%80%99TRA+Companion"><strong>Thermo Scientific™ ARL™ X’TRA Companion</strong></a> X-Ray Diffractometer (c.f. Figure 1) is a simple, easy-to-use benchtop instrument for routine phase analysis as well as more advanced applications. The ARL X’TRA Companion uses a θ/θ goniometer (160 mm radius) in Bragg-Brentano geometry coupled with a 600 W X-ray source (Cu or Co). The radial and axial collimation of the beam is controlled by divergence and Soller slits, while air scattering is reduced by a variable beam knife. An integrated water chiller is available as an option. Thanks to the state-of-the art solid state pixel detector (55x55 μm pitch), the ARL X’TRA Companion provides very fast data collection and comes with single-click Rietveld quantification capabilities and automated result transmission to a LIMS (Laboratory Information Management System) seamless integrated into Thermo Scientific™ SolstiX™ Pronto instrument control software.</p><h4><strong>Your benefits&nbsp;</strong></h4><p>The ARL X’TRA Companion XRD workflow delivers answers in minutes, quantifying MgO, α-Al₂O₃, and MgAl₂O₄ while resolving Ca–Si silicates and trace Fe-spinels. Subtle differences between corundum and spinel are cleanly separated, and LOI drivers—carbonates, brucite, and Mg-sulfates—are identified and trended. These insights let you compute spinel potential, anticipate pore-closing expansion, and set the right pre-dry / prefire schedule to avoid spalling. You can clearly distinguish mid-grade, ore-derived feeds from premium low-silica materials, tighten incoming QA, and optimize formulations for cement/lime kilns, tundish linings, and other carbon-free basic applications.</p>

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Mestrelab Mnova ElViS software

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

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

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