This study aimed to develop a high-throughput HPLC-ICP-MS method for the simultaneous quantification of inorganic mercury (Hg²⁺) and methylmercury (MeHg⁺) in human breast milk. The method was validated using a certified reference material, achieving 99% accuracy.A 4-month analysis of breast milk samples from 10 participants examined the effects of collection method and timing on mercury speciation. The results revealed correlations between mercury species, fat content, and selenium, with MeHg⁺ concentrations being higher in hindmilk. The study highlights the importance of accurate mercury speciation and proper breast milk collection methodologies.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965">High-throughput mercury speciation analysis of breast milk using HPLC-ICP-MS</a></h4>Kenta Iwai, Miyuki Iwai-Shimada, Nozomi Tatsuta, Yayoi Kobayashi, Kaname Asato, Mitsuo Nishimoto, Kunihiko Nakai, Shoji F. NakayamaMicrochemical Journal, Volume 211, April 2025, 113142<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.microc.2025.113142">https://doi.org/10.1016/j.microc.2025.113142</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<h3>1. Introduction</h3>Mercury is a global pollutant released into the oceans and atmosphere as a result of economic activities such as gold mining and natural phenomena such as volcanic eruptions <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0005" name="bb0005" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0005">[1]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0010" name="bb0010" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0010">[2]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0015" name="bb0015" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0015">[3]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0020" name="bb0020" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0020">[4]</a>. This element is present in the environment in various chemical forms, and the sources of exposure, health effects, and toxicokinetics differ significantly among chemical species; therefore, it is necessary to estimate the exposure level of each chemical species <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0025" name="bb0025" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0025">[5]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0030" name="bb0030" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0030">[6]</a>. Methylmercury (MeHg+), the chemical form of mercury of greatest concern in terms of human toxicity, is converted from inorganic mercury (Hg2+) by microorganisms, and individuals are exposed to it through the consumption of seafood. Nervous system disorders as well as cardiovascular and other effects have&nbsp;been reported&nbsp;following exposure to MeHg+ <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0035" name="bb0035" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0035">[7]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0040" name="bb0040" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0040">[8]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0045" name="bb0045" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0045">[9]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0050" name="bb0050" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0050">[10]</a>. The Minamata Convention on Mercury was established&nbsp;to regulate anthropogenic mercury emissions and advocate for the global monitoring of mercury exposure levels <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0055" name="bb0055" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0055">[11]</a>. Nevertheless, given that mercury remains a pervasive environmental pollutant, continuous global monitoring continues to be imperative.Exposure to mercury during the vulnerable period from the fetal stage to infancy is a health concern. Prenatal exposure to MeHg+ may adversely affect child development <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0060" name="bb0060" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0060">[12]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0065" name="bb0065" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0065">[13]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0070" name="bb0070" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0070">[14]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0075" name="bb0075" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0075">[15]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0080" name="bb0080" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0080">[16]</a>. Although measurements of total mercury in breast milk—the&nbsp;main&nbsp;source of exposure in infants—have been reported in many regions <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0085" name="bb0085" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0085">[17]</a>, MeHg+ <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0090" name="bb0090" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0090">[18]</a> and Hg2+ <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0095" name="bb0095" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0095">[19]</a> are released in breast milk; therefore, it is necessary to analyze these chemical species. Blood MeHg+ levels positively correlate with seafood consumption in Japan <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0100" name="bb0100" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0100">[20]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0105" name="bb0105" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0105">[21]</a>, and inorganic mercury exposure is heavily influenced by dental amalgams <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0110" name="bb0110" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0110">[22]</a> and gold mining <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0115" name="bb0115" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0115">[23]</a>.&nbsp;In Japan,&nbsp;dental amalgams have rarely been used&nbsp;since the 1990s <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0120" name="bb0120" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0120">[24]</a>.&nbsp;In addition, Hg2+ exposure is also lower now because of the reduced use of mercury in thermometers and other devices.Thus fat, MeHg+ in breast milk has been analyzed mainly using gas chromatography equipped with electron capture detector (GC-ECD) <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0190" name="bb0190" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0190">[38]</a>; however, the&nbsp;pretreatment for such measurements is complicated, and the analytical volume required is large. It is especially important to establish a method that can adequately analyze even small amounts of colostrum because sample volumes are small. Consequently, the following prerequisites must be met for a comprehensive investigation of breast milk, including colostrum: 1) the minimum analytical volume of the sample must be feasible; 2) pretreatment must be straightforward; and 3) validation must be viable, with the methodology being proved highly reliable. In this study, we developed a high-throughput and sensitive analytical method for the separate determination of chemical species of mercury in breast milk that satisfies the above requirements.This study was designed to achieve three objectives. The first was to develop a straightforward approach for analyzing mercury speciation in breast milk. The second was to ascertain the impact of the sampling methodology and timing on the concentrations of Hg2+ and MeHg+ in breast milk. The third was to elucidate the relationships between the Hg2+ and MeHg+ contents and the contents of the other elements and fats.<h3>2. Methods</h3><h4>2.3. HPLC apparatus and conditions</h4>Separation was performed on a metal-free YMC-Triart C18 HPLC column (YMC CO., LTD., Kyoto, Japan), installed in an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=1260+Bio-inert+LC">Agilent 1260 Bio-inert LC</a> system consisting of a G5611A Bio-inert quaternary pump, G5611A Bio-inert high-performance autosampler, and a G1390B isocratic pump (Agilent Technologies Inc., Santa Clara, CA, USA). The outlet of the HPLC column was connected directly to an ICP-MS nebulizer (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#t0010" name="bt0010" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="t0010">Table 2</a>).<h4>2.4. ICP-MS device and conditions</h4>An <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=8900+ICP-QQQ">Agilent 8900 ICP-QQQ </a>(Agilent Technologies Inc.) was used for speciation analysis and elemental concentration measurements. The introduction system consisted of a micro-mist nebulizer, a quartz double-pass spray chamber, and a quartz torch with a diameter of 2.5 mm. The total element concentration analysis was performed using the ICP-MS equipped with an SPS 4 Autosampler (Agilent Technologies, Inc.) [49].<h3>3. Results and discussion</h3><h4>3.2.3. Comparison of total mercury levels measured by HPLC-ICP-MS and ICP-MS</h4>To validate the developed method, we compared the sum of the Hg2+ and MeHg+ concentrations obtained using HPLC-ICP-MS with the total mercury concentration quantified via ICP-MS analysis. We analyzed foremilk and hindmilk samples from 10 participants in four sampling rounds (N = 80). The median concentrations (ng g−1) for the 80 samples were 0.109 (5th–95th percentile: 0.014–0.365) for Hg2+ and 0.220 (5th–95th percentile: 0.046–0.792) for MeHg+. The Hg2+ and MeHg+ concentrations in all the breast milk samples were above the MDL. The ICP-MS determination method for the total mercury and other elemental contents has been previously published <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#b0245" name="bb0245" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="b0245">[49]</a>. NIST SRM 1953 was used as the reference material to validate the determination of total mercury, as well as other elements using ICP-MS (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#s0120" name="bs0120" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="s0120">Supplemental Table 2</a>). No mercury chemical species other than Hg2+ and MeHg+ were present in breast milk, as determined by HPLC-ICP-MS; therefore, the sum of Hg2+ and MeHg+ was taken to be the total mercury in breast milk. To validate the analytical method, the distributions of total mercury as measured using HPLC-ICP-MS and ICP-MS were plotted. The results from the two methods were found to be strongly correlated (R2&nbsp;=&nbsp;0.895, Pearson’s correlation coefficient&nbsp;=&nbsp;0.946) (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#f0010" name="bf0010" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="f0010">Fig. 2</a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#s0120" name="bs0120" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="s0120">Supplemental Table 3</a>). The effects of adjusting the fat content were also investigated. The values showed better agreement in samples with low fat contents, whereas there was more variation in samples with high fat contents. For samples with high fat contents, the correlation improved upon fat content adjustment (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#s0120" name="bs0120" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="s0120">Supplemental Table 4</a>). A Bland–Altman plot was used to assess the agreement between the two methods (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0026265X25004965#f0015" name="bf0015" data-sd-ui-side-panel-opener="true" data-xocs-content-type="reference" data-xocs-content-id="f0015">Fig. 3</a>). The results of the two measurement methods were generally in agreement. However, discrepancies were observed at lower concentrations. These discrepancies are thought to be due to errors in the ICP-MS measurements, which increase as the measured concentration approaches the limit of quantification.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Microchemical_Journal_Volume_211_April_2025_113142_Fig_2_Regression_correlation_between_the_total_mercury_concentration_as_obtained_via_ICP_MS_analysis_02f63d9d9f.jpg" alt="Microchemical Journal, Volume 211, April 2025, 113142: Fig. 2. Regression correlation between the total mercury concentration as obtained via ICP-MS analysis and the sum of Hg2+ and MeHg+ as obtained via HPLC-ICP-MS analysis (N = 80). THg: total mercury." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Microchemical_Journal_Volume_211_April_2025_113142_Fig_2_Regression_correlation_between_the_total_mercury_concentration_as_obtained_via_ICP_MS_analysis_02f63d9d9f.jpg 168w," sizes="100vw" width="1400" height="1300"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/MICROC_3_c4a9ebef9e.jpg" alt="Microchemical Journal, Volume 211, April 2025, 113142: Fig. 3. A Bland–Altman plot and regression line (dotted line) and 95% limits of agreement (dashed lines) for total mercury in 80 samples as measured by ICP-MS and HPLC-ICP-MS." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_MICROC_3_c4a9ebef9e.jpg 156w," sizes="100vw" width="1400" height="1404"><h3>4. Conclusion</h3>We developed a high-throughput and sensitive method for analyzing mercury speciation in breast milk using HPLC-ICP-MS. The method was validated by analyzing NIST SRM 1953, comparing the values obtained with the total mercury concentrations obtained via ICP-MS analysis.&nbsp;Hg2+ and MeHg+ were detected in all the samples of foremilk and hindmilk provided monthly for 4&nbsp;months by the 10 participants. MeHg+ concentrations differed significantly between foremilk and hindmilk samples but not between samples obtained 2 and 5&nbsp;months postpartum. In contrast, Hg2+ concentrations in the foremilk and hindmilk did not differ and were correlated with selenium concentrations. Breast milk composition differs significantly between colostrum, transitional milk, and mature milk. Hence, a large cross-sectional study using our method will reveal the level of infant exposure to each chemical species of mercury.

High-throughput mercury speciation analysis of breast milk using HPLC-ICP-MS

Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 22nd December 2025? Check out new documents from the field of spectroscopy/spectrometry and related techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">LabRulezGCMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies, Metrohm and Shimadzu!<h3>1. Agilent Technologies: Trace Elemental Analysis of Precursor Materials Using ICP-MS/MS</h3>Robust detection of Pt, Ag, Cd, and Ti in high-metal matrices using the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=8900+ICP-QQQ">Agilent 8900 ICP-QQQ</a> and reactive cell gases&nbsp;<ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_precursor_materials_8900_icp_ms_5994_8841en_agilent_81f1d8ffb5.pdf">Full PDF for download</a></li></ul>Precursor materials are fundamental to the fabrication of integrated circuits (ICs). In atomic layer deposition (ALD), they enable the controlled growth of ultrathin films on semiconductor substrates—an essential capability for scaling advanced device architectures. Because even trace metal impurities in precursors can degrade film performance and device yield, both suppliers and manufacturers routinely analyze these materials. Due to its sensitivity and multi-elemental capabilities, inductively coupled plasma mass spectrometry (ICP-MS) is the industry standard for assessing elemental impurities in semiconductor materials.1 However, the high concentration of the metallic (high-matrix) content of precursors and the prevalence of polyatomic and doubly charged ion (M++) spectral overlaps arising from the matrix on key analytes present challenges for accurate analysis by single-quadrupole ICP-MS. At one time, high-resolution (HR)-ICP-MS (m/Δm up to 10000) was the best choice for solving these kinds of analytical problems. But some interferences require a resolution that exceeds the capability of current HR-ICP-MS instruments. In these cases, the samples are sometimes diluted to reduce the concentration of matrix elements, but dilution cannot totally solve the interference and may negatively affect detection limits.&nbsp;Triple quadrupole ICP-MS (ICP-QQQ or ICP-MS/MS) combined with collision/reaction (CRC) gases overcomes these limitations by selectively removing or avoiding interferences without compromising sensitivity. Agilent ICP-QQQ systems use two quadrupoles (Q1 and Q2) as unit mass filters, enabling MS/MS operation and the controlled use of reactive cell gases. Q1 controls which ions enter the CRC and Q2 controls which ions reach the detector.2–4&nbsp;In this study, we evaluated the performance of the Agilent 8900 ICP-QQQ to determine key analytes in three simulated precursor materials. Each sample contained a highconcentration matrix of hafnium (Hf), zirconium (Zr), or molybdenum (Mo), which cause interferences on platinum (Pt), silver (Ag), and cadmium (Cd) and titanium (Ti), respectively. To demonstrate the 8900 ICP-QQQ system’s ability to remove spectral interferences without sacrificing sensitivity, we quantified the four analytes in the three matrices using optimized instrument operating conditions.<h4>Experimental</h4><h4>Instrumentation&nbsp;</h4>An <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=1&amp;search=8900+ICP-QQQ">Agilent 8900 Semiconductor configuration ICP-QQQ</a> (#200) fitted with the optional m-lens was used for all measurements. The 8900 #200 includes an inert (HF-resistant) sample introduction system comprising a 200 μL/min MicroFlow PFA nebulizer, a PFA spray chamber, endcap, and connector tube, and a demountable torch with a 2.5 mm internal diameter (id) sapphire injector. The cones comprised a Pt-tipped sampling cone and Pt-tipped, Ni-based skimmer cone for m-lens. The samples were self-aspirated using an Agilent SPS 4 autosampler. Hot plasma conditions (1% CeO+ /Ce+ ) were used during the analysis of the high-matrix samples.&nbsp;The 8900 ICP-QQQ was operated in MS/MS mode for all measurements. Ammonia (NH3 ) —plus a flow of helium (He) as a buffer gas—and oxygen (O2 ) were used as reactive cell gases to resolve the matrix-based interferences. Various CRC tuning modes and Q1–Q2 mass setting combinations were tested to obtain the best conditions for each analyte. The optimized operating conditions for the analysis are detailed in Table 1.<h4>Conclusion&nbsp;</h4>The Agilent 8900 Semiconductor configuration ICP-QQQ was evaluated for its ability to eliminate spectral interferences on four analytes—Pt, Ag, Cd, and Ti—in simulated semiconductor precursor materials using MS/MS and reactive cell gases. To accommodate challenging samples comprising Hf (500 ppm), Zr (500 ppm), and Mo (360 ppm) under hot plasma conditions (1% CeO+ /Ce+ ), the 8900 was equipped with the optional m-lens.&nbsp;Ammonia was applied in both on-mass and mass-shift modes to quantify Pt, Ag, and Cd at single-digit or sub-ppt levels. Oxygen on-mass mode effectively removed Mo-based doubly charged ion interferences on Ti, enabling accurate detection at single-digit ppt concentrations. Repeated analysis of the MoCl5 samples spiked at 50 ppt with %RSDs below 10% demonstrated the instrument’s robustness and consistent performance for ultratrace quantification in complex matrices.&nbsp;The 8900 ICP-QQQ delivered low background, high sensitivity, and reliable interference control, making it suitable for method development and trace-level screening of semiconductor materials.<h3>2. Metrohm: Fat content analysis in olive pomace with NIR spectroscopy</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_nir_137_download_bb86ed4a7f.pdf">Full PDF for download</a></li></ul>Olive pomace is the main residue of the olive oil extraction process. It is a thick sludge – the remaining pulpy material after most of the oil is removed from the olive paste. To extract the remaining oil it contains, the olive pomace is treated with solvents. After refining and mixing this with edible virgin olive oil for flavor, aroma, and color, olive pomace oil is obtained. Olive pomace oil is used in cooking for its milder flavor and stability at high temperatures [1], as well as being nutritionally relevant due to its high oleic acid content (C18:1) [2]. Olive pomace fat analysis is necessary to check the efficiency of the oil removal process. The official method to determine this requires a time-consuming drying step followed by solvent extraction. Near-infrared spectroscopy (NIRS) is a fast, chemical-free method for olive pomace testing without any sample preparation.<h4>EXPERIMENTAL EQUIPMENT</h4>140 samples of olive pomace were measured on a <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=56&amp;search=NIR+Analyzer">Metrohm NIR Analyzer</a>. All measurements were performed in reflection mode (1000–2250 nm) using the large cup accessory. The samples were measured in rotation to collect spectral data from several areas. Spectral averaging of signals from several spots helped to reduce sample inhomogeneity. Metrohm software was used for all data acquisition and prediction model development.<h4>RESULTS</h4>The obtained NIR spectra of olive pomace samples (Figure 1) were used to create a prediction model for quantification of fat content. The quality of the prediction model was evaluated using a correlation diagram (Figure 2) which displays a high correlation between the NIR prediction and the reference values measured with Soxhlet extraction. The respective figures of merit (FOM) display the expected precision of a prediction during routine analysis.<h4>CONCLUSION&nbsp;</h4>This Application Note shows the feasibility of using NIR spectroscopy for olive pomace fat analysis. By using NIRS, chemical-free analysis can be conducted within seconds—a quick, easy, cost-effective alternative to determine the efficiency of the olive pomace oil removal process.<h3>3. Shimadzu: Analysis of Pharmaceutical Raw Materials Compliant with ICH Q3D Guideline Using ICP-MS with Collision Mode</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00925_en_c760bf6c08.pdf">Full PDF for download</a></li></ul><h4>User Benefits</h4><ul><li>The 24 elements identified in the ICH Q3D guideline can be measured with high sensitivity.</li><li>Pharmaceutical raw materials that are dissolved in ethanol can be easily analyzed using the Shimadzu ICP-MS organic solvent system.</li></ul>The presence of impurities in pharmaceutical ingredients is a concern within the medical pharmaceutical industry. Therefore, the International Council on Harmonisation of Technical Requirementsfor Registration for Pharmaceuticalsfor Human Use requires the management of metallic impurities in pharmaceuticals through guidelines using ICP-MS (ICH Q3D) 1), 2) . This standard specifies the Permitted Daily Exposure (PDE) for 24 elements of toxicological concern in oral preparations, injectables, and inhalants.&nbsp;In the case of oral preparations, only the seven elements of Classes 1 and 2A are considered, except in instances where elements are intentionally added, such as when used as catalysts during synthesis. However, the sources of elemental impurities are diverse and include not only components such as active pharmaceutical ingredients and excipients but also manufacturing equipment and utensils.&nbsp;ICP-MS has the advantage of being able to analyze various trace elements simultaneously. It is often used for the analysis of elemental impuritiesin pharmaceuticals. The analyte for ICP-MS is usually a liquid sample, so solid samples require pretreatment.&nbsp;In this Application News, we measured three types of pharmaceutical raw materials and analyzed the impurities using the Shimadzu ICP-MS organic solvent system. Furthermore, the accuracy, precision, detection limits and specificity of 24 target elements are presented according to USP3), 4) and EP, assuming measurements below the Control Threshold of 30 % of the PDE.<h4>Instrument Configurations and Analysis Conditions&nbsp;</h4>Table 2 shows the instrument configuration for ICP-MS (<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?manufacturerItems=3&amp;search=ICPMS-2040">ICPMS-2040</a>). The organic solvent system was used to introduce the ethanol. By using the platinum sampling cone, damage from long-term exposure to organic solvents during analysis can be reduced. To reduce the labor required for sample preparation, the internal standards were added using the Online Internal Standard Kit. The analysis conditions are shown in Table 3.<h4>Accuracy and Precision Validation Results&nbsp;</h4>Measurements were carried out for n = 3 spiked samples with the target concentration of 50 % and 150 %, and n = 6 spiked samples with the target concentration and the blank (unspiked) sample. Spike recovery was calculated by averaging the quantitative values of all samples. Tables 6 to 8 show the measurement results.&nbsp;Accuracy: Spike recovery of the samples at each of the target concentrations of 50 %, 100 %, and 150 % was in the range of 94 % to 111 %, thereby satisfying the acceptance criterion. Precision (Repeatability): RSD of the spiked samples with the target concentration ranged from 0.27 % to 3.79 %, satisfying the acceptance criterion.&nbsp;Limit of quantitation: As accuracy for 50 % of the target concentration satisfied the acceptance criterion, the limit of quantitation could be confirmed.&nbsp;<h4>Specificity&nbsp;</h4>ICP-MS analysis often requires evaluation for potential interference by isotypes, polyatomic ions originating from Ar, oxides of coexistent elements and chloride ions. Here, the Cd was assessed. MoO, originating from the Class 3 element Mo, causes interference with Cd. A 1350 μg/L Mo solution (concentration equivalent to 1.5J) was analyzed, and the amount of interference with Cd was measured. Here, in addition to using collision gas, interelement correction was also performed. Table 5 shows the results.&nbsp;When the collision gas was used without interelement correction, interference by MoO was reduced but not eliminated. However, the quantitative value was below the lower limit of detection when the collision gas was used in combination with interelement correction. This result confirmed that the use of interelement correction, together with a collision gas, enables the appropriate correction of interference that could not be eliminated by the collision gas alone.<h4>Conclusion&nbsp;</h4>In this Application News, we measured three types of pharmaceutical raw materials and analyzed 24 elements indicated in the ICH Q3D guideline using the Shimadzu ICP-MS organic solvent system. In accordance with the pharmacopeias (USP, EP), the accuracy, precision, quantification limit, and specificity were confirmed. The results of the validation demonstrated that the performance meets the acceptance criteria with a sufficient margin. Based on the above, it was determined that the Shimadzu ICPMS-2040 is an effective analytical method for the 24-element analysis of pharmaceutical raw materials that can be dissolved in ethanol.

News from LabRulezICPMS Library - Week 52, 2025

Analytical investigation of metal distribution from e-cigarette aerosols to lung deposition using multi-platform mass spectrometry

This study evaluates how microwave digestion temperature, time, and power affect the extraction and ICP-MS determination of Pb, Cu, Zn, As, Se, Cd, and Hg in apricot flesh, kernels, and shells. Digestion was performed according to EPA Methods 3051A and 3052, and method accuracy was confirmed using certified reference material.The optimized conditions significantly improved extraction efficiency and enabled reliable quantification of toxic elements across the different apricot fractions. Estimated Daily Intake and Hazard Quotient models were also applied to assess potential consumer health risks.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/acsodf/article/11/25/36454/5162246/The-Effect-of-Microwave-Oven-Extraction">The Effect of Microwave Oven Extraction Temperature, Time, and Power Optimization on the Determination of Heavy Metals in Apricot by ICP-MS&nbsp;</a></h4>Servet Askin; Nevin Çankaya* ; Halim YılmazACS Omega (2026) 11 (25): 36454–36467&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c13020">https://doi.org/10.1021/acsomega.5c13020</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div>Apricot (Prunus armeniaca L.) is a species belonging to the genus Prunus, the family Rosaceae, and the order Rosales. (<a href="javascript:;">10</a>) The apricot is a drupe in structure. Anatomically, it consists of four parts: the epicarp, mesocarp, endocarp, and kernel. (<a href="javascript:;">10−13</a>) Heavy metals such as As, Hg, Cd, Se, Zn, Pb, and Cu in apricots, which are consumed as fruit and nuts, can accumulate in the human body during consumption and lead to central nervous system disorders, skeletal system disabilities (osteodystrophy), urinary symptoms associated with renal failure, liver diseases and cancer, gastrointestinal disorders, hypertension, mental disorders in children in early periods, and ulceration. (<a href="javascript:;">14−19</a>) As a result of the MWOD process of foods, Zor found heavy metals in spinach, lettuce, and parsley sold in the Marmara region. (<a href="javascript:;">20</a>) In their study, TokalıoĞlu et al., found heavy metals in flaked red pepper (<a href="javascript:;">21</a>) and Pardinho et al., identified trace elements in the Yerba mate plant by ICP-MS. (<a href="javascript:;">22</a>) Umaz et al. found heavy metals in the topsoil parts of some plant species, (<a href="javascript:;">23</a>) and Magalhâesa et al. identified heavy metals in edible, chia, flax, and coconut oils by ICP-MS measurement as a result of the MWOD process. (<a href="javascript:;">24</a>) A study conducted by Chatterjee reported arsenic compounds in oyster tissue. (<a href="javascript:;">25</a>) Ashoka et al. found trace elements in fish tissue samples, (<a href="javascript:;">26</a>) Lao et al. identified heavy metals in seafood, (<a href="javascript:;">27</a>) and Türkmen et al. found trace elements in fish species in the Mediterranean and Aegean seas by ICP-MS as a result of MWOD. (<a href="javascript:;">28</a>) In studies conducted by Öztürk and Lee et al., using the MWOD process with cow’s milk, they determined major and minor elements in cow’s milk by ICP-MS. (<a href="javascript:;">29, 30</a>) These studies demonstrate the effectiveness of MWOD coupled with ICP-MS for trace element analysis across diverse food matrices. Using the MWOD process, Lee et al. found minor and major elements in rice in Brazil, (<a href="javascript:;">31</a>) and Barnet et al. identified minor and major elements in rice in South Korea by ICP-MS. (<a href="javascript:;">32</a>) Shishov et al. found major and minor elements in tobacco and lettuce leaves, (<a href="javascript:;">33</a>) Bhandari and Amarasiriwardena found trace elements in maple sap, (<a href="javascript:;">34</a>) and Sucharova and Suchara found major and minor elements in peach leaves, spinach, pine leaves, aquatic plants, and lagarosiphon by ICP-MS as a result of the MWOD process. (<a href="javascript:;">35</a>) Using the MWOD process, TokalıoĞlu found major and minor elements in medicinal herbs, (<a href="javascript:;">36</a>) Barbosa et al. detected trace elements in soybean products, (<a href="javascript:;">37</a>) Komorowicz et al. identified arsenic in mushroom species, (<a href="javascript:;">38</a>) and Bengü et al. found major and minor elements in honey by ICP-MS. (<a href="javascript:;">39</a>)</div><div>In microwave-assisted ultraviolet (UV) acid digestion, Hartwig et al. identified Ni, Pd, and Pt elements as catalysts in the production of margarine oils, (<a href="javascript:;">40</a>) and Aydin identified major and minor elements in wool by ICP-MS-OES. (<a href="javascript:;">41</a>) In a study conducted by Chen et al. on the quantitative identification of major and minor elements in oranges, they used the MWOD process and the ICP-MS device. (<a href="javascript:;">42</a>)</div><div>This study analyzed major and minor elements separately in orange seeds, orange pulp, and orange peel. The results showed that orange is rich in Mg, K, and Ca elements. The highest amount of Mg was found in orange peel, while the highest amount of K element was found in the seeds. As, Cd, and Pb elements were found at low concentrations in orange. The wet digestion method was used for the identification of K, Mg, Ca, Cu, Zn, Fe, and Mn minerals in IĞdır apricot flesh by AAS, and the following concentrations were found, respectively: K: 262.20–144.86, Mg: 14.54–8.39, Ca: 13.66–7.53, Cu: 0.27–0.11, Zn: 0.15–0.06, Fe: 1.06–0.37, and Mn: 0.09–0.04 mg 100 g–1; whereas they were 74.5–467.6, 164.53–126.86, 40.63–30.46, 1.51–1.05, 2.39–2.01, 2.91–2.11, 0.53–0.36 mg 100g–1, respectively, in the apricot kernel. (<a href="javascript:;">43</a>)In this context, the present study applies EPA Methods 3051A (<a href="javascript:;">57</a>) and 3052 (<a href="javascript:;">58</a>) to determine heavy metal concentrations in the Şalak apricot variety. Microwave digestion parameters (time, temperature, and power) were optimized, and Pb, Cu, Zn, As, Se, Cd, and Hg concentrations were quantified using ICP-MS. Statistical analyses were performed using SPSS to evaluate differences among apricot parts. Furthermore, the study aims to assess potential synergistic or additive effects of heavy metals and to evaluate associated health risks using Estimated Daily Intake (EDI) and Hazard Quotient (HQ) models. (<a href="javascript:;">54, 55, 59, 60</a>)<h4>2. Material and Method</h4><h5>2.3. Chemicals and Tools Used</h5>Apricot samples were digested using a microwave digestion system. Suprapure reagents including 65% HNO3, 36.5% HCl, and 30% H2O2 (Merck, Darmstadt, Germany) were used. The microwave system (ETHOS One, Milestone, Italy) consisted of polytetrafluoroethylene (PTFE)-sealed vessels (10–100 mL). Optimum extraction was achieved by microwave digestion (MWOD) with concentrated acid, using temperature (tp), time (tm), and power (pw) parameters in sealed containers. All the analyses were performed by the multielement method using an <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=7700">Agilent 7700 model </a>(Agilent Corporation, USA) and inductively coupled plasma–mass spectrometry (ICP-MS). Measurement parameters obtained using ICP-MS are given in <a href="javascript:;">Table 1</a>, while microwave digestion parameters are given in <a href="javascript:;">Tables 5</a>, <a href="javascript:;">8</a>, and <a href="javascript:;">11</a>.<h4>3. Results and Discussion</h4><div>After separation and drying of apricot kernel, kernel shell, and flesh samples, comprehensive results were obtained through microwave digestion and ICP-MS analysis. Lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are classified as priority toxic elements in international food safety frameworks due to their cumulative toxicity and adverse health effects, even at low exposure levels. (<a href="javascript:;">1, 63</a>) In contrast, copper (Cu), zinc (Zn), and selenium (Se) are essential trace elements with important physiological roles but may pose health risks when intake exceeds recommended limits. Therefore, monitoring both deficiency and excess levels is critical. (<a href="javascript:;">63</a>) The simultaneous evaluation of toxic and essential elements in apricot matrices provides a comprehensive assessment of dietary exposure and potential health risks in line with established food safety guidelines.</div><div>In the literature, studies using ICP-MS have included: a. Solvent method: nitric acid+hydrochloric acid+hydrogen peroxide; b. Use of CRM 414 plankton material for verification. c. Different optimization applications: d. Statistical calculations and verification and evaluation methods. (<a href="javascript:;">18, 21, 22, 26</a>) In addition to these established approaches, the present study was designed based on EPA Method 3052 and its analytical framework, ensuring methodological robustness and reliability. (<a href="javascript:;">57, 58</a>)<h5>3.1. Optimization of Temperature Variation</h5><a href="javascript:;">Table 4</a> shows the experimental parameters used for temperature optimization. <a href="javascript:;">Figure 1</a> presents the ICP-MS measurement results of toxic elements in apricot kernel, kernel shell, and flesh samples after microwave digestion at 180 °C, 200 °C, and 220 °C. The highest values obtained from ICP-MS measurements under these digestion conditions were as follows: Cu: 0.357 mg kg–1, Zn: 6.65 mg kg–1, As: 10.76 μg kg–1, Se: 8.66 μg kg–1, Cd: 21.13 μg kg–1, Hg: 0.45 μg kg–1, and Pb: 1.45 mg kg–1. When compared with World Health Organization (WHO) concentration limits (Cw), Cu, Zn, As, Cd, and Hg exceeded the reference values, whereas Se and Pb remained below the limits. (<a href="javascript:;">14</a>) The lowest measured concentrations were 0.08 mg kg–1 (Cu), 0.13 mg kg–1 (Zn), 0.14 μg kg–1 (As), 0.08 μg kg–1 (Se), 1.01 μg kg–1 (Cd), 0.05 μg kg–1 (Hg), and 0.08 mg kg–1 (Pb), respectively. (<a href="javascript:;">14</a>) The results further indicated that the highest Zn concentration (2.278 mg kg–1) was observed in the flesh sample from Site I at 180 °C, whereas the highest Cd concentration (17.466 μg kg–1) was detected in the flesh sample from Site III (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/acsodf/article-supplement/5162246/xlsx/ao5c13020_si_002/">Supporting Information 2</a>). Comparison with previously published data on cornflakes provides additional context for these findings. (<a href="javascript:;">54</a>) In that study, the reported concentrations of As (0.0831 μg kg–1), Cd (0.0636 μg kg–1), Hg (0.0233 μg kg–1), and Pb (0.0689 mg kg–1) were substantially lower than the maximum values obtained in the present study, particularly for Cd and Zn. These differences may be attributed to variations in geographical origin, soil–plant transfer mechanisms, agricultural practices, and analytical procedures, including digestion efficiency and matrix effects.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2026_11_25_36454_36467_Table_4_Parameters_of_the_Temperature_Variation_Optimization_e99d752128.jpg" alt="ACS Omega (2026) 11 (25): 36454–36467: Table 4.Parameters of the Temperature Variation Optimization" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2026_11_25_36454_36467_Table_4_Parameters_of_the_Temperature_Variation_Optimization_e99d752128.jpg 245w," sizes="100vw" width="1603" height="292"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2026_11_25_36454_36467_Figure_1_ICP_MS_measurements_of_apricot_s_kernel_regions_92e37d2d10.jpg" alt="ACS Omega (2026) 11 (25): 36454–36467: Figure 1. ICP-MS measurements of apricot’s kernel regions I.K, II.K, III.K, IV.K, kernel’s shell, I.S, II.S, III.S, IV.S, and flesh I.F, II.F, III.F, IV.F in a microwave oven (at 800 W for 15 min) at 180 °C, 200 °C, and 220 °C temperature variations (Cu, Zn, and Pb concentrations: C × 103 μg kg–1)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2026_11_25_36454_36467_Figure_1_ICP_MS_measurements_of_apricot_s_kernel_regions_92e37d2d10.jpg 224w," sizes="100vw" width="1600" height="1119"><div>Furthermore, the trace element levels determined in this study were compared with previously reported data for dried apricots from Türkiye. Overall, the elemental distribution pattern showed good agreement with the literature, especially for essential elements such as Fe, Zn, and Cu, which were within reported ranges. Observed quantitative differences may be associated with variations in soil composition, environmental conditions, cultivation techniques, and postharvest processing methods such as drying. These findings suggest that, despite some variability, the trace element profiles of the analyzed apricot samples are generally consistent with previously published studies. The results of microwave digestion at 200 °C (the second stage of temperature optimization) showed that the highest concentration of Pb (1.174 mg kg–1) was detected in the flesh sample from Site II, while the highest concentration of Cd (15.932 μg kg–1) was also observed in the flesh sample from Site II. Similarly, the results obtained at 220 °C (the third stage of temperature optimization) indicated that the highest concentration of Zn (6.650 mg kg–1) was found in the kernel sample from Site III, whereas the highest Cd concentration (21.127 μg kg–1) was detected in the flesh sample from Site III. Based on the μg kg–1-level measurements, it was observed that increasing temperature significantly enhanced the detection and extraction of cadmium.</div><div>An increase in temperature during microwave-assisted acid digestion improved the extraction efficiency of Zn and Pb, while the extraction efficiencies of As, Hg, Cd, Se, and Cu showed a decreasing trend. When these results were compared with World Health Organization (WHO) (2007) concentration limits (Cw), it was determined that Pb levels in Sites I, II, and III (flesh samples) and Se levels in the kernel from Site I exceeded recommended limits and may pose potential health risks. (<a href="javascript:;">1</a>)</div><div>In this study, analysis of variance (ANOVA) was used to compare the elemental levels of Cu, Zn, As, Se, Cd, Hg, and Pb among different sample groups (kernel (K), kernel shell (S), and flesh (F)). Prior to analysis, normality and homogeneity of variance assumptions were tested. The sample size (n = 36) for temperature optimization was clearly defined. The Kolmogorov–Smirnov test indicated that the data were normally distributed, as all p-values were greater than 0.05. The assumption of homogeneity of variance was verified using Levene’s test, which also produced p-values above 0.05, confirming homogeneity. According to the results of one-way ANOVA, statistically significant differences were found among the groups for Zn (p = 0.000) and Hg (p = 0.000) (p &lt; 0.05). No statistically significant differences were observed for the other elements (p &gt; 0.05). Post hoc comparisons were performed using Duncan’s multiple range test, and the differences between groups are presented in the corresponding table (p &lt; 0.05).</div></div><h4>4. Conclusion</h4><div>Apricot kernel, kernel shell, and flesh samples collected from four different regions were subjected to digestion processes that were repeated three times. To determine toxic elements (Cu, Zn, As, Se, Cd, Hg, and Pb), a total of 108 ICP-MS measurements were performed for temperature optimization during microwave-assisted acid digestion: 36 from the kernel, 36 from the kernel shell, and 36 from pulp samples. Similarly, 108 ICP-MS measurements were performed for time optimization and another 108 for power optimization with the same sample distribution. As part of the basic optimization grouping, quality control (QC) and quality assurance (QA) results for toxic elements in the ripe apricot were evaluated by using relative standard deviation (RSD) values obtained by ICP-MS. Although the 36 samples in each optimization group came from four different regions, the microwave digestion processes for temperature, time, and power were each repeated three times, increasing the statistical confidence interval for each element and ensuring reliable and accurate results.</div><div>This study demonstrates the importance of statistically supported optimization in microwave-assisted acid extraction for determining the elemental composition of the food samples. Analysis of apricot samples also shows that, although the daily dietary intake hazard quotient (HQ) values of toxic elements are not considered dangerous to human health, their accumulation in the body over time can contribute to the development of serious and potentially fatal diseases.</div><div>The presence of high concentrations of toxic metals in fish is of particular importance in relation to the FAO/WHO standards for Pb and Cd. The maximum permissible doses for an adult are 3 mg of Pb and 0.5 mg of Cd per week, but the recommended doses are only one-fifth of those quantities. Lead is known to induce reduced cognitive development and intellectual performance in children, as well as increased blood pressure and cardiovascular disease in adults.</div><div>The results demonstrated that heavy metal concentrations in apricot fruit are significantly influenced by the geological characteristics of cultivation areas, microwave digestion parameters, and the compositional structure of the fruit. Statistical analysis of microwave-assisted digestion processes (temperature, time, and power) further supported significant correlations among the Cu–Zn, As–Hg, and Se–Cd element pairs.</div></div></div>

The Effect of Microwave Oven Extraction Temperature, Time, and Power Optimization on the Determination of Heavy Metals in Apricot by ICP-MS

Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 31st August 2026? Check out new documents from the field of spectroscopy/spectrometry and related techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT SPECTROSCOPY/SPECTROMETRY RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">LabRulezGCMS</a> libraries.</blockquote>This week we bring you brochures by Agilent Technologies and Waters Corporation and application notes by Shimadzu and Thermo Fisher Scientific!<h3>1. Agilent Technologies: Agilent OpenLab ICP-MS Software</h3>Simplify every step of ICP-MS analysisFor Agilent <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7800">7800</a>, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7850">7850</a>, and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=7900">7900</a> ICP-MS systems, and Agilent <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=8900">8900</a> and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=9500">9500 ICP-QQQ</a> systems.<ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/br_openlab_icp_ms_software_5994_9288en_agilent_05f01dcdef.pdf">Full PDF for download</a>&nbsp;</li></ul>The brochure introduces <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=OpenLab+ICP-MS+Software">Agilent OpenLab ICP-MS Software</a>, designed to simplify routine operation and data handling for Agilent 7800, 7850, and 7900 ICP-MS systems and the 8900 and 9500 ICP-QQQ systems. The software supports the complete analytical workflow—from checking instrument readiness and creating batches to acquisition, data review, reporting, and compliance. A central dashboard provides an overview of system status, maintenance requirements, instrument health, and utilization, while Smart EMF and post-run performance checks help users identify when maintenance may be required. Guided Diagnostics, Meter Monitor, and time-stamped system records also support troubleshooting by tracking parameters such as vacuum levels, gas flows, and temperatures.Method and batch setup are designed to reduce manual configuration. Batches can be created from preset methods, templates, existing batches, Method Wizard, or Method Advisor, and legacy Agilent ICP-MS MassHunter batches can be opened directly in OpenLab ICP-MS. For the 9500 ICP-QQQ, Method Advisor provides an element-focused approach in which users select target elements and calibration information while the software applies recommended acquisition masses and cell-gas conditions. Preset methods are available for applications including EPA 200.8/EPA 6020, USP &lt;232&gt;, ICH Q3D, and Chinese Pharmacopoeia requirements, while an IntelliQuant Screening preset supports semiquantitative screening of unknown samples. Routine instrument tasks can also be automated through autotuning, startup checks, batch queues, end-of-run verification, rinse functions, and automatic shutdown.For data evaluation, OpenLab ICP-MS provides a real-time workspace where users can review analyte concentrations, internal-standard recoveries, stability plots, spectra or chromatograms, calibration information, QC charts, and IntelliQuant results in a single view. IntelliQuant acquires a complete mass spectrum and displays semiquantitative elemental concentrations using a color-coded periodic table, while IntelliQuant Star Rating assigns a zero-to-five-star data-quality score based on factors such as potential interferences, calibration quality, background, detection limits, and precision. Reporting options include whole-batch reports, elemental impurity reports for USP &lt;232&gt;/&lt;233&gt; and ICH Q3D, customizable reports, structured CSV export, and direct data transfer to LIMS.The software can be expanded for specialized applications, including LC-ICP-MS and GC-ICP-MS, advanced QC workflows, single-particle ICP-MS and FFF-ICP-MS, ADS 2 automation, semiconductor analysis, and peripheral-device control. Supported analytical approaches include external calibration, standard additions, semiquantitative calibration, isotope ratios, and isotope dilution. For regulated laboratories, OpenLab ICP-MS can be combined with scalable OpenLab compliance solutions providing electronic signatures, user access control, configurable roles, system activity logs, and audit trails, supporting workflows subject to 21 CFR Part 11, EU Annex 11, and equivalent regulations. Overall, the platform is positioned as a unified environment for routine and advanced ICP-MS workflows, combining instrument control, method setup, diagnostics, data-quality assessment, reporting, and compliance support.<h3>2. Shimadzu: Identification of Virgin PET and Recycled PET by X-ray Fluorescence Spectrometry</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/an_01_01204_en_f3f20ff020.pdf">Full PDF for download</a></li></ul><h5>User benefits</h5><ul><li>EDX enables the identification of recycled PET.&nbsp;</li><li>Measurements are quick and simple, require no cumbersome pretreatment, and can be automated using a multi-sample turret.&nbsp;</li><li>The instrument offers excellent cost-performance and supports the detection of a wide range of elements.</li></ul>Establishing a sustainable circular economy is a major societal challenge, and recycling plastic materials such as PET (polyethylene terephthalate) plays a particularly important role in reducing environmental impact. Large numbers of PET bottles and molded products are used every day in the food and beverage sectors. To recycle these materials safely and reliably, technologies are required that can accurately identify their origin. In practice, however, appearance and conventional physical properties alone are insufficient to distinguish virgin material from recycled material, creating a need for a more reliable identification method.&nbsp;For rapid identification in industrial settings, a method requiring little or no pretreatment is highly desirable. In some cases, nondestructive analysis is also needed to enable subsequent application of other analytical techniques. This article presents a practical approach in which metal catalysts present in PET are detected by EDX (Energy-Dispersive X-ray Spectroscopy) and used to distinguish material-recycled PET from virgin PET.<h4>Principle and Overview&nbsp;</h4>Metal catalysts such as antimony (Sb) and germanium (Ge) are used in the synthesis of PET resin. These catalyst metals have been reported to be useful indicators of material origin. 1),2) For example, Sb and Ge are frequently detected together in material-recycled PET, whereas only one catalyst metal is detected in chemically recycled PET and virgin PET. This likely reflects the fact that material-recycled PET is derived from a mixture of feedstocks using different catalysts.&nbsp;ICP-AES (ICP optical emission spectrometry) and ICP-MS (ICP mass spectrometry), which are commonly applied to catalyst metal analysis, provide high sensitivity and accuracy. However, they require pretreatment to convert the sample into a solution, thereby lengthening the analytical workflow. By contrast, EDX can measure solid samples directly, making it well-suited to applications requiring rapid results, such as incoming material inspection and in-line analysis in molding plants. Because EDX is non-destructive, it is also suitable for multimodal analytical workflows.&nbsp;On the other hand, some commercially available handheld EDX instruments do not support elements such as Sb and Ge. At the same time, higher-end systems may not offer the specifications or price range needed for industrial deployment. The <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=1&amp;search=EDX-7200">EDX-7200</a> supports a broad range of elements with excellent cost performance. This article describes the analysis of Sb and Ge in PET using this instrument.<h4>Conclusion&nbsp;</h4>EDX enables rapid and straightforward elemental analysis without cumbersome sample pretreatment. Previous studies have shown that the presence of Sb and Ge can be used to identify recycled PET. Building on this concept, this article presents a practical analytical approach based on EDX. Using the EDX-7200, which combines broad elemental coverage with excellent cost performance, the proposed method provides a practical tool for rapid on-site identification, quality control, and traceability management. When combined with physical property data and other information, this approach is expected to contribute to the safety assessment and quality assurance of recycled materials, thereby supporting a more advanced circular economy.<h3>3. Thermo Fisher Scientific: Optimizing lithium-ion battery recycling operations using handheld XRF analysis</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/lithium_ion_battery_recycling_handheld_xrf_analysis_an1120_en_4c00477b78.pdf">Full PDF for download</a></li></ul>The shift to a low-carbon economy and the adoption of electric vehicles will drive an exponential increase in the demand for lithium-ion batteries. As a corollary to this, the International Energy Agency expects the demand for some key commodities to increase significantly over the next two decades: the demand for Copper (Cu) is anticipated to increase by more than 40%; for nickel (Ni) and cobalt (Co), by 60-70% each; and for lithium (Li), by nearly 90%.¹ This will translate into increased mining activities and potentially generate larger amounts of deleterious waste.&nbsp;To reduce the environmental impact and limit reliance on scarce commodities associated with economical and geopolitical implications, several countries have implemented regulations regarding producers’ responsibilities.² These regulations contain both incentives and accountability guidelines for lithium-ion battery and electric vehicles manufacturers. Their goal is to facilitate the emergence of a circular economy in which recycling will increasingly contribute to the supply of the strategic raw materials.&nbsp;Lithium-ion batteries are made of multiple components, the most valuable being the cathode that contains between 40 and 70% ³, ⁴ of the total value of the battery, depending on its exact chemistry. As of 2023, lithium nickel cobalt manganese oxides (NCM) account for 66% of lithium-ion battery cathode active materials for electric vehicles, followed by lithium iron phosphates (LFP) which account for 24%, and lithium nickel cobalt aluminum oxide (NCA) accounts for the remaining 10%.³<h4>Handheld X-ray Fluorescence Analysis&nbsp;</h4>Handheld X-ray fluorescence analysis (HHXRF) is an elemental analysis technique that has proven to be cost-effective in recycling areas such as scrap metal and automotive catalytic converters. HHXRF can measure elements from magnesium (Mg) to uranium (U) in various types of materials such as metals and alloys; non-metallic inorganic materials such as ceramics; or ores or plastics. The <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/1993">Thermo Scientific™ Niton™ XL2</a> and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/1992">Thermo Scientific™ Niton™ XL5 Plus Handheld XRF Analyzers </a>deliver accurate elemental analysis in real time, with little or no sample preparation, across multiple steps of the lithium-ion battery recycling workflow. Although handheld XRF does not detect lithium, it can measure most elements from the periodic table including nickel or cobalt which often command much higher value than the lithium in a lithium-ion battery.⁴<h4>Conclusion&nbsp;</h4>There is no ideal process to recycle lithium-ion batteries that simultaneously incorporates low environmental impact, high metal recovery yield, and economic viability. Because of the variety of technologies and materials used, recycling lithium-ion batteries is a complex journey with multiple possible paths. HHXRF helps recyclers by generating lab-quality data in real time, allowing them to optimize their processes and make fast decisions that generate significant benefits:&nbsp;<ul><li>Unwanted materials containing heavy metals such as lead or cadmium can be prevented from entering subsequent steps in the recycling workflow.&nbsp;</li><li>Materials can be accurately sorted and adequate processes for recovery selected depending on the material type (e.g., LFP vs. MCM).&nbsp;</li><li>The economic value of incoming and outgoing material can be more accurately estimated.</li></ul><h3>4. Waters Corporation: Waters Aura Systems</h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/720009268_7b9ad02cb4.pdf">Full PDF for download</a></li></ul>The presentation introduces the Waters Aura Systems, a family of high-throughput, fluidics-free instruments for the characterization and identification of visible and subvisible particles. All Aura platforms provide particle counting, identification, morphology assessment, and flexible sample volumes ranging from microliters to milliliters. The portfolio includes configurations tailored to different applications: AuraBMI for biologics particle analysis, Aura for particle analysis with identification, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2437">AuraPTx</a> for protein formulation development, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2438">AuraGT</a> for gene therapy, <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2439">AuraCL</a> for cell therapy, and <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2190">Aura+</a> as the most comprehensive platform. The systems are designed for automation-ready operation, straightforward method transfer, and continuity from research through QC.A key feature of the Aura platform is the combination of Backgrounded Membrane Imaging (BMI), Fluorescence Membrane Microscopy (FMM), and Side Illumination Membrane Imaging (SIMI). BMI captures particles on a membrane and subtracts the membrane background to improve particle contrast, while FMM uses fluorescent dyes or conjugated antibodies to distinguish specific particle types. This enables differentiation of protein aggregates, cells, viral capsids, degraded excipients, plastics, fibers, and other contaminants that may look similar based on morphology alone. SIMI adds further information for identifying unlabeled inorganic particles such as glass, fibers, and Dynabeads. Together, these techniques provide more specific particle identification than morphology-based classification alone.The systems are particularly suited to biologics, gene therapy, and cell therapy development. For gene therapy, Aura can evaluate AAV stability, distinguish capsid-related particles, and monitor DNA leakage using SYBR Gold fluorescence, with measurements possible from as little as 5 µL of sample. For cell therapy, the system can differentiate single cells, cell aggregates, protein aggregates, and extrinsic particles. The platform also supports flexible sample volumes and 24- or 96-well membrane formats, allowing the same general approach to be used from sample-limited early development through larger-volume and QC workflows. The brochure reports excellent quantitative linearity across serial dilutions and emphasizes the ability to preserve sample for additional orthogonal analyses.From a technical perspective, Aura Systems provide 100% sampling efficiency, a particle detection and quantitation range above 1 µm, and can handle concentrations above 3,000,000 particles/mL for 1.6 µm particles. Brightfield membrane imaging takes approximately one minute per sample, while fluorescence measurements require about 15–30 seconds per sample. The instruments are compatible with robotic automation and offer different fluorescence-channel configurations depending on the selected Aura model. Overall, the platform is positioned as a comprehensive solution for obtaining quantitative particle counts together with information on particle identity and composition across the development and quality-control lifecycle.

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News from LabRulezICPMS Library - Week 36, 2026

The Effect of Microwave Oven Extraction Temperature, Time, and Power Optimization on the Determination of Heavy Metals in Apricot by ICP-MS

Analytical investigation of metal distribution from e-cigarette aerosols to lung deposition using multi-platform mass spectrometry

News from LabRulezICPMS Library - Week 52, 2025

High-throughput mercury speciation analysis of breast milk using HPLC-ICP-MS

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