Discover how the iCAP PRO ICP-OES helps reduce method complexity, maintain stability with challenging matrices, and gain faster sample insight using AI-powered SemiQuantFor decades, ICP-OES has been a trusted workhorse for laboratories analyzing everything from clean water to complex brine, alloys, organics, and other challenging matrices. But today’s laboratories are being asked to do more: meet stricter regulatory and industry requirements, process more samples, reduce turnaround time, develop methods for complex samples, and improve workflow efficiency.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_f6b97f35e6.jpg" alt="Thermo Fisher Scientific: How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_f6b97f35e6.jpg 245w," sizes="100vw" width="1024" height="512">That is where next-generation ICP-OES technology can make a meaningful difference.The <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?category=227&amp;manufacturerItems=3&amp;search=PRO">Thermo Scientific iCAP PRO Series ICP-OES</a> is designed to help laboratories improve productivity, robustness, sensitivity, and ease of use — while introducing intelligent, AI-powered tools that give analysts faster insight into their samples.In the on-demand webinar, “<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6198">Time to Go PRO: Discover the future of ICP-OES analysis</a>,” Thermo Fisher Scientific experts take ICP users inside the iCAP PRO ICP-OES platform for a practical virtual demonstration of the hardware, software, and differentiating technologies that support modern trace elemental analysis.<h3>Small footprint, big laboratory benefits</h3>Laboratory space is valuable, and ICP-OES instruments need to fit efficiently into busy workflows. The iCAP PRO ICP-OES features a compact vertical design that helps conserve bench space while keeping maintenance and service areas easy to access.Key benefits include:<ul><li>Space-saving vertical footprint that allows the instrument to sit flush against the wall.</li><li>Front and side access to routine maintenance points and service connections.</li><li>Simplified installation, service, and maintenance.</li><li>Streamlined sample introduction system with familiar, quick-connect components.</li><li>A smoother transition for laboratories currently using previous iCAP systems.</li></ul>For ICP analysts, the result is a system designed not only for performance but also for everyday usability.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_2_dd16d95db6.jpg" alt="Thermo Fisher Scientific: How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_2_dd16d95db6.jpg 245w," sizes="100vw" width="1000" height="553"><h3>Robust performance for high-matrix samples</h3>High-matrix samples and high-throughput workloads are common in industrial, environmental, materials, and contract testing laboratories. To keep up, an ICP-OES system must maintain plasma stability, reduce drift, and minimize downtime.The iCAP PRO ICP-OES is built with design features that support robust, reliable operation across demanding sample types.<h3>Optimized vertical torch designed for extended stability and uptime</h3>The iCAP PRO ICP-OES vertical torch design optimizes airflow within the torch box to support a stable, matrix-tolerant plasma. This helps reduce deposition on the torch and injector, which can support longer, more stable analytical runs.For ICP analysts, the benefits are clear:<ul><li>Reduced signal drift</li><li>Fewer sample failures and reruns</li><li>Less matrix buildup on key components</li><li>Reduced maintenance-related downtime</li><li>Long-term stability and reliability when analyzing challenging samples</li></ul>If cleaning is needed, the passivated aluminum torch box can be removed with just three screws, making maintenance more straightforward.<h3>Purged Optical Path for efficient UV transmission</h3>The iCAP PRO ICP-OES also uses a Purged Optical Path, or POP, interface to help maintain efficient light transmission from the plasma to the spectrometer. Low-flow argon purge gas exits through ceramic axial and radial POP nozzles, creating a protected optical path that helps reduce UV absorption by air and helps prevent plasma byproducts, dust, soot, and sample material from reaching the optical system.This is especially important for ICP-OES analysis because many critical emission lines are in the UV range.Key benefits include:<ul><li>Improved UV sensitivity.</li><li>Lower detection limits for important elements such as arsenic, selenium, and lead.</li><li>Reduced contamination of the optical interface.</li><li>No need for a separate shear gas supply or air compressor.</li><li>Simplified operation and lower running complexity.</li></ul><h3>Mirror protection for long-term optical performance</h3>The intense ultraviolet light produced by the plasma can gradually degrade optical mirrors over time. To help protect the optical system, the iCAP PRO ICP-OES uses an automated beam blocker that shields mirrors from unnecessary UV exposure when measurements are not being taken.This helps support:<ul><li>Long-term optical stability.</li><li>Reduced mirror wear.</li><li>Lower maintenance requirements.</li></ul><h3>High-efficiency optics and detection for confident results</h3>The iCAP PRO Series ICP-OES combines a compact, high-efficiency optical design with proprietary Charge Injection Device, or CID, detector technology to help maximize sensitivity, speed, and confidence in results.The optimized optical path helps transmit more light from the plasma to the detector, while the 5th generation CID821 detector captures high-quality spectral information across the wavelength range with more than 2 million pixels and smaller pixel size than in the previous iCAP platform.Together, these technologies support:<ul><li>Efficient light transmission</li><li>Improved sensitivity</li><li>Better spectral resolution</li><li>Faster analysis</li><li>More confident results in complex matrices</li></ul><h3>The CID detector advantage: more spectral detail, less compromise</h3>In real-world ICP-OES analysis, a single sample can contain trace-level analytes that produce weak signals and major matrix elements that produce very intense emission lines. The detector must manage both at the same time.That is where CID detector technology provides a significant advantage.The iCAP PRO ICP-OES CID821 smaller pixel size and high pixel density help improve physical separation between adjacent wavelengths, giving analysts more flexibility when choosing emission lines and background correction points. This also makes it easier to resolve trace analytes from nearby matrix lines, such as trace molybdenum in a tungsten matrix or trace lead in high-concentration copper.For the ICP-OES user, this means:<ul><li>Better peak separation</li><li>Easier background correction</li><li>Reduced spectral overlap</li><li>Less reliance on correction strategies and mathematical equations</li><li>Greater confidence in complex sample analysis.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_3_e3e0cb27d9.jpg" alt="Thermo Fisher Scientific: How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_3_e3e0cb27d9.jpg 245w," sizes="100vw" width="768" height="357"><h3>Why anti-blooming matters in ICP-OES</h3>Instruments have advanced significantly from classical Paschen-Runge, or Rowland Circle, spectrometers that used individual photomultiplier tubes, or PMTs, positioned at fixed wavelengths. While effective, these systems required larger optical layouts and offered less flexibility because each PMT was dedicated to a specific emission line. The transition to solid-state chip detectors, known as Charge Transfer Devices, or CTDs, helped make ICP-OES instruments more compact, flexible, and information-rich by enabling many wavelengths to be captured across a detector array. This shift supported faster multi-element analysis, easier wavelength selection, improved method development, and access to full-spectrum data from a smaller instrument platform. The two primary CTD technologies used in ICP-OES are the Charge-Coupled Device, or CCD, and the CID.In CTD detector-based ICP-OES, photons from emission lines are converted into electrical charge within individual pixels. When an intense emission line saturates a detector region, excess charge can spread into neighboring pixels, an effect known as blooming. In CCD detectors, where charge is shifted pixel by pixel across the chip during readout, blooming can distort nearby weaker signals and lead to overestimation of their intensity.The CID detector within the iCAP PRO ICP-OES works differently. In a CID, each pixel is an independent site with its own set of electrodes. The electrical charge generated by incoming light is collected and stored directly inside that specific pixel. If a pixel fills to capacity, the excess charge drains into the underlying silicon substrate rather than spilling into neighboring pixels. Pixels are addressed individually and read without shifting charge across the array, helping contain intense signals. In this way, CID detectors are inherently anti-blooming.For analysts, anti-blooming helps protect trace-level analyte peaks near strong matrix emission lines. This can reduce the need to choose less sensitive wavelengths, add extra dilutions, or rely on additional correction strategies that may increase method complexity.Additionally, because the charge doesn’t have to move across the chip to be read (unlike a CCD), a CID can perform Non-Destructive Readout (NDRO) where the CID detector can measure the charge in an individual pixel without emptying it. For ICP-OES analysis, this provides two important benefits.<ul><li>First, weak emission lines can be integrated for a longer time to collect more signal.</li><li>Second, bright emission lines can be monitored during the exposure and read before they approach saturation.</li></ul>In practical terms, NDRO helps the CID detector capture weak trace-level signals more effectively while managing intense matrix lines — improving signal-to-noise ratio resulting to better sensitivity, extending dynamic range, and leading to more confident trace-level results.The blooming effect experienced in CCD detectors can be minimized by setting integration times, segmenting the detector, or by adding anti-blooming or overflow drains. These approaches can help reduce the impact of very intense emission lines on weaker trace-level signals and reduce blooming, but they are mitigation strategies rather than the inherent pixel-level readout advantage of CID technology.<h3>Intelligent Full Range and enhanced UV modes</h3>The larger CID detector also enables Intelligent Full Range, or iFR, mode, which captures the full elemental spectrum in one simultaneous acquisition. This reduces analysis time by eliminating sequential low- and high-wavelength exposures.For applications requiring improved UV performance, enhanced UV, or eUV, mode provides added sensitivity for key elements with emission lines in the low UV range.<h3>Key takeaways from the optical and detector design</h3>The practical benefits of the iCAP PRO ICP-OES compact optical design combined with the unique and proprietary CID technology are clear:<ul><li>High sensitivity,</li><li>Faster analysis,</li><li>Reduced spectral interferences,</li><li>Better performance in high-matrix samples, and</li><li>Greater confidence in the analysis of complex sample matrices.</li></ul>The same rich, simultaneous spectral data captured by the CID detector also helps power advanced software capabilities such as the innovative AI-driven SemiQuant, giving analysts rapid insight into unknown samples without having to build a calibration curve.<h3>AI-powered SemiQuant: instant sample insight</h3>One of the most compelling parts of the on-demand webinar is the software demonstration, including a deep dive into the new AI-powered SemiQuant feature within the<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1489"> Thermo Scientific Qtegra Intelligent Scientific Data Solution (ISDS) software</a>.Traditional ICP-OES method development often starts with several questions: Which elements are present? What concentration ranges should be expected? Which wavelengths should be selected? What interferences might occur? Which samples need dilution?AI-powered SemiQuant helps answer those questions faster. Using the rich, simultaneous spectral information captured by the CID detector, SemiQuant evaluates many wavelengths for each element rather than relying on a single emission line. The AI model interprets these spectral patterns to provide rapid, semi-quantitative concentration estimates without requiring instrument calibration.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_4_56ce880ee0.jpg" alt="Thermo Fisher Scientific: How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_4_56ce880ee0.jpg 245w," sizes="100vw" width="768" height="409">For ICP users, this provides immediate insight into unknown samples.The SemiQuant feature can help analysts quickly assess:<ul><li>What elements are present.</li><li>Which elements are high or low in concentration.</li><li>What calibration ranges to build.</li><li>Which matrix components may cause spectral interferences.</li><li>Which samples may need dilution before quantitative analysis.</li><li>How to streamline method development and sample preparation.</li></ul>This makes AI-powered SemiQuant especially valuable for sample screening, unknown identification, method development, contamination investigations, and troubleshooting before committing to a full quantitative workflow<h3>Watch the on-demand iCAP PRO ICP-OES technical showcase</h3>The on-demand virtual technical showcase gives ICP users an insider’s look at the iCAP PRO ICP-OES from both the hardware and software perspective.In the hardware demonstration, you will see:<ul><li>The compact vertical instrument design</li><li>The vertical torch and torch box access</li><li>The simplified sample introduction system</li><li>Maintenance and user-friendly design features</li><li>How analysts interact with the instrument during routine operation</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_5_4485b11821.jpg" alt="Thermo Fisher Scientific: How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_How_to_Simplify_ICP_OES_Analysis_Method_Development_High_Matrix_Samples_and_Daily_Operation_5_4485b11821.jpg 245w," sizes="100vw" width="768" height="480">In the Qtegra ISDS software demonstration, you will see:<ul><li>How to develop a method</li><li>Wavelength selection</li><li>Acquisition settings</li><li>Interference correction tools</li><li>Sample list setup</li><li>AI-powered SemiQuant analysis</li><li>A deep dive into how SemiQuant provides fast insight into unknown samples</li></ul>Whether you are running environmental samples, industrial materials, or high-salt brines, the iCAP PRO Series ICP-OES is designed to help your laboratory move from sample to reliable result with greater speed, confidence, and ease.Register now to watch the on-demand virtual technical showcase, “<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6198">Time to Go PRO: Discover the future of ICP-OES analysis</a>,” and get firsthand insight from our application experts into the innovations that make the iCAP PRO ICP-OES our most powerful, user-friendly instrument yet. To learn more, visit the <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?category=227&amp;manufacturerItems=3&amp;search=PRO">iCAP PRO Series ICP-OES</a> instruments.<blockquote>Visit us on&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://www.linkedin.com/showcase/72003132/admin/dashboard/">LinkedIn:</a>&nbsp;#ICP-OES #ElementalAnalysis #MetalsAnalysis #AtomicSpectroscopy</blockquote>

How to Simplify ICP-OES Analysis: Method Development, High Matrix Samples, and Daily Operation

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

In this leaflet we discuss whether or not you should correct for an observed significant bias and the impact this may have on the measurement uncertainty (MU). How to apply the correction and how to increase the uncertainty to take account of an uncorrected bias is outside the scope of this leaflet.&nbsp;Important issues for deciding on how to treat an observed significant bias are:&nbsp;<ul><li>whether we understand the cause of the bias, and&nbsp;</li><li>whether its size can be reliably determined.&nbsp;</li></ul>Further we must decide:&nbsp;<ul><li>whether the bias is consistent for all test samples within the scope of the method and&nbsp;</li><li>whether any correction for bias should be multiplicative or additive, depending on whether the magnitude of the bias is constant or changes with the concentration level.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Treatment_of_an_observed_bias_2_d221a32b8b.jpg" alt="Eurachem: Treatment of an observed bias" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Treatment_of_an_observed_bias_2_d221a32b8b.jpg 245w," sizes="100vw" width="1528" height="448"><h4>Should we correct, and should we increase the measurement uncertainty?&nbsp;</h4>The ISO Guide to the expression of uncertainty in measurement, GUM [1], assumes that “the result of a measurement has been corrected for all recognised significant systematic effects” (GUM 3.2.4). This implies that when developing a measurement method all known sources of bias should be investigated and if possible, eliminated or their effect minimised. If this cannot be achieved, then, if appropriate, a correction should be applied and the measurement uncertainty revised.&nbsp;Let’s assume we have available a standardised method or a fully developed in-house method, with a clear description of the measurand, where any known bias has been minimised during method development (e.g. the effect of interferences has been minimised or a correction is included in the method). The next step, as a part of the validation, is to reliably determine any additional bias for the concentration interval and different matrices specified within the scope of the method. On the next page we present a roadmap outlining how to handle any additional significant bias.&nbsp;Note that the observed bias in a laboratory could be due to laboratory as well as method bias. For empirical methods where the measurand is operationally defined by the method, the method bias is by definition zero, however, the laboratory bias still needs to be considered.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Treatment_of_an_observed_bias_3_5d99940abd.jpg" alt="Eurachem: Treatment of an observed bias" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Treatment_of_an_observed_bias_3_5d99940abd.jpg 62w," sizes="100vw" width="426" height="1060"><ol><li>There is no point in trying to eliminate or correct a small bias, since both elimination and correction need resources.&nbsp;</li><li>If bias is not negligible then the best approach, if possible, is to try to eliminate it by modifying the method.&nbsp;</li><li>If bias is not negligible, but eliminating it is either impossible or impractical, then we can consider correcting for bias. There are three possibilities:&nbsp;<ul><li>Correction may be required (e. g. by regulations).&nbsp;</li><li>Correction may be forbidden, in which case no correction should be made.&nbsp;</li><li>Correction may be allowed. Then we should look at two more criteria to determine whether correction is justified.&nbsp;</li></ul></li><li>If bias can be reliably determined and a correction method can be applied that is relevant for all test samples within the scope of the method we may decide to correct for bias. However, if the cause of bias is not known then correcting for bias cannot be generally recommended. If bias cannot be reliably determined then we should not correct for it. If we correct the result on the basis of an unreliable bias estimate then we may even increase the uncertainty of the results.&nbsp;</li><li>Correcting for bias is meaningful only if a useful reduction of measurement uncertainty (MU) is achieved. Correcting for the bias is only meaningful if the MU of the correction is smaller than the component of the MU arising from not applying the correction.&nbsp;</li></ol>If a significant bias is not corrected, it is difficult to give clear guidance on what action to take. For the case of recovery correction, IUPAC [2] lists some possibilities if no correction is applied:&nbsp;<ol><li>No action;&nbsp;</li><li>Report recovery separately, including the uncertainty of both the result and the recovery&nbsp;</li><li>Take the bias into account in the uncertainty estimate of the results</li></ol>These principles can be applied to other forms of bias. Further options have also been reviewed in the literature: See, for example, reference [3]

Treatment of an observed bias

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