This study compares loss-on-ignition, acid decarbonation, and combustion elemental analysis for measuring organic carbon in sediments using certified reference materials and real samples. Loss-on-ignition conversions were inaccurate, while acid washing before elemental analysis caused significant organic carbon loss and poor precision.Combustion pretreatment followed by elemental analysis provided accurate and precise organic and inorganic carbon measurements, confirmed by temperature-dependent combustion. Across more than 150 intertidal sediment samples, acid decarbonation underestimated organic carbon by 37% ± 8%, supporting combustion-based elemental analysis as the preferred approach for reliable sediment carbon determination.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#s0055">Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice</a></h4>Robert B. Sparkes, Stuart A. Rae, Lucy McMahon, Hannah L. Mossman, David McKendry, Clare E. Dean, Graham Tinsley, Frances Fowden, Connor J. Grisdale, Hafiza Begum, Holly Hope, Charlotte McEnhill, Samuel O’Boyle, Nawal Sohail, Marvyn Wachira, Rachel M. DunkOrganic Geochemistry, Volume 218, 2026, 105215<div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.orggeochem.2026.105215">10.1016/j.orggeochem.2026.105215</a></div><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><div><div><div>Environmental and Earth scientists frequently require measurements of carbon contents, including both the total carbon concentration (TC) and the fractions contributed by carbon from organic matter (organic carbon; OC) and carbonate minerals (inorganic carbon; IC; <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0025">Bendle et al., 2009</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0220">Radabaugh et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0230">Saderne et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0240">Scheingross et al., 2019</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0260">Smeaton et al., 2021</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0295">Vonk et al., 2012</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0315">Wood et al., 2021</a>).Accurate, precise, and affordable carbon concentration measurements are critical to the drive towards net-zero emissions, which requires monitoring, reporting and verification of carbon burial and release from an array of global environments (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0210">Post et al., 2001</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0265">Smith, 2004</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0270">Smith et al., 2020</a>). Carbon concentration measurements also underpin further analyses (e.g. normalising the concentration of individual biomarker compounds against the total organic carbon concentration, calculating elemental carbon to nitrogen ratios (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0290">van Dongen et al., 2008</a>), determining the correct sample load for isotope ratio measurements). Despite carbon concentration measurements being ubiquitous throughout multiple disciplines there is no standard approach for making these, especially determining the organic component of the sedimentary carbon (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0080">Fest et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0320">Young et al., 2018</a>). Different disciplines have often generated their own consensus approach, which may not align with studies from another field, even those working on similar samples and asking similar questions (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0070">Farmer et al., 2014</a>). Use of an agreed standard measurement protocol for carbon concentrations, or understanding the agreement/variability between protocols, is vital for regulatory monitoring, reporting and verification (MRV), such as the ongoing development of carbon offsetting standards that, if implemented robustly, could provide real-world benefits to climate mitigation (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0035">Bonn et al., 2014</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0045">Burden et al., 2023</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0105">Guizar-Coutiño et al., 2022</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0190">Needelman et al., 2018</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0205">Pan et al., 2022</a>). Determining a standardised, verified approach to carbon measurement is important for those performing MRV, who may not have access to advanced and/or expensive analytical instrumentation.This study compares three common methods for quantifying the total and organic carbon concentrations in sedimentary samples: i) loss-on-ignition, ii) elemental analysis combined with acid decarbonation, and iii) elemental analysis combined with organic matter combustion. The comparison focuses on assessing the accuracy, precision, and repeatability of each method, identifying their respective strengths and weaknesses, and independently confirming findings via a fourth method (temperature dependent combustion and infrared CO2 measurement) to provide recommendations for best practice. A summary review of these methods is provided below.<h4>2. Materials and methods</h4><h5>2.3. Independent testing of reference materials via TDC</h5>Standards were analysed by the supplier (Elemental Microanalysis Ltd, UK) as part of the certification process: 15/30 elemental analysis measurements gave expected values of TC, OC and IC and the uncertainty for TC, following ISO/IEC17025. These values were independently checked using an <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=16&amp;search=Soli+TOC">Elementar SoliTOC cube</a> instrument at the instrument manufacturer’s headquarters (Elementar Analysensysteme, Hanau, Germany), which used the TDC method. Two combustion ramps were applied, first a two-step OC and IC determination at 500 °C and 900 °C, and then a three-step programme at 400 °C and 600 °C for easily-combustible and residual OC fractions and 900 °C for IC, with three repeat measurements of each. The SoliTOC cube instrument also analysed the agricultural soil sample, again making three repeat measurements of both the two-step and three-step temperature dependent combustion protocols (see Supplementary Table 3).<h5>2.5. Elemental analysis</h5>Total carbon concentrations were measured following the protocol described in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0180">Mossman et al. (2022)</a>. 20&nbsp;mg of either untreated or pre-treated sample was weighed into tin capsules (Elemental Microanalysis Ltd.) and analysed for carbon concentrations using a Vario EL cube elemental analyser (Elementar Ltd.). Before each analytical run, the instrument was cleaned using three blanks without oxygen and seven blanks with oxygen, and calibrated using five analyses of up to 14&nbsp;mg ethylenediaminetetraacetic acid (EDTA) standard (Elemental Microanalysis Ltd.) and three analyses of an independent Soil Standard (Clay) CRM, acquired separately to the Soil Standard (Clay) CRM used to create analytical samples for this study (Elemental Microanalysis Ltd.). Three analyses of the EDTA calibration standard were repeated after every twenty samples. Long-term drift was monitored by repeated measurements of an internal laboratory standard sample, which has been analysed since 2015. Over the course of 65 measurements across 10 instrument runs for this study the laboratory standard showed no systematic drift.<h4>3. Results and discussion</h4><h5>3.1. Testing of certified reference materials (CRMs) using temperature dependent combustion (TDC)</h5>Both two-step and three-step TDC protocols consistently measured carbon contents close to but slightly below the supplier-certified values (see <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0005">Fig. 1</a>). For Soil Standard (Clay), the TDC instrument under-estimated OC by 0.10&nbsp;wt% (two-step) and 0.14&nbsp;wt% (three-step) and IC by 0.15&nbsp;wt% (two-step) and 0.087&nbsp;wt% (three-step). For Soil Standard (Peaty) the TDC instrument underestimated OC by 0.26&nbsp;wt% (two-step) and 0.34&nbsp;wt% (three-step), and IC by 0.021&nbsp;wt% (two-step) and 0.090&nbsp;wt% (three-step). Relative differences for OC are small, 5.1 – 7.0% for Clay and just 1.7 – 2.2% for Peaty, but higher for IC due to the small amount of IC present in the standards (25 – 44% for Clay, 7.0 – 30% for Peaty).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 1. Comparison of organic carbon and inorganic carbon measurements on two certified reference materials using four methods. Upper panel is Soil Standard (Clay) and lower panel Soil Standard (Peaty). Yellow diamonds show the certified value (Elemental Microanalysis Ltd) with manufacturer stated uncertainty in total carbon used as error bars for OC and IC (n = 30). White and grey symbols represent Loss-on-Ignition results using four different organic matter to organic carbon conversion equations (grey sqaures use the “van Bemmelen” scaling factor (Pribyl, 2010), grey circles the Craft et al. (1991) factor, white diamonds the Ouyang and Lee (2020) equation and white circles the Smeaton et al. (2022) equation. IC values for all LoI results use the C/CO2 mass fraction (Wang et al., 2011). Blue circles show the acid decarbonation and elemental analysis protocol, orange circles the combustion and elemental analysis protocol. Green and purple circles show mean results from temperature dependent combustion method (2-step and 3-step protocols respectively). Error bars represent one standard deviation after multiple measurements (n = 2 to n = 4). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_1_Comparison_of_organic_carbon_and_inorganic_carbon_measurements_on_two_certified_reference_materials_using_four_methods_31537717b3.jpg 116w," sizes="100vw" width="2756" height="3698"><h5>3.4. Reproducibility tests</h5>Having shown that combustion coupled with elemental analysis was the most robust method for measuring OC in pure and diluted standards, the next experiment tested the reproducibility of these protocols over multiple implementations. A dried, ground agricultural soil sample was repeatedly analysed using each method (separate aliquots were measured using LoI at 500 °C and 800 °C, separate untreated aliquots were analysed via EA, separate aliquots were combusted at 500 °C and analysed via EA, and separate acid decarbonations were carried out prior to EA measurements). This required a large amount of starting material, more than was available in the CRMs. Independent external measurement of the soil sample using temperature dependent combustion (Elementar, Germany) gave mean OC&nbsp;=&nbsp;2.50&nbsp;±&nbsp;0.002&nbsp;wt% and IC&nbsp;=&nbsp;1.29&nbsp;±&nbsp;0.008&nbsp;wt% for combustion to 500 °C (n&nbsp;=&nbsp;3), whereas combustion to 600 °C gave OC&nbsp;=&nbsp;2.52&nbsp;±&nbsp;0.075 and IC&nbsp;=&nbsp;1.12&nbsp;±&nbsp;0.010&nbsp;wt% (n&nbsp;=&nbsp;3).<div>LoI showed differences in mean OC depending on the equation chosen (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010">Fig. 2</a>). The conversions of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0050">Craft et al. (1991)</a> and <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0200">Ouyang and Lee (2020)</a> were similar to the independent measurements (2.90&nbsp;±&nbsp;0.05&nbsp;wt% and 2.60&nbsp;±&nbsp;0.07&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each), but the equations of <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#b0255">Smeaton et al. (2022)</a> and van Bemmelen over-estimated OC (4.19&nbsp;±&nbsp;0.05 and 4.21&nbsp;±&nbsp;0.08&nbsp;wt% respectively; n&nbsp;=&nbsp;9 for each). IC estimates using the C/CO2 mass fraction conversion were higher than the expected value (1.91&nbsp;±&nbsp;0.06&nbsp;wt%, n&nbsp;=&nbsp;9). Note that the relative standard deviation for OCLoI was&nbsp;∼&nbsp;1.8%, and 3.2% for ICLoI, showing acceptable run-to-run precision.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg" alt="Organic Geochemistry, Volume 218, 2026, 105215: Fig. 2. Organic and inorganic carbon measurements following repeated analysis of a homogenised soil sample via multiple methods. Markers for each method are shown in the legend and are the same as Fig. 1. Small black circles and error bars show the mean and one standard deviation after multiple repetitions of each protocol (n = 9 for LoI, C-EA and AD-EA, n = 3 for TDC)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Organic_Geochemistry_Volume_218_2026_105215_Fig_2_Organic_and_inorganic_carbon_measurements_following_repeated_analysis_of_a_homogenised_soil_sample_via_multiple_methods_c5d62ece0b.jpg 204w," sizes="100vw" width="2363" height="1815">Since OCC-EA and ICAD-EA were calculated by subtraction from mean TCEA, all data from the C-EA and AD-EA methods plotted on a straight line in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S0146638026000902#f0010">Fig. 2</a>, representing OC&nbsp;+&nbsp;IC&nbsp;=&nbsp;TC. However, position and scatter along that line varied between the two pre-treatment techniques. OCC-EA clustered just higher than the independent measurements using TDC (2.60&nbsp;±&nbsp;0.03&nbsp;wt%, n&nbsp;=&nbsp;9). Propagating the errors in TC and IC gave a combined error estimate of&nbsp;±&nbsp;0.1&nbsp;wt% for the repeated OCC-EA measurements. ICC-EA was also close to but slightly higher than the TCD results, 1.35&nbsp;±&nbsp;0.03&nbsp;wt%. AD-EA, however, gave mean OC values of 2.00&nbsp;±&nbsp;0.12&nbsp;wt% (n&nbsp;=&nbsp;9) which was more variable run-to-run than the other methods and gave a significantly (p&nbsp;&lt;&nbsp;0.0001) lower mean OC value than the TCD results. Once again, acid decarbonation results suggested that a large amount of organic matter was lost during the acid treatment, tested further in the next section.</div><h4>4.3. Conclusions</h4>An accurate, precise, efficient, and standardised method for measuring total, organic and inorganic carbon contents of sediment samples is critical for both scientific research and environmental monitoring and reporting and verification. We rigorously tested three methods for measuring carbon contents in soils and intertidal sediments, using certified reference materials and&nbsp;&gt;&nbsp;150 sediment samples. Loss-on-ignition (LoI) is cheap and easy but showed moderate run-to-run variability and, without careful calibration, it is not accurate.Elemental analysis (EA) is an accurate and precise method for total carbon (TC) analysis. Measuring organic and inorganic carbon via EA requires pre-treatment of the sample, and this study tested both acid decarbonation and combustion preparation techniques. Acid decarbonation led to significant and variable organic carbon loss, meaning that true organic carbon concentrations are likely higher than those measured following acidification. Combustion pre-treatment was shown to efficiently remove organic matter, leading to accurate and precise results, and we would recommend making organic carbon measurements in this way.</div></div></div></div></div>

Comparing loss-on-ignition, acid decarbonation and combustion elemental analysis for measuring organic carbon concentrations – recommendations for best practice

Wiley, a global leader in authoritative content and research intelligence, today announced the launch of its spectral analysis application programming interface (API) portfolio, providing users with a new, faster way to access its trusted spectral data, analytical algorithms, and predictive models—within their own workflows.Wiley has one of the most comprehensive and trusted collections of scientific reference databases in the world, relied upon as a gold standard for identifying unknown chemical compounds by laboratories, instrument vendors, and software platforms across pharmaceuticals, forensics, environmental science, chemicals, food, materials, and other fields. The new portfolio extends the data and tools into modern, API-driven environments—allowing users to integrate high-quality spectral intelligence directly into their own systems and workflows.As sample volumes grow and lab workflows become more automated and software-driven, identifying and verifying compounds quickly and reliably becomes even more critical. The new API portfolio meets that need directly, giving labs and platforms a faster way to complete the process, whether catching a quality issue on a production line or identifying an unknown compound in the field. The result is less manual effort, faster analysis, and more confidence in the answer.The launch builds on Wiley's mission to transform knowledge into actionable intelligence for corporate and academic researchers around the world."Speed matters when you're trying to identify an unknown substance; a delayed analysis can mean an assembly line sitting idle or a forensic lab holding up a case,” said Armughan Rafat, Wiley senior vice president and chief AI and data services officer. “We've spent decades building the reference data this industry trusts. Now, by making that spectral data and analytical intelligence available through APIs, we're setting the standard for how labs and vendors turn unknowns into answers faster and at scale."<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_API_spectral_search_5bfcb1dd9b.png" alt="Wiley: spectral analysis API" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Wiley_API_spectral_search_5bfcb1dd9b.png 128w," sizes="100vw" width="802" height="977"><h5>The API portfolio launches with four core capabilities:</h5><ul><li>Spectral Database Search API – Query Wiley’s comprehensive reference collections for rapid compound identification.</li><li>Spectrum-Structure Validation API – Confirm proposed chemical structures against experimental spectral data.</li><li>Compound Classification API – Classify unknown substances, including drugs, based on compound class.</li><li>Spectra Prediction API – Generate predicted spectra to support research, method development, and validation.</li></ul>Unlike fragmented in-house databases or instrument-specific solutions, these APIs offer a continuously curated, vendor-neutral foundation that supports multi-technique workflows, regulatory compliance, and cross-platform interoperability. As a result, laboratories can increase throughput, minimize downtime, and protect the value of the instrumentation and data in which they've already invested.<h4>Spectral Databases &amp; APIs</h4>Spectral databases contain unique molecular “fingerprints” of substances captured through analytical techniques such as infrared (IR), mass spectrometry (MS), nuclear magnetic resonance (NMR), and Raman spectroscopy. By comparing experimental spectra against validated reference data, researchers can accurately identify unknown compounds across a wide range of applications.An API is a common way for computer systems, from lab instruments to software platforms, to request information from one another and get an answer back automatically.<blockquote>For more information about the Wiley spectral analysis API portfolio, please <a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/spectral-analysis-apis/">visit this website</a></blockquote><h4>About Wiley</h4>Wiley is a global leader in authoritative content and research intelligence for the advancement of scientific discovery, innovation, and learning. With more than 200 years at the center of the scholarly ecosystem, Wiley combines trusted publishing heritage with AI-powered platforms to transform how knowledge is discovered, accessed, and applied. From individual researchers and students to Fortune 500 R&amp;D teams, Wiley enables the transformation of scientific breakthroughs into real-world impact. From knowledge to impact—Wiley is redefining what's possible in science and learning.&nbsp;

Wiley Launches Its First Spectral Analysis API Portfolio

Our Library never stops expanding. What are the most recent contributions to LabRulezICPMS Library in the week of 14th September 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 technical note by Agilent Technologies, application notes by Metrohm and Thermo Fisher Scientific and poster by Shimadzu / AOAC!<h3>1. Agilent Technologies: Transitioning from the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=Cary+630">Agilent Cary 630 FTIR</a> to the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=10&amp;search=Cary+635">Agilent Cary 635 FTIR Spectrometer</a></h3>Demonstrating methodology equivalence for pharmaceutical applications<ul><li>Technical note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/te_cary_630_ftir_transition_cary_635_ftir_5994_9408en_agilent_a2ea2ce512.pdf">Full PDF for download</a>&nbsp;</li></ul>Fourier transform infrared (FTIR) spectroscopy is a powerful analytical tool used to analyze a wide variety of materials by interpreting their infrared (IR) wavenumber absorptions. Within pharmaceutical and biopharmaceutical industries, FTIR analysis is integral for material identification and quality assurance of pharmaceutical&nbsp;substances.&nbsp;In the pharmaceutical industry, the Agilent Cary 630 FTIR is used for the identification of drug products, incoming goods, and packaging materials, as specified in global pharmacopoeias, and provides quantitative information such as the concentration of components within pharmaceutical formulations.&nbsp;Agilent now presents the Cary 635 FTIR spectrometer—an ultracompact, easy-to-use, high-performing, portable FTIR system. With greater IR power, the Cary 635 FTIR features greater detection sensitivity and faster scanning times. With its predecessor's modular design, the Cary 635 FTIR can analyze the same samples with a newly enhanced performance quality and efficiency.&nbsp;For users in the pharmaceutical industry who have experience with the Cary 630 FTIR, this technical overview demonstrates the common methodologies and workflows between the Cary 635 FTIR and Cary 630 FTIR spectrometers for pharmaceutical purposes. It will compare the ability of the two instruments to measure and label IR spectra, identify relevant compounds such as active pharmaceutical ingredients (APIs), and quantify substances within commercial&nbsp;formulations.<h4>Instrumentation and software&nbsp;</h4>For comparison, all three application tests were conducted using the Cary 630 FTIR and Cary 635 FTIR spectrometers with ZnSe optics and equipped with a single-reflection diamond attenuated total reflectance (ATR) accessory. The specific method parameters are outlined within each corresponding application. Both the Cary 635 FTIR and Cary 630 FTIR use Agilent MicroLab software, which provides step-by-step instructions with supporting images to guide users through the entire analytical workflow. Both spectrometers follow the same methods and operational procedures from sample loading to data processing (Figure 1). Results are presented in an easy-to-understand, color-coded format with customizable limits, ensuring a rapid and intuitive data review.<h4>Conclusion&nbsp;</h4>The comparison presented here of both the Agilent Cary 635 FTIR and Cary 630 FTIR spectrometers demonstrated shared methodologies that produced consistent results across key applications within pharmacopoeia, including spectrum production, compound identification, and substance&nbsp;quantification. Featuring the same modularity, robustness, and ease of use as the Cary 630 FTIR, the Cary 635 FTIR also offers greater sensitivity and signal-to-noise ratio, resulting from an enhanced IR source intensity. Therefore, users operating in regulated environments can confidently upgrade to the Cary 635 FTIR with the understanding that the workflow from the Cary 630 FTIR is directly transferrable, and that their routine analyses will remain consistent and reliable.<h3>2. Metrohm: Coconut water analysis by NIRS</h3>Determination of Brix, salinity, conductivity, and pH with one instrument<ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/an_nir_145_download_05589da42d.pdf">Full PDF for download</a></li></ul>Fresh coconut water is the main edible part of the coconut (aside from the coconut meat) and has gained popularity as a health drink in recent years. Multiple factors influence the quality, such as harvesting time, growth location, and storage duration. This poses a challenge for industrial producers to achieve and maintain the desired product quality.&nbsp;Testing the properties of coconut water with standard laboratory methods can be challenging, since trained lab personnel are needed. In contrast, near-infrared spectroscopy (NIRS) is fast, chemical-free, and does not require sample preparation. This Application Note explains how NIRS is used for the multiparameter analysis of coconut water. The NIRS solution is easy to use and can be implemented either atline or in a quality control lab.<h4>EXPERIMENTAL EQUIPMENT&nbsp;</h4>An <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=220&amp;search=OMNIS+NIR">OMNIS NIR Analyzer </a>Liquid with a 1 mm flowthrough cell (Figure 1) was used to analyze 206 coconut samples from a commercial supplier. In this configuration, a peristaltic pump injects the sample into the flow-through cell, which is inserted into the cuvette holder. The cell is rinsed with water after each measurement.<h4>CONCLUSION</h4>This Application Note shows the feasibility of using NIR spectroscopy for the quality control of coconut water. The parameters Brix, conductivity, turbidity, pH value, and salinity can be monitored by NIRS in seconds without any sample preparation. The presented setup with a flow-through cell not only simplifies sample handling but also allows full automation when using an OMNIS Sample Robot. In addition to coconut water, the coconut fruit itself can also be analyzed using near-infrared spectroscopy.<h3>3. Shimadzu / AOAC: Analysis of Heavy Metals in Baby Food Using ICP-MS</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/aoac_2026_46_d9c14b32e0.pdf">Full PDF for download</a></li></ul>Babies are more vulnerable to the effects of toxic metals, and baby food therefore requires careful testing. US and EU regulations1)–3) set limits for toxic metals in baby food. This study analyzed five baby foods using the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/icpms/library?mainauthorItems=1&amp;search=ICPMS-2040%2F2050">ICPMS-2040/2050</a> and evaluated spike recovery and long-term stability.<h4>Conclusion&nbsp;</h4>Elemental analysis of baby food was conducted using ICP-MS.&nbsp;We achieved detection limits that met stringent standards. The spike recovery tests provided good results for all samples with different matrices, confirming the validity of the analysis. Additionally, we analyzed 50 samples of infant formula with a high matrix approximately six hours and confirmed good stability.<h3>4. Thermo Fisher Scientific: Process Raman as a comprehensive solution for downstream buffer workflow</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/process_raman_as_comprehensive_solution_an1440_en_c8049b8ede.pdf">Full PDF for download</a></li></ul>Raman technology is rapidly gaining interest as a promising Process Analytical Technology (PAT) solution for real-time, non-invasive monitoring and control of downstream biopharma processes, especially for therapeutics like monoclonal antibodies (mAbs) and nucleic acids. Raman measurement, based on the vibration of molecular bonds, is highly specific for identification and quantification, even in complex or interfering matrices.&nbsp;As an in-line PAT tool, Raman spectroscopy offers direct and rapid measurement in aqueous phases without sample preparation. These features make it ideal for monitoring and controlling dynamic processes such as downstream processing.This study demonstrates a real-time methodology for accurately quantifying formulation excipients in the dynamic ultrafiltration/diafiltration (UF/DF) process using the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2233">Thermo Scientific™ MarqMetrix™ All-In-One Process Raman Analyzer</a> (Figure 1). In addition, this study also illustrates a case where process Raman was able to provide real-time information on buffer quality.<h4>Results&nbsp;</h4>The Partial Least Squares (PLS) models for L-histidine, L-arginine, and sucrose were initially tested using seven independent samples collected on three different process Raman analyzers. Data were mathematically processed to standardize spectra across instruments before applying the models. All the spectra were interpolated to have a common x-axis by equally spacing the 2048 pixels across 60 to 3250 cm-1 Raman shift, followed by relative y-axis standardization using the SRM fluorescence data as described in the NIST standardization protocol.² Figure&nbsp;3 shows the correlation plot of predicted versus reference values for L-histidine, L-arginine, and sucrose for the validation samples. A correlation coefficient of over 95% and a root means square error (RMSE) of less than 5% of the reference value for calibration, cross-validation, and prediction across three instruments demonstrate the reliability of process Raman to accurately predict the concentrations of these excipients, as well as easy model transferability.<h4>Conclusion</h4>This work, combined with our previous demonstrations of accurate in-line protein quantification during UF/DF processes,⁷, ⁸ clearly highlights the value of process Raman for downstream process monitoring. Raman spectroscopy allows for the simultaneous measurement of multiple critical process parameters (CPPs) with a single scan. These findings establish process Raman as a PAT tool with unparalleled benefits compared to other analytical methods.&nbsp;Simultaneous measurement of protein and excipient concentrations not only allows tighter process control but also opens opportunities for automating downstream processing.&nbsp;The ability of process Raman to provide real-time insights into buffer quality before its use in UF/DF runs offers substantial value by preventing batch failures. This capability enhances quality control, making Raman spectroscopy an essential tool for integration as an in-line sensor to improve downstream process monitoring, control, and automation.

News from LabRulezICPMS Library - Week 38, 2026

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rHGH fragment or AOD-9604 (LCMS+CHNS) analysis

Combined LC-MS, CHNS and HPLC analysis of rHGH fragment 176–191 and AOD-9604 for identification, amount, peptide purity and contamination screening.

This analysis is designed to identify and determine the amount and purity of recombinant Human Growth Hormone fragment 176–191 and AOD-9604. It combines LC-MS, CHNS and HPLC to provide information about peptide identity, net peptide content, peptide purity and potential contamination with other peptides or degradation products.The test can detect and measure either target compound. It is therefore not necessary to know in advance whether the sample contains rHGH fragment 176–191 or AOD-9604.<h4>rHGH Fragment 176–191</h4>rHGH fragment 176–191, also known as somatropin 176–191, is a peptide corresponding to amino acids 176 to 191 of Human Growth Hormone. It consists of 16 amino acids and represents the C-terminal region of the HGH molecule.Its molecular weight is approximately 1,799 Da. It differs from AOD-9604 in the first amino acid of its sequence.<h4>AOD-9604</h4>AOD-9604 is a synthetically modified peptide derived from Human Growth Hormone fragment 176–191. Like the rHGH fragment, it contains 16 amino acids, but its sequence begins with tyrosine instead of phenylalanine.This structural difference allows the two peptides to be distinguished by LC-MS. The molecular weight of AOD-9604 is approximately 1,815 Da.<h4>Analytical Methods</h4><h5>LC-MS – Identification and Contamination Screening</h5>LC-MS is used to confirm the identity of the peptide in the sample. It also provides screening for detectable contamination with other peptides and peptide degradation products.<h5>CHNS – Net Peptide Content Determination</h5>CHNS elemental analysis measures the carbon, hydrogen, nitrogen and sulphur content of the sample. The measured values can be used to determine Net Peptide Content.Because the sample is combusted during CHNS analysis, the result is not affected by solubility issues that may complicate chromatographic measurement of peptide amount.<h5>HPLC – Peptide Purity Determination</h5>HPLC is used to determine peptide purity and the relative proportion of the main peptide component compared with chromatographically detectable impurities.<h4>What the Analysis Determines</h4><ul><li>Identity of rHGH fragment 176–191 or AOD-9604</li><li>Amount of peptide in the sample</li><li>Net peptide content determined by CHNS</li><li>Peptide purity determined by HPLC</li><li>Presence of other peptides or degradation products detected by LC-MS</li><li>Differentiation between rHGH fragment 176–191 and AOD-9604</li></ul><h4>Analysis Parameters</h4><ul><li>Target compounds: rHGH fragment – somatropin 176–191 and AOD-9604</li><li>CAS number of AOD-9604: 221231-10-3</li><li>Molecular weight of AOD-9604: 1,815 Da</li><li>AOD-9604 sequence: H-Tyr-Leu-Arg-Ile-Val-Gln-Cys(1)-Arg-Ser-Val-Glu-Gly-Ser-Cys(1)-Gly-Phe-OH</li><li>CAS number of rHGH fragment: 66004-57-7</li><li>Molecular weight of rHGH fragment: 1,799 Da</li><li>rHGH fragment sequence: H-Phe-Leu-Arg-Ile-Val-Gln-Cys(1)-Arg-Ser-Val-Glu-Gly-Ser-Cys(1)-Gly-Phe-OH</li><li>Identification method: LC-MS</li><li>Amount determination method: CHNS</li><li>Peptide purity method: HPLC</li><li>Detection limit: less than one twentieth of the usual dosage per vial</li><li>Result format for lyophilizates: mg per vial and peptide purity</li><li>Alternative result format: m/m purity and peptide purity</li><li>Minimum lyophilizate sample: one whole unopened vial containing the lyophilizate</li><li>Minimum raw API powder sample: 30 mg in a sealed glass vial to prevent moisture absorption</li><li>Average turnaround time: 7 business days</li></ul><blockquote><h5>Important Notice</h5>The alternative m/m purity result format is intended exclusively for raw API powders. For lyophilized samples, the standard result format provides the peptide amount in mg per vial together with peptide purity.</blockquote><h4>Additional Analyses</h4><h5>TFA Analysis</h5><ul><li>Detection and measurement of trifluoroacetic acid (TFA) in the sample. Results are reported in mg per vial.</li></ul><h5>Raw Data</h5><ul><li>Delivery of the available raw analytical data from the primary test and all applicable additional analyses.</li></ul><h5>Fentanyl Presence Analysis</h5><h5>Alternative Result Format – m/m Purity</h5><ul><li>Alternative reporting of the result as mass-by-mass purity. This option is intended exclusively for raw API powders.</li></ul><h5>Variance Testing</h5><ul><li>Analysis of multiple sample units to assess variation within a single batch. All submitted samples must originate from the same batch of the same product.</li></ul><h5>pH Measurement</h5><h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2522">Endotoxin Analysis</a></h5><ul><li>Measurement of bacterial endotoxins, particularly lipopolysaccharides (LPS), in the sample. This analysis is available only for finished products and should not be ordered for raw powders.</li><li>The standard test provides accurate results up to 10 EU per vial. Values above 10 EU per vial are estimated and require an additional test with a different dilution for precise determination. Sending two vials is therefore recommended.</li><li>Where endotoxin levels above 10 EU per vial are expected, the method can be calibrated for the higher range in advance.</li></ul><h5>Additional Report</h5><ul><li>A separate additional laboratory report issued under a different client name.</li></ul><h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2527">Sterility Testing</a></h5><ul><li>TAMC and TYMC testing checks the product for the presence of bacteria, yeasts and moulds. Additional unopened sample units must be submitted for analysis. The average turnaround time for this test is 14 days.</li></ul><h5><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis</a></h5><ul><li>Detection of arsenic, cadmium, lead and mercury in the sample. Testing for other metals can be arranged individually.</li></ul>

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