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News from LabRulezLCMS Library - Week 28, 2026

We, 8.7.2026
| Original article from: LabRulezLCMS Library
This week we bring you application notes by Agilent Technologies and Metrohm and posters by Shimadzu/ASMS, Thermo Fisher Scientific/ASMS and Waters Corporation/ASMS!
<p><strong>LabRulez / AI:</strong> News from LabRulezLCMS Library - Week 28, 2026</p>

LabRulez / AI: News from LabRulezLCMS Library - Week 28, 2026

Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 6th July 2026? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!

👉 SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES

👉 Need info about different analytical techniques? Peek into LabRulezGCMS or LabRulezICPMS libraries.

This week we bring you application notes by Agilent Technologies and Metrohm and posters by Shimadzu/ASMS, Thermo Fisher Scientific/ASMS and Waters Corporation/ASMS!

1. Agilent Technologies: Determination of 41 Per- and Polyfluoroalkyl Substances (PFAS) in Cosmetics

Per- and polyfluoroalkyl substances (PFAS) are synthetic compounds composed of highly stable carbon–fluorine (C–F) bonds, giving them exceptional thermal and chemical stability, leading to poor environmental degradability. PFAS have been used in cosmetics to provide water resistance, oil repellency, or application smoothness. The unintentionally presented PFAS residues in raw materials may remain in finished cosmetic products, and can be absorbed through the skin. Lip products and spray type products can also lead to inhalation or oral exposure to residues.1,2 

Agilent Captiva EMR PFAS Food cartridges are designed to provide streamlined and comprehensive matrix removal after QuEChERS extraction for complex sample matrices, demonstrating effective matrix cleanup efficiency and excellent performance for quantitative PFAS analysis.3,4 In this study, we performed QuEChERS extraction followed by cleanup with Captiva EMR PFAS Food II cartridges for PFAS analysis in five types of cosmetics.

Experimental

Equipment and materials 

The study was performed using an Agilent 1290 Infinity III LC system consisting of an Agilent 1290 Infinity III high-speed pump (G7120A), an Agilent 1290 Infinity III multisampler (G7167B), and an Agilent 1290 Infinity III multicolumn thermostat (G7116B). The LC system was coupled to an Agilent 6495D triple quadrupole LC/MS system (G6495D) equipped with an Agilent Jet Stream Electrospray ion source. Agilent MassHunter Workstation software was used for data acquisition and analysis. 

The 1290 Infinity III LC system was modified using an Agilent InfinityLab PFAS Analysis HPLC conversion kit (part number 5004-0006), including an Agilent InfinityLab PFC delay column, 4.6 × 30 mm (part number 5062-8100). Chromatographic separation was performed using an Agilent ZORBAX RRHD Eclipse Plus C18, 95 Å, 2.1 × 100 mm, 1.8 µm (part number 959758-902).

Results and discussion 

Chromatography and calibration curve linearity 

Figure 2 shows the overlaid chromatograms for 41 PFAS calibration standards, including 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, and 10.0 ng/mL in ACN. The table on the right shows the peaks identification and corresponding retention time. Across 41 PFAS compounds, R² values ranged from 0.9940 to 0.9998, indicating excellent linearity for all analytes' calibration curves across the calibration range of 0.05 to 10.0 ng/mL.

PFAS detection in real cosmetic samples 

Of the 15 cosmetic products assessed, one eyeshadow sample was detected with a trace level of positive PFAS analytes, including PFHeA, PFHpA, PFNA, and PFOA, at about 0.1 ng/g in the sample. Figure 5 shows the method blanks and positive detection of PFHpA and PFNA in this eyeshadow product.

Conclusion 

A simplified, rapid, and reliable method using QuEChERS extraction followed by EMR mixed-mode passthrough cleanup using the Agilent Captiva EMR PFAS Food II cartridge and LC/MS/MS detection was developed for 41 PFAS analysis in cosmetics.

2. Metrohm: brightRC – Advanced brightener analysis with a response curve

Next-level brightener determination in nonlinear systems

WHAT IS BRIGHTRC? 

brightRC is a response curve calibration approach for the determination of brightener-type additives by CVS or CPVS (Cyclic Pulse Voltammetric Stripping), hence the name «brightRC». This technique is suitable to measure brightener content in copper baths for plating. The method is based on signal normalization using the electrolyte value, which is the response for the virgin makeup solution (VMS) saturated with suppressor, and brightener defined as Q0 in the viva software. All recorded signals are divided by Q0 and the normalized values are plotted against concentration to generate an external calibration curve. This calibration can then be used for routine sample determination without repeated standard additions. 

The sample is simply measured using the same method as the calibration, signals are normalized and evaluated using the mathematically fitted calibration curve. By decoupling calibration from sample measurement, brightRC removes the need for repeated standard additions with each sample measurement, enabling faster and more efficient routine analysis. 

The brightRC calibration approach supports flexible regression models including quadratic and nonlinear regression. This allows reliable quantification of additive systems that do not exhibit a wide linear working range or exhibit nonlinear response characteristics, thereby expanding the range of applicable brightener chemistries. 

One important detail that further differentiates brightRC from the MLAT approach relates to normalization of the signal for calibration and sample analysis. This normalization approach improves measurement robustness as variations in absolute signal intensity naturally occurring over the working day are mathematically compensated.

HOW DOES BRIGHTRC WORK IN PRACTICE? 

The brightRC workflow separates calibration and sample analysis. It can be implemented in different setups (from manual to fully automated) depending on the additive system and concentration range. After recording the electrolyte value, calibration standards are measured to establish the calibration curve, otherwise known as the response curve. Once the calibration is stored, samples are determined with the same CVS or CPVS method, without further standard additions. The sample concentration is automatically calculated from the normalized signal using the calibration curve. 

For sample measurement, viva 4.0 provides two practical approaches: 

  1. «brightRC with solution exchange» 
  2. «brightRC with sample dilution» Both variants follow the same calibration principle and differ only in the sample handling strategy.

WHEN TO USE BRIGHTRC? 

brightRC is ideal for routine brightener control in stable plating baths where high sample throughput is required. It is particularly suitable when matrix conditions are consistent and electrode fouling is limited. The approach is especially advantageous for additive systems exhibiting nonlinear signal behavior. If strong matrix variations, significant breakdown products, or severe electrode fouling are expected and a linear correlation between signal and analyte concentration is guaranteed, standard addition techniques such as MLAT are preferred because they may provide additional robustness.

3. Shimadzu / ASMS: PFAS Analysis of Leachate: Evaluating Key Sample Preparation and LC–MS/MS Parameters for Improved Performance

Landfill leachate is a highly complex and variable matrix that poses significant challenges for the analysis of per- and polyfluoroalkyl substances (PFAS). Although U.S. Environmental Protection Agency Method 1633A provides a standardized framework for PFAS quantitation,1 analytical performance remains strongly influenced by sample preparation, chromatographic separation, and mass spectrometric conditions. Landfill leachate samples were extracted using WAX SPE following EPA 1633A and analyzed by LC–MS/MS using a Shimadzu LCMS-8060 under negative ESI conditions. This study evaluates the key factors governing LC–MS/MS sensitivity, signal response, and reproducibility in landfill leachate, including a systematic assessment of SPE, chromatographic behavior, and ionsource conditions across PFAS classes (Figure 1).

Results 

Chromatographic performance was evaluated across four C18 columns (A–D) using peak area, height, asymmetry, and HETP. Two columns produced a higher signal and improved peak shape, while the remaining columns showed greater efficiency. Under repeated batches followed by storage in acetonitrile, the Shimadzu GIST column produced a consistent signal (≤15% change), whereas another column showed 20–50% signal loss, particularly for long-chain PFAS and precursors, indicating greater susceptibility to matrix accumulation (Figure 2).

Conclusion 

  • PFAS analytical performance was dependent on compoundclass-dependent extraction, chromatography, and ionization behavior. 
  • GIST demonstrated lower signal variability under repeated matrix exposure, with lower signal degradation than alternative C18 phases.
  • Neutral PFAS showed poor retention on WAX sorbents, requiring optimization beyond standard EPA 1633A extraction conditions. 
  • PFCA response was more influenced by gas flow conditions, whereas PFSA response was more sensitive to source temperature conditions.

4. Thermo Fisher Scientific / ASMS: Ultra-high throughput proteomics at >500 samples/day using advanced Vanquish Neo UHPLC methods with Orbitrap Astral Zoom MS

High-throughput mass spectrometry is increasingly critical for large-cohort proteomics, population-scale studies, and screening applications. While established LC–MS platforms routinely support several hundred samples per day, further increases in throughput are often limited by chromatographic stability, robustness, and reproducibility under ultra-short gradient conditions, as well as the acquisition speed of the mass spectrometer. Here, we describe an advanced ultra-high-throughput LC–MS methodology developed on the Thermo Scientific Vanquish Neo UHPLC system and Thermo Scientific Orbitrap Astral Zoom mass spectrometer. The goal of this work was to extend the practical throughput limits of LC–MS while achieving deep proteome coverage and maintaining run-to-run reproducibility.

Materials and methods

LC-MS analysis 

Samples were separated on a Thermo Scientific 150 µm × 70 mm C18 column (prototype, not commercially available yet) operated at 4 µL/min using a Vanquish Neo UHPLC system in NanoCap Trap&Elute configuration at room temperature. Prototype methods allowing for reduced LC duty cycles (approx. 1 min of sample pickup/loading and integrated trap-column offline wash) were applied, the standard gradients were adjusted to 1 – 3 min. The Thermo Scientific PepMap Neo 5 µm C18 300 µm × 5 mm cartridge was used as trap column and the separation column connected with a 15 µm ID capillary Thermo Scientific EASY-Spray emitter (ES994) to an EASY-Spray source. The Orbitrap Astral Zoom MS (ICSW AST 2.2) was operated in a dataindependent acquisition (DIA) mode, MS method parameters are given in Table 1. Comparative data for the chemoproteomics samples at lower throughputs (60-300 SPD) were acquired using an EASY-Spray 150 µm × 150 mm C18 column (ES906), as described before. 

Data analysis 

The raw data files were processed with Spectronaut® 20.5 software (Biognosys AG), Thermo Scientific Proteome Discoverer 3.3 SP1 with CHIMERYS 5.0 or DIA-NN Enterprise 2.3.2 (Aptila Biotech GmbH) nodes using default settings as specified in the individual figures. The data was searched against the Homo sapiens SwissProt database (TaxID=9606: 42,252 sequences) and contaminants FASTA file. Peptide numbers correspond to unique stripped peptides

Conclusions 

  • The advanced Vanquish Neo UHPLC methodology in combination with the Orbitrap Astral Zoom MS delivered highly reproducible, deep and precise proteome profiling across ultra-high-throughput regimes (600 SPD) exceeding current field benchmarks. 
  • The workflow enables rapid chemoproteomic screening, as demonstrated with activity-based protein profiling (ABPP) samples, reliably identifying enriched putative target proteins of an antibiotic small molecule probe.

5. Waters Corporation / ASMS: Assessing Relative Response of Four European-Regulated PFAS in Human Serum Using Cyclic Ion Mobility MS

The poster presents an LC-Cyclic-IMS-MS approach for assessing four European-regulated PFAS—PFOS, PFOA, PFNA, and PFHxS—in human serum. The study focuses on environmental exposure monitoring in Ghanaian firefighters and e-waste handlers, using ion mobility mass spectrometry to add separation power and improve identification confidence in complex biological matrices. The work is motivated by increasing regulatory concern around PFAS toxicity, bioaccumulation, and the need for reliable exposure assessment in human biofluids.

The experimental workflow combined reversed-phase LC with a quadrupole-cyclic ion mobility-time-of-flight MS system (SELECT SERIES Cyclic IMS). Human serum samples were prepared using SPE µElution plates with a polymeric reversed-phase/weak anion exchange sorbent. The LC system was modified with PFAS-free components and an isolator column to reduce background contamination, which is especially important for trace-level PFAS analysis. Identification was supported by retention time, accurate mass, isotopically labelled internal standards, and collision cross section values.

The results showed detectable levels of the four regulated PFAS in both occupational groups, with a combined detection rate of 83% across human serum samples and NIST reference standards. Firefighter samples generally showed higher concentration ranges than e-waste handler samples, especially for PFOS, PFOA, PFNA, and PFHxS. For example, PFNA reached up to 5.713 ng/mL in firefighter serum, compared with up to 1.513 ng/mL in e-waste handler serum.

Overall, the poster concludes that LC-Cyclic-IMS-MS provides a highly specific strategy for PFAS analysis in complex biological samples. Ion mobility and CCS values improve confidence in trace-level identification and help distinguish target PFAS from co-eluting matrix background. The authors suggest that this approach can support both targeted PFAS monitoring and broader non-targeted screening of known and emerging PFAS, while also helping future studies connect exposure data with environmental fate and potential toxicological effects.

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