This study explored the chemical composition and antioxidant capacity of stingless bee honey (SBH) produced by Melipona species in Espírito Santo, Brazil. Using FT-MIR and NMR spectroscopy combined with chemometrics, clear separation between M. capixaba and M. quadrifasciata was observed.Volatile compounds identified by GC–MS included carboxylic acids, ketones, aldehydes, and alcohols, with simple phenols such as p-cresol and allyl guaiacol also detected. Phenolic and flavonoid contents correlated selectively with antioxidant capacity, especially between total phenolics and CRAC. These findings highlight chemometrics combined with phenolic profiling as a robust approach for quality control and standardization of SBH.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c01134">Influence of Chemical Profile on the Antioxidant Capacity of Brazilian Stingless Bee Honey</a></h4>Lucas R. de O. Dias, Bruna M. Damm, Bruno F. Paqueli, Bruno Q. Araújo, Gislane C. Oliveira, Diolina M. Silva, Eustaquio V. R. de Castro, Rafael de Q. Ferreira*, and Álvaro C. NetoACS Omega 2025, 10, 20, 20550–20561<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c01134">https://doi.org/10.1021/acsomega.5c01134</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.Stingless bees (Meliponini), also known as native bees, are found in tropical and subtropical regions representing more than 500 bee species worldwide. <a href="javascript:void(0);">(1)</a> Although international legislation governs the quality and identity parameters of Apis melliferahoney, there is a need to establish specific standards and regulatory guidelines for stingless bee honey (SBH). <a href="javascript:void(0);">(2)</a> Currently, SBH quality is assessed based on its physicochemical, sensory, and microbiological properties. <a href="javascript:void(0);">(3)</a>In general, honey is a complex mixture of sugars (primarily glucose and fructose), and its chemical composition can vary considerably depending on climatic conditions, stages of maturation, bee species, and flower type as well as processing and storage methods. <a href="javascript:void(0);">(4)</a>SBH is recognized for its characteristic sweetness combined with an acidic taste, a more fluid texture, and slow crystallization. In addition to sugars, enzymes, amino acids, organic acids, minerals, volatile organic compounds, pigments, and pollen grains, SBH contains phenolic compounds that can impact the antioxidant capacity. <a href="javascript:void(0);">(3)</a> Due to the antioxidant, nutritional, and biological properties of SBH, it has attracted economic interest in the past few years. <a href="javascript:void(0);">(5,6)</a>Phenolic compounds are widely distributed in plants from bee pastures. <a href="javascript:void(0);">(7)</a> Several phenolic acids and flavonoids, such as gallic acid, vanillic acid, protocatechuic acid, cinnamic acid derivatives, quercetin, and rutin have been identified in SBH. These molecules contribute to honey’s flavor, aroma, and antioxidant capacity. <a href="javascript:void(0);">(8)</a>Furthermore, several stingless bee species are capable of producing phenolic compounds-rich honey. Therefore, the chemical characterization of this natural product is essential to know its compositional and functional profiles. <a href="javascript:void(0);">(9)</a>In 2020, Kozłowicz and collaborators characterized honey derived from chestnut flowers and honey with dark orange berries, both produced by stingless bees, identifying phenolic compounds using gas chromatography–mass spectrometry (GC–MS) and Fourier transform mid-infrared (FT-MIR) spectroscopy. Several chemical compounds were identified, including 3-phenylpropionic acid and ferulic acid in both samples, while m-coumaric acid, benzoic acid, and cinnamic acid were predominantly found in only one honey sample. <a href="javascript:void(0);">(10)</a>The presence of phenolic compounds in honey contributes to its antioxidant responses. In general, the antioxidant capacity can be determined by using spectrophotometric methods such as ferric reducing antioxidant power (FRAP) and DPPH (2,2-diphenyl-1-picrylhydrazyl). <a href="javascript:void(0);">(11,12)</a> Some works have determined the total phenolic and flavonoid contents and verified their correlations. While spectrometric methods are widely employed for the quantification of phenolic compounds and the evaluation of antioxidant capacity, the accuracy of these assessments can be compromised by matrix effects and interfering substances, resulting in weak correlations between measured properties. <a href="javascript:void(0);">(13)</a>Consequently, alternative approaches are necessary to verify the correlation among these physicochemical characteristics. Ceric reducing antioxidant capacity (CRAC) electrochemical assay offers several advantages over spectrophotometric techniques. These include the ability to specifically target the redox state of antioxidants, low cost, potential for miniaturization, rapid analysis when compared to reference methods, insensitivity to light, and the absence of interference from sample coloration. <a href="javascript:void(0);">(14)</a>The objective of the present study is to conduct detailed chemical characterization combined with an evaluation of antioxidant capacity to ensure authenticity and quality control of honey. This knowledge is crucial to addressing existing gaps, encouraging proper regulation, and promoting sustainability in the production of SBH. Consequently, it enables broader acceptance and distribution of this product, benefiting consumers, producers, and biodiversity.<h4>2. Materials and Methods</h4><h5>2.3. Fourier Transform Mid-infrared (FT-MIR) Analyses</h5>For physicochemical characterization using FT-MIR, a drop of SBH was directly applied in the attenuated total reflectance (ATR) module (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=Cary+630">Cary 630</a>, Agilent Technologies Inc., USA). FT-MIR spectra were acquired from 4000 to 650 cm–1 with 32 scans and a resolution of 4 cm. ATR module was cleaned with analytical-grade isopropyl alcohol for each sample. <a href="javascript:void(0);">(16)</a><h5>2.4. Nuclear Magnetic Resonance (NMR) Analyses</h5>SBH samples (20.0 mg) were dissolved in D2O. 1H NMR spectra were obtained on a 400 MHz spectrometer (VNMRS 400 model, Varian, USA) operating at a magnetic field of 9.4 T, using a probe of 5 mm 1H/X/D Broadband. All spectra were processed using MestReNova software. <a href="javascript:void(0);">(17)</a><h5>2.5. Identification of Volatile Organic Compounds by Static Headspace Sampling and Gas Chromatography–Mass Spectrometry (SHS-GC-MS)</h5>The volatile fraction of SBH was analyzed using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GC-MS+QP2010">GC-MS QP2010</a> Ultra system (Shimadzu Corporation, Japan) equipped with a static headspace sampler (SHS) and an OV-WAX capillary column (30 m length, 0.25 mm internal diameter, and 0.25 μm film thickness). A 20 mL headspace vial containing 2 g of the sample was incubated in an SHS oven at 95 °C for 15 min. Subsequently, 1 mL of the volatile fraction was collected using a 2.5 mL syringe (Hamilton, USA) and injected in splitless mode (0.45 min) at an injector temperature of 240 °C. The oven temperature program had an initial temperature of 40 °C for 2 min, ramp of 4 °C min–1 to 120 °C, and a heating rate of 11 °C min–1 to 230 °C, where it was held for 8 min. Helium (99.999%, White Martins, Brazil) was used as the carrier gas at a flow rate of 1.5 mL min–1. The mass spectrometer operated in scan mode over a mass range of 35–300 Da, with an ion source temperature of 230 °C, an interface temperature of 220 °C, and a solvent cutoff time of 2.5 min. Chromatographic data were processed using LabSolutions GC–MS Solutions software, version 4.20 (Shimadzu Corporation, Japan). Volatile compound identification was performed by comparing the experimental mass spectra with those in the NIST11 and WILEY7 libraries. Results were expressed as relative area (%) by the integration of peaks in the total ion chromatogram (TIC). <a href="javascript:void(0);">(18)</a> Retention index (RI) was calculated using the equation RI = 100 (n) + 100 (m – n) (tRi – tRn)/(tRm – tRn), where m and n are the numbers of carbons of the n-alkanes that elute before and after “i”, respectively, and “i” is a VOC, based on retention time (tR) of a mixture of nC7 to nC40 alkanes (Supelco Analytical).<h4>3. Results and Discussion</h4><h5>3.1. Spectroscopy Characterization</h5><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c01134#fig1">Figure 1</a> shows the FT-MIR and NMR spectra for the physicochemical characterization of the Melipona capixaba and Melipona quadrifasciata SBH samples. The FT-MIR spectra exhibited absorption frequency from 3500 to 3100 cm–1, which was attributed to O–H stretching from carbohydrates. <a href="javascript:void(0);">(24)</a> The presence of carboxylic acid derivatives, including triacylglycerols and fatty acids, in these samples was attributed to infrared bands around 2935 and 1650 cm–1, which are characteristics of the C–H bond with hybridization sp3 and the C═O group. In addition, C–O band was observed in absorption frequency around 1020 cm–1. <a href="javascript:void(0);">(25,26)</a> As previously highlighted, the chemical groups detected by the FT-MIR spectroscopy data were related to carbohydrates and lipids. These structures were also detected on 1H NMR spectra analyses from the SBH samples. The signals at δ 1.0 and 1.2 ppm may be attributed to methyl hydrogens and methylene hydrogens from (−CH2−)n hydrocarbon chains of fatty acids and triacylglycerols. The signal at δ 2.1 ppm is characteristic of α-methylene hydrogen linked to carbonyl groups from carboxylic acid, such as acetic acid. In addition, two doublets at δH 4.5 (J = 7.8 Hz) and 5.1 (J = 3.9 Hz) ppm were attributed to β and α hydrogens (H1) in anomeric carbon from d-glucose, respectively. The signals from δH 3.0 to 4.0 ppm were attributed to H2, H3, H4, H5, and H6 from α- and β-d-glucose. <a href="javascript:void(0);">(27)</a> Although carbohydrates and lipids, including carboxylic acids, were major compounds found in SBH samples, minor differences were observed in the NMR fingerprinting of M. capixaba and M. quadrifasciata honey.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_20_20550_20561_Figure_1_a_FT_MIR_and_b_NMR_spectra_of_stingless_bee_honey_samples_v_infrared_absorption_frequencies_stretching_or_bending_e8eab423fb.jpg" alt="ACS Omega 2025, 10, 20, 20550–20561: Figure 1. (a) FT-MIR and (b) NMR spectra of stingless bee honey samples. v─infrared absorption frequencies (stretching or bending)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_20_20550_20561_Figure_1_a_FT_MIR_and_b_NMR_spectra_of_stingless_bee_honey_samples_v_infrared_absorption_frequencies_stretching_or_bending_e8eab423fb.jpg 245w," sizes="100vw" width="1789" height="723"><h5>3.2. Volatile Organic Compounds (VOCs)</h5><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c01134#fig3">Figure 3</a> shows GC–MS TIC, highlighting the most abundant volatile compounds in the SBH samples from M. capixaba and M. quadrifasciata.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_20_20550_20561_Figure_3_GC_MS_TIC_for_selected_M_quadrifasciata_and_M_capixaba_stingless_bee_honey_ae7301dfe9.jpg" alt="ACS Omega 2025, 10, 20, 20550–20561: Figure 3. GC–MS TIC for selected M. quadrifasciata and M. capixaba stingless bee honey. TPC was expressed in mg GAE 100 g–1 of SBH. (21) 2-Hydroxypropanoic acid ethyl ester. (30) Acetic acid. (44) Hotrienol. (110) 3,5-Dimethoxybenzaldehyde." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_20_20550_20561_Figure_3_GC_MS_TIC_for_selected_M_quadrifasciata_and_M_capixaba_stingless_bee_honey_ae7301dfe9.jpg 179w," sizes="100vw" width="2906" height="2536">The GC–MS analyses allowed the identification of VOCs (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c01134/suppl_file/ao5c01134_si_001.pdf">Supporting Information</a>, Table S1), which were distributed in eight chemical classes: alcohols, aldehydes, carboxylic acids, esters, ethers, phenols, hydrocarbons, and ketones. As observed, carboxylic acids were the most abundant class in all SBH, with values of 74.34 ± 13.03 and 70.95 ± 5.14% for M. capixaba and M. quadrifasciata, respectively, while phenols and hydrocarbons were minor compounds found in these samples (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c01134#fig4">Figure 4</a>).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_20_20550_20561_Figure_4_Box_plot_for_chemical_classes_of_M_quadrifasciata_red_bar_and_M_capixaba_blue_bar_stingless_bee_honey_2de866d99b.jpg" alt="ACS Omega 2025, 10, 20, 20550–20561: Figure 4. Box plot for chemical classes of M. quadrifasciata (red bar) and M. capixaba (blue bar) stingless bee honey." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_20_20550_20561_Figure_4_Box_plot_for_chemical_classes_of_M_quadrifasciata_red_bar_and_M_capixaba_blue_bar_stingless_bee_honey_2de866d99b.jpg 109w," sizes="100vw" width="1282" height="1847"><h4>4. Conclusion</h4>This study conducted a detailed chemical characterization and a comprehensive evaluation of the antioxidant capacity of honey produced by stingless bees of theMelipona genus, collected in the state of Espírito Santo, Brazil. The results revealed a composition rich in carbohydrates, lipids, phenolic compounds, and VOCs.M. capixaba species exhibit the highest antioxidant capacity. The use of advanced techniques such as FT-MIR, NMR, and GC-MS, combined with spectrophotometric methods FRAP and DPPH, as well as the electrochemical CRAC assay, enabled a robust evaluation of the physicochemical and bioactive properties of SBH.The data obtained, along with the application of chemometric tools, allowed for clear differentiation of the SBH samples based on their chemical profiles and antioxidant properties. These findings highlight the nutritional and functional value of SBH. Furthermore, this study contributes to the establishment of authenticity and quality standards, essential for regulatory purposes and for promoting the sustainability and global acceptance of SBH.

Influence of Chemical Profile on the Antioxidant Capacity of Brazilian Stingless Bee Honey

Integrating AF4 and Py-GC-MS for Combined Size-Resolved Polymer-Compositional Analysis of Nanoplastics with Application to Wastewater

Hops (Humulus lupulus) are essential for brewing due to their resins and aromatic oils. In this study, the chemical profiles of 76 commercial hop cultivars were analyzed using GC–MS and metabolomic approaches to explore their ancestral relationships. Unsupervised statistical analyses revealed distinct clustering between North American and European lineages.Molecular networking enabled the annotation of 26 metabolites that distinguished the two groups. American hops were characterized by mono- and sesquiterpenoids, ketones, and esters, while European varieties showed higher abundances of selinenes, trans-α-bergamotene, humulene epoxide II, neophytadiene, and tocopherols. These results establish metabolomic signatures as reliable markers for hop cultivar differentiation and enhance understanding of their chemical diversity and lineage-based classification.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c06162">Mapping Metabolomic Relationships of Hop Cultivars in an Ancestral Lineage Context</a></h4>Guilherme Silva Dias, Marília Elias Gallon, Leonardo Gobbo-Neto*ACS Omega 2025, 10, 40, 47281–47291<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c06162">https://doi.org/10.1021/acsomega.5c06162</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.Highly valued in the global brewing industry, hops are perennial, dioecious, herbaceous, liana-like plants that belong to the Rosales order and to the Cannabaceae family. <a href="javascript:void(0);">(1,2)</a> The genus Humulus, which consists of three species─Humulus lupulus L. (H. lupulus, common hops), Humulus japonicus Siebold &amp; Zucc., and Humulus yunnanensis Hu─ is believed to have originated in China, although wild populations of H. lupulus are known to be widely distributed across the Northern Hemisphere. <a href="javascript:void(0);">(3,4)</a> Accordingly, the history of hops along with their domestication and use in brewing remains surrounded by uncertainties. <a href="javascript:void(0);">(1,4,5)</a>Humulus lupulus is the most economically and culturally significant species within the genus, having been used in beer production for centuries. <a href="javascript:void(0);">(3)</a> Initial hop cultivars were derived through the selective breeding of wild European populations. <a href="javascript:void(0);">(6,7)</a> A general agreement posits that North American and European wild varieties diverged from a common ancestor more than a million years ago. <a href="javascript:void(0);">(4,8)</a> Currently, five geographically and morphologically distinct botanical varieties of H. lupulus are recognized: var. humulus (Europe and Asia), var. cordifolius (East Asia, particularly Japan), var. neomexicanus (southwestern North America), var. pubescens (eastern and north-central USA), and var. lupuloides (northeastern North America and Canada). <a href="javascript:void(0);">(5,9)</a> Over time, cultivar development has evolved from open-pollinated crosses to controlled hybridization, incorporating North American germplasm and thus broadening the genetic base. <a href="javascript:void(0);">(7)</a>The importance of hop cultivation is highlighted by global production, which ranges from 80,000 to 130,000 tons annually, with Germany and the United States being the world’s largest producers. <a href="javascript:void(0);">(10,11)</a> The increasing demand for aromatic hops, driven by the American craft beer revolution dating back to the 1980s, has accelerated the development of new cultivars. Currently, approximately 300 commercial cultivars are available in the brewing market. <a href="javascript:void(0);">(12−14)</a> Beyond imparting bitterness, flavor, and aroma, hops also exhibit antimicrobial and antioxidant properties, playing a crucial role in the organoleptic stability of beer. <a href="javascript:void(0);">(15−17)</a>The main compounds in hops that are valuable for beer production are biosynthesized and accumulated in the inflorescences (strobiles, also referred as cones) of female plants, specifically in the glandular trichomes (lupulin glands) located at the bases of the bracteoles of the cones. <a href="javascript:void(0);">(2,3)</a> The resins contain compounds and precursors that contribute to the bitterness of beer (e.g., α-acids and β-acids), while the essential oils are the main contributors to the aromatic profile, especially in beer styles that emphasize hop character. <a href="javascript:void(0);">(18)</a> The amount of essential oils in hops can range from 0.5 to 4.0% of the dry cone weight and is primarily composed of terpenes (up to 90% of the essential oil), aliphatic hydrocarbons, oxygenated compounds (such as terpenoid, ester, ketone, alcohol, carboxylic acid, aldehyde, and epoxide derivatives), and organosulfur compounds. <a href="javascript:void(0);">(19−21)</a>Analytical approaches such as gas chromatography coupled to mass spectrometry (GC-MS), liquid chromatography coupled to ultraviolet detectors and/or mass spectrometry (LC-UV-MS), and nuclear magnetic resonance (NMR) have been widely employed for hop characterization. <a href="javascript:void(0);">(22−26)</a> For aroma profiling and quality control, GC-MS techniques are employed to analyze volatile compounds, whereas LC-UV-MS techniques are used to characterize bitter acids, polyphenols, and flavonoids. <a href="javascript:void(0);">(22)</a> Volatile and semivolatile metabolites in hops are largely determined by genetic factors and, therefore, define characteristic phytochemical profiles for each hop cultivar. <a href="javascript:void(0);">(2,27−29)</a> In this regard, a comprehensive GC-MS approach encompassing a wide variety of metabolites is essential for capturing lineage-associated differences in metabolite composition that may be overlooked by traditional workflows focusing solely on volatile compounds. <a href="javascript:void(0);">(21,30)</a>Metabolomic approaches are powerful tools for investigating the chemotaxonomic diversity, including that of hop cultivars from various geographical origins. <a href="javascript:void(0);">(26,31−33)</a> Despite the commercial significance of hop cultivars, comprehensive studies utilizing metabolomic techniques to analyze a broad spectrum of commercial varieties remain scarce. Given that beer quality is intrinsically linked to the chemical composition of its raw materials and ingredients (malted barley, hops, yeast, and water), comprehensive analyses of hop metabolites are helpful in selecting the best varieties to produce beers with desirable flavor profiles, balanced bitterness, and optimal aroma intensity. In this context, such metabolomic knowledge may provide valuable support for the brewing industry in cultivar selection, quality standardization, and product development. In this study, 76 hop cultivars, marketed in a pelletized form, were analyzed by using GC-MS metabolomic profiling. The resulting data were subjected to multivariate statistical analyses and molecular network techniques to identify chemotaxonomic patterns linked to the cultivars’ geographical origins. These findings provided novel insights into the chemical diversity of hops and offered frameworks for mapping relationships among cultivars based on their metabolomic signatures.<h4>Methods</h4><h5>Extract Preparation and GC-MS Analysis</h5>The methodologies used for extract preparation and the GC-MS analytical conditions were the same as those previously employed by our research group. <a href="javascript:void(0);">(74)</a> Blank samples (dichloromethane) and hydrocarbon standard solution (C8–C40, Supelco, Sigma-Aldrich, St. Louis, MO, USA) were also injected during the chromatographic analysis.Analyses were conducted using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=QP2010+Ultra">gas chromatograph coupled to a quadrupole mass spectrometer (QP2010 Ultra</a>, Shimadzu Corporation, Kyoto, Japan). The chromatograms and mass spectra were visualized using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GC+Solutions">GC Solutions software</a> (version 4.20 for Windows, Shimadzu Corp., Kyoto, Japan).<h4>Results and Discussion</h4><h5>Exploring the Hop Chemical Content</h5>To explore variations in the chemical profiles of hops based on the clustering patterns observed in the HCA, a molecular network was generated using the same GC-MS data (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c06162/suppl_file/ao5c06162_si_001.pdf">Figure S2, Supporting Information</a>). Each node in the network represents a detected metabolite, which are arranged into clusters according to their MS spectral similarities. This approach enabled the annotation of key metabolites responsible for differentiation between the two main groups observed in the dendrogram.The clusters containing the major annotated metabolites are shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c06162#fig4">Figure 4</a>. The largest cluster is primarily composed of sesquiterpenoids (dark blue border), one diterpenoid (pink border), and several unidentified metabolites. Other clusters are composed of monoterpenoids (light blue border), aliphatic esters (green border), ketones (brown border), and tocopherols (yellow border).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_40_47281_47291_Figure_4_Clusters_containing_annotated_metabolites_selected_from_the_molecular_network_ded5c3e6c3.jpg" alt="ACS Omega 2025, 10, 40, 47281–47291: Figure 4. Clusters containing annotated metabolites selected from the molecular network. The numerical labels inside the nodes indicate the respective identification numbers (node IDs). The nodes are colored according to the groups obtained from the unsupervised hierarchical cluster analysis (HCA), and the border colors correspond to the metabolite classes." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_40_47281_47291_Figure_4_Clusters_containing_annotated_metabolites_selected_from_the_molecular_network_ded5c3e6c3.jpg 137w," sizes="100vw" width="1000" height="1141">Twenty-six metabolites were annotated in level 2 of confidence according to the minimum reporting standards for chemical analysis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c06162#tbl1">Table 1</a>). <a href="javascript:void(0);">(65,66)</a> The group 1 of the HCA dendrogram, composed predominantly of North American hops and their descendants, was characterized by a higher abundance of monoterpenoids, sesquiterpenoids, aliphatic esters, and methyl ketones, annotated as β-myrcene (17), 2,6-octadiene, 2,7-dimethyl (18), trans-geranic acid, methyl ester (65), linalool (36), calarene (79), α-cubebene (67), α-copaene (70), β-caryophyllene (78), α-humulene (82), β-farnesene (83), germacrene D (90), γ-cadinene (110), farnesol (187), isobutyl isobutyrate (7), 2-methylbutyl isobutyrate (22), 2-undecanone (59), 2-tridecanone (101), and 2-pentadecanone (181). The group 2, consisting of European hops and their descendants, was distinguished by higher prevalence of the monoterpenoid geranyl acetate (71), the sesquiterpenoids α- and β-selinene (95 and 93), trans-α-bergamotene (80), humulene epoxide II (139), the diterpenoid neophytadiene (219), and β- and γ-tocopherols (614 and 615).Group 1 hop cultivars were mainly characterized by the presence of the sesquiterpenoids α-humulene and β-caryophyllene, as well as the monoterpene β-myrcene. These three compounds are recognized as the main constituents of hop essential oils and account together for up to 90% of the total composition, depending on the cultivar. <a href="javascript:void(0);">(2)</a> The predominance of β-myrcene in North American hops, consistently reported in the literature, was confirmed in this study. <a href="javascript:void(0);">(67)</a> Alongside β-myrcene, a higher concentration of linalool was also observed, a monoterpenoid with floral aromatic properties that shares the same biosynthetic pathway as β-myrcene, <a href="javascript:void(0);">(68)</a> which explains their co-occurrence and prominence in the North American lineage group.In addition to this chemical profile, the presence of several other sesquiterpenoids (calarene, α-cubebene, α-copaene, germacrene D, γ-cadinene, and farnesol) was also observed in group 1 hops. The biosynthesis of sesquiterpenes generates a wide diversity of chemical structures, which are primarily modulated by genetic factors through the action of specific sesquiterpene synthase enzymes. <a href="javascript:void(0);">(69)</a> The biosynthetic transformation pathway of this metabolite class may explain the simultaneous and abundant presence of these compounds within the same group, corresponding to hops of North American lineage.Also, group 1 cultivars exhibited high levels of β-farnesene, a sesquiterpene initially isolated from the Czech hop Saaz and traditionally reported as a chemical marker of European hops. <a href="javascript:void(0);">(67,70)</a> Despite this long-standing association, the presence of this metabolite is highly variable among hop cultivars, and its absence has been documented in several traditional European varieties such as Hallertau, Goldings, and Fuggles. <a href="javascript:void(0);">(19)</a> Our results, comprising 76 different hop cultivars, revealed a high predominance of β-farnesene in the group of North American hops. Nevertheless, the results are consistent with previous research demonstrating an inverse relationship between farnesene and selinene levels in hops. These findings were also observed in our study, considering that α- and β-selinenes were predominantly detected in the European hop group. <a href="javascript:void(0);">(67)</a> Interestingly, selinenes have been described as effective chemical markers for the identification of hop cultivars, with several studies reporting high concentrations of these compounds in hops of European genetic background. <a href="javascript:void(0);">(41,51,67,70−72)</a> Thus, our findings reinforce the evidence that selinenes are potential metabolites for distinguishing hops from European lineages.<h4>&nbsp;Conclusions</h4>Our metabolomic approach of hops cultivars based on GC-MS analyses and unsupervised statistical methods demonstrated a consistent correlation with their genetic origin (North American and European lineages). Molecular networking analysis enabled the annotation of 26 metabolites that contributed to differentiating North American from European lineage hop cultivars. Overall, hops of North American lineage exhibited chemical profiles characterized by mono- and sesquiterpenoids, ketones, and esters. Furthermore, the results reinforced the evidence supporting selinenes as relevant markers for European-derived hops and highlighted trans-α-bergamotene as an important chemical marker. This metabolomic technique proved to be an effective strategy for mapping relationships among hops based on their chemical profiles and provided a powerful and comprehensive scientific foundation for the characterization of different hop cultivars commercially available to the brewing industry.

Mapping Metabolomic Relationships of Hop Cultivars in an Ancestral Lineage Context

News from LabRulezGCMS Library - Week 44, 2025

Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 8th December 2025? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND 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://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies and Thermo Fisher Scientific and other document by Shimadzu!<h3>1. Agilent Technologies: High-Precision Determination of Four Greenhouse Gases in the Atmosphere Using the Agilent 8890 GC</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_monitoring_greenhouse_gas_8890_gc_5994_8744en_agilent_0e08f0909f.pdf">Full PDF for download</a></li></ul>Currently, China is actively implementing dual-control measures on total energy consumption and intensity. The Ministry of Ecology and Environment issued "The 14th Five-Year Plan for Ecological and Environmental Monitoring", which specifically highlights the advancement of technologies for direct measurement of carbon emissions. It calls for organizing pilot projects in multiple key industries and enterprises to monitor greenhouse gas emissions such as carbon dioxide and methane, establishing greenhouse gas monitoring networks in key cities, and upgrading the greenhouse gas monitoring capabilities of national atmospheric background stations. As a result, conducting greenhouse gas monitoring within the environmental protection stations of provinces and cities has become a critical task.The use of GC for detecting greenhouse gases has a long history. The conventional methods used gas sampling valve for injection, TCD and FID for sequential detection of CO2 and CH4 , or combining a methanizer with an FID to detect CO2 and CH4 in one analytical channel and detecting N2O on ECD in another analytical channel.2 The analytical precision for CO2, CH4, and N2 O achieved with such configurations typically ranges from 0.2% to 0.5%, but it is difficult to obtain precision better than 0.1%, which does not meet the current requirements for greenhouse gas monitoring accuracy and precision. Moreover, the traditional methods rarely report analytical precision for SF6 at ppt concentration levels. To monitor trace levels of CO2 , CH4, N2O, and ultratrace levels of SF6 on the same GC system with high-precision results, it is necessary to optimize multiple factors included in sample injection, separation, and detection. Furthermore, the system configuration must be flexible enough to allow expansion for future analytical needs, such as incorporating trace CO analysis in air. This work demonstrates how the 8890&nbsp;GC achieves high-precision analysis of four key greenhouse gases by optimizing the injection volume, controlling injection reproducibility, and utilizing multichannel separation and high-sensitivity detectors to ensure the system runs stably in long‑term operation.<h4>Experimental</h4><h5>Instrumental&nbsp;</h5>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=8890+GC">8890 GC</a> is configured as a three-channel/multivalve/multicolumn system, enabling independent analysis of four greenhouse gases—CH4 , CO2 , N2O, and SF6 —within 8 minutes using a single injection. The target compounds are analyzed in separate channels, reducing chromatographic peak broadening and thereby improving sensitivity and&nbsp;repeatability.&nbsp;<ul><li>For CO2 analysis, a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=methanizer">methanizer</a> is used to convert CO2 to CH4 , which is then detected by FID. The system eliminates the need for dehydration through backflushing and venting operation via switching valves, preventing interference from moisture and oxygen in the air and extending the lifespan of the methanizer.&nbsp;</li><li>For N2O and SF6 analysis, the ECD with hidden anode design is used for high‑sensitivity detection and strong anti‑contaminant capability.&nbsp;</li><li>The CH4 analysis is conducted on a single column with a dedicated large injection loop, significantly enhancing detection sensitivity and repeatability, achieving a detection limit for methane as low as single-digit ppb levels.</li></ul><h4>Conclusion&nbsp;</h4>Based on its exceptional pneumatic control and valve-based flow path design, the multivalve/multicolumn Agilent 8890 GC system achieves high-sensitivity and high-precision measurements of four key greenhouse gases. This is accomplished through optimized and precise control of sample flow rate/volume, selection of premium analytical columns, and the use of highly stable and robust FID and ECD. Standard gas samples of CO2 , CH4 , N2 O, and SF6 , with concentrations equivalent to atmospheric levels, were measured continuously 10 times. The measurement precision for these gases was better than 0.05%, 0.1%, 0.1%, and 0.5%, respectively, fully meeting the high-precision monitoring requirements of environmental monitoring stations at various levels for target greenhouse gases. Compared to other monitoring solutions, this approach is based on a stable and reliable gas chromatography platform that is technically easier to master and deploy. It represents a trusted solution developed by Agilent to assist in establishing a greenhouse gas monitoring network in China.<h3>2. Shimadzu: GC/MS Mass Spectra Library - Polymer Additives Library Ver.2</h3><ul><li>Other document</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/c146_e506_d4c93c4d60.pdf">Full PDF for download</a></li></ul><h4>Contains Approximately 5,800 Mass Spectra&nbsp;</h4>This library consists of a library*, which contains 5,775 mass spectra for 590 types of polymer additives and GC/MS pyrolysates from the pyrolysis GC/MS analysis of additives, and a library containing the mass spectra for 65 compounds often targeted for analysis, selected based on their usage in the commercial market, and chemical substance regulatory information. In total, this product contains 5,840 mass spectra, which provides strong support for the analysis of polymer additives. In Ver.2, the compound registration has been newly enhanced, for example vulcanizing accelerators utilized in the rubber filed. Approximately 1,000 spectra have been added.&nbsp;*ADD-MS22B F-Search Additives Library from Frontier Laboratories<h4>Can Be Used with a Variety of GC/MS Systems&nbsp;</h4>The library can be used for a variety of GC/MS applications, including pyrolysis GC/MS, which is widely used for the analysis of additives in polymer materials, and liquid sample injection GC/MS. It supports a wide range of additive analysis for customers.<h4>Library Specifications</h4><ul><li>Registered compounds: 5,775+65&nbsp;</li><li>Registered information: Mass spectrum, retention index for each analytical condition, compound name, molecular weight, compositional formula, structural formula, classification of additives</li><li>Applicable models: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMS-QP2050">GCMS-QP™ serie</a>s, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMS-TQ8050">GCMS-TQ™ series</a> (This library does not include MS/MS spectra.)&nbsp;</li><li>Pyrolyzer (Pyrolysis analysis system): <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=PY-2020D">PY-2020D</a>, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=PY-2020iD">PY-2020iD</a>, and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=43&amp;search=EGA%2FPY-3030D">EGA/PY-3030D</a></li><li>Workstations: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=GCMSsolution">GCMSsolution</a>™ Ver. 4.6 and later, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=LabSolutions+GCMS">LabSolutions™ GCMS</a>™ Ver. 5.131 and later, LabSolutions DB/CS GCMS Ver. 6.131 and later</li></ul><h3>3. Thermo Fisher Scientific: Trace analysis of epichlorohydrin in drinking water using GC-MS coupled with purge and trap</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_003894_gc_single_quad_isq_7610_environmental_an003894_na_en_70123385ae.pdf">Full PDF for download</a></li></ul>Epichlorohydrin (ECH) is a versatile starting material in the production of drugs and polymers and is also used as an insect fumigant and solvent for organic synthesis reactions. ECH-based polymer pipes are commonly used in the production of drinking water due to their durability and resistance to corrosion. However, ECH is known for its high reactivity and toxicity, which poses significant health risks if it contaminates drinking water. Exposure to ECH can cause respiratory issues, skin irritation, and has been classified as a probable human carcinogen.1Due to these risks, many countries have imposed strict limits on the amount of ECH allowed in drinking water. Recently, Europe set a minimum detection limit (MDL) of 30 parts per trillion (ppt) for ECH in drinking water.2 Whereas typically required detection limits for most compounds mandated for analysis in Europe can be achieved using methods based on static headspace, the stringent MDL required for ECH requires preconcentration, for example using purge and trap (P&amp;T) technology.3,4 This technology involves purging the water sample with an inert gas to release volatile organic compounds (VOCs), which are then trapped and concentrated for analysis, ensuring reliable detection at very low concentrations. In the United States, the analysis of VOCs in drinking water is mandated by the Environmental Protection Agency (EPA). The EPA requires the use of P&amp;T technology for drinking water analysis to ensure that even trace amounts of harmful compounds like ECH are detected and managed appropriately.&nbsp;The following evaluation describes the use of the ISQ 7610 Single Quadrupole MS system coupled with the Thermo Scientific™ TRACE™ 1610 GC with the Thermo Scientific™ HeSaver-H2 Safer™ split/splitless injector and Teledyne LABS Tekmar Lumin P&amp;T concentrator combined with the AQUATek LVA autosampler for the analysis of ECH in drinking water.<h4>Experimental</h4><h5>GC-MS parameters&nbsp;</h5>A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/486">TRACE 1610 GC</a> was coupled to the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/484">ISQ 7610 MS</a> equipped with the Thermo Scientific™ NeverVent ™ vacuum probe interlock (VPI) and an ExtractaBrite ion source. A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1773">Thermo Scientific™ TraceGOLD™ TG-VMS column</a>, 30 m × 0.25 mm, 1.4 µm film (P/N 26080-3320) was used for compound separation. The HeSaver-H2 Safer SSL injector was utilized to reduce the carrier gas consumption by decoupling the gas used for the chromatographic separation from the gas used to pressurize the inlet and maintain split and purge flows. The critical separations were maintained with a run time of under 15 minutes.For this analysis, the ISQ 7610 MS was operated in Selected Ion Monitoring (SIM) mode for increased selectivity, as required for this application. Extended method parameters for the ISQ 7610 MS are shown in Table 2.<h5>Instrument control and data processing&nbsp;</h5>Data was acquired, processed, and reported using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76">Thermo Scientific™ Chromeleon™ Chromatography Data System (CDS)</a>. This software can control both the GC-MS system and the Tekmar Lumin P&amp;T with the AQUATek LVA. This allows a single software to be utilized for the full workflow, simplifying the instrument operation. This application note is available for download via the Thermo Scientific™ AppsLab Library, which contains all the parameters needed to acquire, process, and report the analytical data for analysis of ECH.5<h4>Conclusion&nbsp;</h4>This study demonstrates the capability of the Tekmar Lumin P&amp;T with the AQUATek LVA system connected to the ISQ 7610 Single Quadrupole GC-MS to detect and quantify low-level ECH in drinking water samples, in compliance with EPA requirements.&nbsp;<ul><li>Utilizing the Tekmar Lumin P&amp;T’s ability to purge with nitrogen, along with using the HeSaver-H₂Safer SSL injector, nearly four times less helium was consumed during the analysis without sacrificing system performance.&nbsp;</li><li>The linearity of the calibration curve from 30 ppt to 5,000 ppt passed method requirements.</li><li>The application proved robust during an extended study with 20 samples of a 1,000 ppt ECH standard injected over a series of 124 injections, obtaining 4.4% precision and 118% accuracy of the recovery.</li></ul>

News from LabRulezGCMS Library - Week 50, 2025

Insecticide-Induced Perturbations in Sugar Metabolism and Alkaloid Biosynthesis in Coffea arabica (L.) Seedlings

This study presents an analytical strategy combining QuEChERS extraction with low-temperature partitioning (LTP) for the determination of multiclass pesticide residues in cocoa beans by GC–MS. Extraction conditions were optimized using a fractional factorial design, resulting in a simple and efficient workflow suitable for complex cocoa matrices.The validated method showed satisfactory limits of detection and quantification, recoveries within 70–120%, and good overall performance. Application to cocoa samples from Brazil detected permethrin in several samples, demonstrating that the QuEChERS–LTP approach is an effective and reliable cleanup strategy for routine pesticide residue analysis in cocoa.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360">QuEChERS Combined with Low-Temperature Partitioning and GC–MS as an Analytical Strategy for the Determination of Multiclass Pesticide Residues in Cocoa Beans</a></h4>Priscilla M. de Freitas Machado, Madson M. Nascimento, Paulo R. R. Mesquita, Manuela B. Nascimento, Lilian Lefol N. Guarieiro, Gisele O. da Rocha, Jailson B. de Andrade, Raildo M. de Jesus*ACS Omega 2025, 10, 51, 63464–63473<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsomega.5c10360">https://doi.org/10.1021/acsomega.5c10360</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.Cocoa is best known as the primary raw material for chocolate but is also processed into products such as cocoa butter, biscuits, cocoa honey, and cocoa powder. Cocoa powder is derived from roasted, pressed, and milled nibs, containing on average 20% (w/w) lipids on a dry basis and less than 9% moisture. <a href="javascript:void(0);">(3,4)</a>According to the Brazilian Health Regulatory Agency (ANVISA), pesticides such as atrazine, bifenthrin, cyproconazole, metalaxyl, tebuconazole, trifloxystrobin, and permethrin are approved for use in Brazil. <a href="javascript:void(0);">(13)</a> However, among these, only metalaxyl, trifloxystrobin, and tebuconazole are authorized by the European Union. <a href="javascript:void(0);">(14)</a> Thus, investigating these contaminants in cocoa samples is of utmost importance, as Brazil is one of the largest cocoa exporters, supplying cocoa commodities to South America, the United States, and the European Union. <a href="javascript:void(0);">(2)</a> Furthermore, Organochlorine pesticides, in particular, belong to the class of persistent organic pollutants (POPs). Although banned in many countries, they continue to be used in some developing nations because of their chemical stability, resistance to degradation, volatility, and high lipophilicity, which promote bioaccumulation and neurotoxicity. <a href="javascript:void(0);">(15)</a> Epidemiological evidence also associates their exposure with Parkinson’s disease, certain cancers, diabetes, and endometriosis. <a href="javascript:void(0);">(16)</a>Residues of pesticides in cocoa have been reported in various studies. <a href="javascript:void(0);">(17)</a> Analyzing pesticide residues in cocoa presents analytical challenges due to the matrix’s complexity, comprising high lipid content, fatty acids and esters, sugars, polyphenols, and caffeine. These components can interfere with extraction efficiency and contaminate analytical instrumentation. <a href="javascript:void(0);">(18)</a> Therefore, efficient sample preparation methods incorporating effective cleanup steps are essential for accurate residue determination.Several techniques have been employed for multiresidue pesticide analysis in cocoa. For instance, Idowu et al. <a href="javascript:void(0);">(19)</a> quantified 14 organochlorines using Soxhlet extraction and silica/Na2SO4 cleanup, with GC ECD detection. Okoffo et al. <a href="javascript:void(0);">(20)</a> determined 13 organophosphates and 9 pyrethroids employing SPE cartridges (Envi-carb/LC-NH2 and Bond Elute C18), acetonitrile extraction, and GC PFPD or GC ECD quantification. Yusiasih et al. <a href="javascript:void(0);">(21)</a> applied dispersive SPE with PSA, Florisil, and MgSO4 for pyrethroids, followed by GC ECD and GC–MS.Therefore, this study aimed to evaluate the effectiveness of a QuEChERS–LTP analytical procedure for the determination of multiclass pesticide residues in cocoa beans. The factors that affect the QuEChERS–LTP procedure efficiency were optimized. After validation, the procedure was employed to investigate the occurrence of 15 multiclass pesticides in cocoa samples.<h4>Experimental Section</h4><h5>Instrumental Apparatus</h5>Pesticide separation and identification were performed on a Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GC-MS-QP2010SE">GC-MS-QP2010SE</a> system (Kyoto, Japan) equipped with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=AOC-20i">AOC-20i autosampler</a>. Separation was achieved using an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=DB-5MS">Agilent DB-5MS capillary column</a> (30 m × 0.25 mm × 0.25 μm) with a stationary phase of 5% phenyl/95% dimethylpolysiloxane. The instrumental conditions were based on Nascimento et al. <a href="javascript:void(0);">(49)</a> and they are summarized in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl4">Table 4</a>.For quantification, the base peak was used, while two additional ions were monitored for confirmation (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c10360/suppl_file/ao5c10360_si_001.pdf">Table S2</a>) in accordance with SANTE guidelines. <a href="javascript:void(0);">(50)</a> A GC–MS/SIM chromatogram illustrating the separation of all target pesticides is presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig5">Figure 5</a>.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_5_Extracted_ion_chromatogram_EIC_in_SIM_mode_of_the_target_pesticides_f2461c14f4.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 5. Extracted ion chromatogram (EIC) in SIM mode of the target pesticides obtained from the injection of a mixed standard solution at 100 μg L–1. Analytes are listed in order of elution: [1] Molinate (6.18 min); [2] α-HCH (8.15 min); [3] atrazine-d5 (8.69 min); [4] atrazine (8.75 min); [5] diazinon (9.31 min); [6] disulfoton (9.67 min); [7] dimethachlor (10.60 min); [8] metalaxyl (11.25 min); [9] p,p′-DDE (15.82 min); [10] cyproconazole I (16.50 min); [11] cyproconazole II (16.56 min); [12] p,p′-DDD (17.29 min); [13] ethion (17.34 min); [14] trifloxystrobin (18.43 min); [15] tebuconazole (19.13 min); [16] bifenthrin (20.47 min); [17] permethrin I (23.92 min); [18] permethrin II (24.18 min)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_5_Extracted_ion_chromatogram_EIC_in_SIM_mode_of_the_target_pesticides_f2461c14f4.jpg 245w," sizes="100vw" width="2080" height="835"><h5>Application of the Proposed Procedure to Real Samples</h5>In a 50 mL Falcon tube, 2.0 g of sample was weighed using an analytical balance (AUX320, Shimadzu, Kyoto, Japan). A total of 2.0 g of salt (NaCl and Na2SO4 in a 1:1 w/w ratio) was added, followed by an aliquot of the atrazine-d5 solution (surrogate standard) to yield a final concentration of 250 μg kg–1. A mixture of acetonitrile (8.0 mL) and ultrapure water (2.0 mL) was then added. The system was vortexed (K40-10208, Kasvi, São Paulo, Brazil) for 3.0 min at 2500 rpm and centrifuged (MPW-351R, MPW Med. Instruments, Warsaw, Poland) for 10.0 min at 15 °C and 10,000 rpm. After phase separation, approximately 6.0 mL of the supernatant was transferred to a Falcon tube containing 150.0 mg of PSA sorbent. The mixture was vortexed again for 3.0 min at 2500 rpm and centrifuged for an additional 10.0 min under the same conditions. The tubes were then placed in the freezer for 24 h. Once the aqueous phase had frozen, the organic extract containing the analytes was collected, filtered through a Cytiva Whatman MiniUniprep G2 vial with a 0.20 μm membrane filter (Marlborough, Massachusetts, USA), and injected into the GC–MS system (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig6">Figure 6</a>).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_6_Schematic_representation_of_the_extraction_procedure_using_the_Qu_E_Ch_ERS_LTP_procedure_for_pesticide_extraction_in_cocoa_powder_samples_ed58549bcf.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 6. Schematic representation of the extraction procedure using the QuEChERS–LTP procedure for pesticide extraction in cocoa powder samples." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_6_Schematic_representation_of_the_extraction_procedure_using_the_Qu_E_Ch_ERS_LTP_procedure_for_pesticide_extraction_in_cocoa_powder_samples_ed58549bcf.jpg 223w," sizes="100vw" width="1458" height="1022"><h4>Results and Discussion</h4><h5>Evaluation of the Effect of Low and Ultralow Temperatures on the Analytical Response</h5>Studies reporting the use of LTP generally employ a conventional freezer, which typically reaches −18 ± 2 °C as the minimum temperature. In this equipment, effective phase separation can require 8 to 24 h. <a href="javascript:void(0);">(35−37)</a> In contrast, ultralow temperature freezers (ultrafreezers) can reach −80 °C, leading to a substantial reduction in partitioning time. In this study, tests performed with the conventional freezer achieved satisfactory partitioning after 24 h, whereas in the ultrafreezer, complete partitioning was observed in only 20 min.Considering the analytical response in terms of peak area (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig3">Figure 3</a>), higher signals were obtained when LTP was performed in a conventional freezer rather than an ultrafreezer. This behavior can be attributed to the partitioning mechanism. <a href="javascript:void(0);">(32)</a> Partitioning depends on cooling and the subsequent freezing of the aqueous phase. In the conventional freezer, the slower freezing rate provides sufficient time for the system to approach thermodynamic equilibrium, favoring analyte diffusion between phases. Under these conditions, analyte molecules have more time to migrate and distribute optimally before complete solidification. In contrast, the rapid freezing in the ultrafreezer may trap analytes in a nonideal distribution, reducing the efficiency of the partitioning process <a href="javascript:void(0);">(32)</a> (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#fig3">Figure 3</a>). According to these observations, the conventional freezer was selected for subsequent experiments.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Figure_3_Comparison_of_chromatographic_peak_areas_of_pesticides_after_partitioning_in_the_conventional_freezer_versus_the_ultrafreezer_9598bcfec3.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Figure 3. Comparison of chromatographic peak areas of pesticides after partitioning in the conventional freezer versus the ultrafreezer." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Figure_3_Comparison_of_chromatographic_peak_areas_of_pesticides_after_partitioning_in_the_conventional_freezer_versus_the_ultrafreezer_9598bcfec3.jpg 175w," sizes="100vw" width="996" height="891"><h5>Application to Real Samples</h5>Fifteen samples were analyzed in triplicate, comprising two from the State of Pará and 12 from the State of Bahia, located in the North and Northeast regions of Brazil, respectively. Among the 15 pesticides investigated, only permethrin isomers were quantified in four samples (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl3">Table 3</a>). The herbicide molinate was also detected in three samples; however, its concentration was below LOQ. Permethrin isomers concentrations ranged from 17.1 ± 0.3 to 49.6 ± 1.8 μg kg–1. These values are below the MRL established by European Union regulation, which is 100 μg kg–1. <a href="javascript:void(0);">(42)</a> Although permethrin is not permitted in the European Union, <a href="javascript:void(0);">(42)</a> it remains authorized for use as insecticide in Brazil, particularly in crops such as cotton, rice, coffee, citrus, cabbage, beans, tobacco, maize, soybean, tomato, wheat, and grape. <a href="javascript:void(0);">(13)</a> However, no MRL values have been established for cocoa beans under Brazilian legislation. <a href="javascript:void(0);">(13)</a> <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsomega.5c10360#tbl3">Table 3</a> shows the concentration of detected compounds in the cocoa samples.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Omega_2025_10_51_63464_63473_Table_3_Mean_Concentrations_mg_kg_1_Standard_Deviation_of_Target_Pesticides_Detected_in_This_Study_2427955699.jpg" alt="ACS Omega 2025, 10, 51, 63464–63473: Table 3. Mean Concentrations (μg kg–1 ± Standard Deviation) of Target Pesticides Detected in This Study" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Omega_2025_10_51_63464_63473_Table_3_Mean_Concentrations_mg_kg_1_Standard_Deviation_of_Target_Pesticides_Detected_in_This_Study_2427955699.jpg 196w," sizes="100vw" width="1070" height="851">To assess method performance for pesticides not detected in the cocoa samples, one of the analyzed samples was fortified with a mixed standard solution at concentrations of 50, 250, and 500 μg kg–1. The fortified analytes were subsequently detected in the chromatograms presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsomega.5c10360/suppl_file/ao5c10360_si_001.pdf">Figures S2–S4</a>, indicating good response of the proposed method.<h4>Conclusions</h4>The QuEChERS–LTP procedure was successfully developed for the determination of 16 multiclass pesticide residues in cocoa beans. For the first time, ultralow temperature (−80 °C) was applied to accelerate the low-temperature partitioning process during pesticide extraction from cocoa beans. Nevertheless, conventional freezing at −12 °C proved to be more effective for the extraction of the target analytes.Evaluation of the matrix effect demonstrated that matrix-matched analytical curves, constructed in an analyte-free cocoa bean extract, were more effective in compensating for matrix interferences than the use of analyte protectants.The pyrethroid permethrin was quantified in four cocoa samples. However, the concentrations did not exceed the MRL established by European Union legislation.Overall, the QuEChERS–LTP procedure proved to be a promising approach for pesticide residue determination, as it requires smaller volumes of solvents and reagents, involves fewer cleanup and extraction steps, and shows strong potential for application to other complex matrices.

QuEChERS Combined with Low-Temperature Partitioning and GC–MS as an Analytical Strategy for the Determination of Multiclass Pesticide Residues in Cocoa Beans

This study compares the metabolomic profiles of dwarf and commercial tomato cultivars using nontargeted GC–MS metabolomics. The dwarf cultivar UFU TOM MC 1 showed elevated levels of osmoprotectants, antioxidants, and stress-responsive amino acids, including myo-inositol and proline.Pathway analysis revealed enrichment in carbohydrate, amino acid, and protein metabolism pathways associated with stress adaptation. These findings highlight the potential of dwarf tomato cultivars for breeding programs focused on improving stress tolerance, resilience, and nutritional quality.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsagscitech.5c00253">Comparative Metabolomic Analysis Reveals Stress-Resilient Metabolites in Dwarf Tomato Plant</a></h4>Brena R. M. Ikehara*, Natália R. Almeida, Gabriel M. Maciel, Ana C. S. Siquieroli, Camila S. de Oliveira, Lucas M. Pereira, Ana L. A. Ribeiro, and Frederico G. PintoACS Agric. Sci. Technol. 2026, 6, 1, 83–91<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsagscitech.5c00253">https://doi.org/10.1021/acsagscitech.5c00253</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>Tomato (Solanum lycopersicum) is one of the most important vegetable crops globally, extensively cultivated and consumed across diverse regions. In 2022, global production was estimated at about 180 million tons, of which roughly 39 million tons were processed by the food industry. <a href="javascript:void(0);">(1)</a> Belonging to the Solanaceae family, along with other important vegetables, tomato fruits offer several health benefits due to their rich range of phytonutrients, including carotenoids, flavonoids, and phenolic acids, and also contribute to strengthening the agroeconomic chain. <a href="javascript:void(0);">(2,3)</a> This fruit is considered a model species due to their diploid genome, filling a niche in the studies of fleshy fruits that cannot be occupied by Arabidopsis. <a href="javascript:void(0);">(4)</a>Tomato plants can be cultivated in temperate to hot climatic zones, but significant amounts of agricultural input and irrigation are required. However, they can be harshly affected by biotic and abiotic stresses such as drought, salinity, extreme temperatures, and attacks by herbivores or pathogens, compromising productivity and fruit quality. <a href="javascript:void(0);">(5)</a> The impacts of global warming on temperatures threaten the development and production of many crops worldwide, including tomato plants, and therefore food and nutrition security. <a href="javascript:void(0);">(6)</a> For example, low humidity triggers calcium depletion and increases soil salinity, leading to plant diseases and greatly reducing the tomato crop yield. High temperatures also affect important steps for fruit development, including fruit setting, flowering, and ripening, further reducing tomato yield. <a href="javascript:void(0);">(7,8)</a>To reduce the consequences of these changes on tomato production, several breeding programs use wild species, such as S. pimpinellifolium, S. chileno, S. peruvianum, S. pennellii, and S. hirsutum, along with improved varieties S. lycopersicum var. Lycopersicum, for crosses that resulted in enhanced biotic and abiotic resistances. In the search for resistance genes in tomatoes, plants with dwarfism genes have stood out in recent years. <a href="javascript:void(0);">(9,10)</a> Besides being easy to work with, due to their monogenic recessive heredity, they are known for being capable of bringing positive morphological and physiological changes, such as smaller plant architecture, shorter internodes, resistance to pests <a href="javascript:void(0);">(11,12)</a> and diseases, <a href="javascript:void(0);">(13)</a> and saline and water stress. <a href="javascript:void(0);">(14)</a>While recognizing the benefits of tomato plants with the dwarfism gene, such as the production of hybrids with higher yield and resistance, <a href="javascript:void(0);">(15)</a> there are still gaps in the germplasm of this species regarding their chemical composition, including primary and secondary metabolites in leaflets, fruits, and roots. Addressing these gaps requires research focusing on elucidating the chemical composition of tomato varieties, which allows describing their nutritional potential. In order to achieve this, metabolomics, a set of analytical techniques applied to identify substantial amounts of metabolites in biological samples, is employed. Specifically, a nontargeted metabolomics approach, through analyses in gas chromatography coupled to mass spectrometry (GC-MS), has the potential to help better understand the plant metabolome and explain the correlation of metabolites with quality and yield, generating a greater understanding of the physiological and biochemical processes of the dwarf tomato plant. <a href="javascript:void(0);">(16,17)</a>This research aimed to investigate the chemical composition of leaflets from different tomato cultivars through GC-MS metabolomic analysis. The main goal was to better understand the differences and advantages of genetic variation, adaptability, and resilience of dwarf and commercial tomato cultivars through metabolomic analysis of their leaflets.<h4>Experimental Section</h4><h5>Gas Chromatography-Mass Spectrometry Analyses</h5>The analysis was performed using gas chromatography coupled with a mass spectrometer (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-QP2010">GCMS-QP2010</a>, Shimadzu, Kyoto, Japan), equipped with a DB-5MS capillary column (30 m × 250 μm internal diameter). The sample injection temperature used was 280 °C, with a constant helium gas flow of 1.0 mL/min throughout the process. The initial temperature for chromatographic separation was 80 °C, which was maintained for 2 min. Subsequently, the temperature was increased by 5 °C every minute, up to 290 °C, which was maintained for 5 min. The injection volume was 1 μL with a split of 20:1. The interface and ion source temperatures used were 250 °C. The detector voltage was 1.2 kV, and the electron impact ionization mode was selected for the ionization of metabolites at 70 eV. An alkane standard (C10–C40) was used for the quality control and retention index calculation.<h4>Results</h4>The metabolomic analysis on GC-MS generated the chromatogram (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsagscitech.5c00253/suppl_file/as5c00253_si_001.pdf">Figure S1</a>), in which 45 compounds with significantly differentially expressed among UFU MC TOM 1, Santa Clara, and Paron Set (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsagscitech.5c00253#fig1">Figure 1</a>A) were annotated, according to analysis of variance (ANOVA) (Fisher’s LSD test, p &lt; 0.05, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsagscitech.5c00253/suppl_file/as5c00253_si_001.pdf">Table S1</a>) and shown as a heatmap (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsagscitech.5c00253#fig1">Figure 1</a>B). Notably, the metabolomic profile of the UFU MC TOM 1 cultivar is statistically different from the others, as confirmed by Partial Least Squares Discriminant Analysis (PLS-DA) with values of R2 = 0.9940 and Q2 = 0.7928 (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsagscitech.5c00253#fig1">Figure 1</a>C). PLS-DA analysis, using compounds with significant differences in their abundances, showed a distinct clustering for each group, with components 1 and 2 explaining 91.4 and 5.4% of the total variance, respectively (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acsagscitech.5c00253/suppl_file/as5c00253_si_001.pdf">Figure S2</a>).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Agric_Sci_Technol_2026_6_1_83_91_Figure_1_A_Visual_comparison_of_the_three_cultivars_e7fd3114e0.jpg" alt="ACS Agric. Sci. Technol. 2026, 6, 1, 83–91: Figure 1. (A) Visual comparison of the three cultivars. (B) Heatmap of the mean intensity levels across the analyzed groups: UFU MC TOM 1 (yellow), Paron Set (red), and Santa Clara (green). (C) PLS-DA plot, accounting for 37.3% of the observed variation in group separation." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Agric_Sci_Technol_2026_6_1_83_91_Figure_1_A_Visual_comparison_of_the_three_cultivars_e7fd3114e0.jpg 194w," sizes="100vw" width="2100" height="1691">The divergent compounds between the groups underwent pathway analysis enrichment, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The ten metabolic pathways identified as significantly impacted were galactose metabolism (p = 0.0003); valine, leucine, and isoleucine biosynthesis (p = 0.0023); cyanoamino acid metabolism (p = 0.0051); glyoxylate and dicarboxylate metabolism (p = 0.0051); glycine, serine, and threonine metabolism (p = 0.0074); sulfur metabolism (p = 0.0145); butanoate metabolism (p = 0.0185); alanine, aspartate, and glutamate metabolism (p = 0.0302); and biosynthesis of unsaturated fatty acids (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsagscitech.5c00253#fig2">Figure 2</a>).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Agric_Sci_Technol_2026_6_1_83_91_Figure_2_Metabolic_pathway_analysis_overview_c2744a562b.jpg" alt="ACS Agric. Sci. Technol. 2026, 6, 1, 83–91: Figure 2. Metabolic pathway analysis overview. The analysis took into consideration the significant metabolites identified previously in this work. The color of the circles represents −log(p), with red being the highest, while the size of each circle is proportional to the impact of the pathway." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Agric_Sci_Technol_2026_6_1_83_91_Figure_2_Metabolic_pathway_analysis_overview_c2744a562b.jpg 196w," sizes="100vw" width="1000" height="799"><h4>Discussion</h4>Several metabolites were significantly more abundant in UFU MC TOM 1, revealing distinct metabolic profiles among the evaluated cultivars. These metabolites encompass various chemical classes, primarily amino acids, carbohydrates, and organic acids. As the experiment was carried out under controlled and uniform conditions, without the imposition of abiotic or biotic stress, the observed variations represent relative quantitative distinctions between cultivars. Consequently, potential links to stress responses or other biological functions are inferred from the literature rather than demonstrated experimentally.Previous studies have demonstrated that the UFU TOM MC 1 cultivar displays broad-spectrum resistance to a variety of pests, with these defense responses being largely attributed to elevated acyl sugar concentrations in the leaf tissue. <a href="javascript:void(0);">(11,12)</a> The metabolite profile identified in UFU TOM MC 1 indicates the presence of metabolic pathways potentially linked to resistance against multiple biotic stresses, highlighting its value for future functional and breeding studies. Currently, tomato pest management remains largely dependent on insecticide application. <a href="javascript:void(0);">(53)</a> The insights gained from this study indicate that advancing the development of pest-resistant tomato cultivars could markedly decrease the reliance on pesticides, promoting more sustainable production systems. Previous studies have reported UFU MC TOM 1 as resistant to Xanthomonas euvesicatoria pv perforans, but the underlying mechanisms remain unclear. <a href="javascript:void(0);">(13)</a>The pronounced differences in metabolites and metabolic pathways between UFU TOM MC 1 and commercial cultivars offer important evidence of the superior resilience to biotic and abiotic stresses of this dwarf tomato lineage. In general, the accumulation of osmoprotectants, antioxidants, and stress-responsive amino acids, along with the activation of defense-related pathways, sheds further light on the dwarf genetic resource for breeding programs aimed at improving crop resilience under changing environmental conditions.Although untargeted metabolomics does not replace the rigorous validation protocols typical of targeted quantitative assays, it provides a robust and widely accepted approach for detecting meaningful metabolic differences. This strategy enables the identification of variations that are more likely to reflect genuine biological differences. While some metabolites may have nutritional relevance, the experimental design does not allow definitive conclusions on this parameter. Although the findings do not provide direct evidence of resistance mechanisms, they advance our understanding of the biochemical and physiological bases of the resilience of UFU TOM MC 1 and generate testable hypotheses about its adaptive potential, which may support tomato productivity, especially in the context of global climate change.</div>

Comparative Metabolomic Analysis Reveals Stress-Resilient Metabolites in Dwarf Tomato Plant

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