Analytical Challenges Research Articles

Process Research Methods for Studying Supply Chains and Their Management

ABSTRACTProcesses are fundamental to supply chains and their management. Yet, traditional research approaches to supply chain management (SCM) reflect only a limited understanding of process, offering accounts that overlook the constitutive role of dynamically interrelated processes and how their interplay over time shapes the trajectories of supply chains. This article argues that a process‐philosophical perspective is better suited as a starting point for identifying, analyzing, and interpreting the fluid and interwoven processes of supply chains and their co‐evolving environments. Drawing on examples from SCM research, the article offers insights into the nature of process‐thinking and process‐theoretical assumptions, including the analytical choices and challenges entailed in process research. Besides providing methodological guidance, the article highlights how process research methods equip SCM scholars with a powerful lens for studying transformational issues in this field, including sustainability, resilience, and the use of digitalization and technology.

Exploring the utility of complementary separations in liquid chromatography

An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in-silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.

Customizing Bayesian multivariate generalizability theory to mixed-format tests.

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

“Green mathematical approaches in fluorescence spectroscopy for quality control and clinical studies”

In the past decade, significant advancements have been made in mathematical spectral approaches for estimating binary and ternary drug combinations in raw materials and dosage forms. These approaches established on mathematical spectral manipulations of mixture analytes, without the need for separation techniques.The idea of using mathematical spectral approaches in fluorescence spectroscopy was resolving analytical issues and reach to biological concentrations thus, Ratio subtraction (RD) and ratio subtraction coupled with constant multiplication (RS-CM) have been proposed in fluorescence spectroscopy to evaluate the content uniformity of Flunarizine dihydrochloride (FLZ) and Propranolol hydrochloride (PRO) combination in raw materials, tablets, and spiked biological fluids (blood-urine). These approaches have demonstrated the ability to overcome the quantitative analytical challenges posed by the high native fluorescence intensity of the major component (PRO) at λex = 286/λem = 341 nm and the low native fluorescence intensity of the minor component (FLZ) at λex = 254/λem = 311 nm. The linearity range for (PRO) was found to be 25–500 ng/mL, and for (FLZ), it was 50–500 ng/mL. However, the spectrum addition technique was required to estimate (FLZ) in mixtures with concentrations less than 300 ng/mL.The overall validity of the proposed approaches was confirmed through adherence to ICH guidelines, and no statistical variation was observed in comparison to official studies. Additionally, the greenness and whiteness of the proposed approaches were evaluated using the Analytical greenness metric (AGREE), Green Solvents Selecting Tool (GSST), and Red-Green-Blue (RGB) algorithm.

Chromatographic Comparison of Commercially Available Columns for Liquid Chromatography in Polar Pesticide Detection and Quantification Using a Score-Based Methodology.

The detection and quantification of polar pesticides in liquid chromatography coupled with mass spectrometry present significant analytical challenges. This study compares the performance of three LC columns (Hypercarb™, Raptor Polar X™, and Anionic Polar Pesticide™) in separating and quantifying eleven polar pesticides in chicken eggs using a score-based methodology. Analytes include glyphosate, its metabolites, and other high-polarity pesticides like Ethephon, Glufosinate, and Fosetyl aluminum, included in the EU's official control plan. Polar pesticides, characterized by high polarity and hydrophilicity, lead to analytical issues such as poor retention and unconventional peak shapes with traditional reversed-phase methods. Their weak interaction with hydrophobic stationary phases complicates separation, necessitating specific stationary phases to enhance retention and selectivity. This study evaluates these columns' efficacy in complex matrices like chicken eggs and other food samples. Chromatographic separation was performed using a UPLC system coupled with a Q-TOF mass spectrometer; extraction and purification involved freeze-out, centrifugation, and filtration steps. The study highlights the critical role of column selection in achieving accurate and reliable separation and quantification of highly polar analytes in matrices of animal origin, offering in the meantime an easy-to-apply methodology of selection for the right determination of the best chromatographic column for different purposes.

Deep learning for enhanced spectral analysis of MA-XRF datasets of paintings.

Recent advancements of noninvasive imaging techniques applied for the study and conservation of paintings have driven a rapid development of cutting-edge computational methods. Macro x-ray fluorescence (MA-XRF), a well-established tool in this domain, generates complex and voluminous datasets that pose analytical challenges. To address this, we have incorporated machine learning strategies specifically designed for the analysis as they allow for identification of nontrivial dependencies and classification within these high-dimensional data, thereby promising comprehensive interrogation. We introduce a deep learning algorithm trained on a synthetic dataset that allows for fast and accurate analysis of the XRF spectra in MA-XRF datasets. This approach successfully overcomes the limitations commonly associated with traditional deconvolution methods. Applying this methodology to a painting by Raphael, we demonstrate that our model not only achieves superior accuracy in quantifying the fluorescence line intensities but also effectively eliminates the artifacts typically observed in elemental maps generated through conventional analysis methods.

Evaluating the Compatibility of Three Aluminum Salt-Adjuvanted Recombinant Protein Antigens (Trivalent NRRV) Combined with a Mock Trivalent Sabin-IPV Vaccine: Analytical and Formulation Challenges.

In this work, we describe compatibility assessments of a recombinant, trivalent non-replicating rotavirus vaccine (t-NRRV) candidate with a mock trivalent Sabin inactivated polio vaccine (t-sIPV). Both t-sIPV and t-NRRV are incompatible with thimerosal (TH), a preservative commonly used in pediatric pentavalent combination vaccines (DTwP-Hib-HepB) distributed in low- and middle-income countries (LMICs), preventing the development of a heptavalent combination. The compatibility of t-NRRV with a mock DTwP-Hib-HepB formulation is described in a companion paper. This case study highlights the analytical and formulation challenges encountered when combining a mock t-sIPV vaccine (unadjuvanted) with Alhydrogel® (AH) adjuvanted t-NRRV. Selective and stability-indicating competition ELISAs were implemented to monitor antibody binding to each of the six antigens (±AH). Simple mixing caused the undesired desorption of t-NRRV from AH with the concomitant binding of t-sIPV to AH. Although the former effect was mitigated by dialyzing sIPV bulks, decreased sIPV storage stability was observed at accelerated temperatures in the bivalent combination with a rank-ordering of P[8] > P[6] > P[4] and sIPV3 > sIPV2 > sIPV1. The compatibility of AH-adsorbed t-sIPV with alternative preservatives was evaluated, and parabens (methyl, propyl) were identified for potential use in this multi-dose bivalent formulation. Along with a companion paper, the lessons learned are discussed to facilitate the future formulation development of pediatric combination vaccines with new antigens.

Authentication of indigenous Brazilian specialty canephora coffees using smartphone image analysis

The prevention of coffee fraud through the use of digital and intelligence-based technologies is an analytical challenge because depending on the adulterant, visual inspection is unreliable in roasted and ground coffee due to the similarity in color and texture of the materials used. In this work, a 3D-printed apparatus for smartphone image acquisiton is proposed. The digital images are used to authenticate the geographical origin of indigenous canephora coffees produced at Amazon region, Brazil, against canephora coffees from Espírito Santo, Brazil, and to capture the adulteration of indigenous samples. The results evidenced that the technology is favorable to identify the geographical origin and adulteration with multiple substances using smartphone technology. Pure coffees were adulterated with arabica coffee, spent coffee ground, low-quality Canephora coffee, coffee husks, açaí, corn, and soybean in increasing proportions of 10, 20, 30, 40, 50, 60, and 70 %. These adulterants were roasted and grounded similarly to Canephora coffees to mimetize a highly-sophisticated fraud. The images were converted into Red-Green-Blue (RGB) fingerprinting and used as analytical response to construct Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) models. A total of 95 % of all target and non-target samples in the test set were correctely identified, aiding producers and consumers in ensuring accurate labeling and supporting traditional communities economically and culturally. Smartphone-based method demonstrated potential to innovate the coffee safety control representing a new analytical tecnology.

Reproducible 3D culture of multicellular tumor spheroids in supramolecular hydrogel from cancer stem cells sorted by sedimentation field-flow fractionation

Three-dimensional (3D) cancer models, such as multicellular tumor spheroids (MCTS), are biological supports used for research in oncology, drug development and nanotoxicity assays. However, due to various analytical and biological challenges, the main recurring problem faced when developing this type of 3D model is the lack of reproducibility. When using a 3D support to assess the effect of biologics, small molecules or nanoparticles, it is essential that the support remains constant over time and multiples productions. This constancy ensures that any effect observed following molecule exposure can be attributed to the molecule itself and not to the heterogeneous properties of the 3D support. In this study, we address these analytical challenges by evaluating for the first time the 3D culture of a sub-population of cancer stem cells (CSCs) from a glioblastoma cancer cell line (U87-MG), produced by a SdFFF (sedimentation field-flow fractionation) cell sorting, in a supramolecular hydrogel composed of single, well-defined molecule (bis-amide bola amphiphile 0.25% w/v) with a stiffness of 0.4 kPa. CSCs were chosen for their ability of self-renewal and multipotency that allow them to generate fully-grown tumors from a small number of cells.The results demonstrate that CSCs cultured in the hydrogel formed spheroids with a mean diameter of 336.67 ± 38.70 µm by Day 35, indicating reproducible growth kinetics. This uniformity is in contrast with spheroids derived from unsorted cells, which displayed a more heterogeneous growth pattern, with a mean diameter of 203.20 ± 102.93 µm by Day 35. Statistical analysis using an unpaired t-test with unequal variances confirmed that this difference in spheroid size is significant, with a p-value of 0.0417 (p < 0.05).These findings demonstrate that CSC-derived spheroids, when cultured in a well-defined hydrogel, exhibit highly reproducible growth patterns compared to spheroids derived from unsorted cells, making them a more reliable 3D model for biological research and drug testing applications.

A graph network-based learnable simulator for spatial-temporal prediction of rigid projectile penetration

Predicting plate penetration by rigid projectiles (PPRP) is crucial in terminal ballistics, with broad applications in civil and military engineering. Empirical and analytical methods face challenges in predicting field variables like displacement and stress in target plates. Although numerical methods offer high accuracy, they suffer from low computational efficiency. Herein, we introduce an efficient data-driven machine learning (ML) method based on graph neural networks (GNNs), named PGN, specifically tailored to address the PPRP problem. Unlike traditional ML methods that establish direct input-output mappings, PGN predicts comprehensive spatial-temporal information pertaining to the projectile-target interaction process. A thorough analysis of PGN's performance in terms of accuracy, computational efficiency and generalization ability was performed. Compared to validated results of numerical simulations, PGN maintained high precision with RMSE for displacement, stress, and strain predictions below 0.5 %, 9.5 %, and 2.1 %, respectively. It also achieved R2 values exceeding 0.92 for the time history of projectile velocity and acceleration, while requiring only 9.8 % of the computation time compared to LS-DYNA. In generalization tests, PGN exhibited remarkable adaptability in tackling challenging scenarios that extend far beyond the training data distribution, with overall RMSE between 11 % and 13 %. Furthermore, we find that the maximum information propagation capacity of a simulated physical system must meet or exceed the information propagation need of the real-world physical phenomenon it aims to replicate. Consequently, an approach was proposed to determine the critical connectivity radius of the massage passing method directly from the wave speed in the target medium, which greatly improved the accuracy and efficiency of PGN.

Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach with Structural Neighboring Mechanism.

Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a structural neighboring mechanism tailored to enhance the prediction and interpretation of infrared spectra. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. Total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

Advancements in Assays for Micro- and Nanoplastic Detection: Paving the Way for Biomonitoring and Exposomics Studies.

Although plastic pollution and exposure to plastic-related compounds have received worldwide attention, health risks associated with micro- and nanoplastics (MNPs) are largely unknown. Emerging evidence suggests MNPs are present in human biofluids and tissue, including blood, breast milk, stool, lung tissue, and placenta; however, exposure assessment is limited and the extent of human exposure to MNPs is not well known. While there is a critical need to establish robust and scalable biomonitoring strategies to assess human exposure to MNPs and plastic-related chemicals, over 10,000 chemicals have been linked to plastic manufacturing with no existing standardized approaches to account for even a fraction of these exposures. This review provides an overview of the status of methods for measuring MNPs and associated plastic-related chemicals in humans, with a focus on approaches that could be adapted for population-wide biomonitoring and integration with biological response measures to develop hypotheses on potential health effects of plastic exposures. We also examine the exposure risks associated with the widespread use of chemical additives in plastics. Despite advancements in analytical techniques, there remains a pressing need for standardized measurement protocols and untargeted, high-throughput analysis methods to enable comprehensive MNP biomonitoring to identify key MNP exposures in human populations. This review aims to merge insights into the toxicological effects of MNPs and plastic additives with an evaluation of analytical challenges, advocating for enhanced research methods to fully assess, understand, and mitigate the public health implications of MNPs.

Food for fish: Challenges and opportunities for quantifying foodscapes in river networks

AbstractRiverine fishes face many challenges including habitat degradation and climate change, which alter the productivity of the riverscapes in which fish live, reproduce, and feed. Understanding the watershed portfolio of foraging and growth opportunities that sustain productive and resilient fish populations is important for prioritizing conservation and restoration. However, the spatiotemporal distribution and availability of fish food are poorly understood relative to other factors such as abiotic habitat quantity and quality (e.g., water temperature). In this paper, we build on the concept of “foodscapes,” and describe three components of food for fish, including abundance, accessibility, and quality. We then discuss methodological advances to help address three key questions: (1) Why is food availability hard to estimate? (2) What are the consequences of uncertainty in food availability estimates? and (3) What approaches are available or emerging for quantifying food available to fish? To address the first question, we characterize data acquisition and analytical challenges; for the second, we demonstrate the importance of evaluating and communicating potential consequences of uncertainty; and for the third, we posit opportunities for future work. Collectively, we highlight the need for greater appreciation of the role food plays in stream fish conservation, especially given its critical influence on responses to warming temperatures.This article is categorized under: Water and Life &gt; Nature of Freshwater Ecosystems Water and Life &gt; Conservation, Management, and Awareness Water and Life &gt; Methods

Revisiting Dissolved Organic Matter Analysis Using High-Resolution Trapped Ion Mobility and FT-ICR Mass Spectrometry.

The molecular level characterization of complex mixtures remains an analytical challenge. We have shown that the integration of complementary, high-resolution, gas-phase separations allows for chemical formula level isomeric content description. In the current work, we revisited the current challenges associated with the analysis of dissolved organic matter using high-resolution trapped ion mobility separation (TIMS) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). In particular, we evaluated the separation capabilities provided by TIMS-MS compared to MS alone, the use of ICR complementary data acquisition (DAQ) systems and transient processing strategies, ICR cell geometries (e.g., Infinity cell vs harmonized cell), and magnetic field strengths (7 T vs 9.4 T vs 21 T) for the case of a Harney River DOM sample. Results showed that the external high-performance DAQ enables direct representation of mass spectra in absorption mode FT (aFT), doubling the MS resolution compared to the default magnitude mode FT (mFT). Changes between half- vs full-apodization result in greater MS signal/noise vs superior MS resolving power (RP); in the case of DOM analysis, a 45% increase in assigned formulas is observed when employing the DAQ half (Kaiser-type)-apodization window and aFT when compared to the default instrument mFT. Results showed the advantages of reprocessing 2D-TIMS-FT-ICR MS data with higher RP and magnetic field chemical formulas generated list acquired (e.g., 21 T led to a 24% increase in isomers reported) or the implementation of alternative strategies.

Transport and Accumulation of Microplastics from Biosolids to Australian Agricultural Soils: Detection of Microplastics Down to 1 μm.

The shift toward sustainable agriculture involves replacing inorganic fertilizers with organic alternatives like biosolids. However, concerns arise over emerging contaminants, such as microplastics (MPs), which remain largely unregulated. Despite their common use in Australia, the transfer of MPs from biosolids to agricultural soils remains largely unknown. Herein, we have investigated the abundance, characteristics, and transport of MPs resulting from biosolids application in two Queensland agricultural sites. MP concentrations were significantly higher in biosolid-amended soils (average of 1137 MPs/kg) than in reference sites (average of 36 MPs/kg), correlating with the volume, time since application, and frequency of biosolids application. MPs > 25 μm were predominantly polyethylene, polypropylene, and poly(methyl methacrylate) fragments (up to 85%). Fibers constitute only 15-30% of MPs and mainly in larger sizes (average 1011 μm), whereas fragments (average 188 μm) and beads (average 72 μm) had smaller size ranges. Despite analytical challenges using Raman spectroscopy, detected smaller MPs (1-25 μm) were fragments composed of polyethylene, polypropylene, and poly(vinyl alcohol). This study provides the first report of MPs down to 1 μm in Australian agricultural soils. Our findings suggest a need to assess the long-term impact of MPs in biosolids on soil health and food safety. We call on policymakers to consider the implementation of effective MP source control strategies and the development of guidelines for acceptable biosolids application rates.

Study of authenticity, quality characteristics and bioactivity in honey samples from different botanical origins and countries

Honey is a widely consumed commodity featuring beneficial nutritional properties resulting in a high market price. Therefore, honey is a target of fraudulent practices and analytical strategies to assess honey quality and origin are highly needed. Within this context, we performed a comprehensive analytical testing for 39 honey samples originating from Greece and other countries. Analytical parameters related to honey quality control (Directive 2001/110/EC) were monitored. In detail, hydroxymethylfurfural (HMF) and diastase activity were measured using spectrophotometric assays. Importantly, 6 samples imported by the American market exceeded the regulatory limit (40 mg/kg). The content of the main honey sugars was monitored using an accredited ultra-high-performance liquid chromatography coupled to evaporative light scattering detector (UHPLC-ELSD) and all samples were in line with the regulatory standards. In addition, chromatographic analysis was performed to determine 23 phenolic compounds as these are bioactive compounds contributing towards the health promoting effects of honey. Besides chemical characterisation, in vitro testing was also performed by monitoring the inhibitory effect of the honey samples towards acetylcholinesterase (AChE) and pancreatic lipase (PNLIP). Indeed, inhibition was noticed and in the case of PNLIP the monitored differences between Greek and samples from other countries were statistically significant. To assess honey sample authenticity, chemometric tools were employed, namely, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). The multivariate analysis revealed that the taxifolin content may be used as a marker for thyme honey from Cyprus while the total content of gallic and protocatechuic acids could be used as a marker for oak honeys. All in all, implementing several different methods based on different principles (both physicochemical and bioanalytical) in a complex analytical matrix such as honey is a great analytical challenge that we successfully faced and comprehensively demonstrated in this study.

Multi-Technique Characterization of Cartonnage and Linen Samples of an Egyptian Mummy from the Roman Period

The historical and cultural significance of artistic works and archaeological artifacts underscores the imperative use of non-destructive testing methods in cultural heritage objects. Analyzing pigments in artwork poses a specific analytical challenge that demands a combination of various techniques to accurately determine chemical compositions. In this context, our work focused on the multi-analytical characterization of samples derived from fragments of a Roman-era Egyptian mummy named Kherima, dating back to around 200 AD. To identify the layers and elemental composition of the pigments used in the decoration, various techniques were employed: X-ray microfluorescence (µXRF), X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), high-resolution optical microscopy (OM), and X-ray computed microtomography (microCT). This multi-analytical approach facilitated the identification of the original pigments in the analyzed mummy fragments, along with insights into the materials used in the ground layer and the techniques applied in artifact manufacturing, indicating their accordance with the historical period and region to which they originally belonged.

Progress in multifactorial single-cell chromatin profiling methods.

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

The Detri2match conceptual framework: Matching detritivore and detritus traits to unravel consumption rules in a context of decomposition

Abstract From soil to freshwater ecosystems, decomposition can be conceived as the result of interactions between organic matter and a diversity of organisms. This function is driven in part by detritivores, invertebrates that feed on detritus or graze on its associated microbes and that have a significant but extremely variable contribution to decomposition. In order to better understand and predict detritivore–detritus pairwise interactions, we propose a conceptual framework, called Detri2match, to study the consumption of detritus by detritivores, using a trait‐matching approach at the individual detritivore level. Here, we focus on the interaction between saprophagous detritivores that fragment plant detritus. We propose a novel definition of a saprophagous detritivore as an animal that consumes plant detritus when its traits match sufficiently the traits of its resource, passing through five interaction facets of consumption. These include (1) a spatial match rule regarding the encounter, (2) a biomechanical match rule regarding ingestion, (3) a digestive match rule regarding assimilation, (4) an energetic match rule regarding the fulfilment of metabolic needs and (5) a nutritional match rule regarding the fulfilment of chemical element needs in adapted proportions. The main goal of this framework is to guide future research to establish generic rules of misunderstood detritus–detritivore pairwise interactions by identifying relevant interaction facets and their key associated traits for both detritivores and detritus. This investigation should be conducted over the temporal variability of trait‐matching constraints throughout the whole decomposition process. Coupled with adequate accumulation of trait information, the Detri2match framework could also facilitate predictions by inference of non‐tested pairwise detritivore–litter interactions. We also outline conceptual, methodological and analytical challenges of this framework. The main challenge would be to scale up these pairwise rules at the detrital network level and to test their genericity, which would contribute to a better understanding of the functioning of the detrital network and its contribution to decomposition. Read the free Plain Language Summary for this article on the Journal blog.