Analytical science advances最新文献

{"title":"What Is the Outlier—Consistent Outlier or Inconsistent Outlier?","authors":"Hiromasa Kaneko","doi":"10.1002/ansa.70030","DOIUrl":"10.1002/ansa.70030","url":null,"abstract":"<p>In the design of molecules, materials and processes, outliers or outlier samples can be included in a dataset when performing machine learning or regression analysis. Although outlier samples with high prediction errors in regression analysis have been divided into bad leverage points and vertical outliers (good leverage points have low prediction errors), this study classifies the outlier samples into consistent outliers (CO) and inconsistent outliers (ICO) for a detailed discussion of outlier samples and their effective utilisation. The relationship between the explanatory variables (<i>x</i>) and dependent variables (<i>y</i>) is consistent with the other samples for CO but not for ICO. Furthermore, an index of ICO-likeness based on triple cross-validation and the mean absolute error is proposed, and a method to determine whether an outlier sample is an ICO or a CO is developed. Data analysis using numerical simulation datasets and a compound dataset with boiling points confirms that the proposed method can appropriately discriminate between ICO and CO. When an outlier sample is determined to be an ICO, the errors in <i>x</i> and <i>y</i> should be checked first for the sample. If no errors exist in <i>x</i> and <i>y</i>, a new <i>x</i> should be added to explain <i>y</i> of the ICO. When an outlier sample is determined to be CO, it is expected that exploring the extrapolation from CO in <i>x</i> will further improve the <i>y</i> values using a model that includes CO.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-Friendly Spectrophotometric Methods for Resolving the Spectral Inference of Benzalkonium Chloride During Alcaftadine and Ketorolac Tromethamine Quantification","authors":"Ola G. Hussein,&nbsp;Mamdouh R. Rezk,&nbsp;Liliya Logoyda,&nbsp;Mina Wadie","doi":"10.1002/ansa.70028","DOIUrl":"10.1002/ansa.70028","url":null,"abstract":"<p>The pharmaceutical industry has recently introduced a new combination of eye drop containing alcaftadine (ALF) and ketorolac tromethamine (KTC) utilized in managing allergic conjunctivitis. This new formulation elevates the necessity for developing sensitive, reliable, rapid and simple spectrophotometric methods to accurately quantify ALF and KTC in their dosage form with full consideration of its preservative benzalkonium chloride. In this study, three simple green spectrophotometric methods were developed to simultaneously determine ALF, KTC, and benzalkonium chloride in their ternary combinations without requiring preliminary separation steps. The expected methods employed unique spectral properties of the mixture including the extension of KTC's spectrum beyond that of ALF. The suggested methods were direct spectrophotometric, absorbance resolution, and factorized zero-order methods. The proposed methods were assessed for validity in accordance with ICH guidelines and were determined to be linear within concentration ranges of 1.0–14.0 µg/mL for ALF and 3.0–30.0 µg/mL for KTC. A statistical comparison between the developed methods versus the reported and official methods revealed lack of any difference in terms of accuracy and precision. The work was also considered a successful attempt to stick to <i>Green Analytical Chemistry</i> principles and preserve our environment via using water as the main solvent. This was reflected by the highest greenness score of the proposed methods upon adopting state-of-the art assessment metric tools including Complementary Green Analytical Procedure Index (ComplexGAPI) and analytical GREEnness (AGREE). Considering these benefits, our developed methods were demonstrated to be green, sensitive, and accurate making it suitable for routine drug analysis.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling Particulate Matter (PM10) Variations During Transboundary Haze Events Using a Modified Quantile Regression Approach","authors":"Nur Alis Addiena A. Rahim,&nbsp;Norazian Mohamed Noor,&nbsp;Izzati Amani Mohd Jafri,&nbsp;Ahmad Zia Ul-Saufie,&nbsp;Mohamad Anuar Kamaruddin,&nbsp;Mohd Remy Rozainy Mohd Arif Zainol,&nbsp;Andrei Victor Sandu,&nbsp;Petrica Vizureanu,&nbsp;Gyorgy Deak","doi":"10.1002/ansa.70027","DOIUrl":"10.1002/ansa.70027","url":null,"abstract":"<p>Severe haze episodes in Southeast Asia, largely attributed to transboundary pollution from neighbouring countries, have led to substantial environmental degradation and adverse health effects. This study presents the development of a novel air quality forecasting model specifically developed to predict particulate matter with a diameter of less than 10 µm (PM₁₀) concentrations 1 to 3 days in advance during transboundary haze events in Malaysia. The innovation lies in the integration of quantile regression (QR) with advanced feature selection techniques—namely, Relief-based ranking, correlation-based selection and principal component analysis (PCA)—to form modified predictive models. These hybrid models, referred to as QR-Relief, QR-correlation and QR-PCA, demonstrated superior performance over traditional QR and multiple linear regression models across four urban locations: Klang, Melaka, Pasir Gudang and Petaling Jaya. Model accuracy was evaluated using selected performance metrics, including mean absolute error, normalized absolute error and root mean square error. The results indicate that reducing the dimensionality of input variables through analytical feature selection significantly improves predictive reliability. Furthermore, model validation using an independent dataset from 2019 confirmed their real-world applicability. This methodological advancement provides a robust analytical framework for developing early warning systems during haze events, offering valuable decision-support tools for environmental and public health management.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Diagnosis of Acute Kidney Injury in Liver Transplant Patients by an Untargeted Metabolomic Approach","authors":"Larissa C. Motta,&nbsp;Camila M. de Almeida,&nbsp;Gabriely S. Folli,&nbsp;Marcos V. V. Lyrio,&nbsp;Bruno M. M. Siqueira,&nbsp;José Brango-Vanegas,&nbsp;Rosiane A. Costa,&nbsp;Octávio L. Franco,&nbsp;Ana Paula C. Figueiredo,&nbsp;Vandack A. Nobre Junior,&nbsp;Paulo R. Filgueiras,&nbsp;Valério G. Barauna,&nbsp;Paula F. Vassallo,&nbsp;Wanderson Romão","doi":"10.1002/ansa.70025","DOIUrl":"10.1002/ansa.70025","url":null,"abstract":"<p>Acute kidney injury is a common complication in patients undergoing orthotopic liver transplantation, being associated with increased mortality and prolonged hospitalization. This study aimed to develop a novel analytical strategy for the early diagnosis of acute kidney injury in liver transplant recipients by combining an untargeted metabolomic approach with advanced chemometric techniques. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with partial least squares discriminant analysis, we successfully classified patient samples according to the severity of renal dysfunction. This integration of high-resolution mass spectrometry with multivariate analysis offers a minimally invasive, cost-effective and precise diagnostic method, requiring only a single serum sample per analysis. The study analysed 132 serum samples from 59 patients at three postoperative time points (0–6 h, 24 h and 48 h). The mass spectra revealed distinct metabolic profiles across different stages of acute kidney injury, highlighting biochemical shifts related to oxidative stress, inflammation and impaired renal function. The discriminant models demonstrated high sensitivity (80%–98%) and specificity (80%–97%), especially in distinguishing advanced stages of kidney injury, where metabolic alterations were most evident. These results support the use of this analytical workflow as a promising tool for early detection and monitoring of acute kidney injury, representing a significant advance in the application of untargeted metabolomics to clinical diagnostics. This work lays the foundation for future clinical translation of metabolomics-based diagnostics in liver transplantation, enabling more effective and timely therapeutic interventions.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 2","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Liquid Chromatography–Mass Spectrometry (LC–MS) Applications in Biological and Applied Sciences","authors":"Samyah Alanazi","doi":"10.1002/ansa.70024","DOIUrl":"10.1002/ansa.70024","url":null,"abstract":"<p>Liquid chromatography–mass spectrometry (LC–MS) is a highly sophisticated analytical technique that has become indispensable for sample analysis across various scientific domains. In recent years, LC–MS has emerged as a cornerstone technology in comparative replicate sample analysis, particularly in the fields of proteomics, lipidomics and metabolomics. This review provides a comprehensive and in-depth exploration of LC–MS applications in biological, and applied sciences from the key aspects include (1) a historical perspective on LC–MS development and advancements; (2) its critical role in forensic investigations and environmental science; (3) its applications in food safety, quality control and pharmaceutical analysis and (4) its significance in the examination of pharmaceuticals and biological specimens. By presenting a broad and integrated understanding of LC–MS, this review underscores its transformative impact on life sciences and its expanding role in scientific research and innovation.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results","authors":"Yuta Sakai,&nbsp;Shota Horikawa,&nbsp;Mamoru Aizawa,&nbsp;Hiromasa Kaneko","doi":"10.1002/ansa.70021","DOIUrl":"10.1002/ansa.70021","url":null,"abstract":"<p>The proportion of older people in the world's total population is expected to increase. Bone diseases are more prevalent in older people; therefore, the number of patients with such diseases is expected to increase worldwide. Artificial bone is a biomaterial used in the treatment of bone diseases. Artificial bones with high bone formation rates are desired; however, the results of artificial bone implantation vary. There are also ethical issues associated with animal experiments. Our purpose in this study is to predict the variation in bone formation rates. We created multiple sub-datasets and constructed a machine learning model to predict the variation in bone formation rates by considering the results of multiple measurements. We also propose a metric, Jensen–Shannon (JS) divergence, to evaluate the accuracy of the model for predicting variation. We tested the validity of JS divergence by comparing combinations of explanatory variables. Additionally, we found an optimal combination of explanatory variables to construct a model with high predictive accuracy. We expect that the prediction of variation will be useful for improving the practical development of materials and medicines, such as artificial bones, for which stable effects are required, regardless of the individual.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current PAT Landscape in the Downstream Processing of Biopharmaceuticals","authors":"Pavithra Sathiyapriyan,&nbsp;Shatanik Mukherjee,&nbsp;Thomas Vogel,&nbsp;Lars-Oliver Essen,&nbsp;David Boerema,&nbsp;Martin Vey,&nbsp;Uwe Kalina","doi":"10.1002/ansa.70013","DOIUrl":"10.1002/ansa.70013","url":null,"abstract":"<p>Protein-based therapeutics have revolutionized modern medicine, addressing complex diseases with unprecedented specificity and efficacy. The rising demand for biologics has driven the evolution of biomanufacturing practices to ensure consistent quality and operational efficiency. Traditional batch testing, with its inherent limitations, is being replaced by quality by design (QbD) frameworks and process analytical technology (PAT). PAT facilitates real-time monitoring and control by integrating advanced analytical tools and data-driven methodologies to optimize downstream processing (DSP). This review highlights the recent advancements in PAT tools, including spectroscopy, chromatography and biosensors. Spectroscopic techniques provide rapid, non-invasive measurements, while biosensors offer high specificity for monitoring critical quality attributes. Additionally, the integration of chemometric modelling and digital twins enables predictive analytics and enhances process control, paving the way for real-time release (RTR) of the product. Despite challenges in regulatory compliance and technology integration, innovations in automation and machine learning are bridging these gaps, accelerating the transition to intelligent manufacturing systems. This article provides a comprehensive evaluation of emerging analytical technologies and strategic insights into their integration, aiming to support the biopharmaceutical industry's shift towards robust, continuous and adaptive manufacturing paradigms.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Waste PET Plastic-Derived UIO-66(Zr)-MOF Adsorbent for Dispersive Solid Phase Extraction and Removal of Penicillin Antibiotics Water Systems","authors":"Inga Skans,&nbsp;Mthokozisi Mnguni,&nbsp;Philiswa Nosizo Nomngongo","doi":"10.1002/ansa.70023","DOIUrl":"10.1002/ansa.70023","url":null,"abstract":"<p>The extensive use or misuse of broad-spectrum antibiotics such as penicillins and their existence in various water bodies is still one of increasing global concern due to their persistent nature, which could potentially lead to human health and environmental risks. Recycling or upcycling plastic waste to value-added products such as metal-organic frameworks (MOF) to solve other environmental problems, such as water pollution, has received more attention. As a result, UiO-66(Zr)-MOF derived from recycled waste PET bottles (PET-derived UiO-66(Zr)-MOF) was investigated for enrichment and removal of amoxicillin (AMX) and penicillin G (PNG) in water bodies. The synthesised adsorbent was characterised using different analytical characterisation techniques. UiO-66(Zr)-MOF produced the maximum adsorption capacities of 139 and 147 mg/g for AMX and PNG, respectively. Freundlich and pseudo-second-order kinetics models best explained the adsorption process. The dispersive micro-solid phase extraction (D-μ-SPE) of the target analytes was performed using a high-performance liquid chromatography-diode array detector (HPLC-DAD). The effectiveness of the developed method was confirmed by its remarkable analytical performances, such as limits of detection (0.19–0.21 µg/L) while the linear range was 0.6–1000 µg/L. The feasibility and applicability of the validated UiO-66(Zr)-MOF-D-μ-SPE/HPLC-DAD method were confirmed by applying it in real water analysis. The AMX and PNG were detected in wastewater samples in the ranges of 1.51–4.71 µg/L and 0.195–1.19 µg/L for influent and effluent. The spike recoveries ranged from 89.9% to 101%, while the precision (intraday and interday) of the method was less than 5%.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Suspended Droplet Microextraction Method for Spectrophotometric Determination of Serum Iron","authors":"Aruna Jyothi Kora,&nbsp;K. Madhavi,&nbsp;N. N. Meeravali","doi":"10.1002/ansa.70022","DOIUrl":"10.1002/ansa.70022","url":null,"abstract":"<p>A facile, selective and sensitive method was devised for the Fe³⁺ quantification in low volume samples, such as bovine serum based on suspended droplet microextraction (SDME). Various process parameters such as concentrations of acid (1.5% hydrochloric acid), complexing agent (0.7% ammonium thiocyanate), quaternary ammonium salt (0.2% Aliquat 336) and extracting solvent (500 µL octanol) were optimised. Ammonium thiocyanate forms water soluble, red coloured, anionic ferric thiocyanate complex [Fe(SCN)₆]³⁻ with Fe³⁺ ions released from the iron–protein complex under an acidic medium. Negatively charged [Fe(SCN)₆]³⁻ complex forms hydrophobic ion associate Fe(SCN)₆³⁻–Aliquat 336₃³⁺ with hydrophilic NH₄⁺ head groups of Aliquat 336 and drives out the formed micelle from aqueous solution along with the iron complex. After stirring, ion associate bonded micelles are separated into a hanging micro droplet of octanol. Red coloured ferric thiocyanate complex in a suspended droplet is solubilised in methanol and Fe³⁺ concentration in serum samples is obtained by recording the spectrophotometric absorbance at 505 nm. The recoveries ranged from 96.2%–98.8% with relative standard deviation (RSD) (%) values from 1.4% to 5.0% at 100–400 ng/mL confirming interference free quantification at optimised conditions. The developed method was linear over the range of 20–1000 ng/mL of Fe³⁺ with a limit of detection of 2.4 ng/mL for the serum matrix. The developed method is applied to various bovine serum samples and Fe³⁺ concentration values ranged from 62.7 to 1582.5 ng/mL. The obtained values were in accordance with the results obtained from the electrothermal atomic absorption spectrometry at 99% confidence level using <i>t</i>-test indicating the accuracy of the developed method. The proposed procedure offers various advantages such as enhanced sensitivity of the spectrophotometer towards iron determination, low-cost complexing agent, low sample volume, metal and biological interference free, simplicity and selectivity. Thus, the developed method can be an alternative to the routine spectrophotometric analysis of low volume samples such as serum and other biological fluids.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Dark Metabolome/Lipidome and In-Source Fragmentation","authors":"Winnie Uritboonthai,&nbsp;Linh Hoang,&nbsp;Aries Aisporna,&nbsp;Martin Giera,&nbsp;Gary Siuzdak","doi":"10.1002/ansa.70012","DOIUrl":"10.1002/ansa.70012","url":null,"abstract":"&lt;p&gt;To the editor,&lt;/p&gt;&lt;p&gt;Tandem mass spectrometry (MS/MS) is valued for its ability to facilitate molecular identification and deliver highly consistent data across a wide range of mass spectrometry platforms. Distinct from MS/MS is the fragmentation that occurs during electrospray ionization (ESI), commonly referred to as in-source fragmentation (ISF) (Figure 1). ISF was first observed in the 1950s with electron ionization and has been recognized as an inherent yet often overlooked feature of the ESI process, albeit less prevalent than with electron ionization. Recently, ISF has been associated with the overrepresentation of peaks in liquid chromatography mass spectrometry (LC/MS) data, where it accounts for the majority of observed unfiltered peaks [&lt;span&gt;1&lt;/span&gt;]. Due to its overrepresentation in LC/MS data, and the subsequent inability to identify the molecules associated with these peaks using MS/MS data, ISF has been linked to the so-called “dark metabolome” [&lt;span&gt;2, 3&lt;/span&gt;] (also encompassing the lipidome), a term used to describe uncharacterized molecular species in metabolomics and lipidomics. This association [&lt;span&gt;1&lt;/span&gt;] was determined by an examination of MS/MS data acquired at 0 eV collision energy from METLIN's extensive library of over 931,000 molecular standards. However, while the similarity of ISF and MS/MS at 0 eV data has been described in previous studies [&lt;span&gt;1, 4&lt;/span&gt;–&lt;span&gt;6&lt;/span&gt;], it has yet to be directly established that they correlate with each other. We explored the consistency between MS/MS (0 eV) data and ISF across various molecular species to assess whether mining METLIN's MS/MS (0 eV) data—comprising over 931,000 molecular standards—can effectively link ISF to the dark metabolome and lipidome.&lt;/p&gt;&lt;p&gt;Liquid chromatography-tandem mass spectrometry (LC-MS/MS) with ESI has become a cornerstone in metabolomics, lipidomics, and clinical analysis due to its accuracy in identifying small molecules within complex biological matrices. With LC-MS/MS, after ionization occurs in the ESI source, charged molecules are directed into a collision cell where they undergo fragmentation for structural analysis. This procedure is typically repeated for all charged analytes present in a sample. However, despite its utility, this method has revealed an unexpectedly vast array of spectral features associated with the “dark metabolome.” However, given the limited number of protein-coding genes [&lt;span&gt;7, 8&lt;/span&gt;] with only a fraction producing enzymes, the chemical diversity [&lt;span&gt;3, 9, 10&lt;/span&gt;] detected through LC-MS/MS—potentially hundreds of thousands or even millions of metabolites—far exceeds biological expectations. Current estimates suggest that less than 2% of observed LC-MS/MS spectra can be annotated, a potentially broad spectrum of unknown compounds [&lt;span&gt;3&lt;/span&gt;]. Recent research [&lt;span&gt;1&lt;/span&gt;] using the METLIN database and its data at 0 eV has shed light on this discrepancy, and much of the perceived compl","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}