Spectral Library Research Articles

Abstract 2334: Immunologic and proteomic biomarkers of progression risk in Barrett’s esophagus

Abstract Barrett’s esophagus (BE) is an established risk factor for the development of high-grade dysplasia (HGD) or esophageal adenocarcinoma (EC), but clinical predictors of progression remain poorly defined. Biomarkers of progression risk are therefore in high demand, and in this study we examined the utility of immune cell density and proteomic profiling to identify progression risk indicators in biopsy samples from patients in surveillance for BE. Formalin-fixed paraffin embedded tissue samples (n=58) from patients with non-dysplastic BE undergoing surveillance biopsy were examined. During follow-up, 25 patients were diagnosed with HGD or EC (“progressors”), while 33 patients did not during at least 5 years of subsequent surveillance (“non-progressors”). To analyze immune markers for cell densities, samples were stained for CD3 and CD8 positive lymphocytes, CD68/CD86 co-positive (“M1- like”) macrophages, and CD68/CD206 co-positive (“M2-like”) macrophages. Regions of interest (ROI) were selected from the lamina propria of the gastroesophageal junction. Subgroup comparisons of cell density were carried out using the Wilcoxon rank sum test and logistic regression for the progression outcome. Proteomic profiling was assessed using label-free, liquid chromatography-tandem mass spectroscopy (LC-MS/MS), using data-independent acquisition. The raw LC-MS/MS data were searched using Spectronaut against a spectral library generated from 6 progressor and 6 non-progressor samples. Differential protein expression analysis was conducted using Perseus to compare the progressor and non-progressor groups. A total of 728 ROIs were examined for lymphocytes. lymphocyte cell densities were lower in progressors vs. non-progressors (CD3+: p=0.03; CD8+: p=0.003), as was the CD8:CD3 cell density ratio (x̄: 0.66 vs 0.84, p<0.0001). On logistic regression, CD8+ cell density (p=0.003) and CD8:CD3 ratio (p=0.007) were significantly associated with progression. A total of 587 and 489 ROIs were examined for M1 and M2 cell densities, respectively. M1 like macrophages were significantly increased in progressors vs non-progressors (x̄: 93.4 vs 35.4, p=0.0004). A total of 7, 209 proteins were identified across the samples, among which 163 were differentially-expressed based on progression risk (false positive rate<0.05). 4 major GO processes and KEGG pathways were associated with progressors: rRNA processing, Wnt signaling, bacterial invasion of epithelial cells, and G protein-coupled receptor signaling. In this study, we present immunologic and proteomic biomarkers of progression risk in BE. More comprehensive assays for immune microenvironment and mechanistic studies are planned in a prospective BE surveillance cohort obtained from an ongoing registry-repository study. The enhanced understanding of malignant progression in BE could provide opportunities to improve surveillance or prevent progression in the future. Citation Format: Hyun Young Park, Catherine Lewis, Ashten Omstead, Erin Grayhack, Christopher Sherry, Kunhong Xiao, Gursimran Kochhar, Michael Landau, Douglas Stairs, Ali H. Zaidi, Patrick L. Wagner. Immunologic and proteomic biomarkers of progression risk in Barrett’s esophagus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2334.

Deep Learning-Based Prediction of Decoy Spectra for False Discovery Rate Estimation in Spectral Library Searching.

With the advantage of extensive coverage, predicted spectral libraries are becoming an attractive alternative in proteomic data analysis. As a popular false discovery rate estimation method, target decoy search has been adopted in library search workflows. While existing decoy methods for curated experimental libraries have been tested, their performance in predicted library scenarios remains unknown. Current methods rely on perturbing real spectra templates, limiting the diversity and number of decoy spectra that can be generated for a given library. In this study, we explore the shuffle-and-predict decoy library generation approach, which can generate decoy spectra without the need for template spectra. Our experiments shed light on decoy method performance for predicted library scenarios and demonstrate the quality of predicted decoys in FDR estimation.

Library Enabling Annotation of Botanical Natural Products (LEAFBot): An Open-Access Library of Mass Spectrometry Fragmentation Spectra for Plant Metabolites.

Many existing mass spectral libraries focus on human or microbially derived molecules. Few plant-specific MS2 databases exist, making annotation of botanical samples difficult. To fill this gap in mass spectrometry data availability, the Library Enabling Annotation of Botanical Natural Products (LEAFBot) was constructed. Using a flow injection mass spectrometry method that allowed for rapid throughput data collection, the MS2 spectra of >300 pure botanical secondary metabolites were experimentally measured and complied into a single library housed in the Global Natural Products Social Molecular Networking (GNPS) spectral database. Of these compounds, over 20% were not present in the existing GNPS database, and 11% were not present in any of three main mass spectral databases (GNPS, Metlin, and MassBank). Additionally, LEAFBot contains a wider range of adducts compared to other plant-based mass spectral libraries, enabling more effective annotation of unknown features. The LEAFBot database represents a new resource to the mass spectrometry and metabolomics community seeking to characterize plant-based samples. The possibility of searching against a taxonomically specific library decreases the likelihood of false positives in database searches, and the ease of adding new spectra, following procedures outlined herein, will enable community-lead expansion of the database.

Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification.

Microplastics persist as ubiquitous environmental contaminants, and efficient methods to quantify and identify their presence are essential for assessing their environmental and health impacts. Common identification approaches typically fall under either vibrational spectroscopy or thermoanalytical techniques with thermogravimetric analysis (TGA) coupled with Fourier transform infrared (FTIR) spectroscopy bridging the intersection. Despite its potential, TGA-FTIR remains relatively underutilized for microplastic analysis, even though each thermogram is associated with approximately 200 FTIR spectra that can be rapidly assessed with targeted automated data analysis. This work explores the development of data analysis routines specialized in identifying plastic components from TGA-FTIR. A dedicated spectral library and a matching algorithm were created to identify polymers from their gas-phase FTIR spectra. The approach was further enhanced by utilizing machine learning (ML) classification techniques, including k-nearest neighbor, random forest, support vector classifier, and multilayer perceptron. The performance of these classifiers for complex data sets was evaluated using synthetic data sets generated from the spectral library. ML techniques offered precise and unambiguous identification compared with a custom spectral matching algorithm. By correlating polymer identities with mass loss in the thermogram, this approach combines qualitative insights with semiquantitative analysis, enabling a streamlined assessment of plastic content in samples.

A reference database enabling in-depth proteome and PTM analysis of mouse immune cells

Spectral libraries fulfill multiple functions in biological and analytical applications. For biologists, these libraries provide a valuable resource to verify the presence and abundance of proteins or pathways within a selected cell type thus determine the feasibility of further experiments. Despite advances, existing libraries are incomplete and provide researchers only a limited amount of information. To address this, we introduce the reference database - Spectral Library of Immune Cells (SpLICe), a resource covering B-cells, CD4 and CD8 T-cells, macrophages and dendritic cells containing nearly 9,000 protein groups and 110,346 proteotypic peptides. Additionally, the database provides data on > 20,000 post-translationally modified proteotypic peptides (oxidation, phosphorylation, methylation, acetylation, deamidation and N-glycosylation) across the selected immune cell populations. SpLICe supports the quantification of more than half of total murine proteins annotated by UniProtKB/Swiss-Prot, enabling monitoring of selected proteins or pathways from Reactome pathways and Gene Ontology databases. The platform provides relative protein abundances and supports the generation of targeted mass spectrometry assays by identifying and scoring proteotypic peptides.

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.

This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reproducible models for peptide property predictions. Key limitations of the current models, including difficulties in handling diverse post-translational modifications and instrument variability, highlight the need for large-scale, harmonized datasets, and standardized evaluation metrics for benchmarking.

Analysis of Fragmentation Pathways of New-Generation Synthetic Cannabinoids (INACA, IATA, FUPPYCA, and OXIZID Type Compounds) Based on Electrospray Ionization High-Resolution Mass Spectrometry.

A rapid introduction of new psychoactive substances onto the illicit drug market and the dynamic changes in their structures cause the identification of novel compounds to be difficult and time-consuming. This is related to the possible lack of mass spectra of new substances in spectral libraries or the unavailability of certified reference materials. Therefore, this study analyzed the fragmentation patterns of 24 synthetic cannabinoids recently reported to the European Early Warning System. The tested compounds were divided into subgroups based on similarity of core structure and linker moiety, i.e., the synthetic cannabinoids with the acronym INACA (containing an indazole core and a carboxamide linker), the IATA compounds with an indole core and an acetamide linker, the FUPPYCA synthetic cannabinoids (containing a 4-fluorophenyl-pyrazole core and a carboxamide linker), and the OXIZID compounds which are characterized by a 2-oxindole core and a hydrazide linker. The analysis was carried out by quadrupole time-of-flight mass spectrometry with electrospray ionization (ESI-QTOFMS). High mass accuracy of the applied technique allowed us to determine the preferred fragmentation routes of novel synthetic cannabinoids. The main cleavage for the compounds with the acronyms INACA, IATA, and FUPPYCA occurred on the bond adjacent to the carbonyl group constituting part of the molecule linker. Instead, for the OXIZID type group, neither ions originating from the core of the molecule nor ions from the linker and tail were observed. Knowledge of the characteristic ions of compounds from a certain chemical group can be helpful in the elucidation of the structure of new substances that may appear in products seized on the drug market.

Establishing Visible–Near Infrared Spectroscopy for the Prediction of Soil Organic Carbon in Arid Croplands of Arequipa, Peru

ABSTRACT The expansion of irrigated agriculture into deserts can rapidly change soil chemistry, including the soil organic carbon (OC) content, with major impacts on the soil structure and biogeochemical cycling. Direct soil sampling and wet chemistry for frequent monitoring of OC changes can be impractical; a low-cost OC predictive model using Visible Near Infrared (VNIR) spectroscopy was established for arid croplands. To ensure broader model application, soils were sampled under distinct agricultural management types in the southern Arequipa Region of Peru: (1) traditional terraced arid agriculture (with OC contents from 0.38 to 2.31%) and (2) newly developed hyperarid croplands under varying levels of irrigation (with lower OC contents of 0.18–1.37%). Using the two datasets with distinct soil chemistry, soil management representation was increased within this regional spectral library. With the combined dataset, the optimal pre-processing procedure was determined using an international soil standard correction of absorbance, robust baseline correction, multiplicative scatter correction, and Savitzky−Golay smoothing (R2 = 0.81, RMSE = 0.12 (equivalent to 0.09% OC), RPD = 2.5). An equal to or improved predictive power above previous arid VNIR spectroscopic analyses was achieved. These results demonstrate how VNIR predictive modeling can assess rapidly changing soil OC, with implications for arid agricultural management and land development in the region.

Identification of Lithium-Bearing Pegmatite Dikes Based on WorldView-3 Data: A Case Study of the Shaligou Area in Western Altyn

Shaligou, located in the southwestern Altyn Mountains, is a newly discovered lithium-bearing pegmatite deposit that is driving increased exploration in the region. However, the challenging environmental conditions of the Altyn Mountains pose challenges for the exploration of lithium-bearing pegmatites. Consequently, remote sensing technology has emerged as an effective exploration tool. In this study, the spectral data of typical samples were collected to establish a spectral library of rocks and minerals in the study area capable of serving as a foundation for remote sensing analysis. Firstly, ASTER data were utilized successfully for lithological interpretation of the area. Secondly, high-resolution WorldView-3 data with a spatial resolution of 0.31 m were used to establish interpretation criteria for pegmatite dikes. Ground validation results were highly consistent with the remote sensing interpretations, confirming that the use of WorldView-3 data significantly enhances the accuracy of lithium-bearing pegmatite dike identification, providing valuable guidance for further exploration.

Generic spectral library framework for urban land cover mapping with optical remote sensing imagery

Generic spectral library framework for urban land cover mapping with optical remote sensing imagery

Nationwide suspect screening of new psychoactive substances (NPSs) and other controlled substances in Taiwan wastewater using liquid chromatography-High resolution mass spectrometry (LC-HRMS).

Nationwide suspect screening of new psychoactive substances (NPSs) and other controlled substances in Taiwan wastewater using liquid chromatography-High resolution mass spectrometry (LC-HRMS).

Simple, fast, and non‐targeted detection of multi‐pesticides in fruit with chemometrics‐assisted magnetic "one‐step" strategy

AbstractA chemometrics‐assisted magnetic “one‐step” method was proposed for the fast and non‐targeted detection of multi‐pesticides in fruit samples with gas chromatography–mass spectrometry (GC–MS). In this method, extraction/partitioning and cleanup were integrated into one step with magnetic adsorbent, which thus was simple and time‐saving (within 3.5 min) in sample preparation. Fast temperature program was used to further shorten analysis time in GC–MS. However, the measured GC–MS signal obtained with the fast method was easily composed of overlapping peaks and a high level of background. Automatic information mining of unknown multi‐pesticides in complex GC–MS data was achieved with the adaptive iteratively reweighted penalized least squares‐moving window target transfer factor analysis V2.0 algorithm, with reference to the pesticide mass spectral library. Two mixed standards (34 and 39 pesticides) and three kinds of fruit samples were used to verify this method. All pesticides in the mixed pesticide standards were detected with standard temperature program. Furthermore, all spiked pesticides in different fruit samples were detected with a total detection time of 8.5 min.

High-throughput screening of acetals/ketals in edible essences via GC-Orbitrap-MS and their formation rates at room temperature.

High-throughput screening of acetals/ketals in edible essences via GC-Orbitrap-MS and their formation rates at room temperature.

Pyrolysis-GC/MS differentiates polyesters and detects additives for improved monitoring of textile labeling accuracy and plastic pollution.

Polyesters comprise the greatest proportion of textile fibers and are found in various everyday goods; hence, polyester fibers are a significant source of microplastic pollution and textile waste. The specific chemical composition of commercial polyester fibers is often proprietary and mostly assumed to be poly(ethylene terephthalate) (PET). Polyester is a class of polymers that include poly(butylene terephthalate) (PBT), poly(cyclohexylenedimethylene terephthalate) (PCT), and poly(ethylene naphthalate) (PEN), as well as biodegradable polymers. Our study aims to clarify whether household polyester products are primarily PET, are labeled accurately, or contain phthalate additives by applying double-shot pyrolysis-gas chromatography/mass spectroscopy (Py-GC/MS). We analyzed four scientific-grade polyester reference standards, 52 manufacturer-grade polyester fibers or pellets, and 229 samples from 193 consumer polyester products. From the pyrograms, samples were predominantly identified as PET (87.4%, 95% CI [93.5-81.3%]), but five samples were identified as a different polyester, nine as non-polyester polymers, and 23 as a blend of PET with another polymer. From the thermal desorption chromatograms, diethyl phthalate was the most frequently detected phthalate, found in 23.3% (95% CI [17.3-29.3%]) of the consumer products, including children's toys. Double-shot py-GC/MS advantageously results in these empirical data that (1) counter the assumption that products labeled polyester are always PET, (2) emphasize the importance of creating spectral libraries with well-characterized materials for accurate polymer identification of unknown plastic particles, and (3) demonstrate that phthalates are common additives in household products.

Combining non-targeted high resolution mass spectrometry with effect-directed analysis to identify contaminants of emerging concern in the field of ecotoxicology: A systematic quantitative literature review.

Methods for measuring environmental toxicity and identifying chemical toxicity drivers using non-targeted analysis (NTA) were reviewed in this systematic quantitative literature review. Effect-directed analysis (EDA) was used to assess sample toxicity and prioritise NTA sample analysis. The most common bioassays performed were estrogen, androgen and aryl hydrocarbon receptor assays, with many studies using test batteries. Across the 95 studies in this review, the toxicity could be explained (>75%) for eight studies, four studies had toxicity endpoints explained and unexplained, and 38 studies had unexplained (<75%) toxicity. The addition of NTA allowed for toxicity to be explained with a median of 47% for TOXnon-target studies and 34% for TOXtarget+non-target, far higher than the 13% median for TOXtarget studies within this review. The outcomes of identification were affected by method factors including sample extraction, chromatography method, data acquisition and data processing. Method factors with the biggest potential to introduce selection bias were sample extraction and chromatography technique. These factors were characterised by a high representation of reverse phase liquid chromatography contributing to the selective exclusion of polar, highly polar and ionic compounds from sample analysis. This reduces compound identification and excludes unknown chemical contaminants from analysis. Not all studies reported the explained toxicity contribution of identified compounds, however it was evident that many compound features could not be identified using NTA processing software. There were severe limitations for liquid chromatography data compared to gas chromatography data with insufficient spectral library databases for spectra matching. This bottleneck is partially solved through the rise in in silico and retention time prediction software. Future work, including increasing spectral databases for liquid chromatography, use of less biased chromatography and sample preparation techniques and inclusion of EDA-NTA into risk assessment frameworks, will allow for better toxicity assessment of emerging contaminants.

Mid-infrared absorption spectra and mass absorption coefficients for 23 chondrites

Aims. We present mid-infrared transmission spectra from 2 to 23 μm of the 23 Atacama Desert chondrites of different types (carbonaceous Ornans and ordinary of H, L, and LL groups) as well as of some pure minerals (olivine and diopside). We focus on the characteristics of silicate at 10 μm and 20 μm, analyzing the influence of composition and grain size on peak strengths and spectral shapes. We present the first results of the Cosmic Dust Laboratory, a dedicated facility at the Universidad Diego Portales equipped with a VERTEX 80v vacuum Fourier transform infrared spectrometer. Methods. Through milling and sieving samples, we obtained different ranges of particle sizes to study the effect of grain size on the intensity and shape of the spectrum. Results. The resulting spectral library can be compared with astronomical data of protoplanetary disks, debris disks, and even white dwarf disks obtained with instruments such as MIRI on board the James Webb Space Telescope and MATISSE on the Very Large Telescope Interferometer. We also present mass absorption coefficient values, which can be used for radiative transfer modeling of astronomical observations. This study aims to improve dust opacities for astronomical applications, with a focus on circumstellar disks.

DiaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens.

diaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens.

Time-lapse tryptic digestion: a proteomic approach to improve sequence coverage of extracellular matrix proteins.

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes, including proliferation, adhesion, and migration. Alterations of ECM structure and/or composition have been linked to many pathological processes, including cancer and fibrosis. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundances in the ECM. They also do not permit to achieve the high coverage needed to gain finer biochemical on ECM proteoforms ( e.g. , isoforms, post-translational modifications) and topographical information critical to better understand ECM protein functions. Here, we present the development of a time-lapsed proteomic pipeline using limited tryptic proteolysis and sequential release of peptides over time. This experimental pipeline was combined with data-independent acquisition mass spectrometry and the assembly of a custom matrisome spectral library to enhance peptide-to-spectrum matching. This pipeline shows superior protein identification, peptide-to-spectrum matching, and significantly increased sequence coverage against standard ECM proteomic pipelines. Exploiting the spatio-temporal resolution of this method, we further demonstrate how time-resolved 3-dimensional peptide mapping can identify protein regions differentially susceptible to trypsin, which may aid in identifying protein-protein interaction sites.

Химический состав дистиллята из пшеницы в процессе выдержки в контакте с древесиной дуба

Traditionally, grain distillates are left to age in oak barrels. This process needs intensification, and this problem remains understudied despite numerous scientific publications. The study expands and clarifies the data on the chemical composition of grain distillates after a long-term contact with oak wood. The research featured samples of 60% immature grain distillate from wheat harvested in 2023 and model solutions prepared from immature grain distillate in industrial oak chips (1.0×0.5 cm, Ecochips, France) subjected to a weak, medium, and strong heat treatment modes and stored in the dark for 7, 14, 21, and 28 days. The study relied on a combination of gas chromatography, chromatography / mass spectrometry, and capillary electrophoresis. The thermal treatment mode and extraction time were variables. The authors developed a set of express methods to define the composition of volatile organic impurities and the ionic composition of grain distillates. Isoamylol, isobutanol, and 1-propanol were the most abundant higher alcohols (≤ 80% total volatile organic impurities). The variation ranges were 1 354.3–1 489.3 mg/dm3 for isoamylol, 235.0–261.9 mg/dm3 for isobutanol, and 119.6–134.9 mg/dm3 for 1-propanol. The Whiskey22 thematic spectral library provided a 90% identification accuracy. The aging affected not only the mass concentrations but also the list of ions. In the model solution with the highest degree of thermal treatment, the maximal concentrations belonged to acetates (67.3 mg/dm3), potassium (4.2 mg/dm3), phosphates (2.9 mg/dm3), lactates (2.4 mg/dm3), and chlorides (2.3 mg/dm3). The optimal ratios and ranges were as follows – isoamylol:isobutanol (5.7–5.9), isobutanol:1-propanol (1.9–2.0), ethyl caprate:ethyl laurate (2.1–2.3), and ethyl caprate:ethyl caprylate (2.9–3.2). The new methods proved effective in determining the chemical composition of grain distillates and can be recommended for commercial production of alcoholic beverages

Multilaboratory Study of a Nontarget Data Acquisition for Target Analysis (nDATA) Workflow Using Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry for the Screening of 1087 Pesticides in Fresh Fruits and Vegetables.

A nontarget Data Acquisition for Target Analysis (nDATA) workflow was developed to screen pesticides in fresh produce based on ultrahigh-performance liquid chromatography-high-resolution full scan mass spectrometry/variable data-independent tandem mass spectrometry acquisition (LC-FS MS/vDIA MSMS) and a pesticide database. The MSMS spectral library was generated to create a database consisting of 1087 pesticides based on authentic pesticide standards. The retention time (±0.5 min), precursor ion (≤± 5 ppm), and product ions (≤± 5 ppm) were extracted for each pesticide from LC-FS MS/data-dependent MSMS acquisition (LC-FS MS/DDA MSMS). Mass accuracy criteria, along with ±0.1 min retention time tolerance, were used for the identification of pesticides. Three laboratories evaluated and validated the nDATA workflow to screen and identify pesticides from produce extracts (apples, bananas, broccoli, carrots, grapes, lettuce, oranges, potatoes, strawberry, and tomatoes) prepared by the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) procedure. Of the 1087 pesticides evaluated, false-positive rates were ≤5% for 977 pesticides in blank matrices and false-negative rates were ≤5% for 921 and 985 pesticides in fortified matrices at 10 and 100 μg/kg, respectively. False positives detected were misidentified pesticides, incurred residues, or contaminants possibly resulting from process or system contamination detected below the threshold level of 10 μg/kg. False negatives were attributed to pesticides that did not sufficiently ionize or fragment or had poor stabilities and QuEChERS extraction efficiencies. Incurred residues in archived produce samples (apple, Chinese broccoli, grape, kale, kohlrabi, orange, pepper, strawberry, tomato, and turnip green) were prepared using QuEChERS, evaluated by the nDATA workflow, and the results were compared and confirmed, if possible, to targeted GC-MS/MS, LC-MS/MS, and LC-FS MS/DDA MSMS methods. The three laboratories identified 25 parent pesticides at levels >10 μg/kg that were consistent with findings from targeted procedures and discovered 10 different metabolites that were not provided in the multiple reaction monitoring method or inclusion list of the targeted procedures. GC-MS/MS identified two pesticides, chlorothalonil and dacthal, and a possible chlorothalonil metabolite, pentachlorobenzonitrile, that were not amenable to LC-low or LC-high-resolution mass spectrometry analysis in produce samples. To improve the identification quality, the nDATA workflow further implemented quality control, operational, and processing measures to reduce the number of false detects, and the data evaluation workload. As demonstrated in this study, the validated nDATA workflow creates new opportunities for chemical residues analysis, offering a potential screening complement to targeted LC-MS/MS, GC-MS/MS, and nontargeted methods for pesticides and other contaminants of interest.