Mass spectral libraries: A statistical review of the visible use

Analysis of Microheterogeneous Glutelin Subunits and Highly Efficient Identification of Selenylation Peptides by In-Gel Proteolysis: Focus on Se-Enriched Rice

Selenylation of cysteine and methionine thiols through selenate supplements increases the content of selenium-containing amino acids in various agricultural products. This modification results in numerous biological and health benefits. Despite their critical roles in human physiology, methods for high-coverage and efficient identification of selenylation peptides are limited. This study systematically developed a mass spectrometric method to identify selenylation peptides combined with in-gel trypsin proteolysis. In-gel proteolysis identified the two well-separated bands containing rice glutelin. We identified 11 rice glutelin subunits along with 42 selenylation peptides from the glutelin acidic subunits and 30 selenylation peptides from the glutelin basic subunits with high confidence. A comprehensive evaluation disclosed the mapping of selenium-containing rice glutelin subunits. Additionally, the selenylation modification of peptides coexisted with oxidation and iodoacetamide (IAM) alkylation. Moreover, the multidimensional MS criteria validated the results, while spectral statistics revealed the veritable Se/S substitution degree in Se-enriched rice. These findings collectively demonstrated the presence of numerous selenation sites in microheterogeneous glutelin subunits, thereby enhancing our understanding of the seleno-peptidomics of rice proteins. As significant bioactive organic compounds, the identified peptides in this study are promising candidates for a variety of bioactivities, including neuroprotective, anti-inflammatory, antioxidant, hepatoprotective, and immunomodulatory effects.

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries

Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.

Unlocking the therapeutic potential of Nigella sativa extract: phytochemical analysis and revealing antimicrobial and antioxidant marvels

The growing global threat of antimicrobial resistance endangers both human and animal life, necessitating the urgent discovery of novel antimicrobial solutions. Medicinal plants hold promise as sources of potential antimicrobial compounds. In this study, we investigated the phytochemical constituents and microbicidal capabilities of the ethanolic extract from Nigella sativa (black seed). Gas chromatography analysis (GC) identified 11 compounds, among them thymoquinone, and thymol, contributing to antimicrobial and antioxidant properties. Antimicrobial assays demonstrated notable inhibition zones against broad spectra of bacteria, including Pseudomonas aeruginosa, Escherichia coli, Salmonella typhi, Staphylococcus aureus, Enterobacter, and Bacillus subtilis, along with potent antifungal activity against Aspergillus niger, Penicillium, and Candida albicans. Notably, when combined with antibiotics, the extract displayed exceptional synergistic antimicrobial efficacy. The black seed extract demonstrated membrane-damaging activity and disrupted virulence factors that protect microbes from antimicrobial agents, including the formation of bacterial biofilm and protease secretion. Thymoquinone, the primary active constituent of the extract, exhibited similar antimicrobial and ant virulence properties. In silico analysis targeting key regulators of quorum sensing and biofilm formation in P. aeruginosa, such as RhlG, LasR, and PqsR, showed a remarkable affinity of thymol and thymoquinone for these targets. Moreover, the N. sativa extract exhibited dose-dependent cytotoxicity against both the promastigote and amastigote forms of Leishmania tropica parasites, hinting at potential antiparasitic activity. In addition to its antimicrobial properties, the extract displayed potential antioxidant activity at a concentration of 400 μg/mL.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Odor perception of aromatherapy essential oils with different chemical types: Influence of gender and two cultural characteristics

Olfactory perception, and especially affective responses of odors, is highly flexible, but some mechanisms involved in this flexibility remain to be elucidated. This study investigated the odor perceptions of several essential oils used in aromatherapy with emotion regulation functions among college students. The influences of people's characteristics including gender, hometown region, and fragrance usage habit on odor perception were further discussed. Odor perception of nine essential oils, which can be divided into the ester-alcohol type (e.g., lavender oil) and terpene type (e.g., lemon oil) were evaluated under three odor concentrations. The results indicated that chemical type, but not concentration, significantly influenced the odor perception and there was no interaction between the two factors in this study. The arousal and emotional perception scores of odors with terpene-type oil were significantly higher than odors with ester-alcohol type. In terms of people's characteristics, participants from the southern Yangtze river gave a higher familiarity rating to almost all of these odors. The habits of fragrance usage also significantly influenced some of the odors' subjective intensity and emotional perception ratings. However, there were no significant gender differences in most of the odor perceptions. In addition, familiarity and pleasantness were positively correlated, and emotional perception and subjective intensity also showed a weak correlation. These results suggested that users' cultural characteristics could be considered to be important factors that affect the essential oil's odor perception in aromatherapy.

Biological Potentials and Phytochemical Constituents of Raw and Roasted Nigella arvensis and Nigella sativa

Nigella species are widely used to cure various ailments. Their health benefits, particularly from the seed oils, could be attributed to the presence of a variety of bioactive components. Roasting is a critical process that has historically been used to facilitate oil extraction and enhance flavor; it may also alter the chemical composition and biological properties of the Nigella seed. The aim of this study was to investigate the effect of the roasting process on the composition of the bioactive components and the biological activities of Nigella arvensis and Nigella sativa seed extracts. Our preliminary study showed that seeds roasted at 50 °C exhibited potent antimicrobial activities; therefore, this temperature was selected for roasting Nigella seeds. For extraction, raw and roasted seed samples were macerated in methanol. The antimicrobial activities against Streptococcus agalactiae, Streptococcus epidermidis, Streptococcus pyogenes, Candida albicans, Escherichia coli, Enterobacter aerogenes, Klebsiella pneumoniae, and Klebsiella oxytoca were determined by measuring the diameter of the zone of inhibition. The cell viability of extracts was tested in a colon carcinoma cell line, HCT-116, by using a microculture tetrazolium technique (MTT) assay. Amino acids were extracted and quantified using an automatic amino acid analyzer. Then, gas chromatography–mass spectrometry (GC–MS) analysis was performed to identify the chemical constituents and fatty acids. As a result, the extracts of raw and roasted seeds in both Nigella species showed strong inhibition against Klebsiella oxytoca, and the raw seed extract of N.arvensis demonstrated moderate inhibition against S. pyogenes. The findings of the MTT assay indicated that all the extracts significantly decreased cancer cell viability. Moreover, N. sativa species possessed higher contents of the measured amino acids, except tyrosine, cystine, and methionine. The GC–MS analysis of extracts showed the presence of 22 and 13 compounds in raw and roasted N. arvensis, respectively, and 9 and 11 compounds in raw and roasted N. sativa, respectively. However, heat treatment decreased the detectable components to 13 compounds in roasted N. arvensis and increased them in roasted N. sativa. These findings indicate that N. arvensis and N. sativa could be potential sources of anticancer and antimicrobials, where the bioactive compounds play a pivotal role as functional components.

Hyphenated Mass Spectrometry versus Real-Time Mass Spectrometry Techniques for the Detection of Volatile Compounds from the Human Body

Mass spectrometry (MS) is an analytical technique that can be used for various applications in a number of scientific areas including environmental, security, forensic science, space exploration, agri-food, and numerous others. MS is also continuing to offer new insights into the proteomic and metabolomic fields. MS techniques are frequently used for the analysis of volatile compounds (VCs). The detection of VCs from human samples has the potential to aid in the diagnosis of diseases, in monitoring drug metabolites, and in providing insight into metabolic processes. The broad usage of MS has resulted in numerous variations of the technique being developed over the years, which can be divided into hyphenated and real-time MS techniques. Hyphenated chromatographic techniques coupled with MS offer unparalleled qualitative analysis and high accuracy and sensitivity, even when analysing complex matrices (breath, urine, stool, etc.). However, these benefits are traded for a significantly longer analysis time and a greater need for sample preparation and method development. On the other hand, real-time MS techniques offer highly sensitive quantitative data. Additionally, real-time techniques can provide results in a matter of minutes or even seconds, without altering the sample in any way. However, real-time MS can only offer tentative qualitative data and suffers from molecular weight overlap in complex matrices. This review compares hyphenated and real-time MS methods and provides examples of applications for each technique for the detection of VCs from humans.

Statistics of the Popularity of Chemical Compounds in Relation to the Non-Target Analysis

The idea of popularity/abundance of chemical compounds is widely used in non-target chemical analysis involving environmental studies. To have a clear quantitative basis for this idea, frequency distributions of chemical compounds over indicators of their popularity/abundance are obtained and discussed. Popularity indicators are the number of information sources, the number of chemical vendors, counts of data records, and other variables assessed from two large databases, namely ChemSpider and PubChem. Distributions are approximated by power functions, special cases of Zipf distributions, which are characteristic of the results of human/social activity. Relatively small group of the most popular compounds has been denoted, conventionally accounting for a few percent (several million) of compounds. These compounds are most often explored in scientific research and are practically used. Accordingly, popular compounds have been taken into account as first analyte candidates for identification in non-target analysis.

BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics

Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.

Relationships in Gas Chromatography—Fourier Transform Infrared Spectroscopy—Comprehensive and Multilinear Analysis

Molecular spectroscopic detection techniques, such as Fourier transform infrared spectroscopy (FTIR), provides additional specificity for isomers where often mass spectrometry (MS) fails, due to similar fragmentation patterns. A hyphenated system of gas chromatography (GC) with FTIR via a light-pipe interface is reported in this study to explore a number of GC–FTIR analytical capabilities. Various compound classes were analyzed—aromatics, essential oils and oximes. Variation in chromatographic peak parameters due to the light-pipe was observed via sequentially-located flame ionization detection data. Unique FTIR spectra were observed for separated mixtures of essential oil isomers having similar mass spectra. Presentation of GC×FTIR allows a ‘comprehensive’-style experiment to be developed. This was used to obtain spectroscopic/separation profiles for interconverting oxime species with their individual spectra in the overlap region being displayed on a color contour plot. Partial least square regression provides multivariate quantitative analysis of co-eluting cresol isomers derived from GC–FTIR data. The model resulted in an R2 of 0.99. Prediction was obtained with R2 prediction value of 0.88 and RMSEP of 0.57, confirming the method’s suitability. This study explores the potential of GC–FTIR hyphenation and re-iterates its value to derive unambiguous and detailed molecular information which is complementary to MS.

Predicting the absence of an unknown compound in a mass spectral database

Only a small subset of known organic compounds (amenable for gas chromatography/mass spectrometry) is present in the largest mass spectral databases (such as NIST or Wiley). Nevertheless, library search algorithms available in the market are not able to predict the absence of a compound in the database. In the present work, we have tried to implement such prediction by means of supervised classification. Training and validation set contained 1500 and 750 compounds, respectively. Two prediction sets (containing 750 and about 3000 mass spectra) were considered. The easiest-to-use models were built with only one input variable: match factor of the best candidate or InLib factor (both parameters were calculated within MS Search (NIST) software). Multivariate classification models were built by partial least squares discriminant analysis (PLS-DA); match factors of top n candidates were used as input variables. PLS-DA was found to be the most effective approach. The prediction efficiency strongly depended on the 'uniqueness' of mass spectra presented in the test set. PLS-DA model was able to correctly predict the absence of a compound in the database in 29.9% for prediction set #1 and in 74.4% for prediction set #2 (only 1.3% and 2.5% of compounds actually presented in the database were wrongly classified).

Non-targeted Screening in Environmental Monitoring Programs

Contaminant monitoring programs have been tasked with understanding the fate and transport of toxic chemicals in the environment. Mass spectrometry based methods have traditionally been developed to maximize sensitivity and accuracy of a select set of target compounds. As mass spectrometry methods have advanced, so has the breadth of questions proposed by environmental chemists. Incorporating these methods in chemical monitoring programs provides large data sets to explore the effects of complex mixtures on environmental systems.

A Comprehensive LC-QTOF-MS Metabolic Phenotyping Strategy: Application to Alkaptonuria

BACKGROUND

Identification of unknown chemical entities is a major challenge in metabolomics. To address this challenge, we developed a comprehensive targeted profiling strategy, combining 3 complementary liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) techniques and in-house accurate mass retention time (AMRT) databases established from commercial standards. This strategy was used to evaluate the effect of nitisinone on the urinary metabolome of patients and mice with alkaptonuria (AKU). Because hypertyrosinemia is a known consequence of nitisinone therapy, we investigated the wider metabolic consequences beyond hypertyrosinemia.

METHODS

A total of 619 standards (molecular weight, 45–1354 Da) covering a range of primary metabolic pathways were analyzed using 3 liquid chromatography methods—2 reversed phase and 1 normal phase—coupled to QTOF-MS. Separate AMRT databases were generated for the 3 methods, comprising chemical name, formula, theoretical accurate mass, and measured retention time. Databases were used to identify chemical entities acquired from nontargeted analysis of AKU urine: match window theoretical accurate mass ±10 ppm and retention time ±0.3 min.

RESULTS

Application of the AMRT databases to data acquired from analysis of urine from 25 patients with AKU (pretreatment and after 3, 12, and 24 months on nitisinone) and 18 HGD−/− mice (pretreatment and after 1 week on nitisinone) revealed 31 previously unreported statistically significant changes in metabolite patterns and abundance, indicating alterations to tyrosine, tryptophan, and purine metabolism after nitisinone administration.

CONCLUSIONS

The comprehensive targeted profiling strategy described here has the potential of enabling discovery of novel pathways associated with pathogenesis and management of AKU.

Annotating Nontargeted LC-HRMS/MS Data with Two Complementary Tandem Mass Spectral Libraries

Tandem mass spectral databases are indispensable for fast and reliable compound identification in nontargeted analysis with liquid chromatography–high resolution tandem mass spectrometry (LC-HRMS/MS), which is applied to a wide range of scientific fields. While many articles now review and compare spectral libraries, in this manuscript we investigate two high-quality and specialized collections from our respective institutes, recorded on different instruments (quadrupole time-of-flight or QqTOF vs. Orbitrap). The optimal range of collision energies for spectral comparison was evaluated using 233 overlapping compounds between the two libraries, revealing that spectra in the range of CE 20–50 eV on the QqTOF and 30–60 nominal collision energy units on the Orbitrap provided optimal matching results for these libraries. Applications to complex samples from the respective institutes revealed that the libraries, combined with a simple data mining approach to retrieve all spectra with precursor and fragment information, could confirm many validated target identifications and yield several new Level 2a (spectral match) identifications. While the results presented are not surprising in many ways, this article adds new results to the debate on the comparability of Orbitrap and QqTOF data and the application of spectral libraries to yield rapid and high-confidence tentative identifications in complex human and environmental samples.

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4-5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.