Multivariate Data Analysis Research Articles

Large Language Models (such as ChatGPT) as Tools for Machine Learning-Based Data Insights in Analytical Chemistry.

Artificial intelligence (AI), especially through the development of deep learning techniques like convolutional neural networks (CNNs), has revolutionized numerous fields. CNNs, introduced by Yann LeCun in the 1990s (Hubbard, W.; Jackel, L. D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989, 1 (4), 541- 551. https://doi.org/10.1162/neco.1989.1.4.541), have found applications in healthcare for medical diagnostics, autonomous vehicles in transportation, stock market prediction in finance, and image recognition in computer vision to name just a few. Similarly, in analytical chemistry, deep learning has enhanced data analysis from techniques like MS spectrometry, NMR, fluorescence spectroscopy, and chromatography. Another AI branch, Natural Language Processing (NLP), has surged recently with the advent of Large Language Models (LLMs), such as OpenAI's ChatGPT. This paper demonstrates the application of an LLM via a smartphone to conduct multivariate data analyses, in an interactive conversational manner, of a hyper-spectral imaging data set from laser-induced breakdown spectroscopy (LIBS). We demonstrate the potential of LLMs to process and analyze data sets, which automatically generate and execute code in response to user queries, and anticipate their growing role in the future of analytical chemistry.

Multi-omics profiling in spinal muscular atrophy (SMA): investigating lipid and metabolic alterations through longitudinal CSF analysis of Nusinersen-treated patients.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease caused by biallelic mutations in the SMN1 gene, leading to progressive muscle weakness due to degeneration of the anterior horn cells. Since 2017, SMA patients can be treated with the anti-sense oligonucleotide Nusinersen, which promotes alternative splicing of the SMN2 gene, by regular intrathecal injections. In this prospective study, we applied metabolomic, lipidomic, and proteomic analysis to examine sequential CSF samples from 13 SMA patients and controls. This multi-omic approach identified over 800 proteins and 400 small molecules including lipids. Multivariate analysis of multi-omic data successfully discriminated between the CSF derived from SMA patients and control subjects. Lipidomic analysis revealed increased levels of cholesteryl esters and lyso-phospholipids, along with reduced levels of cholesterol and phospholipids in the CSF of SMA patients as compared to healthy controls. These data, combined with results from functional assays, led us to conclude that SMA patients exhibit altered levels and function of high-density-lipoprotein (HDL)-like particles in the CSF. Notably, Nusinersen therapy was observed to reverse disease-specific profile changes toward a physiological state, potentially explicable by restoring HDL function.

Metabolome profiling dissects the oat (Avena sativa L.) innate immune response to Pseudomonas syringae pathovars.

One of the most important characteristics of successful plant defence is the ability to rapidly identify potential threats in the surrounding environment. Plants rely on the perception of microbe-derived molecular pattern chemicals for this recognition, which initiates a number of induced defence reactions that ultimately increase plant resistance. The metabolome acts as a metabolic fingerprint of the biochemical activities of a biological system under particular conditions, and therefore provides a functional readout of the cellular mechanisms involved. Untargeted metabolomics was applied to decipher the biochemical processes related to defence responses of oat plants inoculated with pathovars of Pseudomonas syringae (pathogenic and non-pathogenic on oat) and thereby identify signatory markers that are involved in host or nonhost defence responses. The strains were P. syringae pv. coronafaciens (Ps-c), P. syringae pv. tabaci, P. syringae pv. tomato DC3000 and the hrcC mutant of DC3000. At the seedling growth stage, metabolic alterations in the Dunnart oat cultivar (tolerant to Ps-c) in response to inoculation with the respective P. syringae pathovars were examined following perception and response assays. Following inoculation, plants were monitored for symptom development and harvested at 2-, 4- and 6 d.p.i. Methanolic leaf extracts were analysed by ultra-high-performance liquid chromatography (UHPLC) connected to high-definition mass spectrometry. Chemometric modelling and multivariate statistical analysis indicated time-related metabolic reconfigurations that point to host and nonhost interactions in response to bacterial inoculation/infection. Metabolic profiles derived from further multivariate data analyses revealed a range of metabolite classes involved in the respective defence responses, including fatty acids, amino acids, phenolic acids and phenolic amides, flavonoids, saponins, and alkaloids. The findings in this study allowed the elucidation of metabolic changes involved in oat defence responses to a range of pathovars of P. syringae and ultimately contribute to a more comprehensive view of the oat plant metabolism under biotic stress during host vs nonhost interactions.

Principal component analysis of multivariate spatial functional data

Principal component analysis of multivariate spatial functional data

Anethum graveolens L. (dill) seed polyphenolic fractions: Assessment of high-voltage electrical discharges, subcritical water, and microwave-assisted extraction systems.

Continuous exploration of innovative and sustainable solutions within green chemistry is imperative to tackle challenges inherent in traditional production processes. This study examined three novel methods for obtaining polyphenolic extracts from dill seeds: nonthermal high voltage electric discharge (HVED), subcritical water extraction (SWE), and microwave-assisted extraction (MAE). SWE was conducted at temperatures ranging from 100 to 200°C under 30bar pressure, while MAE utilized a hydroethanolic solvent at temperatures from 40 to 120°C. HVED extraction was performed at varying frequencies (40-100Hz) and durations (5-15min) in water. The total phenolic and flavonoid content of the extracts was quantified, and antioxidant activity was evaluated via 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Polyphenol content and DPPH radical scavenging capacity varied across techniques, with MAE at 40°C yielding the highest values. Increasing MAE temperature caused a steep decline in total phenol and flavonoid contents and a rise in DPPH IC50 values. Conversely, SWE significantly increased phenolic recovery at temperatures between 150-200°C. HVED extraction revealed frequency as the most critical parameter for phenolic recovery. Catechin (224.88µg/mL), epicatechin (120.62µg/mL), epigallocatechin (107.56µg/mL), and procyanidin B1 (116.36µg/mL) emerged as the primary phenolics. SWE demonstrated superior suitability for individual phenolic recovery compared with the other two techniques. Multivariate data analysis reveals relationships between extraction parameters and polyphenol recovery, underscoring the potential for eco-friendly and efficient extraction processes. This study highlights the transition toward sustainable extraction methods in modern industrial processing. PRACTICAL APPLICATION: This study provides insights into innovative extraction techniques for obtaining polyphenols from dill seeds, which could be applied in the food, cosmetic, and pharmaceutical industries. The findings suggest that these eco-friendly methods can improve the efficiency of extracting antioxidants, offering manufacturers a greener and more effective approach to producing health-promoting ingredients.

On the estimation of the number of components in multivariate functional principal component analysis

Happ and Greven developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this article, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution.

Profiling of Australian Stingless Bee Honey Using Multivariate Data Analysis of High-Performance Thin-Layer Chromatography Fingerprints

The complex chemical composition of honey presents significant challenges for its analysis with variations influenced by factors such as botanical source, geographical location, bee species, harvest time, and storage conditions. This study aimed to employ high-performance thin-layer chromatography (HPTLC) fingerprinting, coupled with multivariate data analysis, to characterise the chemical profiles of Australian stingless bee honey samples from two distinct bee species, Tetragonula carbonaria and Tetragonula hockingsi. Using a mobile phase composed of toluene:ethyl acetate:formic acid (6:5:1) and two derivatisation reagents, vanillin–sulfuric acid and natural product reagent/PEG, HPTLC fingerprints were developed to reveal characteristic patterns within the samples. Multivariate data analysis was employed to explore the similarities in the fingerprints and identify underlying patterns. The results demonstrated that the chemical profiles were more closely related to harvest time rather than bee species, as samples collected within the same month clustered together. The quality of the clustering results was assessed using silhouette scores. The study highlights the value of combining HPTLC fingerprinting with multivariate data analysis to produce valuable data that can aid in blending strategies and the creation of reference standards for future quality control analyses.

Predictors of Consumer Intention Towards Upcycling Practice and Consumption in Brazil

Objective: The aim of this study is to investigate the predictors that influence consumer intention to adopt upcycling practices in Brazil. Theoretical Framework: Upcycling emerges as an innovative consumption approach, standing out as a more sustainable alternative compared to traditional methods of goods production. Method: The research adopted a quantitative methodology, using a survey to assess consumers' perceptions of upcycling. Multivariate data analysis and structural equation modeling were applied to identify correlations between the proposed constructs and gain insights into participants' perspectives. Results and Discussion: The results indicate that the factors positively predicting consumer willingness to engage in upcycling are, in order of influence: behavioral intention, followed by the role of beliefs and perceived facilitating conditions, which include skills, knowledge, and inspiration. Research Implications: The research makes a theoretical contribution by providing valuable insights, derived from primary data, into consumers' perceptions of their willingness to engage in upcycling. Practically, the study offers significant implications for management and public policies by promoting a more sustainable consumption approach, raising environmental awareness, and generating tangible social benefits. Originality/Value: The study advances the development of the proposed theoretical model by including and analyzing the influence of predictive factors such as autonomy, empowerment, and entrepreneurial opportunities in the creation of new businesses. The results demonstrate that these factors play crucial roles and make significant contributions to the advancement of consumer behavior theory, deepening the understanding of this phenomenon in practice and promoting more sustainable consumption practices.

Predicting competitive alpine skiing performance by multivariable statistics-the need for individual profiling.

Predicting competitive alpine skiing performance using conventional statistical methods has proven challenging. Many studies assessing the relationship between physiological performance and skiing outcomes have employed statistical methods of questionable validity. Furthermore, the reliance on Fédération Internationale de Ski (FIS) points as a performance outcome variable presents additional limitations due to its potential unreliability in reflecting short-term, sport-specific performance. These factors complicate the selection of appropriate tests and the accurate prediction of competitive outcomes. This study aimed to evaluate the predictive power of a generalized physiological test battery for alpine skiing performance, as measured by FIS points, utilizing multivariable data analysis (MVDA). Physiological test results from a total of twelve (n = 12) world-class female skiers were included in the analysis. The result on goodness of regression (R2) and goodness of prediction (Q2) in this study indicate that valid Orthogonal Projection to Latent Structures (OPLS) models for both Slalom and Giant Slalom can be generated (R2 = 0.39 to 0.40, Q2 = 0.21 to 0.15), but also that competition performance still cannot be predicted at a group level (low Q2). In contrast, higher predictive power of competitive performance was achieved on an individual level using the same data (R2 = 0.88 to 0.99 and Q2 = 0.64 to 0.96). The findings of this investigation indicate that the selected tests employed in this study exhibit limited generalizability for the assessment of elite alpine skiers, as the predictive value of specific physiological parameters on competitive performance appears to be highly athlete-dependent.

Causal analysis for multivariate integrated clinical and environmental exposures data

Causal analysis for multivariate integrated clinical and environmental exposures data

Revisiting the Metabolism of Donepezil in Rats Using Non-Targeted Metabolomics and Molecular Networking.

Background/Objectives: Although donepezil, a reversible acetylcholinesterase inhibitor, has been in use since 1996, its metabolic characteristics remain poorly characterized. Therefore, this study aims to investigate the in vivo metabolism of donepezil using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) based on a molecular networking (MN) approach integrated with a non-targeted metabolomics approach. Methods: After the oral administration of donepezil (30 mg/kg) in rats, urine, feces, and liver samples were collected for LC-HRMS analysis. Chromatographic and spectrometric data were processed through MN and multivariate data analysis to identify the in vivo metabolites of donepezil. Results: A total of 50 metabolites were characterized, including 23 newly identified metabolites. Donepezil was biotransformed by O-demethylation, N-debenzylation, and hydroxylation, and these metabolites are further conjugated with glucuronic acid and sulfurous acid. N-Desbenzyldonepezil (M4), didesmethyldonepezil (M5), and N-desbenzyldonepezil (M4) were identified as the most abundant metabolites in urine, feces, and liver samples, respectively. Conclusions: The metabolic characteristics of donepezil in rats were comparable to those in humans, indicating that a rat is a reliable model for studying donepezil metabolism. This study indicates that a MN approach combined with a metabolomics approach is a reliable tool to identify unknown metabolites of drugs and drug candidates.

MetaboLabPy-An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis.

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.

Robust scaling strategies for outlier handling in orthogonal projection to latent structures discriminant analysis (OPLS-DA)

Orthogonal projection to latent structures discriminant analysis (OPLS-DA) is a widely used statistical method in multivariate data analysis, particularly in class pattern recognition. In addition, OPLS-DA is an extension of partial least square (PLS) that separately considers the variance of correlated and orthogonal predictors in the projection process to the response. PLS is a linear method that is very sensitive to outliers, making OPLS-DA susceptible to the influence of outliers. Investigations have shown that outliers commonly exist in datasets and can significantly affect statistical analysis. Therefore, this research aimed to propose a robust scaling method in the pre-processing step of OPLS-DA to address outliers. The proposed method was tested using both simulated and actual datasets, and the result showed effectiveness in handling outliers and improving classification accuracy. Consequently, Robust OPLS-DA could be designed to achieve optimal classification results by effectively separating classes and increasing robustness against outliers.

Information, social networks and emotions: fear and anger towards Pedro Sánchez 2019–2021

IntroductionThe objective of this research is to determine the relationship between the use citizens make of social networks to learn about politics and the emotions expressed towards political leaders, taking as a case study the current Prime Minister of Spain Pedro Sánchez.MethodsA quantitative methodological approach was used in this longitudinal study. This approach proposes both a descriptive and multivariate analysis of data from three databases.ResultsThe analysis of results shows important differences between consuming political information on social networks and not doing so in terms of the type of negative emotions (anger and fear) generated towards Pedro Sánchez. Connecting these findings with previous cognitive approaches led us to conclude that, while fear encourages citizens to seek alternative information, when they seek it through social networks the expression of anger increases.DiscussionIn this sense, preliminary results suggest that fear towards Pedro Sánchez becomes anger when information is proactively sought in social networks.

Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis.

Condition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data analytics, machine learning (ML), Industry 4.0, and Internet of Things (IoT) applications. Multi-sensor data provides opportunities to predict component conditions; however, environments characterized by multiple sensors and diverse fault states across various components complicate the fault classification process. To address these challenges, this study introduces a novel multivariate Functional Data Analysis (FDA) framework based on Multivariate Functional Principal Component Analysis (MFPCA) for classifying failure conditions in hydraulic systems. The proposed method systematically tackles condition-based diagnostics and addresses fundamental issues in multi-fault classification. Experimental results demonstrate that this approach achieves high classification accuracy using raw multi-sensor data, establishing multivariate FDA as a powerful tool for fault diagnosis in complex systems.

PGI Chianina meat traceability by means of multivariate HRMAS-NMR data analysis.

Food quality is a crucial issue for producers and consumers, either dealing with commodities according to basic standards or with top quality products. Among the parameters contributing to quality, the place of origin is considered to be one of the most relevant, especially for protected denomination of origin and protected geographical indication foods, PDO and PGI, respectively. These labels have been designed by the EU to protect and valorise high quality foodstuff produced in limited areas and to ensure higher incomes to farmers. Such economic interest has prompted the use of several analytical techniques for the traceability issue. Here we proposed the multivariate HRMAS-NMR (i.e. High-Resolution Magic Angle Spinning-Nuclear Magnetic Resonance) data analysis for the traceability of PGI Chianina meat, specifically for the semitendinosus muscle. The metabolic profile of Chianina meat assessed by HRMAS-NMR spectroscopy was analysed by means of PCA (Principal Component Analysis), PLS-DA (Partial Least Square-Discriminant Analysis) and OPLS-DA (Orthogonal Partial Least Square-Discriminant Analysis) in order to classify samples according to the geographical origin. The built models provided an excellent separation between PGI and non-PGI, and the use of the VIP (Valuable Influence on Projection) values allowed us to identify metabolites contributing significantly to classification. Specifically, we found molecules such as amino acids, carnosine, some nucleosides, and fatty acids to be responsible for the discrimination: the fatty acid profile of meat is affected by the different feeding systems, while the other metabolites are involved in the ageing process of meat (ATP degradation during post mortem and proteolysis).

Laser Desorption-Ion Mobility Spectrometry of Explosives for Forensic and Security Applications.

The detection of explosives in crime scene investigations is critical for forensic science. This study explores the application of laser desorption (LD) ion mobility spectrometry (IMS) as a novel method for this purpose utilising a new IMS prototype developed by MaSaTECH. The LD sampling technique employs a laser diode module to vaporise explosive traces on surfaces, allowing immediate analysis by IMS without sample preparation. Chemometric approaches, including multivariate data analysis, were utilised for data processing and interpretation, including pre-processing of raw IMS plasmagrams and various pattern recognition techniques, such as linear discriminant analysis (LDA) and support vector machines (SVMs). The IMS prototype was validated through experiments with pure explosives (TNT, RDX, PETN) and explosive products (SEMTEX 1A, C4) on different materials. The study found that the pre-processing method significantly impacts classification accuracy, with the PCA-LDA model demonstrating the best performance for real-world applications. The LD-IMS prototype, coupled with effective chemometric techniques, presents a promising methodology for the detection of explosives in forensic investigations, enhancing the reliability of field applications.

The effect of interleukin-17 on the course of juvenile idiopathic arthritis

Background. Juvenile idiopathic arthritis (JIA) is a heterogeneous group of chronic joint diseases in children who have different course and prognosis, so they continue to be investigated. In the presented part of the study, the content of soluble ІL-17-retseptoru, ІL-1 and γ -interferon in blood serum was determined to assess the role of their ratios and cooperation in the course of the disease in various subvariants of JIA. This will make it possible to predict the further course of the disease and choose the right treatment tactics. Purpose – The aim is to study the influence of interleukin 17 on the course of idiopathic arthritis. Materials and Methods. A statistical analysis of the immunological examination was carried out, which included the assessment of the content of interferon-ɣ, interleukin-1β and IL-17 receptor in blood serum using multivariate correlation and linear dispersion analysis of data. An analysis of the medical documentation of 40 patients with JIA, who during 2017-2019 at the clinical base of the Department of Pediatrics, Childrenʼs Infectious Diseases, Immunology and Allergology of P.L. Shupyk Scientific and Practical Center for Pediatric Rheumatology and Transitional Support of Children and Adolescents with Rheumatic Diseases State Institution «Institute of Pediatrics, Obstetrics and Gynecology of the National Academy of Medical Sciences of Ukraine». Results. The analysis of the content of pro-inflammatory cytokines in the blood serum of patients with JIA, depending on the subvariant, showed that the content of IL-1β in the blood serum was significantly increased only in sJIA, which was significantly (p < 0.05) higher than in other JIA. An increase in its content was found in 2/3 of patients, and it was also higher (p < 0.05) in teenagers compared to children aged 4–12 years. And when analyzing depending on age, the highest values of IL-17R were in the middle age group. The obtained data indicate the compensatory value of the increase of IL-17R and the simultaneous initiation of inflammatory and anti-inflammatory processes in the exacerbation of JIA. The level of IL-17R in blood serum was the highest to a greater extent in patients with pJIA and eJIA, in these groups the highest frequency of detection of increased IL-17R was also noted. A similar regularity was not observed in sJIA. The obtained data indicate the compensatory role of its increase and the simultaneous initiation of inflammatory and anti-inflammatory processes during an active inflammatory process against the background of JIA. The assessment of the ratio of stimulating and inhibitory cytokines showed that in patients with uveitis the ratio of IFN-γ/IL-1β (4379.29 ± 476.83) was higher than in other JIA (from 60.84 ± 14.92 in oJIA to 105 .20 ± 66.01 in pJIA) and IFN-γ/IL-17R (4474.01 ± 3899.19 vs. 20.14 ± 11.48 in pJIA to 934.55 ± 931.37 in sJIA). An increase in the IL-1β/IL-17R ratio was characteristic only for sUIA (34.12 ± 26.17). Conclusions. The balance of cytokines determines the directionality of the inflammatory process in JIA. The most unfavorable is the presence of increased IL-1β/IL-17R. The ratio of IFN-γ/IL-1β and IFN-γ/IL-17R can be considered as potential markers of uveitis and IL-1β/IL-17R – cJIA.

Effect of Various Stabilizers on the Characteristics of a Nitrate Esters-Double Base Propellant under Thermal Aging

ABSTRACT The current study examined the stabilizing effect of lignin biopolymer in comparison to traditional 2-nitrodiphenylamine (2-NDPA) and 1,3-Dimethyl-1,3-diphenylurea (C-II) stabilizers on the stability of double-base nitrocellulose (NC)/diethylene glycol dinitrate (DEGDN) propellant during artificial aging (at 338.65 K for 60 days). Various spectroscopic techniques, including Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD), were used, along with traditional stability tests like the Bergmann-Junk (BJ) test and isothermal thermogravimetric analysis (TGA). Experimental results confirmed that FTIR and XRD provided valuable insights into chemical changes during aging. Particularly, lignin-stabilized NC/DEGDN exhibited comparable stability to C-II and 2-NDPA at all stages of aging, as evidenced by the amount of the released nitrous gases measured by BJ test and thermal stability assessed by TGA. Additionally, multivariate data analysis using principal component analysis (PCA) of FTIR spectra successfully differentiated between aged and unaged samples and highlighted trends in stability. Overall, the findings suggest that lignin offers a comparable stabilizing effect for NC/DEGDN propellants relative to traditional stabilizers.

A-SIMA/A-MAP: a comprehensive toolkit for NMR-based metabolomics analysis.

Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features. In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics. A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs. Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu , with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere. The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency.