Multivariate Data Analysis Research Articles

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

IntroductionPredicting 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.MethodThis 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.ResultsThe 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).DiscussionThe 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.

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.

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.

Monitoring of the Biotechnological Production of Dihydroxyacetone Using a Low-Field 1H NMR Spectrometer.

The concept of sustainable production necessitates the utilization of waste and by-products as raw materials, the implementation of biotechnological processes, and the introduction of automated real-time monitoring for efficient use of resources. One example is the biocatalyzed conversion of the reusable by-product glycerin by acetic acid bacteria to dihydroxyacetone (DHA), which is of great importance to the cosmetic industry. The application of compact spectrometers enables the rapid measurement of samples while simultaneously reducing the consumption of resources and energy. Yet, this approach requires comprehensive data preprocessing and, on occasion, multivariate data analysis. For the process monitoring of the production of DHA, a low-field 1H nuclear magnetic resonance (NMR) spectrometer was implemented in on-line mode. Small-volume samples were taken from a bypass and transferred to the spectrometer by an autosampler. Complete analysis within minutes allowed real-time process control. To this purpose, reliable automated spectral preprocessing preceded the creation of a univariate model. The model enabled the acquisition of process knowledge from chemical kinetics and facilitated the tracking of both substrate and product concentrations, requiring independent calibration. As a second multivariate approach, principal component analysis was utilized to monitor the process in a semi-quantitative manner without the necessity for calibration. The results of this study are beneficial for real-time monitoring applications with the objective of exerting control over the process in question while minimizing expenditure.

Circadian Rhythms of Skin Surface Lipids and Physiological Parameters in Healthy Chinese Women Reveals Circadian Changes in Skin Barrier Function.

Circadian rhythms are driven by the biological clock, an endogenous oscillator that generates approximately 24 h cycles in mammals. The circadian regulation of the lipid metabolism plays a crucial role in overall metabolic health. An analysis of the correlation between the skin's physiological parameters and skin lipids can provide a better insight into the rhythmic changes in skin condition. The aim was to reveal how skin surface lipids (SSLs) participate in the regulation of circadian rhythms in the skin and the importance of the circadian oscillation of facial lipid molecules in maintaining epidermal homeostasis. Changes in SSLs were assessed using UPLC-QTOF-MS. The skin's physiological parameters were quantified using non-invasive instruments. Multivariate data analysis was employed to evaluate the differences. Both skin surface lipids and physiological parameters exhibited certain circadian variation patterns. Four major lipid classes (fatty acids, glycerophospholipids, prenol lipids, saccharolipids) exhibited circadian rhythmic trends, with seven lipid subclasses contributing most significantly to the overall patterns observed. Among the physiological parameters assessed, sebum secretion, transepidermal water loss, moisture measurement value, and skin surface temperature exhibited sinusoidal circadian rhythms. Further analysis revealed significant correlations between fatty acids and saccharolipids with moisture measurement values, and between glycerolipids and pH value. In addition, lipids closely associated with the barrier such as unsaturated fatty acids and ceramide chain lengths correlated significantly with moisture measurement values. Through correlation analysis, the study elucidates the influence of diurnal fluctuations in skin surface lipids on skin barrier function. These findings hold significant implications for understanding skin barrier impairment associated with circadian rhythm disruptions.

Study on the Chemical Characterization and Hypolipidemic Function of Nelumbo nucifera Based on Its Flavonoid Components.

Nelumbo nucifera has great value and development prospects in hypolipidemic applications. In this study, we comprehensively screened out multi-index components relevant to the quality of N. nucifera based on the hypolipidemic function of the flavonoid fraction of N. nucifera (FFN) combined with chemical characterizations. Firstly, in vitro antioxidant and cell experiments evaluated the hypolipidemic function of the FFN. Secondly, the chemical compositions of N. nucifera were identified by UPLS-MSn technology. Then, the multi-index flavonoid components (rutin, hyperoside, isoquercitrin, quercetin-3-O-β-D-glucuronide, astragalin, and quercetin) were determined using a quantitative fingerprint combined with multivariate statistical data analysis. Finally, the quality of N. nucifera was scientifically evaluated by multi-index quantitative analysis combined with multivariate statistical data analysis, which was used to study the relationship between the content of flavonoid components and the overall quality. The above-mentioned research lays a material foundation for improving the quality standards of N. nucifera, providing a basis for developing functional foods to improve dyslipidemia.

Identification of Metabolites From Halamphora Sp. and Its Correlation With Quorum Sensing Inhibitory Activity via UHPLC-ESI-MS/MS-Based Metabolomics and Molecular Networking.

The UHPLC-MS/MS metabolomics approach was employed to profile and characterize multi-components in Halamphora sp. extracted with different solvents that contribute to quorum sensing inhibitory (QSI) activity. A total of 37 and 34 metabolites were tentatively identified from negative and positive ion modes, respectively. The metabolites have been assigned to various groups, including fatty acids, glycolipids, sterols, diazines, flavonoids, peptides, carotenoids, and pigments. Multivariate data analysis showed that the QSI activity in the acetone extract was due to tumonoic acid A, terpeptin derivatives, pheophorbide A, hydroxyhexadeca-1,5-dien-3-ynoxy]propane-1,2-diol, l-methionyl-l-tyrosine, stearidonic, hexadecadienoic, tricosenoic, palmitic, and linolenic acids. These metabolites were more concentrated and differed significantly in acetone extract compared to other extracts. Acetone extract displayed a cluster of nodulisporic acid and fucoxanthin through MS/MS-based molecular networking (MN) platform. The present study shows that the liquid chromatography mass spectrometry (LCMS)-based metabolomics and MN effectively identify QSI-active metabolites in Halamphora sp. extracts, which can be promoted as a natural antifoulant.

Quality Characteristics and Volatile Components of Chili Oil Prepared from the Combination of Shuanla and Erjingtiao Peppers.

This study aimed to investigate the influence of varying weight ratios of Shuanla and Erjingtiao peppers (10:0, 8:2, 6:4, 5:5, 4:6, 2:8, and 0:10, corresponding to samples PA, PB, PC, PD, PE, PF, and PG, respectively) on the sensory attributes, chromatism, acid values (AVs), peroxide values (POVs), capsaicinoids, and volatile organic compounds (VOCs) of seven chili oil samples. GC-IMS was employed to detect the VOCs of the chili oil samples, which were subsequently analyzed using multivariate statistical methods. The results revealed significant differences in pungency among the samples, with the PA sample exhibiting the strongest pungency. The PG sample demonstrated the highest values for a*, b*, and C*, while the PA sample displayed the highest L* and h*. The AVs of seven samples ranged from 0.490 ± 0.005 to 1.727 ± 0.015 mg/g. The POVs of the chili oil samples, ranging from 0.094 ± 0.000 to 0.127 ± 0.002 g/100 g, were significantly lower than those of extra virgin olive oil, 0.183 ± 0.001 g/100 g. The contents of capsaicinoids ranged from 15.26 ± 0.07 g/kg in the PA sample to 0.38 ± 0.00 g/kg in the PG sample (p < 0.05). Additionally, 56 volatile flavor substances were identified, and 10 key flavor compounds (ROAV ≥ 1) were screened among them. Multivariate data analysis via OPLS-DA indicated that 20 VOCs (VIP > 1) could serve as flavor markers in a clustering heat map to differentiate among the seven chili oil varieties. The findings of this study provide a valuable reference for the promotion of Shuanla and Erjingtiao peppers in chili oil production and the development of specific flavor profiles in chili oil to cater to diverse consumer preferences.

Diversity of Picea omorika (Pančić) Purk. Populations Based on Morpho-anatomical Needle Traits and Bioclimatic Parameters

Picea omorika (Pančić) Purkyne is a well-known endemic conifer species. However, there have been few morpho-anatomical studies to date. Several years ago, preliminary population studies of P. omorika were published, but with the inclusion of additional populations, it became necessary to confirm whether the southernmost population from Mileševka Canyon, also known as var. vukomanii, (Pavlović and Matović 1994), remains distinct from the others. Two-year-old needles from the lower third of the crown were collected in late autumn from seven natural populations of P. omorika from Serbia and from the border with Bosnia and Herzegovina. The variability of ten morpho-anatomical traits and the cross-sectional anatomy of Serbian spruce needles were examined in seven natural populations in Serbia. Needle length was measured with a digital calliper, and the nine other needle traits, needle width, needle thickness, vascular bundle diameter, resin ducts diameter, number of resin ducts, distance between vascular bundle and resin duct, cuticle and epidermal thickness, hypodermal thickness, and hypodermal width, were examined under a light microscope. For most of the traits studied, the Štula population had the highest values, whereas the Bilo population had the lowest. Needles with triangular sections are most common in the southern part of Mt. Tara, while elliptical and rhomboidal sections are characteristic in populations at the edge of the distribution range in Serbia. Multivariate analyses separated populations into two main groups. Both principal component analysis and discriminant analysis indicated population overlap, while cluster analysis identified three main groups. Multivariate analysis of bioclimatic data and a correlation study of morpho-anatomical traits were also performed. Based on bioclimatic parameters, individuals in the populations from Mileševka and Štula showed the most distinct characteristics. Mantel and partial Mantel tests confirmed no correlation between the complete set of morphological traits and bioclimatic parameters, even after adjusting for regional differences.

Nonparametric Analysis of Multivariate Data in Factorial Designs with Nondetects: A Case Study with Microbiome Data

AbstractThe term “nondetects” describes observations that are not fully observed because the true value is below a detection threshold—and can therefore not be precisely detected. One may also consider them a special case of left-censored data. Nondetects occur frequently, for instance, in life sciences research in medicine or microbiology. This article examines the use of nonparametric inference methods for multivariate data in factorial designs in situations where nondetects are present, and it evaluates their performance. The focus is on testing hypotheses regarding interaction and main factor effects. The nonparametric centerpiece of the methodology is assuming the nonparametric relative effect (probabilistic index) and its generalizations as the functional on which inference is built, along with the respective invariance properties of the resulting tests. On this basis, we apply and evaluate recently proposed nonparametric analogs to the following types of multivariate test statistics: (1) Wald-type statistic (WTS), (2) ANOVA-type statistic (ATS), (3) Lawley–Hotelling trace, (4) Wilks Lambda (Likelihood ratio), (5) Bartlett–Nanda–Pillai trace. Except for the WTS, all the mentioned methods are available through the R-package nparmd. Extensive simulations and a case study from the field of microbiology demonstrate that the proposed methods can handle commonly occurring rates of nondetects without substantial impairment of specificity and sensitivity.

Polyploids and geographic divergence blur species delimitation in the Paspalum stellatum complex (Poaceae: Paspaleae)

Abstract Paspalum stellatum comprises a diploid and several polyploid cytotypes. The widely distributed cytotype 2n = 32 is an amphidiploid, an outcome of hybridization between diploid P. stellatum (2n = 20) and the related species Paspalum schesslii (2n = 12). Several analyses were conducted upon a set of 24 accessions of P. stellatum and representatives of three related species. Multivariate analyses of morphological data discriminated between the two potentially sexual cytotypes, 2n = 20 and 2n = 32, but the putatively apomictic higher polyploids were interspersed between them. Instead, Iner Simple Sequence Repeat (ISSR) markers separate two main groups geographically, west and east from the Brazilian-Bolivian Pantanal. Both groups include diploid (2n = 20) and tetraploid (2n = 32) cytotypes, but higher polyploids belong exclusively to the eastern group. Considering that diploids and polyploids behave as different biological entities, we propose to treat the diploid (2n = 20) cytotype as a different species, for which the name Paspalum cujabense should be rehabilitated. Meanwhile, the name P. stellatum remains for a complex including the tetraploid and all polyploid cytotypes.

Parameter Dependency of Electrochemical Reduction of CO2 in Acetonitrile - A Data Driven Approach.

The electrochemical CO2 reduction reaction (CO2RR) is a promising technology for the utilization of captured CO2. Though systems using aqueous electrolytes is the state-of-the-art, CO2RR in aprotic solvents are a promising alternative that can avoid the parallel hydrogen evolution reaction (HER). While system parameters, such as electrolyte composition, electrode material, and applied potential are known to influence the reaction mechanism, there is a lack of intuitive understanding as to how. We show that by using multivariate data analysis on a large dataset collected from the literature, namely random forest modelling, the most important system parameters can be isolated for each possible product. We find that water content, current density, and applied potential are powerful determinants in the reaction pathway, and therefore in the Faradaic efficiency of CO2RR products.

Impact of Nitro Substituents on Dopamine Sensing and Nanostructure Morphology: A Machine Learning Approach for PANI:2- and 3-Nitro-1H-Pyrrole Nanocomposite Sensors

Abstract In this study, we explore the effects of nitro substituents on the morphology and dopamine (DOP) sensing performance of polyaniline (PANI) nanocomposites (NCs). The novelty of the study is the unique integration of 2-nitro-1H-pyrrole (D9A) and 3-nitro-1H-pyrrole (D9B) into PANI to develop advanced non-enzymatic voltammetric sensors, combined with machine learning for DOP sensitivity and morphology analysis. Structural and morphological insights were obtained through comprehensive characterization techniques including ¹H NMR, ¹³C NMR, Fourier transform infrared spectroscopy, scanning electron microscopy, and artificial intelligence-enhanced SEM analysis. The PANI: D9B NCs sensor demonstrated superior DOP detection in the range of 0.625–5 µM, with exceptional sensitivity (329.72 µAµM⁻¹ cm⁻²) and an ultra-low limit of detection of 0.078 µM. Its rapid sensing capability within 1 minute indicates potential for use in biomedical diagnostics. In contrast, the PANI NCs sensor exhibited lower sensitivity, which was linked to higher Zreel values and space charge effects. To further enhance DOP prediction accuracy, we employed machine learning (ML) models—ANN, SVM, XGBoost, and Linear Regression—to analyze sensor outputs, with a focus on feature extraction and multivariate data analysis. Our combined approach provides a robust framework for optimizing nitro-substituted PANI NCs for high-performance sensing applications.