Chemometric Tools Sharpen Analysis of Levodopa and Dopamine Across Complex Matrices

Levodopa and dopamine sit at the center of Parkinson’s disease care, yet measuring them reliably in blood, urine, serum, and pharmaceutical formulations remains a stubborn analytical problem. A new review published in Future Journal of Pharmaceutical Sciences on 8 October 2026 compiles the chemometric-assisted analytical methods reported between 2010 and 2025 for determining these two compounds, along with the co-administered drugs, metabolites, and impurities that complicate their measurement. Written by Finan T. Hindam, Reham M. Arafa, Basma M. Eltanany, and Amal M. Abou Al Alamein, the review organizes the field around three perspectives: quantitative determination, experimental optimization, and discriminant analysis. Its central message is that multivariate data analysis, when paired with the right spectroscopic or electrochemical technique, can untangle mixtures that defeat conventional one-signal-at-a-time approaches.

The core difficulty is chemical similarity. Levodopa is a first-line therapy for Parkinson’s disease and acts as a direct precursor to dopamine, which is itself an essential neurotransmitter in the brain’s neural circuits. Drugs commonly given alongside levodopa, including carbidopa, entacapone, tolcapone, methyldopa, and benserazide, share almost the same physicochemical properties as the target analytes, producing interfering signals in instruments. Endogenous compounds add another layer of trouble: dopamine’s electrochemical response overlaps heavily with that of uric acid, ascorbic acid, adrenaline, noradrenaline, serotonin, and magnesium ions. In traditional univariate analysis, managing data from multiple drugs in a biological matrix becomes extremely challenging, which is precisely where chemometrics offers its advantage.

Chemometrics, or multivariate data analysis, applies mathematical and statistical methods to chemical data for enhanced interpretation. Rather than measuring one factor at a time, it studies several factors simultaneously, making the process time-saving and cost-effective, particularly in laboratories with limited resources. The review describes how these models resolve severe overlap of instrumental signals as an alternative to chromatographic separation, eliminate drift from nonlinear measurements, remove interference between components with similar signals, and build classification and quantification models. Models fall into supervised categories, such as partial least squares, principal component regression, artificial neural networks, and support vector machines, and unsupervised ones, including principal component analysis, hierarchical cluster analysis, and K-means clustering.

A critical step before any multivariate analysis is data preprocessing. In reported spectrophotometric methods, mean centering, blank subtraction, automated scatter identification, and data decomposition have been used to extract the desired signal from background noise. Voltammetric methods rely on tools that correct potential shifts and baselines, with interval correlation optimized shifting and correlation optimized warping handling shift correction, and adaptive iteratively reweighted penalized least squares or asymmetric least square spline regression managing baselines. The Savitzky-Golay approach frequently reduces background noise while enhancing peak intensity and sensitivity. The payoff can be dramatic: in one chemometric-assisted differential pulse voltammetric method analyzing levodopa, carbidopa, and entacapone in a pharmaceutical formulation, the root-mean-square error of prediction for levodopa dropped from 6.02 × 10−5 to 1.98 × 10−6 M after preprocessing with airPLS, icoshift, and Savitzky-Golay treatment.

Spectrophotometry and spectrofluorimetry coupled with chemometrics form one major branch of the reviewed literature. Kinetic versions of these techniques are of particular interest: analytes with similar characteristics react with the same reagent, and differences in reaction rates allow differentiation and quantification without physical separation. Continuous wavelet transform, alternating quadrilinear decomposition, artificial neural networks, partial least squares, and principal component regression have all proven effective at processing kinetic data. In one study, continuous wavelet transform applied to kinetic spectrophotometric data determined dopamine and uric acid in the presence of ascorbic acid, with the method applied to human serum and urine samples achieving high percentage recoveries. Another team processed four-way kinetic fluorescence data with alternating quadrilinear decomposition, showing that a third-order calibration model could accurately determine levodopa and tyrosine in complex systems with significant collinearity and unknown interferents. A digital-video-based method recording color development during reaction with silver nitrate quantified dopamine in the presence of ascorbic acid, with a principal component analysis-artificial neural network model outperforming partial least squares in nonlinear calibration of spiked human serum samples.

Voltammetric techniques coupled with chemometrics constitute another major branch, and the review highlights several model comparisons. For simultaneous determination of the neurotransmitters dopamine, adrenaline, noradrenaline, and serotonin, a Tchebichef curve moment model outperformed multivariate curve resolution-alternating least squares and partial least squares, owing to its extraction of invariant features from voltammetric data and strong multiresolution capability when handling first-order signals from differential pulse voltammetry in spiked human serum. For levodopa with co-formulated drugs in human serum, multivariate curve resolution-alternating least squares beat parallel factor analysis 2 with lower prediction errors, an advantage attributed to its ability to handle nonlinearity and potential shifts common in electrochemical measurements. In another comparison of five models for levodopa, carbidopa, and benserazide, the same curve resolution approach produced the smallest ellipse in an elliptical joint confidence region test, indicating the highest precision. Unfolded partial least squares with residual trilinearization showed superior predictive ability for ten target analytes in the presence of three unanticipated interferences, handling four-way data arrays and full voltammetric matrices.

Beyond routine quantification, the review covers spectroelectrochemistry, which combines electrochemical and spectroscopic detection in a single analytical run and generates two independent datasets for the same chemical system, making it autovalidated. Using a parallel factor analysis model, one derivative spectroelectrochemical method analyzed a mixture of dopamine, uric acid, and ascorbic acid, with thin-layer diffusion achieving 36 percent higher absorbance than semi-infinite diffusion and relative error and relative standard deviation values of 9.00 and 4.10 percent for dopamine. Chromatography also benefits: an alternating trilinear decomposition-based second-order calibration method resolved overlapping chromatographic peaks of matrix interference with levodopa, carbidopa, and methyldopa, and was applied to human plasma samples with low prediction errors and excellent recovery. Design of experiments tools, including Doehlert matrix, Box-Behnken, and fractional factorial designs, have optimized electrode fabrication, stability-indicating HPLC separations, and extraction techniques, saving cost, effort, and time by reducing the number of experiments required.

Discriminant analysis represents a third frontier. Electronic tongues and noses, known as differential sensing techniques, identify analytes using arrays of receptors or sensors with different binding affinities, with response patterns decoded through numerical processing. A colorimetric sensor array built from aggregated gold nanoparticles classified the neurotransmitters levodopa, dopamine, adrenaline, and noradrenaline by linear discriminant analysis and quantified them by partial least squares, accurately predicting them in spiked urine samples with root-mean-square error of prediction values of 1.33 for levodopa and 0.27 for dopamine. An electronic tongue based on chemosensory microspheres successfully discriminated five L-tyrosine derivatives, including levodopa and dopamine, in dietary supplements using unsupervised principal component analysis. The review also weighs environmental sustainability: an ATR-FTIR method coupled with chemometrics scored higher on the analytical greenness metric than a chromatographic method, 0.75 versus 0.57, and a chemometric-assisted voltammetric method scored 90 versus 77 on the analytical Eco-scale, although the chemometric model required far more calibration samples, 109 versus 7.

The authors are candid about limitations. Some reported spectroscopic and spectrofluorimetric techniques showed lower sensitivity and limited dynamic linear range compared to electrochemical methods, potentially struggling with trace levels in biological fluids and not covering the full physiological concentration range relevant to disease monitoring. Most in vivo voltammetric techniques face biological matrix variability, including endogenous interferents, differing ionic strengths, and pH changes that can alter voltammetric profiles and lead to inaccurate neurochemical predictions, while prolonged chronic measurements may cause electrode response variability from biofouling. Broader validation across different brain regions or disease models is still needed, and because most reported methods used human serum or urine, the authors call for more methods developed in cerebrospinal fluid. They also recommend optimizing training set sizes, including interferents and physiological variations in training data, and creating an open-source benchmark dataset with explicit rules for comparing model performance across studies.

Looking forward, the review argues that deep learning and machine learning algorithms, including support vector machines, convolutional neural networks, and artificial neural networks, are increasingly applied to overcome limitations of traditional chemometric approaches, particularly in handling nonlinear data distributions and complex classification tasks in biological matrices. These approaches reduce dimensionality, facilitate feature selection, and enhance model training efficiency, minimizing overfitting risk, a strategy the authors describe as feature engineering. Strategies to reduce biofouling in electrochemical sensing, such as self-cleaning surfaces, protective coatings, and biocompatible materials, are proposed to extend sensor lifetime for real-time monitoring. The authors’ key recommendation is to develop chemometric-assisted methods that simultaneously determine levodopa and dopamine along with co-administered drugs and related impurities in biological matrices such as cerebrospinal fluid and human serum, supporting therapeutic drug monitoring and the optimization of personalized treatment regimens for patients with Parkinson’s disease.

Subject of Research: Chemometric-assisted analytical methods for determining levodopa and dopamine in various matrices

Article Title: Chemometrics in paving the way to analyze levodopa and dopamine in various matrices for monitoring Parkinson’s disease and other neurological disorders

Article References: Hindam, F. T., Arafa, R. M., Eltanany, B. M., & Al Alamein, A. M. A. (2026). Chemometrics in paving the way to analyze levodopa and dopamine in various matrices for monitoring Parkinson’s disease and other neurological disorders. Future Journal of Pharmaceutical Sciences, 12(1), Article 94. https://doi.org/10.1186/s43094-026-01029-z

Image Credits: AI Generated

DOI: 10.1186/s43094-026-01029-z

Keywords: chemometrics, levodopa, dopamine, Parkinson's disease, therapeutic drug monitoring, spectrophotometry, voltammetry, multivariate data analysis, design of experiments, discriminant analysis, electronic tongue, green analytical chemistry