Multivariate calibration combined differential pulse voltammetry for simultaneous electroanalytical determination of phenolic compounds using a Fe3O4-modified carbon paste electrode

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Simultaneous electrochemical determination of morphine and methadone by using CMK-5 mesoporous carbon and multivariate calibration

For the first time, a sensitive electrochemical sensor using a glassy carbon electrode modified with CMK-5 Ordered mesoporous carbon was fabricated for simultaneous analysis of morphine and methadone. Modern electrochemical FFT-SWV techniques and partial least-squares as a multivariable analysis were used in this method. CMK-5 nanostructures were characterized by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction analysis, and Raman spectroscopy. Variables such as accumulation time and pH for the proposed sensor were optimized before quantitative analysis. To train the proposed sensor, standard mixtures of morphine (MOR), and methadone (MET) were prepared in the established linear ranges of the analyzes. The results obtained from training samples were used for PLS modeling. The efficiency of the model was determined using test and real matrix samples. The root mean square error of prediction and the squared correlation coefficients (R²_p) for MET and MOR were estimated to be 0.00772 and 0.00892 and 0.948 to 0.990, respectively. The recoveries in urine samples were reported to be 97.0 and 105.6% for both MOR and MET, respectively.