Simultaneous chemiluminescence determination of thebaine and noscapine using support vector machine regression

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Prediction of surface tension of solution in the presence of hydrophilic silica nanoparticle and anionic surfactant by ATR-FTIR spectroscopy and chemometric methods

In the current research, an analytical method was proposed for the quantitative determination of surface tension of anionic surfactant solutions in the presence of hydrophilic silica nanoparticles using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The surface tension behavior of anionic surfactant solutions considerably changes by the addition of silica nanoparticles with different particle size. The spectral data of solutions were used for prediction of surface tension using two calibration methods based on support vector machine regression (SVM-R) as a non-linear algorithm and partial least squares regression (PLS-R) as a linear algorithm. For preprocessing of data, baseline correction and standard normal variate (SNV) were also applied. Root mean square error of prediction (RMSEP) in SVM-R and PLS-R methods were 4.203 and 4.507, respectively. Considering the complexity of the samples, the SVM-R model was found to be reliable. The proposed method is fast and easy for measurement of the surface tension of surfactant solutions without any sample preparation step in chemical enhanced oil recovery (C-EOR).

ATR-FTIR spectroscopy and chemometric techniques for determination of polymer solution viscosity in the presence of SiO2 nanoparticle and salinity

An analytical method was proposed for quantitative determination of rheological properties of polyacrylamide (PAM) solution in the presence of SiO₂ nanoparticle and NaCl. The viscosity of PAM-SiO₂ nanohybrid solution was predicted using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in the wavenumber range of 800-3000 cm^-1 and chemometrics methods. Support vector machine regression (SVM-R) as a non-linear multivariate calibration procedure and partial least squares regression (PLS-R) as a linear procedure were applied for calibration. Preprocessing methods such as baseline correction and standard normal variate (SNV) were also utilized. Root mean square error of prediction (RMSEP) in SNV-SVM and SNV-PLS methods were 3.231 and 6.302, respectively. Considering the complexity of the samples, the SVM-R model was found to be reliable. The proposed method is rapid and simple without any sample preparation step for measurement of the viscosity of polymer solutions in chemical enhanced oil recovery (CEOR).

Simultaneous chemiluminescence determination of citric acid and oxalic acid using multi-way partial least squares regression

A novel kinetic chemiluminescent method proposed for the simultaneous determination of oxalic acid and citric acid in their mixtures.

Ultrasound-assisted ionic liquid-based microextraction combined with least squares support vector machines regression for the simultaneous determination of aluminum, gallium, and indium in water and coal samples

A new simple and rapid ultrasound-assisted ionic liquid-based microextraction method was applied to preconcentrate aluminum(III), gallium(III), and indium(III) ions from water samples as a prior step to their simultaneous spectrophotometric determination using least squares support vector machines regression. In the novel procedure, 1-hexyl-3-methylimidazolium hexafluorophosphate [C(6)MIM][PF(6)] was dispersed into the aqueous sample solution as fine droplets by ultrasonication, and the analytes were extracted into the ionic liquid phase after complexation with 1,2,5,8-tetrahydroxy anthraquinone (quinalizarine). After centrifuging, the fine droplets of extractant phase were settled to the bottom of the conical-bottom glass centrifuge tube. The detection limits for Al(III), Ga(III), and In(III) were 1.70, 2.02, and 2.06 ng mL(-1), respectively. The precision of the method, evaluated as the relative standard deviation obtained by analyzing a series of ten replicates, was below 3.2% for all elements. The method was successfully applied for the determination of Al(III), Ga(III), and In(III) in real samples.