Filip Ciepiela

Voltammetric classification of ciders with PLS-DA.

Voltammograms recorded on the glassy carbon electrode (GCE) may be a chemical fingerprints of food samples, enabled distinguishing the origin of the considered products. In this work the objects of the study was 5 Polish ciders of various brands. For each sample 10 scans were recorded by DPV in the potential range between -0.2 and 1.0 V in Britton-Robinson buffer at pH 2.0. The signals preprocessing realized by baseline correction with 4-th degree polynomial and normalization (in 0 to 1 interval), performed to reduce problems with insufficient signals repeatability associated with mechanical renovation of the electrode surface before each measurement. The PLS-DA classification models were built using the training set and then validated using the samples absent in the learning process. The final multi-class model with optimized complexity enables classification of the ciders with 100% sensitivity and specificity, with the exception of one cider, where specificity was 95% (for validation set).

NEW MULTIPURPOSE ELECTROCHEMICAL ANALYZER FOR SCIENTIFIC AND ROUTINE TASKS

This work presents a modernized and improved analyzer, different versions of which have been developed by our team over the past 30 years. Modern solutions and fully integrated chips have been applied to this device, which is dedicated to measuring low and very low levels of a recorded value. The up-to-date software fulfills the requirements of scientists, routine analysts, teachers, and students in the areas of electrochemical measurements and the interpretation of recorded signals. The proposed multipurpose analyzer ensures full measuring capability and allows the control of different electrochemical sensors, including the controlled growth mercury drop electrode (CGMDE). Various typical and more advanced signal processing procedures are built-in and are easily accessible.

Faradaic and capacitive current estimation by means of Independent Components Analysis and 1 kHz sampling

In this work it was demonstrated for the first time that Independent Components Analysis non-deterministic multivariate strategy enables estimation of the Faradic and double layer charging current components in differential pulse and anodic stripping voltammetry. The signals separation can be realized based on the different shapes of the capacitive and Faradaic current functions. The set of calibration voltammograms measured with the 1kHz sampling frequency is the sufficient data to retrieve signals compatible with the theoretical shapes, with r2 greater than 0.987 for Faradaic component and greater than 0.994 for capacitive component. In the effect, DP voltammograms of Pb(II) and Cd(II) recorded on silver annular band electrode were constructed using only Faradaic component. Quantitative analysis was performed and calibration models were calculated by different waiting time and sampling time, what demonstrated the possibility of significant improvement of sensitivity in considered experiments.

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness.