A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

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A voltammetric sensor array (or electronic tongue) is developed for the simultaneous quantification of cysteine, glutathione and homocysteine without need of previous separation. It is based on the integration of three commercial screen‐printed electrodes (gold curated at high and low temperature and carbon modified with carbon nanotubes). Linear sweep voltammograms measured simultaneously by all three sensors are processed by Partial Least Squares (PLS) regression and different variables selection algorithms such as Genetic Algorithm and interval‐Partial Least Squares. The method was applied to synthetic mixtures and successfully validated, with correlation coefficients of prediction (Rp2) of 0.9542, 0.9429 and 0.9589 for cysteine, glutathione, and homocysteine respectively.

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