Ethanol fuel adulteration with methanol assessed by cyclic voltammetry and multivariate calibration

• Simple and fast assessment method of ethanol fuel adulteration.
• Simultaneous multivariate electroanalytical determination of methanol and ethanol.
• Variable selection was crucial for accurate quantification.
• GA-MLR shows the best prediction performance.
• Technology: voltammetry.

In this work, it is proposed the one-voltammogram simultaneous determination of methanol and ethanol in hydrated ethyl alcohol fuel (HEAF) samples by cyclic voltammetry and multivariate calibration. To perform the determination, different multivariate calibration approaches were tested namely partial least-squares regression (PLS) and multivariate linear regression (MLR). To minimize collinearity problems in MLR, three variable selection algorithms were evaluated: the successive projections algorithm (SPA), the stepwise (SW) formulation, and the genetic algorithm (GA). Variable selection could also provide an improvement in prediction results when compared to full-voltammogram PLS. In this sense, genetic algorithm PLS (GA-PLS), interval PLS (iPLS), and synergy interval PLS (siPLS) were evaluated. An excellent analytical performance was obtained for both alcohols (RMSEP < 0.8% w.w−1) with GA-MLR performed on data transformed by standard normal variate (SNV). The developed method presented a wide linear working range for both alcohols and a LOD = 0.9% w.w−1 for methanol sufficient to assess the adulteration of HEAF samples in accordance to Brazilian regulatory agency. The proposed method is simple (with no sample pre-treatment), accurate, rapid, presents low-cost, and can be conducted at distribution sites using a portable potentiostat.