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Hydrogen peroxide (H 2 O 2 ) is a reactive oxygen species that serves as an important signaling molecule in normal brain function. At the same time, excessive H 2 O 2 concentrations contribute to myriad pathological consequences resulting from oxidative stress. Studies to elucidate the diverse roles that H 2 O 2 plays in complex biological environments have been hindered by the lack of robust methods for probing dynamic H 2 O 2 fluctuations in living systems with molecular specificity. Background-subtracted fast-scan cyclic voltammetry at carbon-fiber microelectrodes provides a method of detecting rapid H 2 O 2 fluctuations with high temporal and spatial resolution in brain tissue. However, H 2 O 2 fluctuations can be masked by local changes in pH (ΔpH), because the voltammograms for these species can have significant peak overlap, hindering quantification. We present a method for removing ΔpH-related contributions from complex voltammetric data. By employing two distinct potential waveforms per scan, one in which H 2 O 2 is electrochemically silent and a second in which both ΔpH and H 2 O 2 are redox active, a clear distinction between H 2 O 2 and ΔpH signals is established. A partial least-squares regression (PLSR) model is used to predict the ΔpH signal and subtract it from the voltammetric data. The model has been validated both in vitro and in vivo using k-fold cross-validation. The data demonstrate that the double waveform PLSR model is a powerful tool that can be used to disambiguate and evaluate naturally occurring H 2 O 2 fluctuations in vivo.

analytical chemistry

Electrochemical Selectivity Achieved Using a Double Voltammetric Waveform and Partial Least Squares Regression: Differentiating Endogenous Hydrogen Peroxide Fluctuations from Shifts in pH