Hydrogen peroxide (H2O2) is a reactive oxygen species that serves as an important signaling molecule in normal brain function. At the same time, excessive H2O2 concentrations contribute to myriad pathological consequences resulting from oxidative stress. Studies to elucidate the diverse roles that H2O2 plays in complex biological environments have been hindered by the lack of robust methods for probing dynamic H2O2 fluctuations in living systems with molecular specificity. Background-subtracted fast-scan cyclic voltammetry at carbon-fiber microelectrodes provides a method of detecting rapid H2O2 fluctuations with high temporal and spatial resolution in brain tissue. However, H2O2 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 H2O2 is electrochemically silent and a second in which both ΔpH and H2O2 are redox active, a clear distinction between H2O2 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 H2O2 fluctuations in vivo.