Characterizing droplets in spray processes is of high interest in many areas, such as car painting or spray drying. The Time Shift technique provides an efficient and accurate way to measure size and velocity of individual droplets in sprays. Usually peak detection algorithms are applied to extract the information of interest out of the acquired signals. In this work we show that peak detection algorithms are biased when estimating size and velocity of transparent droplets measured by the Time Shift technique. The bias magnitude is quantified with respect to different droplet sizes and velocities as well as aperture configurations. Additionally, based on interpolation, a practical method is provided to reduce the bias. Using the proposed approach, we show that the accuracy in sizing droplets is highly increased, especially for small droplet sizes.