In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach, termed OnePassSampler, has low computational complexity as it avoids multiple scans on its memory buffer by sequentially processing data. Extensive experimentation on a wide variety of datasets reveals that OnePassSampler has a smaller false detection rate and smaller computational overheads while maintaining a competitive true detection rate to ADWIN2.