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This study presents a digital surface-enhanced Raman scattering (SERS) method to enhance the sensitivity of SERS detection for low-concentration analytes. Conventional SERS analysis using average Raman intensity faces limitations in distinguishing low concentrations due to the substrate’s sparse distribution of target molecules. To overcome this challenge, we used a binary code-based data analysis approach. Gold nano popcorn substrates were utilized for high-sensitivity detection, with malachite green isothiocyanate (MGITC) as the target molecule. Raman mapping data were analyzed using both the conventional average Raman intensity method and the proposed digital SERS approach. In the digital SERS method, a threshold value was established based on the mean and standard deviation of Raman signals in the absence of target molecules. Pixels with Raman intensities exceeding this threshold were assigned a value of “1”, while those below were assigned “0”. Quantification was then performed based on these digital counts corresponding to MGITC concentrations. Our results demonstrate that the digital SERS method significantly improved the ability to distinguish and quantify analytes in low-concentration ranges that were indiscernible using the conventional approach. This analytical technique shows promise for ultrasensitive chemical analysis and expands the capabilities of SERS-based detection methods, potentially revolutionizing the field of trace analyte detection. Full article