<p>In this paper, a chemical chip based image analysis strategy was developed for rapid amino acids (AAs) analysis by integrating perturbance system (PtbS) with digital image processing algorithms (DIPA). A CuCl₂ - tartaric acid chemical chip was designed to generate rich colorimetric response patterns from complex AAs containing samples, enabling high-throughput data acquisition without complex pretreatment. Chemical reaction chip images were captured using a charge-coupled device for visual analysis, and the resulting color intensity variations were quantitatively described by grayscale values (GVs), which exhibited a direct correlation between with AAs concentrations. Multivariate calibration models were established using partial least squares regression (PLSR) after image preprocessing, including bright-field correction, standard color chart calibration, region-of-interest (ROI) extraction, contrast enhancement, and weighted grayscale conversion. The models demonstrated good predictive performance, achieving R² values of 0.9212–0.9869 for GVs prediction, 0.9321–0.9972 for high-concentration AAs samples, and 0.6811–0.8737 for low-concentration AAs samples. The method demonstrated high precision for samples with relatively high amino acid content, highlighting its applicability for the rapid profiling of samples with moderate to high concentrations. Overall, this work presents a novel and cost-effective strategy for rapid analysis, eliminating the need for complex pretreatment. It promotes the application of high-throughput chemistry and image processing technology in the fields of agriculture and food, and also lays the foundation for more extensive sample analysis in combination with machine learning in the future.</p>