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Chemical probe-based pattern analysis offers a powerful approach for evaluating complex mixtures, particularly in non-target sensing scenarios where components are unknown or where multivariate interactions, such as those involved in taste perception, dominate the response behavior. However, its broader applicability has been limited by challenges in generating sufficiently diverse probe sets and in acquiring multidimensional response data from large probe arrays. In this study, we address both limitations by constructing a high-capacity sensing platform that integrates artificial DNA-derived chemical probes with conventional fluorescent probes. Artificial DNA probes were synthesized following established modular assembly methods, enabling large-scale generation of structurally diverse sensing elements. An imaging-based detection instrument—combining controlled excitation and high-resolution fluorescence capture—was developed to simultaneously quantify color and intensity responses from up to 88 probes. We applied this system to the analysis of 20 taste-related compounds, demonstrating clear discrimination based on multidimensional fluorescence patterns. Furthermore, systematic evaluation of probe number versus classification accuracy revealed that increased probe diversity substantially enhances non-target discrimination performance, supporting the value of using low-specificity artificial DNA probes in high-density arrays. These results establish a versatile and scalable platform for non-target pattern analysis and highlight the importance of probe multiplicity in complex mixture sensing.
Hitosugi et al. (Wed,) studied this question.