Smart polyester materials with embedded near-infrared (NIR) functionalities offer a promising pathway for low-cost, covert tagging, and object identification. In this study we present the development and characterization of polyester packaging surfaces printed with spectrally matched twin dyes that are invisible under visible light but selectively absorbed in the NIR region. The dye patterns were applied using a Direct-to-Film transfer (DTF) method onto polyester substrates. To validate their optical behavior, we applied a dual measurement approach. Laboratory grade NIR absorbance spectroscopy was used to characterize the spectral profiles of the twin dyes in the 400–900 nm range. A custom photodiode-based detection system was constructed to evaluate the feasibility of low-cost, embedded NIR absorbance sensing. Results from both methods show correlation in absorbance contrast between the dye pairs, confirming their suitability for spectral tagging. The developed materials were evaluated in a real-world detection scenario using commercially available NIR cameras. Under dark field conditions with edge illuminated planar lighting, the twin dye patterns were successfully recognized through custom software, enabling non-contact identification and spatial localization of the NIR codes. This work presents a low-cost, scalable approach for smart packaging applications based on optical detection of actively illuminated twin dyes using accessible NIR imaging systems.

This paper explores a novel approach to integrating machine-readable security elements into textile product design by embedding OCR-B codes within floral graphic patterns intended for cotton tote bags. The concept relies on the aesthetic use of dark leaf motifs, which naturally provide regions of high absorption suitable for concealing information in the visible spectrum. The embedded OCR-B code remains visually imperceptible to the human observer, while becoming detectable under near-infrared (NIR) imaging due to the differential spectral response of the code and surrounding texture. Using multispectral image acquisition, we evaluate the visibility, contrast, and machine readability of the hidden code across selected wavelengths. The study demonstrates that natural pattern structures can serve as an effective substrate for embedding security identifiers without compromising the artwork’s visual integrity. This concept highlights an emerging direction in design-driven protection of textile products, where aesthetic motifs inspired by nature can simultaneously function as carriers of covert authentication data.