The aim of this study is to investigate the effect of hydrogen bonds in natural cellulose on luminescent properties, specifically focusing on controlling optical characteristics by tuning hydrogen bond interactions through the incorporation of light scattering from gold nanoparticles (AuNPs). The results from gold nanoparticles are that all samples exhibited strong plasmonic scattering within the 350–800 nm range, albeit with varying intensities. This study demonstrates that cellulose combined with AuNPs is highly effective for light scattering applications due to the reflective properties of cellulose and the surface plasmon resonance (SPR) effects of AuNPs. The enhancement of the autofluorescence signal increased about 2X relative to the Agave-AuNP sample and 67% higher than pure agave cellulose autofluorescence. Moreover, a quenching effect was observed in the mixture of cellulose, C₉H₂₃NO₃Si, (APTES) and AuNPs attributed to hydrogen bond interactions, which diminished the light scattering properties. To understand the autofluorescence properties of cellulose and its interaction with metal nanoparticles, these composite materials are promising candidates for novel applications in bioimaging, sensing, and optoelectronic devices.

This study presents a custom-built, portable multispectral imaging (MSI) system integrated with computer vision for sodium nitrite detection via the Griess reaction on paper-based substrates. The MSI system was used to investigate the absorption characteristics of sodium nitrite at concentrations from 0 to 10 ppm across nine spectral bands spanning 360–940 nm on para-aminobenzoic acid (PABA) and sulfanilamide (SA) substrates. Upon forming azo dyes with N-(1-naphthyl) ethylenediamine (NED), the PABA and SA substrates exhibited strong absorption near 545 nm and 540 nm, respectively, as measured by a spectrometer. This agrees with the 550 nm MSI images, in which higher sodium nitrite concentration regions appeared darker due to increased absorption. A concentration-correlation analysis was conducted for each spectral band. The normalized difference index (NDI), constructed from the most and least correlated bands at 550 nm and 940 nm, showed a stronger correlation with sodium nitrite concentration than the single best-performing band for both substrates. The NDI increased the coefficient of determination (R²) by approximately 19.32% for PABA–NED and 19.89% for SA–NED. This improvement was further confirmed under varying illumination conditions and through comparison with a conventional smartphone RGB imaging approach, in which the MSI-based NDI showed substantially superior performance. The enhancement is attributed to improved contrast, illumination normalization by the NDI, and the narrower spectral bands of the MSI compared with RGB imaging. In addition, the NDI framework enabled effective image segmentation, classification, and visualization, improving both interpretability and usability and providing a practical guideline for developing more robust models with larger training datasets. The proposed MSI system offers strong advantages in portability, sub-minute acquisition time, and operational simplicity, enabling rapid, on-site, and non-destructive chemical analysis.