A natural pigment-based nanosized colorimetric sensor for freshness evaluation of aquatic products

This work proposes an innovative method for monitoring the freshness of aquatic products using a nanosized colorimetric sensor (NCS). Six porous organic frameworks (POFs) were utilised to modify and sensitize four food-friendly natural pigments. Four optimal nano-pigments were selected based on their RGB-responsive interaction with NH₃. Meanwhile, pigments exhibited significant changes in Vis-NIR spectra after nanosizing. Results indicated that the optimal NCS performs well in predicting aquatic products' total volatile alkaline nitrogen (TVB-N) content. While the prediction model based on image data did not benefit from nanosizing, the model constructed from spectral data demonstrated high accuracy and stability. The best PLS model achieved a prediction set correlation coefficient (Rp) of 0.9891 and a residual prediction deviation value of 6.66 by combining with variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. Thus, the nanofabrication of POFs on pigments shows promise for developing high-precision and stable freshness prediction models.