This paper describes a strategy followed to achieve a sensing phenomenon in aqueous media using water-insoluble organic molecules. A sensory polymeric material for the colorimetric sensing of cyanide in water has been developed based on the reactivity of this anion with a fluorene derivative.

Polymers are extensively used in food and beverage packaging to shield against contaminants and external damage due to their barrier properties, protecting the goods inside and reducing waste. However, current trends in polymers for food, water, and beverage applications are moving forward into the design and preparation of advanced polymers, which can act as active packaging, bearing active ingredients in their formulation, or controlling the head-space composition to extend the shelf-life of the goods inside. In addition, polymers can serve as sensory polymers to detect and indicate the presence of target species, including contaminants of food quality indicators, or even to remove or separate target species for later quantification. Polymers are nowadays essential materials for both food safety and the extension of food shelf-life, which are key goals of the food industry, and the irruption of smart materials is opening new opportunities for going even further in these goals. This review describes the state of the art following the last 10 years of research within the field of food and beverage polymer’s applications, covering present applications, perspectives, and concerns related to waste generation and the circular economy.

Iron in blood serum, wine and water has been visually detected and quantified quickly by processing photographs of an iron colorimetric sensory polymer substrate. The photographs may be taken with a conventional digital camera or Smartphone. The sensory materials were designed following a straightforward strategy. A proven iron organic chelator was easily transformed into an acrylic monomer and further copolymerized with hydrophilic co-monomers to render a membrane comprised of a hydrophilic, gel-like, polymer network. The film-like membrane generated was cut into small-diameter sensory discs. Thus, upon immersion of the sensory disks in blood serum, wine, and water, a colour development was rapidly observed which could be easily correlated with the iron concentration of the samples. RGB digital parameters obtained from photographs of the sensors were processed statistically using principal component analysis (PCA) and used to elaborate titration curves and quantify iron concentrations. The response time of the sensory films was short, 15 min, and the concentrations measured in water ranged from 56 ppb to 56 ppm. This broad range covers the U.S. Environmental Protection Agency (EPA) and European Union (EU) drinking water standards for iron in drinking water (<300 and 200 ppm, respectively), the typical iron content in wines (1 to 10 ppm) and the normal range of iron in the blood serum in men (0.8–1.8 ppm). This methodology for detecting and quantifying chemical species avoids the time-consuming sample preparation, expensive laboratory techniques, and specialized personnel needed to carry out conventional analytical methods.

We have developed an in situ methodology for determining nitrite concentration in processed meats that can also be used by unskilled personnel. It is based on a colorimetric film-shaped sensory polymer that changes its color upon contacting the meat and a mobile app that automatically calculates the manufacturing and residual nitrite concentration by only taking digital photographs of sensory films and analyzing digital color parameters. The film-shaped polymer sensor detects nitrite anions by an azo-coupling reaction, since they activate this reaction between two of the four monomers that the copolymer is based on. The sensory polymer is complemented with an app, which analyzes the color in two different digital color spaces (RGB and HSV) and performs a set of 32 data fittings representing the concentration of nitrite versus eight different variables, finally providing the nitrite concentration of the test samples using the best fitting curve. The calculated concentration of nitrite correlates with a validated method (ISO 2918: 1975) usually used to determine nitrite, and no statistically significant difference between these methods and our proposed one has been found in our study (26 meat samples, 8 prepared, and 18 commercial). Our method represents a great advance in terms of analysis time, simplicity, and orientation to use by average citizens.