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Time-temperature integrators (TTIs) are devices capable of recording the thermal history of a system. They have an enormous impact in the food and pharmaceutical industries. TTIs exploit several irreversible thermally activated transitions such as recrystallization, dewetting, smoothening, chemical decomposition, and polymorphic transitions, usually considered drawbacks for many technological applications. The aim of this article is to sensitize research groups working in organic synthesis and surface science toward TTI devices, enlarging the prospects of many new materials. We reviewed the principal applications highlighting the need and criticisms of TTIs, which offer a new opportunity for the development of many materials.

The development of cheap and reliable time-temperature indicators (TTIs) can greatly improve customer satisfaction with respect to the correct handling and storage of perishable goods. A series of new irreversible thermochromic squaraine dyes enables the preparation of simple TTIs whose time-temperature regime can be controlled by selection of the appropriate molecule/substrate combinations.

An experimental procedure was developed to validate experimentally the theoretical optimal process temperatures giving the maximum quality at the surface of conduction heating products for specified levels of sterility. the method involved filling a can with a 10% bentonite solution, placing glass capillaries containing a food quality indicator at the surface, and processing it at different temperatures for the necessary time in order to attain a specified sterility value at the least-lethality point. the acid catalysed sucrose hydrolysis reaction was used as a chemical time-temperature-integrator for a quality index of heat sterilized products. At a pH of 3.8, the kinetic parameters D121.15°C ard z values of this reaction, were 214min and 31°C, respectively. Final surface retentions of sucrose compared well with theoretically predicted values from simulation modelling. Optimal temperatures calculated using either numerical computer optimization or generalized regression equations were in the range of experimentally determined optimal conditions.