A statistical pattern recognition approach for the classification of cooking stages. The boiling water case

• New acoustical pattern recognition system that takes into account the intrinsic inertia of a generic cooking process.
• This methodology has been tested on the very simple case of water boiling considering audio and vibration as input signals.
• The system developed achieves recognition rates up to 100% for all different types of input (audio, vibration).
• The methodology developed has the potential to be applied in the future for more complex cooking processes.

Although pattern recognition technique has been widely used in many fields, it seems that very few studies have applied this technique to cooking processes. In this preliminary research, a new methodology has been developed and tested on a simple case of water boiling. Besides defining and analysing the efficacy and the performance of a statistical pattern recognition approach when applied to different signals (sound and vibration), an optimisation module has been proposed to boost the classification rates by adding syntactical analysis that enables the inertia of the process to be considered. In the specific case of boiling water, almost 100% successful recognition has been reached. These results prove the validity of this methodology, opening up new research lines for new scenarios, such as different cooking processes, acoustically polluted environments, and sensor optimisation.