How can crispiness be measured objectively, and how can consumer perception be predicted?

This presentation introduces a method developed to analyse the crispiness of products such as bread, biscuits, chips and French fries, as well as the “snap” of chocolate, and to link these characteristics to sensory perception.

The presentation demonstrates how acoustic signals, combined with signal processing and machine learning, can be used to predict texture attributes based on measurement data and sensory panel evaluations.

This approach can be applied in product development, quality control and process optimisation within the food industry.

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