Idea 4: Taste and Smell sensory categorization with EEG wearable in labs

 I have exposure to sensory perception of taste test and smell test data logging in Food laboratories of corporations ( I have been to food labs of many companies). Exposure to IBM, Hyper Taste system.(https://research.ibm.com/projects/hypertaste)

Idea: Implement a data logging system that uses sensory perception analysis to collect and analyze taste and smell test data in food laboratories of corporate entities. This system aims to enhance quality control, research, and product development processes.

Problem Statement: Food laboratories need accurate and efficient methods to analyze taste and smell perceptions of their products. Manual data collection and analysis can be time-consuming and subjective, potentially leading to inconsistencies and inaccuracies in sensory evaluation.

Solution: Develop a comprehensive data logging system that combines sensory perception analysis techniques, digital data collection, and advanced analytics to provide accurate and actionable insights from taste and smell tests.

Business Process Canvas:

1. Requirement Analysis:

   • Collaborate with food laboratories to understand their taste and smell evaluation processes.
   • Identify key sensory parameters and attributes that need to be captured.

2. Sensor Integration:

   • Integrate advanced sensors and devices capable of capturing taste and smell data.
   • Leverage technologies like Hyper Taste system to enhance data accuracy.

3. Test Design and Protocol:

   • Collaborate with food scientists to design standardized taste and smell test protocols.
   • Define the parameters to be evaluated, such as flavor intensity, aroma profiles, and more.

4. Data Collection:

   • Implement a user-friendly digital interface for testers to record sensory perceptions during tests.
   • Allow testers to provide real-time feedback on taste, aroma, texture, etc.

5. Sensory Perception Analysis:

   • Develop algorithms to interpret sensory perception data captured by sensors.
   • Apply statistical methods to analyze taste and smell patterns and preferences.

6. Data Visualization:

   • Create visualizations and dashboards that present sensory perception data in a meaningful way.
   • Enable researchers and product developers to easily interpret and compare results.

7. Cross-Reference with Formulation:

   • Integrate sensory perception data with the formulation and recipe data of tested products.
   • Identify correlations between sensory attributes and ingredients/formulations.

8. Quality Control and Feedback Loop:

   • Implement real-time quality control checks based on sensory data.
   • Automatically alert if a product's sensory attributes deviate from established standards.

9. Product Development Insights:

   • Provide insights to product development teams about preferred taste and smell profiles.
   • Guide formulation adjustments for better consumer acceptance.

10. Collaboration and Feedback:

    • Allow food scientists to collaborate and share insights based on sensory data analysis.
    • Enable teams to collectively refine products and processes.

11. Regulatory Compliance:

    • Ensure that the data logging system complies with relevant food safety and quality regulations.
    • Implement security measures to protect sensitive sensory data.

12. Scalability and Integration:

    • Design the system to handle multiple simultaneous taste and smell tests.
    • Integrate the data logging system with existing laboratory equipment and software.