A major part of the work carried out during the project was to acquire the missing data, reformatting, harmonising and fixing them where necessary and developing a meta-database for the OptiSignFood platform, but also to improve the prediction performance.
Data were used from own measurements, but also from existing databases such as EuroFIR (The European Food Information Resource Network project), which includes more than 20 national and specialised food sub-databases. Within the project, six country-specific food composition databases were licensed from EuroFIR to increase the relevance for the European market and the number of foods covered. Pre-processing was necessary to harmonise and standardise the data. The sub-databases were then merged into an aggregated nutrition database. For the environment database, the Life Cycle Inventory (LCI) data of food ingredients from five different databases were prepared, harmonised and standardised for integration into the meta-database.
Separate datasets on pH, colour and texture parameters such as Bostwick Consistency, collected by The Makers Food and Pascal Processing, were combined into an aggregated food technology database containing information on more than 300 ingredients.
In addition, Metacognis used its Heron data mining application to increase the amount of data available for OptiSignFood, thereby improving its accuracy.
The aggregated databases, the nutrition database, the environment database and the food technology database were used to create the meta-database platform.
By increasing the size and number of databases, algorithms were developed to calculate and predict relevant food parameters. The models for nutrition, pH, colour and texture were improved compared to the prototype or preliminary state of 2020. The implementation of the Life Cycle Inventory databases enabled the calculation of specific environmental indicators. The first step was to select indicators that are widely used. Once the framework is established, additional life cycle indicators can be easily integrated.
In parallel, a secure development lifecycle design and user research were carried out to identify the current pain points of the users, but also to get their feedback on the initial UX/UI design.
In the second reporting period, the focus was on creating an OptiSignFood software application for demonstration and validation by pilot customers. An initial user manual was developed, the UX/UI was further improved, and the frontend and backend software was implemented. User needs were identified and prioritised to create an MVP. The models' performance was evaluated with test and industry data sets. 21 pilot customers tested the OptiSignFood platform with overall positive feedback. This feedback was used to identify opportunities to improve usability, interface design and functionality.
