A cloud-based IoT approach for food safety and quality prediction
Auf einen Blick
- Projektleiter/in : Prof. Dr. Sven Hirsch
- Projektteam : Dr. Jürg Buchli, Dr. Stefan Glüge, Silvana Meyer, Sandro Roth, Dr. Martin Schüle, Dr. Simone Ulzega
- Projektstatus : abgeschlossen
- Drittmittelgeber : KTI (KTI-Projekt / Projekt Nr. 27217.1 PFES-ES)
- Projektpartner : Axino Solutions AG, Genossenschaft Migros Zürich
- Kontaktperson : Sven Hirsch
Beschreibung
Safety and quality prediction are topical issues in food
industry. We are developing a novel IoT approach in the framework
of a collaboration with Genossenschaft Migros Zürich (GMZ), ZHAW
(represented by the institutes IAS and ILGI) and Axino Solutions
AG. The main goal of the project is to provide a robust, reliable
and cost-effective method for real-time monitoring of core
temperatures T_c (t) of food products in various types of coolers
employed in MIGROS shops. Environment temperature measurements, T_e
(t), are provided at regular time intervals by sensors positioned
at specific locations in the coolers. In real circumstances (i.e.,
in a MIGROS shop) core temperatures T_c (t) cannot be measured
directly since it is not allowed to have sensors inside food items.
Therefore, core temperatures have to be estimated using only
environment temperatures T_e (t) and an appropriate mathematical
model describing the physics of the cooling process.
The dynamics of such complex systems cannot be described by a
comprehensive physical model including all possible variables,
parameters and processes. Therefore, a very general approach
consists in designing conceptual models including only a selection
of a few state variables and system parameters. In this
reductionist approach, dominant processes of interest are described
by a physics-compliant deterministic model expressed in terms of a
differential equation (ODE), while all other processes involving
unpredictable and uncontrollable random events, such as
interactions with customers in a crowded shop, are included in the
model as noise. Noise is expressed mathematically by a random term
that has the effect of perturbing in a stochastic way the dynamics
described by the deterministic part of the model. This leads in a
natural way to a so-called stochastic differential equation
model.
For making reliable predictions, the (stochastic) model needs to be
calibrated on some measured data. In other words, model parameters
need to be estimated so that the model can reproduce observations.
This data-driven model calibration is called parameter inference.
Once the model parameters are duly calibrated, an adequate forward
model allows us to make real-time predictions of core temperatures,
given only local air temperature observations.
A network of temperature sensors, appropriately installed in the
coolers in a variety of different shops spread over the territory,
will provide the necessary real-time temperature data. The
real-time, food-specific temperature estimation is one element in a
larger predictive maintenance framework, where we employ
time-series analysis to identify possible cooler malfunction signs
in the shops and continuously monitor the food quality. The system
shall in the end be able to detect early warning signals to deploy
interventions like technical maintenance or initiate quality
management actions.