Periodic Reporting for period 2 - PhasmaFOOD (Portable photonic miniaturised smart system for on-the-spot food quality sensing)
Período documentado: 2018-07-01 hasta 2019-12-31
PhasmaFOOD's main goal achieved, is to deliver an autonomous, multifunctional multi-sensor optical sensing device for the detection of food quality and safety issues such as food spoilage, adulteration, and aflatoxins. The system is based on visible and near infrared spectroscopy technologies and is supported by an open software architecture to support highly differentiated applicative goals throughout the food supply chain. PhasmaFOOD delivers fast characterisation of foods, encompassing an extendable framework for the deployment of smart chemometric algorithms, data fusion strategies and reference laboratory measurements.
The PhasmaFOOD project has been built upon 4 distinct objectives:
(i) To provide a miniaturized, multi-parameter and programmable sensing node for food spoilage (through microbial activity), food safety (i.e. mycotoxin detection) and adulteration, via the integration of heterogeneous vibrational spectroscopy (visible, fluorescence and near infrared), sensors.
(ii) To provide a smart system embedding detective and predictive capabilities by incorporating smart signal processing, data analytic models, communication enablers and smart algorithms.
(iii) To build food analysis platform hosting data sets for training and calibration of food analysis algorithms and providing proactive decision-making deeper insight into patterns and correlations in sensory data.
(iv) To ensure manufacturability, reliability, and cost‐effectiveness of the whole system for food spoilage, safety, and adulteration sensing applications, targeting realistic business model analysis.
The project approach has certainly been proved to be solid for viable and general-purpose food scanning. The key innovations of the project are the PhasmaFOOD device that integrated 3 different spectroscopies and a high-resolution camera and the PhasmaFOOD software platform that was developed to support the standard laboratory procedures with two different versions for stakeholder needs. The creation of high-quality reference data sets and food analysis models can additionally be considered as an added value asset of the PhasmaFOOD project.
The innovation potential can be safely considered high enough after the identification of a market gap in food safety. In addition, through the intense effort of all partners, the PhasmaFOOD prototype managed to attract the interest of several stakeholders from diverse industrial domains. Numerous trials have showcased the reliability of the device achieving high accuracy results in all use cases examined during the project.
The PhasmaFOOD project has been built upon 4 distinct objectives:
(i) To provide a miniaturized, multi-parameter and programmable sensing node for food spoilage (through microbial activity), food safety (i.e. mycotoxin detection) and adulteration, via the integration of heterogeneous vibrational spectroscopy (visible, fluorescence and near infrared), sensors.
(ii) To provide a smart system embedding detective and predictive capabilities by incorporating smart signal processing, data analytic models, communication enablers and smart algorithms.
(iii) To build food analysis platform hosting data sets for training and calibration of food analysis algorithms and providing proactive decision-making deeper insight into patterns and correlations in sensory data.
(iv) To ensure manufacturability, reliability, and cost‐effectiveness of the whole system for food spoilage, safety, and adulteration sensing applications, targeting realistic business model analysis.
The project approach has certainly been proved to be solid for viable and general-purpose food scanning. The key innovations of the project are the PhasmaFOOD device that integrated 3 different spectroscopies and a high-resolution camera and the PhasmaFOOD software platform that was developed to support the standard laboratory procedures with two different versions for stakeholder needs. The creation of high-quality reference data sets and food analysis models can additionally be considered as an added value asset of the PhasmaFOOD project.
The innovation potential can be safely considered high enough after the identification of a market gap in food safety. In addition, through the intense effort of all partners, the PhasmaFOOD prototype managed to attract the interest of several stakeholders from diverse industrial domains. Numerous trials have showcased the reliability of the device achieving high accuracy results in all use cases examined during the project.
Within this period, progress was achieved in all WPs. Highlights include:
• Detailed definition of the target foods and types of measurements, with corresponding functionality requirements of the detection system.
• Market analysis and strategic planning of commercialization approach.
• Business path, strategy and plan for the PhasmaFOOD device and software
• Demonstration of PhasmaFOOD functionality in two expos in various stakeholders
• 46 peer-reviewed scientific publications, 6 of them in Open Access
• 2 PhD degrees that will be awarded for work performed within PhasmaFOOD framework
• Design of sensing hardware and selection of sensor components: a UV-VIS spectrometer, a NIR spectrometer and a VIS board-level camera.
• Establishment of SOP both for IR spectroscopy, fluorescence spectroscopy and visible reflectance spectroscopy.
• Final feasibility tests using the final PhasmaFOOD prototype on use case 1, 2 and 3 samples.
• Assessment of detection capability for Aflatoxin B1 from ppm to tens of ppb range.
• Set-up of a lighting concept within the WP2, based on the sensing requirements of UV-VIS spectrometer, NIR spectrometer and the VIS board-level camera.
• Electronic design of the near-sensing for the UV-VIS spectrometer and its hardware interface towards the main electronic board. The NIR spectrometer, which is being manufactured by partner IPMS, has received design adaptation.
• Design of final prototypes of electronic boards and electronic integration.
• Development of PhasmaFOOD software components, two different mobile applications and PhasmaFOOD cloud platform database, data fusion techniques and decision making approaches.
• High-quality data reference sets produced for each one of the UCs
• Specification of the security and privacy procedures and the software development and integration strategy.
• Manufacturing, assembly and laboratory testing of the optomechanical hardware of the sensing sub-unit, including all sensors and light sources in a robust 3D printed housing.
• Definition of the standard operating procedures for the PhasmaFOOD sensors, sampling strategies, data assessment and conclusions until M36 of the project.
• Development of multivariate analysis and data fusion strategies for each use case.
• Design and development of the laboratory measurements collection database, with specification of experimental data organization and database structure.
• Final implementation of the software for the PhasmaFOOD smart system.
• Implementation of the main electronic board integrated inside the PhasmaFOOD sensing device, including the hardware and layout designs of the main electronic board.
• Implementation of embedded software for the PhasmaFOOD smart food analysis device.
• Implementation of all integration activities leading to the first PhasmaFOOD prototype.
• Detailed definition of the target foods and types of measurements, with corresponding functionality requirements of the detection system.
• Market analysis and strategic planning of commercialization approach.
• Business path, strategy and plan for the PhasmaFOOD device and software
• Demonstration of PhasmaFOOD functionality in two expos in various stakeholders
• 46 peer-reviewed scientific publications, 6 of them in Open Access
• 2 PhD degrees that will be awarded for work performed within PhasmaFOOD framework
• Design of sensing hardware and selection of sensor components: a UV-VIS spectrometer, a NIR spectrometer and a VIS board-level camera.
• Establishment of SOP both for IR spectroscopy, fluorescence spectroscopy and visible reflectance spectroscopy.
• Final feasibility tests using the final PhasmaFOOD prototype on use case 1, 2 and 3 samples.
• Assessment of detection capability for Aflatoxin B1 from ppm to tens of ppb range.
• Set-up of a lighting concept within the WP2, based on the sensing requirements of UV-VIS spectrometer, NIR spectrometer and the VIS board-level camera.
• Electronic design of the near-sensing for the UV-VIS spectrometer and its hardware interface towards the main electronic board. The NIR spectrometer, which is being manufactured by partner IPMS, has received design adaptation.
• Design of final prototypes of electronic boards and electronic integration.
• Development of PhasmaFOOD software components, two different mobile applications and PhasmaFOOD cloud platform database, data fusion techniques and decision making approaches.
• High-quality data reference sets produced for each one of the UCs
• Specification of the security and privacy procedures and the software development and integration strategy.
• Manufacturing, assembly and laboratory testing of the optomechanical hardware of the sensing sub-unit, including all sensors and light sources in a robust 3D printed housing.
• Definition of the standard operating procedures for the PhasmaFOOD sensors, sampling strategies, data assessment and conclusions until M36 of the project.
• Development of multivariate analysis and data fusion strategies for each use case.
• Design and development of the laboratory measurements collection database, with specification of experimental data organization and database structure.
• Final implementation of the software for the PhasmaFOOD smart system.
• Implementation of the main electronic board integrated inside the PhasmaFOOD sensing device, including the hardware and layout designs of the main electronic board.
• Implementation of embedded software for the PhasmaFOOD smart food analysis device.
• Implementation of all integration activities leading to the first PhasmaFOOD prototype.
Improvements of the State of the Art include the following:
• Integration of UV+VIS Fluorescence/VIS, VIS camera and NIR spectroscopy in a single portable, standalone device, i.e. without the need for further light sources.
• Development of a system (device + method) to detect in a fast and low cost way mycotoxin (in particular aflatoxin B1) contamination in the range of ppm (first, fast screening).
• PhasmaFOOD software system and functional platform and architecture which apply a novel approach for distributed data analysis and decision making. Each platform layer (embedded software, mobile application and cloud platform) perform assigned data preparation and analysis operations on collected measurements. .
• Data analysis playground as part of the cloud platform web dashboard which allow PhasmaFOOD system users to test different configurations of data analysis pipelines and assess performance of each step in data pre processing and classification. This also enables users to validate and assess quality of collected data sets.
• Data fusion algorithms that integrate data from the 4 measurement methods (Fluorescence, VIS and NIR spectroscopy, VIS imaging) from a variety of sources (end users) and use cases in the food safety context.
• Electronic board hardware design with the capability of integrating three different sensing components (UV-VIS, NIR, micro camera) for food quality safety.
• Integration of UV+VIS Fluorescence/VIS, VIS camera and NIR spectroscopy in a single portable, standalone device, i.e. without the need for further light sources.
• Development of a system (device + method) to detect in a fast and low cost way mycotoxin (in particular aflatoxin B1) contamination in the range of ppm (first, fast screening).
• PhasmaFOOD software system and functional platform and architecture which apply a novel approach for distributed data analysis and decision making. Each platform layer (embedded software, mobile application and cloud platform) perform assigned data preparation and analysis operations on collected measurements. .
• Data analysis playground as part of the cloud platform web dashboard which allow PhasmaFOOD system users to test different configurations of data analysis pipelines and assess performance of each step in data pre processing and classification. This also enables users to validate and assess quality of collected data sets.
• Data fusion algorithms that integrate data from the 4 measurement methods (Fluorescence, VIS and NIR spectroscopy, VIS imaging) from a variety of sources (end users) and use cases in the food safety context.
• Electronic board hardware design with the capability of integrating three different sensing components (UV-VIS, NIR, micro camera) for food quality safety.