#  Data Summary

The main objective of PhasmaFOOD is to design and implement an autonomous,
multifunctional and programmable optical sensing device, integrated with
spectroscopy technologies for food hazard, microbial activity detection and
shelf-life estimation.

The project will configure a dedicated cloud platform for collecting data from
the PhasmaFOOD device and to perform in-depth analysis of the data obtained
from the project’s device. The cloud platform will enable correlation of the
device measurements with spectral analysis results collected from all
connected PhasmaFOOD smart sensing devices. This will provide the opportunity
for detection of trends, patterns and distribution of food contamination which
can help prevent outbreaks and provide recommendation for improving food
safety at different stages of farm-to-fork production chain.

PhasmaFOOD cloud platform will also host sensory and contextual database which
will be used for training data analysis and machine learning models deployed
on smart sensory device and as part of PhasmaFOOD mobile application (data
analytics calibration). The data collected from the miniaturized device will
be forwarded to the cloud where the spectroscopy analysis will take place
using a reference database. The PhasmaFOOD reference database will be built
upon laboratory measurements of the specific foods and qualities supported by
the PhasmaFOOD Use Cases:

  * **Use case 1: Detection of mycotoxins in various grains and nuts:** Aflatoxins, a special type of mycotoxins, will be detected. A simple, convenient ultraviolet test makes it possible to detect the possible presence of aflatoxin. 

  

  * **Use case 2: Detection of early sign of spoilage and spoilage in fruits, vegetables, meat, fish** : Combined with estimation on product expiration date. 

  

  * **Use case 3: Detection of food fraud:** Adulteration of alcoholic beverages, oil, milk and meat. 

  

In Section 1 and Section 3 we provide summary and descriptions, respectively,
of the Datasets that will be produced by the different laboratory experiment
set-ups that will be used to collect reference measurements. These dataset
descriptions provide useful insights into meat-related laboratory experiments
and may be useful in other food security settings outside the project.

Deliverable updates are planned for Months 18 and 36 to reflect the updates in
the data management framework of PhasmaFOOD.

#  Datasets: Reference and Name

The PhasnaFOOD project has already identified, and is still further
identifying, a set of data sources that will stem from new and existing
laboratory experiments that will produce the required reference measurements
required by the use case scenarios.

Following an iterative process, data sources are established, encapsulating
the project’s use case requirements. A refinement process is foreseen to
continuously take place throughout the lifetime of the project, as new
laboratory experiments will be deployed to collect additional reference
measurements.

##  Naming Methodology

###  Folder nomenclature

For each data source there is a folder containing the data source definition
and the data sample. Each folder has a specific name that is composed by
different parts/elements, containing information about:

  * Use Case (UC1, UC2, UC3), 

  

  * Food & Beverage Type (FBT-<Name>), 

  

  * Contamination & Fraud Type (CFT-<Name>), 

  

  * Measurement Sensor (MS-<Name>), 

  

  * Laboratory (LO-<Organisation short name>). 

  

Based on the above, a data source folder instance example would be:

UC1_FBT-Pork-Meat_CFT-SPOILAGE_MS-FTIR_LO-AU

###  Datasets nomenclature

In each folder the files containing the measurement datasets will follow a
similar nomenclature, adding field information required for the further
identification of the datasets. For each dataset, its specific name is
composed by different parts, containing the following elements:

  * Use Case (UC1, UC2, UC3), 

  

  * Food & Beverage Type (FBT-<Name>), 

  

  * Contamination & Fraud Type (CFT-<Name>),  Measurement Sensor (MS-<Name>), 

  

  * Laboratory (LO-<Organisation short name>), 

  

  * Dataset ID (DID-<ID provided by LO>), 

  

  * Date Provided (DP-<YYYYMMDD>), 

  

  * Dataset File Extension (DFE-<filename extension>). 

  

Based on the above, a dataset instance example would be:

UC1_FBT-Pork-Meat_CFT-SPOILAGE_MS-FTIR_LO-AU_ DID-111_ DP-20170122_ DFE-XLSX

#  Dataset Descriptions

##  Generic Use Case Data Sources

For the implementation of the PhasmaFOOD use cases’ samples spectral databases
will be built up to hold chemical reference data and used later to benchmark
and validate new samples. The general schema for Data Sources descriptions in
PhasmaFood is provided below.  
  
<table>  
<tr>  
<th>

Data Source name: Use-case 1, 2 and 3

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Data, used for use-cases 1, 2 and 3 (see D1.1), populating spectroscopic
databases:

  1. UV-Vis data – spectroscopic data 

  

  2. NIR data – spectroscopic data 

  

  3. Image data – 3D spectroscopic data 

  

  

Chemical reference data (e.g. nitrogen/moisture determination, GC, LC-MS) to
benchmark the samples in use-cases 1, 2, and 3.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Samples used in use-cases 1, 2 and 3 for spectral data base building (WP3) and
validation (WP6)

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

Unknown, pilot equipment is not available so far

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

Unknown so far, probably .csv or excel formats

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

Unknown so far

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

Unknown so far

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

Unknown so far

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

WP 3 – till M 27, WP 6 till M36

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Only available to consortium

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

On demand, when pilot equipment is available

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Unknown so far

</td> </tr>  
<tr>  
<td>

**Raw data sample**

</td> </tr>  
<tr>  
<td>

NA

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td> </tr>  
<tr>  
<td>

(Print screen for a data sample, if not in text format; e.g. Excel Sheet) NA

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Field Descriptions (Description of the fields within the data, whenever
possible; e.g. JSON keys descriptions, Excel Sheet’s column descriptions,
etc.)**

</th> </tr>  
<tr>  
<td>

**Field Name**

</td>  
<td>

**Field Description**

</td>  
<td>

**Type of Data**

</td> </tr>  
<tr>  
<td>

Sample name

</td>  
<td>

Sample name

</td>  
<td>

text

</td> </tr>  
<tr>  
<td>

Reference value

</td>  
<td>

Class1/Class2

</td>  
<td>

category

</td> </tr>  
<tr>  
<td>

Wavenumber

</td>  
<td>

Wavenumber

</td>  
<td>

number

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>

In the remaining sections, detailed instances of PhasmaFOOD laboratory
measurements are presented. These will form the basis of the PhasmaFOOD cloud
reference database and will be specifically considered during the
specification (WP1) and design (WP2) project activities.

_NOTE:_ The measuring methods of the PhasmaFOOD demonstrator will include
spectral data (from visible and near-infrared spectroscopy) and image data
(visible camera). The following data sets are examples from FTIR spectroscopy
and multispectral imaging (MSI). Although these exact measuring methods will
not be implemented into the PhasmaFOOD demonstrator, the data format of FTIR
spectra is representative of our visible and NIR spectra, and MSI is used to
demonstrate the data format of our visible images.

##  Beef meat spoilage experiment with FTIR  
  
<table>  
<tr>  
<th>

Data Source name: Beef meat spoilage experiment with FTIR

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Beef meat spoilage using FTIR spectroscopy. Aerobic storage at chill (0, 5 °C)
and abuse (10, 15, and 20 °C) temperatures. Prediction of the microbial load
on the surface of meat samples directly from FTIR spectral data.

**Dataset entities** Beef meat spoilage

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

FTIR spectral data

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

Comma Separated values text files: *.csv

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

Csv

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

5 (storage temperatures) x [1,7] MB

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

[1,7] MB for each storage temperature. Each csv file is about [70,100] KB.

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

Overall period of 350 h

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

Meat samples stored at 0 and 5 °C were analyzed every 24 h, whereas samples
stored at 10, 15, and 20 °C were analyzed every 8, 6, and 4 h, respectively.

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Data quality**

</th>  
<th>

Complete and published: Argyri et al. (2010)  1  Rapid qualitative and
quantitative detection of beef fillets spoilage based on Fourier transform
infrared spectroscopy data and artificial neural networks Sensors and
Actuators B: Chemical 145, 146-154.

</th> </tr>  
<tr>  
<td>

**Raw data sample**

</td> </tr>  
<tr>  
<td>

TITLE

DATA TYPE INFRARED SPECTRUM

ORIGIN JASCO OWNER

DATE 13/11/09

TIME 15:15:51

SPECTROMETER/DATA SYSTEM

LOCALE 1033

RESOLUTION

DELTAX 0.964233

XUNITS 1/CM

YUNITS ABSORBANCE

FIRSTX 399.1927

LASTX 4000.6047

NPOINTS 3736

FIRSTY 5

MAXY 5

MINY -1.51932

XYDATA

399.1927 5

400.1569 5

401.1211 5

402.0854 5 403.0496 5

404.0138 5

404.9781 5

405.9423 1.17363

406.9065 0.86412

407.8708 0.715091

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**Field Descriptions (Description of the fields within the data, whenever
possible; e.g. JSON keys descriptions, Excel Sheet’s column descriptions,
etc.)**

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**Field Name**

</td>  
<td>

**Field Description**

</td>  
<td>

**Type of Data**

</td> </tr>  
<tr>  
<td>

XYDATA

</td>  
<td>

Spectral data

</td>  
<td>

Wavelengths (cm  -1  )

</td> </tr>  
<tr>  
<td>

Second column (next to XYDATA)

</td>  
<td>

Spectral data

</td>  
<td>

Absorbance at each specific wavelength

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>

##  Pork meat spoilage experiment with FTIR  
  
<table>  
<tr>  
<th>

Data Source name: Pork meat spoilage experiment with FTIR

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Spectral data from FTIR with minced pork meat spoilage during aerobic storage
of meat samples at different storage temperatures (0, 5, 10, and 15 °C).
Prediction of the microbial load on the surface of meat samples directly from
FTIR spectral data.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Pork minced meat spoilage

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

FTIR spectral data

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

Comma Separated values text files: *.csv

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

csv

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Direct data URI**

</th>  
<th>

N/A

</th> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

4 (storage temperatures) x [1,7] MB.

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

[1,7] MB for each storage temperature. Each csv file is about [70,100] KB.

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

Overall period of 350 h

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

Samples stored at 0 and 5 °C were analyzed approximately every 24 and 12 h,
respectively, whereas samples stored at 10 and 15 °C were analyzed every 6–7
h.

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Complete and published: Papadopoulou et al. (2011)  2  Contribution of

Fourier transform infrared (FTIR) spectroscopy data on the quantitative
determination of minced pork meat spoilage. Food Research International 44,
3264.

</td> </tr>  
<tr>  
<td>

**Raw data sample**

</td> </tr>  
<tr>  
<td>

TITLE

DATA TYPE INFRARED SPECTRUM

ORIGIN JASCO

OWNER

DATE 13/11/09

TIME 15:15:51

SPECTROMETER/DATA SYSTEM

LOCALE 1033

RESOLUTION

DELTAX 0.964233

XUNITS 1/CM

YUNITS ABSORBANCE

FIRSTX 399.1927

LASTX 4000.6047

NPOINTS 3736

FIRSTY 5

MAXY 5

MINY -1.51932

XYDATA

399.1927 5

400.1569 5

401.1211 5

402.0854 5

403.0496 5 404.0138 5

404.9781 5

405.9423 1.17363

406.9065 0.86412

407.8708 0.715091

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**Field Descriptions (Description of the fields within the data, whenever
possible; e.g. JSON keys descriptions, Excel Sheet’s column descriptions,
etc.)**

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

**Field Name**

</td>  
<td>

**Field Description**

</td>  
<td>

**Type of Data**

</td> </tr>  
<tr>  
<td>

XYDATA

</td>  
<td>

Spectral data

</td>  
<td>

Wavelengths (cm  -1  )

</td> </tr>  
<tr>  
<td>

Second column (next to XYDATA)

</td>  
<td>

Spectral data

</td>  
<td>

Absorbance at each specific wavelength

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr> </table>

##  Beef meat spoilage experiment with MultiSpectral Imaging (MSI)  
  
<table>  
<tr>  
<th>

Data Source name: Beef meat spoilage experiment with MultiSpectral Imaging
(MSI)

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Beef meat spoilage using Multispectral imaging. Aerobic storage at 2, 8 and 15
°C, sterile and naturally contaminated samples. Prediction and mapping of the
microbial load on the surface of meat samples directly from Multispectral
Imaging data.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Beef meat spoilage.

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

Multispectral images. Each sample consists of 18 images captured at 18
different wavelengths.

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

*.hips files. Graphic classification file. 

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

Hips

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

(105 for sterile + 114 for naturally contaminated) x 100MB ~= 22 GB

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

~100MB for each sample

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

Overall period of 350 h

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request due to the large size

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

Meat samples stored at 2 and 8 °C were analyzed every 24 h, whereas samples
stored at 15 °C were analyzed every 6 h.

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Complete and published: Tsakanikas et al (2016) Exploiting multispectral
imaging for non-invasive contamination assessment and mapping of meat samples.
Talanta 161, 604.

_http://www.sciencedirect.com/science/article/pii/S0039914016306889_

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td> </tr>  
<tr>  
<td>

</td> </tr> </table>

##  Pork minced meat spoilage experiment with MultiSpectral Imaging (MSI)  
  
<table>  
<tr>  
<th>

Data Source name: Pork minced meat spoilage experiment with MultiSpectral
Imaging (MSI)

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Pork minced meat spoilage using Multispectral imaging. Aerobic and MAP
(modified atmospheres packaging) storage at 0, 5, 10, 15 and 20 °C. Prediction
and mapping of the microbial load on the surface of meat samples directly from
Multispectral images data.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Pork minced meat spoilage.

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

Multispectral images. Each sample consists of 18 images captured at 18
different wavelengths.

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

*.hips files. Graphic classification file. 
</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

Hips

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

(160 for aerobic storage + 150 for map storage) x 100MB ~= 300 GB

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

~100MB for each sample.

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

Overall period of 350 h.

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request due to the large size.

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

Meat samples stored at 0 and 5 °C were analyzed every 24 h, samples stored at
8 °C were analyzed every 8 h, whereas samples stored at 15 and 20 °C were
analyzed every 5 h.

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Complete and published: Dissing et al. (2013) Using Multispectral Imaging for

Spoilage Detection of Pork Meat. Food Bioprocess Technol (2013) 6:2268- 2279
3

</td> </tr>  
<tr>  
<td>

**Print screen**

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr> </table>

##  Adulteration experiment Beef & Pork meat with MultiSpectral Imaging (MSI)  
  
<table>  
<tr>  
<th>

Data Source name: Adulteration experiment Beef & Pork meat with MultiSpectral
Imaging (MSI)

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Beef and pork-minced meat was mixed in order to achieve nine different
proportions of adulteration and two categories of pure pork and beef.
Detection of minced beef fraudulently substituted with pork and vice versa
using Multispectral imaging.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Detection of minced beef fraudulently substituted with pork and vice versa

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

Multispectral images. Each sample consists of 18 images captured at 18
different wavelengths.

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

*.hips files. Graphic classification file. 

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

Hips

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

220 meat samples in total from four independent experiments (55 samples per
experiment) x 100MB ~= 22 GB

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

~100MB for each sample

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request due to the large size

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

On demand. Storage is not applicable here.

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Complete and published: Ropodi et al. (2015) Multispectral image analysis
approach to detect adulteration of beef and pork in raw meats. Food Research
International 67, 12-18  4  .

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr> </table>

##  Adulteration experiment Beef & Horse meat with MultiSpectral Imaging (MSI)  
  
<table>  
<tr>  
<th>

Data Source name: Adulteration experiment Beef & Horse meat with MultiSpectral
Imaging (MSI)

</th> </tr>  
<tr>  
<td>

**Data source description**

</td> </tr>  
<tr>  
<td>

Detection of minced beef adulteration with horsemeat, as well as during
storage in refrigerated conditions. For this reason, multispectral images of
110 samples from three different batches of minced beef and horsemeat in 18
wavelengths were acquired. Images were taken again after samples were stored
at 4 °C for 6, 24 and 48 h.

</td> </tr>  
<tr>  
<td>

**Dataset entities**

</td>  
<td>

Detection of minced beef adulteration with horsemeat.

</td> </tr>  
<tr>  
<td>

**Dataset attributes**

</td>  
<td>

Multispectral images. Each sample consists of 18 images captured at 18
different wavelengths.

</td> </tr>  
<tr>  
<td>

**Data type**

</td>  
<td>

*.hips files. Graphic classification file. 

</td> </tr>  
<tr>  
<td>

**Standard**

</td>  
<td>

Hips

</td> </tr>  
<tr>  
<td>

**Direct data URI**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Data Size**

</td>  
<td>

(110 samples from three different batches of minced beef and horsemeat) x
100MB ~= 11 GB

</td> </tr>  
<tr>  
<td>

**Sample size**

</td>  
<td>

~100MB for each sample

</td> </tr>  
<tr>  
<td>

**Data lifetime**

</td>  
<td>

N/A

</td> </tr>  
<tr>  
<td>

**Availability**

</td>  
<td>

Upon Request due to the large size

</td> </tr>  
<tr>  
<td>

**Data collection frequency**

</td>  
<td>

Sampling at 6, 24 and 48 h of storage at 4°C.

</td> </tr>  
<tr>  
<td>

**Data quality**

</td>  
<td>

Complete and published: Ropodi et al. (2017) Multispectral imaging (MSI): A
promising method for the detection of minced beef adulteration with horsemeat.
Food Control 73, 57-63  5  .

</td> </tr>  
<tr>  
<td>

**Print screen (if possible)**

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr> </table>

#  Standards and Metadata

The food sector is highly characterized by non-centralized sources of dynamic
and heterogeneous data which increase the information about, for example, meat
quality in a particular region, but typically, decrease the effectiveness of
sharing that information across stakeholders. The harmonization and
standardization of data structures and data exchange services are fundamental
challenges for both the information society as a whole, as well as for Food
Security applications.

The main focus of PhasmaFOOD is to provide the required data interoperability
and adaptability in a variety of food safety settings. PhasmaFOOD will seek to
establish liaisons within the food security value chain, with a particular
interest in standardizing the use of food related Open Data in the food
security sector and standardizing the types of food related analytics that can
be sought from big data platforms.

##  Rapid alert system for food and feed (RASFF)

Commission Regulation (EU) No. 16/2011 specifies the framework for
implementing measures for the rapid alert system for food and feed (RASFF).
The RASFF notifications are generated from templates that provide guidelines
on how different fields in the notifications are used. In the table below we
provide a sample example of the RASFF fields that are closely related to the
PhasmaFOOD application scenarios. Detailed analysis is provided in the project
specification document D1.2 _“Functional and System Specifications”_ .

__RASFF Fields_ _  
  
<table>  
<tr>  
<th>

**RASFF Fields**

</th>  
<th>

**Explanation**

</th> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**notification classification**

</td>  
<td>

Classification of the notification according to the definitions given in
Regulation 16/2011 and to the guidance given in SOP 5.

</td> </tr>  
<tr>  
<td>

**information source**

</td>  
<td>

Specific source of the information contained in the notification if this is
relevant to the understanding of the content of the notification, e.g. a food
control body in a third country or a consumer association.

</td> </tr>  
<tr>  
<td>

**risk decision**

</td>  
<td>

Gives information about the evaluation of the risk:

\- whether the risk is considered to be serious, not serious or undecided; -
motivate: why was the risk evaluated as serious (only to be added when the
evaluation as serious risk is not straight forward).

</td> </tr>  
<tr>  
<td>

**product category**

</td>  
<td>

Choose the product category from one of the two lists (alphabetical order) or
enter it into the other field if the category is not among the entries of the
lists or if there are more than one (for more than one product belonging to
different categories).

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**product name(s) (on label)**

</td>  
<td>

Precise product name(s), characterising the product(s), without using any
commercial name; often the product name on the label that can be found on the
packaging.

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**product CN code**

</td>  
<td>

Enter the Common Nomenclature code for the product concerned.

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**product aspect**

</td>  
<td>

Here you should enter important characteristics of the product such as the
temperature at which it is kept but also e.g. the kind of packaging, etc.

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**sampling dates**

</td>  
<td>

6 separate fields are provided for a maximum of 6 separate values to be
entered

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**sampling info**

</td>  
<td>

Make a reference to a compulsory sampling methodology or inform about the
circumstances in which the sample was taken (esp. if the sample was taken from
an opened packaging of the product etc.).

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**sampling place**

</td>  
<td>

Place where the samples were taken: use the list box provided or the field
other if the place is not among the list entries or to specify the name of the
operator.

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Analytical method(s)**

</td>  
<td>

If a specific analytical method was applied, e.g. one described in legislation
or in an EN or international standard, enter it here.

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**hazards identified**

</td>  
<td>

Enter the hazards that were evaluated as non-compliant (according to
legislation or risk evaluation) as a result of the analysis or analyses.

</td>  
<td>

</td> </tr> </table>

__RASFF List of values_ _

In RASFF when there is mention of an “open list”, it is a list of entities to
which new entities could be added. The table below outlines the main lists of
values used in RASFF.  
  
<table>  
<tr>  
<th>

**RASFF Lists of Values**

</th>  
<th>

**Explanation**

</th> </tr>  
<tr>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**notification type**

</td>  
<td>

Food, food contact material, feed

</td> </tr>  
<tr>  
<td>

**notification classification**

</td>  
<td>

Alert notification, border rejection notification, information notification
for Attention, information notification for follow-up, news

</td> </tr>  
<tr>  
<td>

**product relation**

</td>  
<td>

Additional lots, different variety, ingredient, processed product, raw
material

</td> </tr>  
<tr>  
<td>

**risk decision**

</td>  
<td>

Serious, not serious, undecided

</td> </tr>  
<tr>  
<td>

**impact on**

</td>  
<td>

Human health, animal health, environment

</td> </tr>  
<tr>  
<td>

**unit weight/volume**

</td>  
<td>

Closed list of units for weight/volume: g, kg, l, ml

</td> </tr>  
<tr>  
<td>

**temperature**

</td>  
<td>

Ambient, chilled, frozen

</td> </tr>  
<tr>  
<td>

**hazard**

</td>  
<td>

Closed list, see annex with an extracted hazards list from RASFF Access
database (where the master data for hazards are kept)

</td> </tr> </table>

**durability date** Best before, sell-by, use-by

##  World Health Organization (WHO) FOSCOLLAB

The World Health Organization (WHO), through its Department of Food Safety and
Zoonoses (FOS), initiated a project named FOSCOLLAB to improve ways of sharing
food safety data and information to support risk assessment and decision-
making in food safety. FOSCOLLAB is a platform accessible from internet and
displaying together within dashboards various data (quantitative and
qualitative) and information (e.g. expert advice) useful for food safety
professionals. FOSCOLLAB allows linkages between databases using four
criteria: food name, hazard name, country of origin and year for data
generation.

**FOSCOLLAB Element Explanation**

**Sample collection, prep and** Important context can be added to sampling
information by also **analysis** Reporting the sample size, including units,
and the sample’s representativeness. Where the user is interested in knowing
the prevalence of an analyte, or knowing that an analyte is not present with
an estimated level of confidence, information about the representativeness of
the sample will be very important. In some cases, this is not necessary, for
example, where the user of

FOSCOLLAB is only seeking an indication of the presence of an analyte

**Country of origin of the** Country of origin is necessary in identifying the
country where **sample** contamination occurred

**Why sample was collected** Outbreak investigation, recall verification,
compliance, random sampling/surveillance, monitoring, baseline studies…

**Action Description** Action taken based on laboratory result; e.g.
International

Health Regulation (IHR) risk assessment/notification

**Instrument name** Analytical instrument used to identify analyte ex: Whole-
Genome Sequencing (WGS), platforms, test kits, etc.

##  Interoperability

PhasmaFOOD will investigate data interoperability with the aforementioned
standard food safety data models and will also consider additional available
food data models.

Data Interoperability will have to be considered at the layer of the
PhasmaFOOD Cloud Platform and the specification of the platform APIs. Adapting
the existing data models will enable the PhasmaFOOD application to operate in
different environments (e.g. food security checks) and exchange information
with existing systems, thus growing the application potential.

#  Data Access and Sharing

Due to the nature of the data involved, some of the results that will be
generated by each project phase will be restricted to authorized users, while
other results will be publicly available. As is our commitment, data access
and sharing activities will be rigorously implemented in compliance with the
privacy and data collection rules and regulations, as they are applied
nationally and in the EU, as well as with the H2020 rules. In the case end-
user testing will be performed, PhasmaFOOD users would be required to pre-
register and consent using the system. Then they will need to authenticate
themselves against a user database. If successful, the users will have roles
associated with them. These roles will determine the level of access that a
user will be given and what they will be permitted to do.

As the raw data included in the data sources will be gathered from the closed
and controlled laboratory experiments, collected measurements will be seen as
highly commercially-sensitive. Therefore, access to raw data can only take
place through the partners involved in the performance of the laboratory
measurements. For the models to function correctly, the data will have to be
included into the PhasmaFOOD cloud database. The results of the food analytics
will be secured and all privacy concerns will be catered during the design
phase. In the cases of trend analytics, anonymization methods will be applied
as part of the built-in cloud platform features.

Publications will be released and disseminated through the project
dissemination and exploitation channels to make external research and market
actors aware of the project as well as appropriate access to the data.

Within the project, our produced conference papers and journal publications
will be Green

Open Access and stored in an appropriate repository – such as OpenAIRE
(European Comission, 2015), Registry of Research Data Repositories (German
Research Foundation, 2015) or Zenodo (CERN Data Centre, 2015).

Since the Data Management is expected to mature during the course of the
project, an updated release of this document will follow on M18, where the
repositories for data storage will be specified and more detailed information
on how these data can be accessed by the wider research community will be
provided.

#  Archiving and Preservation

_Short Term_

All original raw data files and data source processing programs will be
versioned over time and maintained in a date-stamped file structure with text
files documenting the background. As the data will be stored in the PhasmaFOOD
cloud repositories, the data will be automatically backed up based on a
standardized schedule. These backups could be brought back online within a
reasonable timeframe that will ensure that there is no detrimental effect of
the data being lost or corrupted.

_Long Term_

It is in the project’s intentions that the long term high quality final data
product generated by PhasmaFOOD will be available for use by the research
community as well as industry peers. We will identify appropriate archiving
institutions that might serve as long term data preservation.

#  Data Management Plan Checklist

At the end of the project, we will be carrying out the following checklist to
ensure that we are meeting the criteria to have successfully implemented an
Open Access Data Management Plan. By adhering to the items below, we are
confident that the project will provide open access to the appropriate data
and software, and thereby, enable researchers to utilize the findings of this
project to further expand their knowledge capacity and personal gains as well
as to provide the ITS industry with the necessary tools to advance their
business and processes.

  1. Discoverable: 

    1. Are the relevant data that are to be made available, our project publications or any Open software that has been produced or used in the project, easily discoverable and readily located? 

  

    2. Have we identified these by means of a standard identification mechanism? 

  
  

  

  2. Accessible: 

    1. Are the data and associated software in the project accessible, where appropriate, and what are the modes of access, scope for usage of this data and what are the licensing frameworks, if any, associated with this access (e.g. licensing framework for research and education, embargo periods, commercial exploitation, etc.)? 

  
  

  

  3. Useable beyond the original purpose for which it was collected: 

    1. Are the data and associated software, which are made available, useable by third parties even after the collection of the data? 

  

    2. Are the data safely stored in certified repositories for long term preservation and curation? 

  

    3. Are the data stored along with the minimum software, metadata and documentation to make them useful? 

  
  

  

  4. Interoperable to specific quality standards: 

    1. Are the data and associated software interoperable, allowing data exchange between researchers, institutions, organizations, countries, etc. (e.g. adhering to standards for data annotation, data exchange, compliant with available software applications, and allowing re-combinations with different datasets from different origins)? 

  
  

  

  

#  Conclusions

This deliverable has provided an overview on how to build the data collecting
- and sharing plan during the course of the PhasmaFOOD project and after the
project will be finished. This deliverable is regarded as a live document
which will be updated incrementally as the project progresses. This version
sets the overall framework that will form the basis for two additional
iterations on M18 and M36, towards the overall delivery of a comprehensive
document at the end of the project.

In this version of the deliverable, we outlined the descriptions of the Use
Case related Datasets, which are still being collected as part of controlled
laboratory measurements. Standardization and interoperability aspects have
been introduced as well as sharing and access procedures of the project data.

It should be noted that an accurate description of the datasets to be produced
(but also in some cases collected and processed) during the early months of
the project is challenging. It is therefore not feasible to exhaustively list
the datasets that will be subsequently used and/or produced, but to highlight
datasets that the consortium agrees are the most relevant at this stage of the
poject. For these reasons, regular updates of the Data Management Plan are
expected. More specifically, these updates will be done in PhasmaFOOD as part
of the mid- term and final project reviews and at other moments as decided by
the PhasmaFOOD consortium. Updates to the DMP will appear within the
dissemination reports of the project.

The upcoming revisions of this deliverable will focus -among other- to a
fuller presentation of different laboratory experiments data collection,
description of the PhasmaFOOD database characteristics, update of data access
and sharing and update of data interoperability priorities. Data regarding
hardware design and specification as well as software design and
implementation (cloud, mobile and embedded level) will be also addressed.