Sample handling in vegetal R&D
What can be considered as good practices to manage samples in R&D departments? Let's see what processes are implied and why an integrated information system may help:
- Collecting (sampling planning, protocols, forms, notices)
- Tracking (labels, barcodes, white labeling, inventory)
- Control quality (transformation tracking, work stations)
- Analysis and communication of result
Insights of sample handling in vegetal R&D
A sample is not only a measure or a notation, it is often described by criterias (Type, Collection, Transformation method, Analysis process). Such metadata is important since it has an influence on which samples can be compared to each other, but it is not easy to store such information, even harder to integrate it to the analysis.
Sample handling requires:
- A strong R&D organization (e.g. machines, laboratories, teams, data and processing)
- A database tool for measurement storing and analysis
A tool for sharing and communicating results
Another key step consists in integrating the sample-related processes to the decision-making tools, in order to save time and be able to query all the sample data.
Watch all the steps of sample handling in video !
Tristan Duminil, agronomist and sales magager, has presented all the steps of sample handling in vegetal R&D, with real case studies using the transversal research information system RnDExp®, during a live webinar. Click here to ask for the webinar replay.
The same presentation has been done in French language during the AFMEX conference on January 25 2018. Watch the video in French here.
Solutions to set up for quality sample handling
Sample handling mobilizes various resources (human, machines, partnering) along with methodology and organization all along the process from sample collection to decision-making (see figure 1).
An integrated process management system can bring all the methods and tools required to get the best quality sample management practices. Let's see what can be done to track, automatize and improve each of the six steps of sample handling.
Planning and collecting samples
According to the experiment protocol, the researcher creates collection forms and process notices (demo in the video at 7'30''). To-do-lists (above picture) enable to repeat identical experiments and standardize team work.
Codification and white labels
Anonymisation of samples codes (see above and demo in the video at 9'10'')
Handling samples transformation
- Management of project steps and working stations
- Quality Control
Example : Roasting process for cocoa seeds preparation in Chocolate samples evaluation (watch the video at 12'15'')
- Calibration and integration with measuring devices
- Data control
- Automation of data capture (see above)
Example: Automatic capture of fruit weight harvested (watch the video at 15'50'')
Analysis of Agronomical trials:
Analysis of Agronomical trials:
- Validation of trials : interactive graphics, analysis of residues
- Multi criteria statistical analysis and analysis of interactions
See above and live demonstration in the video at 17'40''.
Analysis of agronomical performances of hybrids and varieties (Field crops and vegetables) can be done on a web-based map to share results with colleagues, partners... (see above and this article on web mapping trial data)
In a nutshell
On every step of the sample management, let's try to improve the efficiency of the research department by bringing tools to:
- Automate and codify the sample collection
- Secure by reducing errors, in particular to validate and control the samples
- Standardize with reapeatable methods and compareable data
A departemental database tool is also a "documentary base" that enables to store and inform users on general good practices and particularly on sampling.
Doriane, your R&D IT partner
About DorianeThe expertise of Doriane brings the three players of R&D the resources to organize, share and valorize their research projects:
RESEARCH DEPARTMENTS: Fast implementation of business processes ; Liberation of researchers creativity
IT DEPARTMENTS: Real-time centralization of multilocation data ; Security and integrity of data
MANAGEMENT: Maximization of research investments ; Research departments monitoring ; Intellectual assets valorization and sustainability