Sample handling in vegetal R&D
A sample is not only a measure or a notation: it needs qualification to become valuable. The more you track and describe the sample, from collection to analysis, the more valuable it is for your breeding or testing activity. Identifiers, follow-up monitoring and measures altogether enable you to compare samples with each others, and analyze the results.
Do you gather enough information along your sample processes to take informed decisions ?
How can you integrate sample monitoring to your decision-making tools, save time and make the most of your data ?
"The more you track and describe the sample, from collection to analysis, the more valuable it is for your breeding or testing activity."
Optimize sample identification
Collecting plant samples involves many actors, at least the researcher ordering the test and the technician collecting and inputting the result. It gets even more complex when other departments or external partners are needed, such as analytical, sensorial or MM analysis.
Some tools enable to save time and avoid errors during identification and all along the sample process. Anonymisation of samples codes with "White-labels" make sure the operator is not influenced by the sample name in its notations. Advanced reports can be really handy, such as shipment reports for expeditions, inventories etc... Another example is the preparation of MM sample collection sheet for biotechnicians, according to the traits the breeder needs to check and the needs of the lab.
"Save time and avoid errors during identification and all along the sample processbetter understanding on their interpretation."
Add value to sample handling
The process of sample evaluation gets way more efficient when integrating results related to the sample collection: Calibration of measuring devices, data processes, ontology... All this information helps describe the results, and have a better understanding on their interpretation. To take fast and accurate decisions it matters to have all necessary information on the material environment correctly integrated into the decision making process.
One another concrete example of standardizing within a R&D department the step of sampling has been put into place through the mobile application dedicated to field observations. Standardized forms had been delivered to technicians helping them to make observations of disease tolerance. Such methodologies improve the repeatability of evaluations and standardize collaborative work.
Automation of sample capture limits human mistakes and leads to standardization. Essential for most quality certifications, automation and traceability of sample handling is the key to save time, avoid errors and take informed decisions. A plant breeding software or research IT system such as RnDExperience® provides all the necessary tools to secure and valorize vegetal R&D information.
"All this information helps describe the results, and have a better understanding on their interpretation. "
Enhance trial analysis with sample handling
Sample results need to be analyzed to fully take part in the decision-making workflow, and provide new traits to the trial analysis. Then they give a new view angle to the agronomist on its trial data. Interactive data visualization graphs enable to analyze sample results and take decisions on the population the samples come from.
Agronomical trial analysis tools monitor many objectives such as validation of trials (interactive graphics, analysis of residues) and multi criteria statistical analysis with interactions study. Monitoring the whole sample-related processes in a central database enables to query all the information and provide useful reports.
"[Sample analysis] gives a new view angle to the agronomist on its trial data."
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.
An integrated process management system brings all the methods and tools 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.
On every step of the sample management, we strive 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.
"An integrated process management system brings all the methods and tools to get the best quality sample management practices."
Watch all the steps of sample handling in video !
Tristan Duminil, agronomist and business manager, 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.