|Project Title:||Big data-enabled Collaborative Database for Non-targeted Contaminants Detection|
|Hosting Institution:||The Hong Kong Polytechnic University (PolyU)|
|Abstract:||The objective of this project is to set up an innovative and comprehensive big-dataenabled collaborative database for the detection of unknown contaminants. The technology shall first be applied on milk and milk ingredients.
Traditional product testing by dairy companies often target regulatory requirements which are directed towards known substances with set legal limits. However, such testing fails to alert any previously un-encountered contaminants as they are not being looked for.
To tackle this challenge, the existing vast amount of data in different proprietary formats from dairy industrial partners – covering mainly the demographic regions of China and Europe – will be gathered, and protocols will be established to convert such data to standardized reference chemical fingerprints with tolerance levels. The resulting big-data will be utilized by newly developed innovative algorithms and chemometric protocols to create alerting system for unknown contaminants.
This non-targeted methodology and the database developed can be utilized by the industry to effectively identify any sample anomalies which may arise from potential contaminants or adulterants without our prior knowledge. Hence, immediate and appropriate measures can be taken if such circumstances arise, safe-guarding the quality and safety of milk and milk ingredients for consumers and preventing the next Melamine incident.
|Project Coordinator:||Dr Terence Lau|
|Approved Funding Amount:||HK$5.4 M|
|Project Period:||28 Nov 2018-27 Nov 2020|