The end user sensor tree: An end-user friendly sensor database
Language English Country England, Great Britain Media print-electronic
Document type Journal Article
PubMed
30769289
DOI
10.1016/j.bios.2019.01.055
PII: S0956-5663(19)30084-3
Knihovny.cz E-resources
- Keywords
- Database, Food contaminants, Pesticides, Repository, Sensors, Toxins,
- MeSH
- Biosensing Techniques classification methods MeSH
- Databases, Factual MeSH
- Food Contamination analysis MeSH
- Humans MeSH
- Mycotoxins chemistry isolation & purification MeSH
- Nanostructures chemistry MeSH
- Pesticides chemistry isolation & purification MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Mycotoxins MeSH
- Pesticides MeSH
Detailed knowledge regarding sensor based technologies for the detection of food contamination often remains concealed within scientific journals or divided between numerous commercial kits which prevents optimal connectivity between companies and end-users. To overcome this barrier The End user Sensor Tree (TEST) has been developed. TEST is a comprehensive, interactive platform including over 900 sensor based methods, retrieved from the scientific literature and commercial market, for aquatic-toxins, mycotoxins, pesticides and microorganism detection. Key analytical parameters are recorded in excel files while a novel classification system is used which provides, tailor-made, experts' feedback using an online decision tree and database introduced here. Additionally, a critical comparison of reviewed sensors is presented alongside a global perspective on research pioneers and commercially available products. The lack of commercial uptake of the academically popular electrochemical and nanomaterial based sensors, as well as multiplexing platforms became very apparent and reasons for this anomaly are discussed.
References provided by Crossref.org
ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers
Smartphone-based optical assays in the food safety field
A Hybrid Lab-on-a-Chip Injector System for Autonomous Carbofuran Screening