BB-SPECTRAL is an artificial intelligence (AI) technology for quantify and categorized chemical compounds using spectral information of highly complex samples (e.g. blood). Avoiding the use of reagents, BB-SPECTRAL is able to predict compounds concentrations with an accuracy of about 90% in comparison to the laboratorial results (gold standard).
Explaining to humans the spectral data extracted from samples, BB-SPECTRAL is unique at establishing its own knowledgebase, indicating 'a priori' the predictability, accuracy and precision of new estimates. This technology is applicable to all regions of the electro-magnetic spectra used in spectroscopy analysis, or with any other type of spectroscopy where complex multi-scale interference and chemical/biological variability is present.
Current approaches for spectral analysis comprise artificial neural networks (ANN) and non-linear support vector machines (SVM), which attempt to create complex function models that fit to all data. This strategy fails at analyzing complex samples, requiring large amounts of data, being unable of detecting outliers, and encompassing high computational costs.
BB-SPECTRAL was developed to overcome these technical problems using a local and global approach to provide medical-grade accuracy and precision, quantifying and identifying any chemical compound in highly complex samples.
Works with a wide range of spectral data (spectroscopy analysis (X-ray, UV, vis, nIR, IR, far-IR and microwaves);
Extends non-destructive, non-invasive spectroscopy applications to a high number of Industries;
Avoids the use of reagents.
- Clinical assessment of biological targets in healthy and pathological conditions (Medicine and Veterinary);
- Quality control in Agro-Food;
- Materials characterisation.
Development StageIndustrial Prototype (TRL 5-6)
Further Information<p><a href="https://worldwide.espacenet.com/publicationDetails/biblio?DB=EPODOC&locale=en_EP&FT=E&CC=WO&NR=2018060967&KC=A1" target="_blank">WO2018060967</a></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0925400521007073?dgcid=author#fig0030">Point-of-care Vis-SWNIR spectroscopy towards reagent-less hemogram analysis</a></p> <p><a href="https://www.researchgate.net/publication/336933646_Optical_Sensing_of_Nitrogen_Phosphorus_and_Potassium_A_Spectrophotometrical_Approach_Toward_Smart_Nutrient_Deployment/fulltext/5dbb800a92851c81801dc87d/Optical-Sensing-of-Nitrogen-Phosphorus-and-Potassium-A-Spectrophotometrical-Approach-Toward-Smart-Nutrient-Deployment.pdf">Optical Sensing of Nitrogen, Phosphorus and Potassium: A Spectrophotometrical Approach Toward Smart Nutrient Deployment</a></p>
Tagse-Health, Artificial intelligence, Diagnostics, Spectroscopy, Precision Agriculture, Chemical