Bia Analytical and Camstar Herbs Partner to Advance AI-Driven Authenticity Models for Herbs & Spices
Bia Analytical is delighted to announce a strengthened partnership with Camstar Herbs, supporting the continued development of advanced authenticity screening models for herbs and spices.
The collaboration brings together Bia Analytical’s expertise in food authenticity, spectroscopy and AI-driven chemometric modelling with Camstar Herbs’ extensive experience in sourcing and supplying high-quality herbs and spices. Through the partnership, the organisations will work together to further reinforce models used to support authenticity screening across a growing range of herb and spice commodities.
Access to diverse, commercially relevant samples is critical to building robust models that accurately reflect the natural variation found within global supply chains. By contributing industry insight and real-world sample diversity, Camstar Herbs play an important role in helping Bia Analytical continue to enhance the performance, reliability and practical application of its testing.
“Strong authenticity models are built on strong industry partnerships. We are delighted to be working with Camstar Herbs to further develop our model library and ensure our solutions are grounded in real-world industry requirements. This collaboration will help us continue to expand the breadth, accuracy and robustness of our capabilities while delivering greater value to the wider food industry.”
— Stephanie Heaney, Bia Analytical
“We are pleased to be partnering with Bia Analytical on this initiative. The opportunity to contribute to the development of authenticity models that can strengthen assurance across the herbs and spices sector aligns closely with our commitment to quality, transparency and continuous improvement.”
— Jack Pickerden, Camstar Herbs
The partnership supports Bia Analytical’s ongoing investment in data-driven authenticity solutions, combining analytical science, spectral fingerprinting and machine learning techniques to create models capable of identifying typical and atypical materials within complex food supply chains.