A Comparative Analysis of Methods (LC-MS/MS, LC-MS and Rapid Test Kits) for the Determination of Diarrhetic Shellfish Toxins in Oysters, Mussels and Pipis

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Mapping the development of a Dinophysis bloom in a shellfish aquaculture area using a novel molecular qPCR assay

Diarrhetic shellfish toxins produced by certain species of the marine dinoflagellate Dinophysis can accumulate in shellfish in high concentrations, representing a significant food safety issue worldwide. This risk is routinely managed by monitoring programs in shellfish producing areas, however the methods used to detect these harmful marine microbes are not usually automated nor conducted onsite, and are often expensive and require specialized expertise. Here we designed a quantitative real-time polymerase chain reaction (qPCR) assay based on the ITS-5.8S ribosomal region of Dinophysis spp. and evaluated its specificity, efficiency, and sensitivity to detect species belonging to this genus. We designed and tested twenty sets of primers pairs using three species of Dinophysis - D. caudata, D. fortii and D. acuminata. We optimized a qPCR assay using the primer pair that sufficiently amplified each of the target species (Dacu_11F/Dacu_11R), and tested this assay for cross-reactivity with other dinoflagellates and diatoms in the laboratory (11 species) and in silico 8 species (15 strains) of Dinophysis, 3 species of Ornithocercus and 2 species of Phalacroma. The qPCR assay returned efficiencies of 92.4% for D. caudata, 91.3% for D fortii, and 91.5% for D. acuminata, while showing no cross-reactivity with other phytoplankton taxa. Finally, we applied this assay to a D. acuminata bloom which occurred in an oyster producing estuary in south eastern Australia, and compared cell numbers inferred by qPCR to those determined by microscopy counts (max abund. ∼6.3 × 10³ and 5.3 × 10³ cells L^-1 respectively). Novel molecular tools such as qPCR have the potential to be used on-farm, be automated, and provide an early warning for the management of harmful algal blooms.