SNP Array for Small-Shrimp (Genus Acetes) Origin Determination Using Machine Learning

Abstract

Accurate origin determination of seafood is crucial for consumer trust and safety. This study was performed to develop a machine learning-based single-nucleotide polymorphism (SNP) analysis technique to determine the origin of Acetes species in salted small-shrimp products. Mitochondrial DNA (COI and 16S rRNA) analysis revealed genetic variations among species and origins. Eight candidate SNPs were identified, six of which were developed into markers for genotyping analysis. Using the developed markers, an SNP array was created and SNP data from salted small-shrimp samples were obtained. Machine learning analysis using a supervised learning algorithm achieved 100% accuracy in classifying the origin of Acetes based on SNP data. This method offers a reliable method for regulatory bodies to combat food fraud and ensure product integrity. The approach can be further improved by expanding the data set to encompass a wider range of species and origins. This study highlights the potential of SNP analysis and machine learning for ensuring seafood authenticity and promoting sustainable practices.