Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication.

ABSTRACT: Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discrim

SUBMITTER: Putri LA 

PROVIDER: S-EPMC10275922 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature