4.7 Article

Proposing Two Local Modeling Approaches for Discriminating PGI Sunite Lamb from Other Origins Using Stable Isotopes and Machine Learning

Journal

FOODS
Volume 11, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/foods11060846

Keywords

local modeling; protected geographical indication; Sunite lamb; stable isotopes; machine learning

Funding

  1. National Key Research and Development Program [2017YFE0114400]

Ask authors/readers for more resources

This study used stable isotope analysis and local modeling methods to distinguish PGI Sunite lamb samples from samples of lamb from other brands. The results showed that local modeling was better than global modeling. The Adaptive Kennard-Stone algorithm and the PCA-Full distance method based on DD-SIMCA both achieved high accuracies. Stable isotope ratio analysis combined with local modeling can be used as an effective indicator for protecting PGI Sunite lamb.
For the protection of Protected Geographical Indication (PGI) Sunite lamb, PGI Sunite lamb samples and lamb samples from two other banners in the Inner Mongolia autonomous region were distinguished by stable isotopes (delta C-13, delta N-15, delta H-2, and delta(18)0) and two local modeling approaches. In terms of the main characteristics and predictive performance, local modeling was better than global modeling. The accuracies of five local models (LDA, RF, SVM, BPNN, and KNN) obtained by the Adaptive Kennard-Stone algorithm were 91.30%, 95.65%, 91.30%, 100%, and 91.30%, respectively. The accuracies of the five local models obtained by an approach of PCA-Full distance based on DD-SIMCA were 91.30%, 91.30%, 91.30%, 100%, and 95.65%, respectively. The accuracies of the five global models were 91.30%, 91.30%, 91.30%, 100%, and 91.30%, respectively. Stable isotope ratio analysis combined with local modeling can be used as an effective indicator for protecting PGI Sunite lamb.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available