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

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 (δ13C, δ15N, δ2H, and δ18O) 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.

Detection of goat meat adulteration by real-time PCR based on a reference primer.

Economically motivated adulteration (EMA) has become one of the most concerned food safety issues. However, existing mainstream PCR methods could neither achieve qualitative detection purposes, nor detect all possible meat species involved in adulteration. When meat has been adulterated with unidentified species but the result indicates no adulteration, it is a false negative; when meat has not been adulterated deliberately but has somehow been polluted during its processing or packaging, a false positive emerges. A novel reference primer based real-time PCR approach was developed in this study for quantitative determination of goat meat adulterated with pork. By calculating the ratio of Ct (specificity/reference), a good linear correlation (R2 = 0.9929) could be deduced for the goat meat content. We also successfully amplified simulated samples and the results showed high accuracy with an average recovery of 108.74% for the samples.

[Gold nanoparticle-aptamer based colorimetric biosensing assays].

The single strand nucleic acid based aptamer could bound to targets with high sensitivity and specificity. Gold nanoparticles have strong particle space optical effects and could take a color change from red to blue when the dispersed nanoparticles were aggregated. Aptamer could be immobilized through covalent coupling or direct adsorption to the surface of gold nanoparticle. Various approaches have been designed for biosensing based on the target induced aptamer-gold nanoparticle system color changes. The recent developments in the gold nanoparticle-aptamer based colorimetric biosensing assays were reviewed and the directions for future research were discussed and proposed.