A rapid sample preparation method for the analysis of stable isotope ratios of beef samples from different countries

Characterization of Beef Coming from Different European Countries through Stable Isotope (H, C, N, and S) Ratio Analysis

The need to guarantee the geographical origin of food samples has become imperative in recent years due to the increasing amount of food fraud. Stable isotope ratio analysis permits the characterization and origin control of foodstuffs, thanks to its capability to discriminate between products having different geographical origins and derived from different production systems. The Framework 6 EU-project "TRACE" generated hydrogen (²H/¹H), carbon (¹³C/¹²C), nitrogen (¹⁵N/¹⁴N), and sulphur (³⁴S/³²S) isotope ratio data from 227 authentic beef samples. These samples were collected from a total of 13 sites in eight countries. The stable isotope analysis was completed by combining IRMS with a thermal conversion elemental analyzer (TC/EA) for the analysis of δ(²H) and an elemental analyzer (EA) for the determination of δ(¹³C), δ(¹⁵N), and δ(³⁴S). The results show the potential of this technique to detect clustering of samples due to specific environmental conditions in the areas where the beef cattle were reared. Stable isotope measurements highlighted statistical differences between coastal and inland regions, production sites at different latitudes, regions with different geology, and different farming systems related to the diet the animals were consuming (primarily C3- or C4-based or a mixed one).

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.

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China

Traceability of milk origin in China is conducive to the implementation of the protection of regional products. In order to distinguish milk from different geographical distances in China, we traced the milk of eight farms in four neighboring provinces of China (Inner Mongolia autonomous region, Hebei, Ningxia Hui autonomous and Shaanxi), and multivariate data analysis was applied to the data including elemental analysis, stable isotope analysis and fatty acid analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) is used to determine the optimal classification model, and it is explored whether the combination of different technologies is better than a single technical analysis. It was confirmed that in the inter-provincial samples, the combination of the two techniques was better than the analysis using a single technique (fatty acids: R² = 0.716, Q² = 0.614; fatty acid-binding isotopes: R² = 0.760, Q² = 0.635). At the same time, milk produced by farms with different distances of less than 11 km in each province was discriminated, and the discriminant distance was successfully reduced to 0.7 km (Ningxia Hui Autonomous Region: the distance between the two farms was 0.7 km, R² = 0.771, Q² = 0.631). For short-distance samples, the combination multiple technologies are not completely superior to a single technique, and sometimes, it is easy to cause model over-fitting.

Achieving Food Authenticity and Traceability Using an Analytical Method Focusing on Stable Isotope Analysis

High-value agricultural products are characterized by the geographical conditions of the production areas such as climatic and soil conditions. These products are protected by the geographical indication (GI) protection system, which has been introduced in more than 100 countries. Because GI products are expensive in the market, products are often mislabeled as GI. Thus, there is an urgent need for the development of analytical methods that enable the tracing of geographical origins of food materials. Stable isotope analysis is used to trace the geographical origin of food materials. In this study, we review the applications for tracing the geographical origin of agricultural products (especially rice, beef, and honey) focusing on an analytical method for analyzing stable isotopes (δD, δ¹³C, δ¹⁵N, δ¹⁸O, and δ³⁴S).

Discrimination of mutton from different sources (regions, feeding patterns and species) by mineral elements in Inner Mongolia, China

The traceability of mineral element fingerprints to mutton in a small area of China was studied. The element data of 104 sheep and 24 goat samples from Inner Mongolia were measured, and the data were analyzed by multivariate statistical analysis from different origins, species and feeding patterns. The results shows that 11 elements (Mg, Al, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, Ba) in sheep meat had significant differences between different regions (P < 0.05), and the results of linear discriminant analysis (LDA) showed that the accuracy of the original classification rate was 95.2%, and the cross-validation rate was 85.9%. Goat meat and sheep meat samples from Alxa League were also clearly identified with LDA results showing that the cross-validation accuracy of the two species was 70.2%. Then the feeding patterns of sheep meat were effectively classified. The results showed that the multi-element analysis has certain potential as a method to distinguish mutton in a small area.