Origin assignment by multi-element stable isotopes of lamb tissues

Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb

The study successfully utilized an analytical approach that combined quantitative lipidomics with back-propagation neural networks to identify breed and part source of lamb using small-scale samples. 1230 molecules across 29 lipid classes were identified in longissimus dorsi and knuckle meat of both Tan sheep and Bahan crossbreed sheep. Applying multivariate statistical methods, 12 and 7 lipid molecules were identified as potential markers for breed and part identification, respectively. Stepwise linear discriminant analysis was applied to select 3 and 4 lipid molecules, respectively, for discriminating lamb breed and part sources, achieving correct rates of discrimination of 100 % and 95 %. Additionally, back-propagation neural network proved to be a superior method for identifying sources of lamb meat compared to other machine learning approaches. These findings indicate that integrating lipidomics with back-propagation neural network approach can provide an effective strategy to trace and certify lamb products, ensuring their quality and protecting consumer rights.

Investigation Tracing the Origin of Tan Sheep Visceral Tissues through Mineral Elements

The traceability of quality mineral fingerprints in the viscera of Tan sheep from northwest China was studied. Twenty-five mineral elements in the heart and liver samples of Tan sheep were determined using an inductively coupled plasma mass spectrometer (ICP-MS), and the characteristics of the mineral elements in the visceral tissues of the Tan sheep were further analyzed in combination with a principal component analysis (PCA), hierarchical cluster analysis (HCA), and linear discriminant analysis (LDA) to establish a discriminant model and verify it. The results show that 11 elements (¹³⁷Ba, ⁴³Ca, ⁶³Cu, ⁵⁶Fe, ³⁹K, ³¹P, ⁶⁰Ni, ⁷⁸Se, ¹¹⁸Sn, ¹²⁵Te, and ⁶⁶Zn) in the Tan sheep heart samples had significant differences among different regions (p < 0.05), and the results of the LDA show that the accuracy rate of the return-generation examination was 85.70%, and the accuracy rate of the hand-over-fork examination was 87.50%; 10 elements (¹¹¹Cd, ⁵⁹Co, ⁵²Cr, ⁵⁶Fe, ³⁹K, ⁵⁵Mn, ⁹⁵Mo, ²³Na, ¹²¹Sb, and ⁷⁸Se) in the Tan sheep liver samples had significant differences among different regions (p < 0.05), and the results of the LDA showed that the accuracy rate of the return-generation examination was 96.30%, and the accuracy rate of the hand-over-fork examination was 86.25%. This indicates that the multi-element analysis has potential for determining the origin of Tan sheep viscera in certain regions.

Isotope Fingerprinting as a Backup for Modern Safety and Traceability Systems in the Animal-Derived Food Chain

In recent years, due to the globalization of food trade and certified agro-food products, the authenticity and traceability of food have received increasing attention. As a result, opportunities for fraudulent practices arise, highlighting the need to protect consumers from economic and health damages. In this regard, specific analytical techniques have been optimized and implemented to support the integrity of the food chain, such as those targeting different isotopes and their ratios. This review article explores the scientific progress of the last decade in the study of the isotopic identity card of food of animal origin, provides the reader with an overview of its application, and focuses on whether the combination of isotopes with other markers increases confidence and robustness in food authenticity testing. To this purpose, a total of 135 studies analyzing fish and seafood, meat, eggs, milk, and dairy products, and aiming to examine the relation between isotopic ratios and the geographical provenance, feeding regime, production method, and seasonality were reviewed. Current trends and major research achievements in the field were discussed and commented on in detail, pointing out advantages and drawbacks typically associated with this analytical approach and arguing future improvements and changes that need to be made to recognize it as a standard and validated method for fraud mitigation and safety control in the sector of food of animal origin.

A New and Effective Method to Trace Tibetan Chicken by Amino Acid Profiling

As a "rare bird on the plateau", the Tibetan chicken is rich in nutrition and has high medicinal value. In order to quickly and effectively identify the source of food safety problems and to label fraud regarding this animal, it is necessary to identify the geographical traceability of the Tibetan chicken. In this study, Tibetan chicken samples from four different cities in Tibet, China were analyzed. The amino acid profiles of Tibetan chicken samples were characterized and further subjected to chemometric analyses, including orthogonal least squares discriminant analysis, hierarchical cluster analysis, and linear discriminant analysis. The original discrimination rate was 94.4%, and the cross-validation rate was 93.3%. Moreover, the correlation between amino acid concentrations and altitudes in Tibetan chicken was studied. With the increase in altitude, all amino acid contents showed a normal distribution. For the first time, amino acid profiling has been comprehensively applied to trace the origin of plateau animal food with satisfactory accuracy.

Geographical traceability of gelatin in China using stable isotope ratio analysis

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

Fatty Acid and Multi-Isotopic Analysis (C, H, N, O) as a Tool to Differentiate and Valorise the Djebel Lamb from the Mountainous Region of Tunisia

The objective of this study was to distinguish between the Tunisian Djebel lamb meat and meat from typical Tunisian production systems (PSs) through the fatty acids (FAs) profile and the stable isotope ratio analysis (SIRA). Thirty-five lambs from three different regions and PSs (D = Djebel, B = Bou-Rebiaa, and O = Ouesslatia) were considered for this purpose. The results demonstrated that the PS and the geographic origin strongly influenced the FA profile of lamb meat. It was possible to discriminate between the Djebel lamb meat and the rest of the dataset thanks to the quantification of the conjugated linoleic acids (CLA) and the branched chain FAs. Moreover, statistically different concentrations of saturated, monounsaturated and polyunsaturated FAs and a different n-6/n-3 ratio were found for grazing (D and BR) and indoor (O) lambs, making it possible to discriminate between them. As for the stable isotope ratio analysis, all parameters made it possible to distinguish among the three groups, primarily on the basis of the dietary regimen (δ(¹³C) and δ(¹⁵N)) and breeding area (δ(¹⁸O) and δ(²H)).

Tracing the geographic origin of velvet antlers in China via stable isotope analyses

Multielement (H, C, N, O) stable isotope ratio analysis was used to discriminate the geographical origin of velvet antlers (VAs) of deer from ten provinces in China. Ratios of ²H/¹H, ¹³C/¹²C, ¹⁵N/¹⁴N, and ¹⁸O/¹⁶O in the VA samples were measured using isotope ratio mass spectrometry. The results showed that there were highly significant differences in the mean isotopic values and in four isotopic ratios between VA samples from the ten provinces. The most significant difference among the four isotope ratios was in δ²H ratio of VA samples; regions with a more humid climate and higher average ambient temperatures had higher δ²H ratios than those with dry climates and lower temperatures. These results demonstrate that the multiple stable isotopic ratio approach is a powerful tool to help trace the geographical origin of VAs, and could be adopted by government officials to help protect consumer interests from improper labeling in VA markets.

Multielement (H, C, N, O) stable isotope ratio analysis was used to discriminate the geographical origin of velvet antlers (VAs) of deer from ten provinces in China.

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.

Research progress on mutton origin tracing and authenticity

With the globalization of the food market and the convenience of food transportation between countries, consumers are increasingly worried about the source and safety of the food they eat. Traceability has been identified as an important tool for ensuring food safety and quality. This review mainly introduces the principles of five food traceability technologies, summarizes the progress in mutton application, comprehensively compares and analyzes the five traceability technologies, and discusses their application prospects, advantages and disadvantages. It is aimed at promoting research and application of traceability technology in mutton safety, promoting establishment and improvement of food traceability system.

Preliminary Study on Tracing the Origin and Exploring the Relations between Growing Conditions and Isotopic and Elemental Fingerprints of Organic and Conventional Cavendish Bananas (Musa spp.)

The stable isotopic ratios and elemental compositions of 120 banana samples, Musa spp. (AAA Group, Cavendish Subgroup) cultivar Williams, collected from six countries (Colombia, Costa Rica, Dominica Republic, Ecuador, Panama, Peru), were determined by isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. Growing conditions like altitude, temperature, rainfall and production system (organic or conventional cultivation) were obtained from the sampling farms. Principal component analysis (PCA) revealed separation of the farms based on geographical origin and production system. The results showed a significant difference in the stable isotopic ratios (δ¹³C, δ¹⁵N, δ¹⁸O) and elemental compositions (Al, Ba, Cu, Fe, Mn, Mo, Ni, Rb) of the pulp and peel samples. Furthermore, δ¹⁵N was found to be a good marker for organically produced bananas. A correlation analysis was conducted to show the linkage of growing conditions and compositional attributes. The δ¹³C of pulp and peel were mainly negatively correlated with the rainfall, while δ¹⁸O was moderately positively (R values ~0.5) correlated with altitude and temperature. A moderate correlation was also found between temperature and elements such as Ba, Fe, Mn, Ni and Sr in the pulp and peel samples. The PCA results and correlation analysis suggested that the differences of banana compositions were combined effects of geographical factors and production systems. Ultimately, the findings contribute towards understanding the compositional differences of bananas due to different growing conditions and production systems linked to a defined origin; thereby offering a tool to support the traceability of commercial fruits.

Spatial Fingerprinting: Horizontal Fusion of Multi-Dimensional Bio-Tracers as Solution to Global Food Provenance Problems

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.

Geographic origin discrimination of pork from different Chinese regions using mineral elements analysis assisted by machine learning techniques

Porkis thelargest-producedandmost-consumedmeat intheworld, and the food market globalization has increased public attention to food origin. Therefore, advanced techniques are required to determine the geographical origin of pork. This study investigated the prospects of using fingerprint analysis of mineral elements and machine learning to facilitate the traceability of pork origin in China. The results showed that each of seven regions had a characteristic element content profile. To improve the performance of the origin traceability model, popular machine learning techniques in food authenticity were introduced. This resulted in a high-performance origin tracing model. Comparing various machine learning algorithms, the feedforward neural network achieved superior performance with an overall accuracy of 95.71% and area under the curve close to one. Thus, this study proves that mineral elements analysis assisted by machine learning can be applied to distinguish pork samples within a country.