Rare Earth Element Labeling as a Tool for Assuring the Origin of Eggs and Poultry Products

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

Rare earths and other elements content in hen eggs sold in Italy: comparison among main types of farming methods and consumer's health risk assessment

Recently, in Italy, consumers are choosing hen eggs from farming systems with higher ethical value, due to their perception of a related higher quality and safety. The purposes of this study were to evaluate the existence of differences in elemental content in Italian eggs from organic, barn, and caged hen farming methods and to determine the related potential consumer exposure risk to inorganic contaminants due to the consumption of eggs. One hundred seventy-six egg samples were collected and analyzed using Q-ICP-MS to investigate the content of 14 elements (Pb, As, Hg, Cd, Tl, Fe, Zn, Mn, Cu, Se, Co, Ni, V, and Cr) and 13 rare earth elements (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, and Yb). The scenarios of exposure to rare earth and other elements from eggs were estimated for three age groups of consumers. The daily intake values were always lower than the respective safety reference values. In conclusion, Italian hen eggs contain low levels of rare earth and other elements, and therefore, their consumption does not represent a risk of exposure. Finally, no significant differences in contaminants between conventional and organic farming methods were found.

Eggshell biometrics for individual egg identification based on convolutional neural networks

Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

Metagenomic Insights Into the Effects of Rare-Earth Elements Supplementation on Rumen Digestibility and Meat Quality of Beef Cattle

Rare-earth elements (REE), supplemented as feed additives, effectively improved feed conversion and production performances of monogastrics. However, very little information exists on how REE supplementation affects ruminants. In the present study, twenty-four 18-month-old Jinjiang bull cattle, with initial body weight (BW) of 374.75 ± 14.02 kg, were randomly allotted into four dietary treatments with a 15-day-long preliminary trial: a control treatment (basal diet), a 400 mg/kg REE treatment (basal diet supplemented with 400 mg REE/kg DMI), an 800 mg/kg REE treatment (basal diet supplemented with 800 mg REE/kg DMI), and a 1,200 mg/kg REE treatment (basal diet supplemented with 1,200 mg REE/kg DMI). Based on the results, the optimum supplementation scale was chosen for a 60-day-long follow-up feeding procedure. At the end of the feeding period, all bull cattle were slaughtered. Feed intake, average daily weight gain, carcass performances, meat quality, and rumen microbiota were measured. Results indicate a positive response in terms of growth performance and gastrointestinal digestibility to REE supplementation, and 400 mg/kg DMI treatment presented the most average daily feed intake (ADFI), the best average daily weight gain (ADG), and the least F/G. REE also significantly decreased the ruminal propionate content compared with control treatment. As to microbiota, despite no increases in bacterial community abundance, there was a proliferation of Bacteroidetes and Tenericutes and suppression of Actinobacteria under REE treatment. Furthermore, REE treatment significantly increased the meat protein content and decreased meat fat content. There was also an increase in the activities of the enzymes related to lipid syntheses. Fatty acid synthetase (FAS) and malate dehydrogenase (MDH) were significantly suppressed, while the activity of the lipolysis-related enzyme, lipoproteinesterase (LPL), was enhanced. In summary, REE supplementation provided an effective regulation on ruminal microbiota, facilitation of ruminal fiber digestibility, promotion of feed conversion, suppression of lipid deposition, and finally, improved the production and meat quality of beef cattle.

Authentication of meat and dairy products using rare earth element labeling and detection by solution based and laser ablation ICP-MS

In order to meet the increasing customer demand for local food products, various methods for verification of food origin by means of region specific trace element fingerprinting have been developed. However, for products from conventional agriculture, without a close relationship to the local soil, other methods for food authentication are required. In an alternative approach, foodstuffs produced in a certain region, by a specific producer or under certain conditions can be safeguarded against imitation by chemical labeling. The objective of the present study was to develop a method for labeling lamb meat and goat milk by selective enrichment of terbium and thulium in the feed for the animals. Therefore, a distinctive rare earth element (REE) pattern is artificially introduced which can be determined in labeled food products. Detection of REE labels was carried out using inductively coupled plasma mass spectrometry (ICP-MS) after acid digestion. Alternatively, laser ablation ICP-MS (LA-ICP-MS) was applied, allowing direct analysis of bone samples and analysis of meat and milk samples after dry ashing and pressing pellets. After three weeks of administering 1000-fold terbium and thulium enriched feed to lambs, terbium and thulium enrichment was detected in all sample types except blood, following the trend bones > kidney > liver > heart > meat > kidney fat. Similarly, goat milk was successfully labeled after three weeks of feeding 500-fold terbium and thulium enriched feed. Hence, the present method allows discrimination of labeled from unlabeled animal products, while REE contents in all labeled products remained low enough to avoid any health risk for the consumer.

Analysis of rice and wheat flour by particle nebulization ICP-MS

A simple and green particle nebulization ICP-MS method for the direct measurement of trace toxic elements in rice and wheat samples was developed.

Classification of Hen Eggs by HPLC-UV Fingerprinting and Chemometric Methods

Hen eggs are classified into four groups according to their production method: Organic, free-range, barn, or caged. It is known that a fraudulent practice is the misrepresentation of a high-quality egg with a lower one. In this work, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs. non-organic hen eggs) and demonstrating the suitability of the proposed method.