A comprehensive review of the current trends and recent advancements on the authenticity of honey

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Honey adulteration with exogenous syrup has become a common phenomenon, and current detection techniques that require large instruments are cumbersome and time-consuming. In this study, a simple and efficient method was developed by integrating the rapid extraction of nucleic acids (REMD) and recombinase polymerase amplification (RPA), known as REMD-RPA, for the rapid screening of syrup adulteration in honey. First, a rapid extraction method was developed to rapidly extract corn syrup DNA in five minutes to meet the requirements of PCR and RPA assays. Then, the RPA method for detecting endogenous maize genes (ZssIIb) was established, which could detect 12 copies/μL of the endogenous maize gene within 30 min without cross-reacting with other plant-derived genes. This indicated that the RPA technique exhibited high sensitivity and specificity. Finally, the REMD-RPA detection platform was used to detect different concentrations of corn syrup adulteration, and 1 % adulteration could be detected within 30 min. The 22 commercially available samples were tested to validate the efficacy of this method, and the established RPA was able to detect seven adulterated samples in less than 30 min. Overall, the developed method is rapid, sensitive, and specific, providing technical support for the rapid field detection of honey adulteration and can serve as a reference for developing other field test methods.

Essential spectral pixels-based improvement of UMAP classifying hyperspectral imaging data to identify minor compounds in food matrix

Classifying big data in hyperspectral imaging (HSI) can be challenging when minor (low-concentrated) compounds are present in actual samples, as for chemical additives and adulterants in food matrix. Herein, we propose a new strategy to classify HSI data for the identification of adulterants in food material for the first time. This strategy is based on the selection of essential spectral pixels of full HSI data followed by the feature space construction using uniform manifold approximation and projection as well as the data clustering utilizing hierarchical clustering analysis on the reduced data (named ESPs-UMAP-HCA). We apply our approach to analyze two real NIR datasets and four new Raman datasets. Compared with non-ESPs UMAP-HCA and t-distributed stochastic neighbor embedding combined with ESPs and HCA (ESPs-t-SNE-HCA), the developed strategy provides well-separated clusters for major and minor compounds in food matrix. Finally, the adulterants as minor compounds are accurately identified, which is confirmed by the fact that the extracted spectra of them perfectly match with their pure spectra. In addition, their locations are found in the contribution map even though they are present in a few pixels. What's more, the proposed strategy does not need any a priori knowledge of the data structure and the class memberships and therefore reduced the studied difficulty and confirmation bias in the analysis of big HSI datasets. Overall, the proposed ESPs-UMAP-HCA method could be a potential approach for food adulteration detection.

Bi2WO6/TiO2-based visible light-driven photoelectrochemical enzyme biosensor for glucose measurement

Nowadays, the frequent occurrence of food adulteration makes glucose detection particularly important in food safety and quality management. The quality and taste of honey are closely related to the glucose content. However, due to the drawbacks of expensive equipment, complex operating procedures, and time-consuming processes, the application scope of traditional glucose detection methods is limited. Hence, this study developed a photoelectric chemical (PEC) sensor, which is composed of a photoactive material of bismuth tungstate (Bi2WO6) with titanium dioxide (TiO2) and glucose oxidase (GOD), for simple and rapid detection of glucose. Notably, the composites' absorption prominently increased in the visible light region, and the photo-generated electron-hole pairs were efficiently separated by virtue of the unique nanostructure system, thus playing a crucial role in facilitating PEC activity. In the presence of dissolved oxygen, the photocurrent intensity was enhanced by H2O2 generated from glucose under electro-oxidation specifically catalyzed by GOD fixed on the modified electrode. When the working potential was 0.3 V, the changes of photocurrent response indicated that the PEC enzyme biosensor provides a low detection limit (3.8 µM), and a wide linear range (0.008-8 mM). This method has better selectivity in honey samples and broad application prospects in clinical diagnosis for future.

Potential application of bee products in food industry: An exploratory review

Over the past eight years, bee products such as wax, honey, propolis, and pollen have generated intense curiosity about their potential food uses; to explore these possibilities, this review examines the nutritional benefits and notable characteristics of each product related to the food industry. While all offer distinct advantages, there are challenges to overcome, including the risk of honey contamination. Indeed, honey has excellent potential as a healthier alternative to sugar, while propolis's remarkable antibacterial and antioxidant properties can be enhanced through microencapsulation. Pollen is a versatile food with multiple applications in various products. In addition, the addition of beeswax to oleogels and its use as a coating demonstrate significant improvements in the quality and preservation of environmentally sustainable foods over time. This study demonstrates that bee products and apitherapy are essential for sustainable future food and innovative medical treatments.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

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.