No abstract

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and emerging technologies (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work.The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move towards Industry 5.0.

No abstract

Among developed countries, bovine milk production makes a major contribution towards the economy. Elevating consumer demand for functional foods has triggered a niche for non-bovine milk-based products. Mixing milks from different species can be a strategy to increase the consumption of non-bovine milk and enable consumers and dairy companies to benefit from their nutritional and technological advantages. Thus, this review aimed to gather the most important research on yoghurts derived from processing mixtures of milks of different species. We discuss the impact of milk mixtures (i.e., species and milk ratio) on nutritional, physicochemical, sensory, rheological and microbiological properties of yoghurts. More specifically, this paper only highlights studies that have provided a clear comparison between yoghurts processed from a mixture of two milk species and yoghurts processed from a single species of milk. Finally, certain limitations and future trends are discussed, and some recommendations are suggested for future research.

The present study was aimed to investigate the potential of multispectral images coupled with chemometric tools (PLSDA and PLS-R) for: (1.) discriminating different French blue veined cheeses belonging to four brand products (Fourme d'Ambert, Fourme de Montbrison, Bleu d'Auvergne, and Bleu des Causses) and (2.) predicting some of physicochemical (pH, ash, dry matter, total nitrogen, water soluble nitrogen, Ca 2+ , Na + , Cl − , and P) and rheological properties (softening and dropping points). The results obtained showed that multispectral imaging system applied to anisotropic blue cheeses succeeded to: (1.) discriminate cheeses based on their blue veins features in spite of the visual similarity of their structure and appearance with percentage of correct classification varying between 30 and 100%; and (2.) predict selected parameters (i.e., Ca 2+ , Cl − , WSN, dropping, and softening points) since R 2 cv ≥ 0.62 and RPD ≥ 1.62 were obtained. Moreover, the predictive results suggested that the image texture of cheese was strongly related to its physicochemical composition and rheological characteristics (softening and dropping points).

The present study aimed to evaluate and compare the ability of front face (FFFS) and synchronous fluorescence spectroscopy (SFS) to predict total fat and FA composition of beef LT muscles coming from 36 animals of 3 breeds (Angus, Limousin and Blond d'Aquitaine). The regression models were performed by using Partial Least Square (PLS) method. In spite of the low number of samples used, the results of this preliminary study demonstrated the ability of fluorescence spectroscopy to predict meat lipids. Nonetheless, the results suggested that the fluorescence spectroscopy is more suited to measure SFA (R(2)p≥0.66; RPD≥2.29) and MUFA (R(2)p≥0.48; RPD≥1.49) than PUFA (R(2)p≤0.48; RPD≤1.63). Moreover, R(2) and RPD factors obtained with FFFS were greater compared to the ones obtained with SFS suggesting that FFFS is more adapted to measure lipid composition of beef meat.

Synchronous fluorescence spectroscopy (SFS) coupled with two-dimensional correlation spectroscopy (2DCOS) was employed to monitor, at the molecular level, the coagulation of five mixture ratios of camel’s milk (CaM) and cow’s milk (CM) (100% CaM, 75% CaM:25% CM, 50% CaM:50% CM, 25% CaM:75% CM and 100% CM). The dissimilarities among the different formulations are highlighted on the synchronous 2DCOS-SFS. In addition, according to the cross-peak symbols in synchronous and asynchronous spectra, the rate of response modification in riboflavin, protein and vitamin A matched with common coagulation phenomena usually reported during chymosin coagulation (hydrolysis of κ-casein, destabilization of casein micelles and aggregation). This study demonstrated that 2DCOS-SFS is a successful strategy to discriminate milk mixtures and to monitor molecular structure modifications during coagulation process.