The Impact of Nonthermal Technologies on the Microbiological Quality of Juices: A Review

Fruit and vegetable juices are rich sources of nutrients that support microbiological growth and ultimately undergo rapid deterioration of safety and quality. The loss of nutritional quality of juices due to intensive thermal processing is a major problem encountered during the treatment of commercially preserved liquid foods. Conventional thermal processing technologies inactivate microorganisms and enzymes and extend the shelf life of foods but exert negative effects on nutritional and organoleptic properties of juices, for example, a loss of vitamins, of a desirable flavor, and of bioactive compounds and development of different sensory profiles as a result of heating. Nonthermal technologies including ultrasonication, a pulsed electric field, high‐pressure processing, irradiation, and their combinations are suitable alternatives for achieving the same preservation effect without the adverse effects of heat on the quality of juices and meet consumer demand for clean‐label, safe, and wholesome products without compromising their nutritional properties.     

Do Dairy Minerals Have a Positive Effect on Bone Health?

This review aims to examine the relationship between the consumption of dairy products, mineral absorption, and bone health, and critically evaluates the methods that have been used to investigate this relationship. As people live longer and have lives that are more active in modern societies, bone health is of concern due to the possibility for the increasing incidence of bone disorders, such as osteoporosis. It has been suggested that dairy products can play a key role in bone health due to their high levels of minerals. Whether the positive effect of dairy consumption on bone health is due solely to the concentration of minerals, the action of vitamins, proteins, and lipids present in dairy products, and complex interactions between different milk components remains to be determined. Assessment of how dairy products affect bone health is complex, with apparent contradictory conclusions being reported in the literature. To gain a better understanding of the effects that dairy products have on bone health, this review presents an evaluation of a combination of data obtained using a variety of methods. From those data, we surmise that the preferable approach to investigate the effects of milk on bone health is to obtain data from human, animal, and cell line testing. A combined approach will enable various aspects to be identified, including mechanisms and the assessment of holistic effects, which will enable the effects in the human situation to be ascertained.     

Marine omega‐3 (n‐3) phospholipids: A comprehensive review of their properties,sources, bioavailability,and relation to brain health

For several decades, there has been considerable interest in marine‐derived long chain n‐3 fatty acids (n‐3 LCPUFAs) due to their outstanding health benefits. n‐3 LCPUFAs can be found in nature either in triglycerides (TAGs) or in phospholipid (PL) form. From brain health point of view, PL n‐3 is more bioavailable and potent compared to n‐3 in TAG form, as only PL n‐3 is able to cross the blood–brain barrier and can be involved in brain biochemical reactions. However, PL n‐3 has been ignored in the fish oil industry and frequently removed as an impurity during degumming processes. As a result, PL products derived from marine sources are very limited compared to TAG products. Commercially, PLs are being used in pharmaceutical industries as drug carriers, in food manufacturing as emulsifiers and in cosmetic industries as skin care agents, but most of the PLs used in these applications are produced from vegetable sources that contain less (without EPA, DPA, and DHA) or sometimes no n‐3 LCPUFAs. This review provides a comprehensive account of the properties, structures, and major sources of marine PLs, and provides focussed discussion of their relationship to brain health. Epidemiological, laboratory, and clinical studies on n‐3 LCPUFAs enriched PLs using different model systems in relation to brain and mental health that have been published over the past few years are discussed in detail.     

Utilisation of beef lung protein powder as a functional ingredient to enhance protein and iron content of fresh pasta

Beef lungs are an underutilised co-product of the meat industry that could be used as an ingredient to supplement the protein content of cereal foods. Beef lung powder (BLP) had a protein content of 87% (dry weight basis), an amino acid score of 1, and contained 1 mg g⁻¹ iron. Fresh semolina pasta was used as a model food, and BLP was incorporated up to 20%. Incorporation of 10% BLP improved the indispensable amino acid score of the pasta from 0.48 to 0.91. At that level of incorporation, higher cooking loss and redder (increased ‘a’ value) and darker (decreased ‘L’ value) colour resulted. The fresh pasta with BLP had a lower glycaemic response compared to control samples. BLP improved the functional value of the fresh pasta and is a potential ingredient in the development of new food products.     

Comparison of the Proteolytic Activities of New Commercially Available Bacterial and Fungal Proteases toward Meat Proteins

The hydrolytic activity of 3 commercially available protease preparations (bacterial protease G, fungal 31000, and fungal 60000) were examined using fluorescent‐labeled casein, azo dye‐impregnated collagen, and meat protein extracts from bovine M. semimembranosus and Achilles tendon, and compared to that of papain. Assays showed that all proteases exhibited little activity at low temperature (5 °C), and maximal activity at 45 °C. The pH, at which optimal activity was observed for each of the protease preparations, differed and ranged from pH 5.0 to 8.0. Kinetic parameters (K_M and V_max) were also different between protease preparations, with the bacterial protease G and papain exhibiting significantly higher V_max values (P < 0.001) and lower K_M values (P < 0.01) for the casein substrate than the 2 fungal protease preparations. Meat protein hydrolysis was displayed on SDS‐PAGE and proteins analyzed with mass spectrometry. The protease preparations were shown to have varying affinity toward different meat proteins. The bacterial protease G preparation was efficient at hydrolyzing most myofibril and collagen proteins, and appeared to be more efficient than papain at hydrolyzing collagen proteins. On the other hand the 2 fungal protease preparations showed a selective specificity toward meat myofibrillar proteins, and the fungal 60000 protease preparation exhibited high affinity toward collagen γ and collagen type I chain B proteins. The results generated in this study demonstrated that these commercial proteases have good potential for use in meat tenderization applications due to their mild and complementary effects on different meat proteins. Practical Application : Bacterial and fungal protease preparations exhibited varying affinities for hydrolyzing meat proteins. This selective moderate capability of microbial proteases compared to papain is potentially an advantage in avoiding over‐tenderization in meat. On the other hand, the bacterial protease G preparation, which appeared to be more efficient at hydrolyzing connective tissue proteins than papain, could be beneficial in tenderizing meat with high connective tissue content. The synergistic effect of these protease preparations could be incorporated into a meat tenderizing formula to give the tenderizer a broad activity spectrum, thus able to target different cuts of meat.     

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligent MAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches.