Fouling during food processing – progress in tackling this inconvenient truth

Clean-In-Place (CIP) wastewater management using nanofiltration (NF)-forward osmosis (FO)-direct contact membrane distillation (DCMD): Effects of draw salt

A substantial amount of water is being used during Clean-in-Place (CIP) operation, and is transformed into wastewater that can cause eutrophication to the nearby ecosystem. The present study proposed the Nanofiltration (NF) - Forward Osmosis (FO) - Direct Contact Membrane Distillation (DCMD) to recover the cleaning agents and reclaim freshwater from the model CIP wastewater. NF steps were suggested as prefiltration steps to remove organic compounds from the CIP wastewater. NF steps reduced the lactose and protein contents by 100 % and 95.6 %, respectively. The permeates from NF steps were further managed by the integrated FO-DCMD system. Several draw salts such as NaCl, KCl, MgCl2, and CaCl2 were compared to investigate the influence on FO and DCMD performance. It was found that monovalent salts (NaCl and KCl) outperformed the divalent salts (MgCl2 and CaCl2) in terms of water flux for both FO and DCMD. This can be attributed to the lower viscosity and higher mass transfer coefficient. In addition, the replenishment costs of each salt were evaluated since salts loss occurred during FO and DCMD operation. The cost evaluation revealed that NaCl is most the cheapest salts per reclaimed water. All of this observation indicates that NaCl is preferred in terms of water flux and replenishment cost. The NF permeate kept concentrated using the integrated FO-DCMD or single FO with 2 M of NaCl. Compared to a single FO that showed a consistent decline in draw solution concentration, FO-DCMD could maintain the concentration of the draw solution. Despite the constant concentration, flux decline of FO was observed due to fouling formation caused by the high-temperature operation. However, the FO-DCMD could accomplish the recovery of pure water. Finally, the cleaning agents recovered by the NF-FO-DCMD showed the cleaning efficacy comparable to the fresh NaOH. These results suggest the potential of the proposed system to manage the CIP wastewater.

Application of Emerging Techniques in Reduction of the Sugar Content of Fruit Juice: Current Challenges and Future Perspectives

In light of the growing interest in products with reduced sugar content, there is a need to consider reducing the natural sugar concentration in juices while preserving the initial concentration of nutritional compounds. This paper reviewed the current state of knowledge related to mixing juices, membrane processes, and enzymatic processes in producing fruit juices with reduced concentrations of sugars. The limitations and challenges of these methods are also reviewed, including the losses of nutritional ingredients in membrane processes and the emergence of side products in enzymatic processes. As the existing methods have limitations, the review also identifies areas that require further improvements and technological innovations.

Monitoring and Characterization of Milk Fouling on Stainless Steel Using a High-Pressure High-Temperature Quartz Crystal Microbalance with Dissipation

Fouling at interfaces deteriorates the efficiency and hygiene of processes within numerous industrial sectors, including the oil and gas, biomedical device, and food industries. In the food industry, the fouling of a complex food matrix to a heated stainless steel surface reduces production efficiency by increasing heating resistance, pumping requirements, and the frequency of cleaning operations. In this work, quartz crystal microbalance with dissipation (QCM-D) was used to study the interface formed by the fouling of milk on a stainless steel surface at different flow rates and protein concentrations at high temperatures (135 °C). Subsequently, the QCM-D response was recorded during the cleaning of the foulant. Two phases of fouling were identified. During phase-1, the fouling rate was dependent on the flow rate, while the fouling rate during phase-2 was dependent on the flow rate and protein concentration. During cleaning, foulants deposited at the higher flow rate swelled more than those deposited at the lower flow rate. The composition of the fouling deposits consisted of both protein and mineral species. Two crystalline phases of calcium phosphate, β-tricalcium phosphate and hydroxyapatite, were identified at both flow rates. Stratification in topography was observed across the surface of the QCM-D sensor with a brittle and cracked structure for deposits formed at 0.2 mL/min and a smooth and close-packed structure for deposits formed at 0.1 mL/min. These stratifications in the composition and topography were correlated to differences in the reaction time and flow dynamics at different flow rates. This high-temperature application of QCM-D to complex food systems illuminates the initial interaction between proteins and minerals and a stainless steel surface, which might otherwise be undetectable in low-temperature applications of QCM-D or at larger bench and industrial scales. The methods and results presented here have implications for optimizing processing scenarios that limit fouling formation while also enhancing removal during cleaning.

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

Recent advances and perspectives of ultrasound assisted membrane food processing

Power ultrasound (US) transmits substantial amounts of small mechanical movements serving for particle detaching in membrane filtrations. This topic has been reviewed in recent years mainly focused on the mechanisms by which the flux is improved under specific processing conditions. US also been shown to improve food quality by changing physical properties and modifying the activity of enzymes and microorganisms. Surprisingly, limited information exists regarding on how the application of US results in terms of process and quality during membrane filtration of complex matrices such as liquid foods. This review highlights the recent advances in the use of US in membrane filtration processes focused in the manufacturing of foodstuffs and food ingredients, and perspectives of novel hybrid membrane-US systems that may be quite interesting for this field. The application of US in food membrane processing increases the flux, but the lack of standardization regarding to experimental conditions, make suitable comparisons impossible. In this sense, careful attention must be paid regarding to the ultrasonic intensity (UI), the membrane configuration and type of transducers and volume of the treated solution. Dairy products are the most studied application of US membrane food processing, but research has been mainly focused on flux enhancement; hitherto there have been no reports of how operational variables in these processes affect critical aspects such as quality and food safety. Also, studies performed at industrial scale and economical assessments are still missing. Application of US combined with membrane operations such as reverse osmosis (RO), forward osmosis (FO) and enzyme membrane bioreactors (EMBR) may result interesting for the production of value-added foods. In the perspective of the authors, the stagnation of the development of acoustic filtration systems in food is due more to a prejudice on this subject, rather than actual impedance due to the lack of technological development of transducers. This later has shown important advances in the last years making them suitable for tailor made applications, thus opening several research opportunities to the food engineering not yet explored.

Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.