Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

Thermal processing is a technique for sterilizing foods through heating at high temperatures. Thermal processing plays a significant role in preserving foods economically, efficiently, reliably, and safely. Control in thermal processing of foods is necessary to avoid any decrease in food quality, i.e., color change, reduced content, sensory quality, and nutrition. Artificial Neural Network (ANN) has been developed as a computing method in research and developments on thermal processing methods to discover one suitable for food processing without damaging food quality. To this date, ANN has been used in food industries for modeling many processes. The paper aims to identify the latest trend in intelligent neural network control for the thermal processing of foods. The paper conducted a systematic literature review with five research questions using Preferred Reporting Items for Systematic Review (PRISMA). According to screening results and article selection, 240 potential articles have fulfilled the inclusion criteria. Then, each article was explored to identify the advantage and the advance of intelligent network control in thermal food processing. It can be concluded that the technology in information and computations of food processing has rapidly developed and advanced through the utilization of a combination of ANN with fuzzy logic and/or genetic algorithms

AC-AC Voltage Controller of Power Supply for Heater on Drying System

The coconut fruits is very useful to be processed into cooking oil and it can meet the needs of cooking for Indonesia people, But before the coconuts processed into oil, The coconuts should be dried first. Usually the drying process is manually, that is dried in the sun. Unfortunately this way is not hygienic and can’t be done continuously, causing the coconut fungus overgrow. This paper proposes methode to solve the problem above.This research makes the dryer coconut fruitssystem by using the heater.Dryer system used for dry some coconut fruits that must be reduced the water content up to 5%, this dry coconutis called copra. System requires heater which will be regulated the temperature. Heater temperature setting is done by adjusting the heater supply voltage, and this is the task of ac to ac voltage controller. Ac to ac voltage controller is a circuit converter is capable converting ac voltage with value 220 Vrms at 50 Hz frequency and have waveform pure sinusoidal become ac voltage frequency at 50 Hz with an output voltage suitable with our need but the voltage waveform not impure sinusoidal or defective as a result firing angle effects. The output voltage which we set determines the value of the heater temperature. The output voltage is set from the firing angle of the triac component using the addition and decreasing angle values. Based on the results of experiments that have been done, if the drying chamber which has volume 135.2 liters and the temperature regulated at 700 C then the heater with a power requirement of 400 W should receive supply 216 V from ac to ac voltage controller. So the triac of ac to ac voltage controller fired on 0.55 radians so that the to ac voltage controller that gets 220 V input voltage can produce 216 V output voltage

Application of simulated neural networks as Non-Linear Modular Modeling Method for predicting shelf life of processed cheese / Sumit Goyal and Gyanendra Kumar Goyal

This paper presents the capability of simulated neural network (SNN) models for predicting the shelf life of processed cheese stored at ambient temperature 30o C. Processed cheese is a dairy product generally made from medium ripened Cheddar cheese. Elman and Linear Layer(Train) SNN models were developed. Body & texture, aroma & flavour, moisture, free fatty acids were used as input variables and sensory score as the output. Neurons in each hidden layers varied from 1 to 40. The network was trained with single as well as double hidden layers up to 100 epochs, and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient performance measures were used for testing prediction potential of the developed models. Results showed a 4201 topology was able to predict the shelf life of processed cheese exceedingly well with R2 as 0.99992157. The corresponding RMSE for this topology was 0.003615359. From this study it is concluded that SNN models are excellent tool for predicting the shelf life of processed cheese

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Development of a colorimetric indicator, using a residue from grape juice processing, for food freshness monitoring

Filmes indicadores colorimétricos foram desenvolvidos para avaliação da qualidade de alimentos através do reaproveitamento do resíduo sólido, do tipo não-bagaço (NPG), da centrifugação do suco de uva, atualmente inexplorado em sistemas inteligentes para embalagens de alimentos. As dispersões foram obtidas por complexação polieletrolítica de alginato e gelatina e alginato e quitosana, com e sem adição de NPG, nas seguintes composições (m/m): alginato e gelatina (75 % + 25 %, AG); alginato, gelatina e NPG (75% + 25% + 0.5 g 100 g complexo -1 , AG0.5); alginato, gelatina e NPG (75% + 25% + 1.0 g 100 g complexo -1 , AG1.0); alginato e quitosana (75% + 25%, ACH); alginato, quitosana e NPG (75% + 25% + 0.5 g 100 g complexo -1 , ACH0.5); alginato, quitosana e NPG (75% + 25% + 1.0 g 100 g complexo -1 , ACH1.0). A adição de NPG afetou a estabilidade e o comportamento reológico dos complexos AG e ACH, que apresentaram características predominantemente viscosas e viscoelásticas, respectivamente, evidenciadas pelo comportamento de creep-recovery. O NPG afetou as propriedades hidrofílicas dos filmes indicadores. A umidade diminuiu em até 35 %, a solubilidade dos filmes AG aumentou, e não houve efeito significativo na permeabilidade ao vapor de água, exceto para a amostra ACH1.0, na qual alterações microestruturais promovidas pelo processo de secagem podem ter aumentado a difusão através do filme. As propriedades mecânicas e ópticas também foram alteradas, assim como a morfologia, formando-se filmes de menor transmissão de luz com estruturas mais heterogêneas e rígidas. Esse comportamento corroborou-se pela avaliação da topografia superficial dos filmes, cujos valores de rugosidade média para os filmes AG e ACH aumentaram cerca de 91 % e 52 %, respectivamente, após a adição do maior teor de NPG. Os difratogramas das amostras e os espectros de FTIR demonstraram que o NPG interagiu de maneira diferente com os dois complexos, e os compostos fenólicos do NPG podem ter atuado como um agente de reticulação para o complexo ACH, o que aumentou as interações intermoleculares e intramoleculares entre alginato e quitosana. Isto evidenciou-se na molhabilidade, que aumentou após adição de NPG, bem como na avaliação das propriedades termodinâmicas. As isotermas de adsorção de água dos filmes foram determinadas a 20, 30 e 40 °C, a partir das quais foram obtidas as propriedades termodinâmicas de sorção. O modelo GAB apresentou o melhor ajuste para os dados de adsorção, que foi um processo controlado por entalpia. As menores perdas de massa até 100 °C foram encontradas para os filmes ACH, que apresentaram os maiores módulos de armazenamento e perda na análise dinâmico-mecânica. Um novo método foi proposto para avaliar a biodegradabilidade dos filmes, por meio da avaliação da perda de área por análise digital de imagens. A adição de NPG diminuiu a biodegradabilidade, com perdas de área de 90,02 % a 35,90 % após 30 dias em ambiente fechado. Testes de eficiência colorimétrica dos filmes foram realizados em soluções tampão ácido lático / lactato e na fase gasosa com amônia, como sistemas modelo para a deterioração de produtos lácteos e cárneos, respectivamente. Eles também foram aplicados como indicadores de frescor em amostras de leite integral. Em geral, menores teores de NPG foram mais eficientes para diferenciação de cores pelo visualizador. A quantificação do desvio cromático vermelho em função do pH foi promissora para avaliar a mudança visual de cor por análise digital de imagens para aplicação dos filmes em embalagens de alimentos.Colorimetric indicator films were developed for food freshness assessment through the solid residue from the centrifugal separation process of grape juice upcycling, a non-pomace residue (NPG) currently unexplored in the development of intelligent systems for food packaging. The film-forming matrix was obtained by polyelectrolyte complexation of alginate and gelatin and alginate and chitosan, with and without NPG addition, with the following composition (w/w): alginate and gelatin (75 % + 25 %, AG); alginate, gelatin and NPG (75% + 25% + 0.5 g 100 g complex -1 , AG0.5); alginate, gelatin and NPG (75% + 25% + 1.0 g 100 g complex -1 , AG1.0); alginate and chitosan (75% + 25%, ACH); alginate, chitosan and NPG (75% + 25% + 0.5 g 100 g complex -1 , ACH0.5); alginate, chitosan and NPG (75% + 25% + 1.0 g 100 g complex -1 , ACH1.0). The NPG addition affected the stability and the rheological behavior of the AG and ACH complexes, which presented predominantly viscous and viscoelastic characteristics, respectively, evidenced by their creep-recovery behavior. The NPG affected the water-related properties of the indicator films. It decreased their moisture by up to 35 %, increased the solubility of the AG films, and had no significant effect on their water vapor permeability, except for the ACH1.0 sample, in which microstructure changes promoted by the drying process may have increased the diffusion through the film. The mechanical and optical properties were also affected, as well as the morphology with the formation of films with lower light transmission, and more heterogeneous and rigid structures. This behavior was corroborated by the films’ surface topography evaluation, whose average roughness values for the AG and ACH films increased by about 91 % and 52 %, respectively, after the highest content of NPG addition. The samples’ diffractograms and FTIR spectra demonstrated that NPG interacted differently with the two complexes, and the NPG phenolic compounds may have acted as a crosslinking agent for the ACH complex, which enhanced intermolecular and intramolecular interactions between alginate and chitosan. This behavior was evidenced in the wettability evaluation, which was increased by the NPG addition, as well as in the evaluation of thermodynamic properties. The films’ water adsorption isotherms were determined at 20, 30, and 40 °C, from which the thermodynamic properties of sorption were obtained. The GAB model presented the best fit for the adsorption data, which was an enthalpy-controlled process. The lowest mass losses up until 100 °C were found for the ACH films, which showed the highest storage and loss modules in dynamic mechanical analysis. A new method was proposed to evaluate the biodegradability of films, by area loss assessment through digital image analysis. NPG addition decreased the films’ biodegradability, whose area losses ranged from 90.02 % to 35.90 % after 30 days under indoor soil conditions. Colorimetric efficiency tests of the films were performed in acid lactic / lactate buffer solutions and in the gaseous phase with ammonia, as model systems for the deterioration of dairy and meat products, respectively. They were also applied as freshness indicators in whole milk samples. In general, lower NPG contents were more efficient for color differentiation by the viewer. The red chromatic shift quantification as a function of pH was promising to evaluate the visual color change by digital image analysis for the films’ application on food packaging

Utilization and Impact of Internet of Things (IoT) in Food Supply Chains from the Context of Food Loss/Waste Reduction, Shelf-Life Extension and Environmental Impact

openThe Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study.The Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study

Review on environmental models in the food chain - Current status and future perspectives

Diversity of food systems and their interaction with the environment has become a research topic for many years. Scientists use various models to explain environmental issues of food systems. This paper gives an overview of main streams in analyzing this topic. A literature review was performed by analyzing published scientific papers on environmental impacts in the food chain. The selection criteria were focused on different environmental approaches applied in the food chain and on the perspectives of future research. This review shows that on the one side there are generic environmental models developed by environmental scientists and as such applied on food. On the other side, there are models developed by food scientists in order to analyze food-environmental interactions. The environmental research in food industry can be categorized as product, process or system oriented. This study confirmed that the focus of product based approach is mainly performed through life-cycle assessments. The process based approach focuses on food processes such as heat transfer, cleaning and sanitation and various approaches in food waste management. Environmental systems in the food chain were the least investigated stream analyzing levels of environmental practices in place. Future research perspectives are the emerging challenges related to environmental impacts of novel food processing technologies, innovative food packaging and changes in diets and food consumption in connection with climate and environmental changes