Search results for: artificial food

Authors: Muhasin Koyiloth, Sathyanarayana N. Gummadi

Abstract:

Biological membranes are ordered association of lipids, proteins, and carbohydrates. Lipids except sterols possess asymmetric distribution across the bilayer. Eukaryotic membranes possess a group of lipid translocators called scramblases that disrupt phospholipid asymmetry. Their action is implicated in cell activation during wound healing and phagocytic clearance of apoptotic cells. Cholesterol is one of the major membrane lipids distributed evenly on both the leaflet and can directly influence the membrane fluidity through the ordering effect. The fluidity has an impact on the activity of several membrane proteins. The palmitoylated phospholipid scramblases localized to the lipid raft which is characterized by a higher number of sterols. Here we propose that cholesterol can interact with scramblases through putative CRAC motif and can modulate their activity. To prove this, we reconstituted phospholipid scramblase 1 of C. elegans (SCRM-1) in proteoliposomes containing different amounts of cholesterol (Liquid ordered/Lo). We noted that the presence of cholesterol reduced the scramblase activity of wild-type SCRM-1. The interaction between SCRM-1 and cholesterol was confirmed by fluorescence spectroscopy using NBD-Chol. Also, we observed loss of such interaction when one of I273 in the CRAC motif mutated to Asp. Interestingly, the point mutant has partially retained scramblase activity in Lo vesicles. The current study elucidated the important interaction between cholesterol and SCRM-1 to fine-tune its activity in artificial membranes.

Keywords: artificial membranes, CRAC motif, plasma membrane, PL scramblase

Procedia PDF Downloads 172

Authors: Cristina Munoz-Shuguli, Francisco Rodriguez, Julio Bruna, M. Jose Galotto, Abel Guarda

Abstract:

The microbial contamination in fruits due to the presence of fungal is the most important cause of their deterioration and loss. The development of active food packaging materials with antifungal properties has been proposed as an innovative strategy in order to prevent this problem. In this way, natural compounds as the essential oils or their derivatives, also called volatile compounds (VC), can be incorporated in the food packaging materials to control the fungal growth during fruit packaging. However, if the VC is incorporated directly in the packaging material, it is released very fast due to VC high volatility. For this reason, the formation of inclusion complexes through the encapsulation of VC into beta-cyclodextrin (β-CD) and their incorporation in package materials is an alternative to maintain an antifungal atmosphere around the packaged fruits for longer times. In this context, the aim of this work was to develop inclusion complexes based in β-CD and VC (β-CD:VC) for further application in the antifungal food packaging materials development. β-CD:VC inclusion complexes were obtained with two different molar ratios 2:1 and 1:1, through co-precipitation method. The entrapment efficiency of β-CD:VC as well the release of antifungal compound from inclusion complexes exposed to different relative humidity (25, 50, and 97 %) to headspace were determined by gaseous chromatography (GC). Also, thermal and antimicrobial properties of β-CD:VC were determined through thermogravimetric analysis (TGA) and antifungal assays against Botrytis cinerea, respectively. GC results showed that β-CD:VC 2:1 had a higher entrapment efficiency than β-CD:VC 1:1, with values of 75.5 ± 3.71 % and 59.6 ± 1.51 %, respectively. It was probably because during the synthesis of β-CD:VC 1:1, there was less molecular space to the movement of VC molecules. Furthermore, the release of VC from β-CD:VC was directly related with the relative humidity. High amount of VC was released when the inclusion complexes were exposed to high humidity, possibly due to the interactions between the water molecules and the β-CD hydrophilic wall. On the other hand, a better thermal stability of VC in inclusion complexes allowed to verify its effective encapsulation into β-CD. Finally, antimicrobial assays showed that the inclusion complexes had a high antifungal activity at very low concentrations. Therefore, the results obtained in this work allow suggesting the β-CD:VC inclusion complexes as potential candidates to the development of fruit antifungal packaging materials, which activity is relative humidity dependent.

Keywords: Botrytis cinerea, fruit packaging, headspace release, volatile compounds

Procedia PDF Downloads 113

Authors: Shweta Kumari, Parmjit S. Panesar, Manab B. Bera

Abstract:

The hydrolysis of lactose using β-galactosidase is one of the most promising biotechnological applications, which has wide range of potential applications in food processing industries. However, due to intracellular location of the yeast enzyme, and expensive extraction methods, the industrial applications of enzymatic hydrolysis processes are being hampered. The use of permeabilization technique can help to overcome the problems associated with enzyme extraction and purification of yeast cells and to develop the economically viable process for the utilization of whole cell biocatalysts in food industries. In the present investigation, standardization of permeabilization process of novel yeast isolate was carried out using a statistical model approach known as Response Surface Methodology (RSM) to achieve maximal b-galactosidase activity. The optimum operating conditions for permeabilization process for optimal β-galactosidase activity obtained by RSM were 1:1 ratio of toluene (25%, v/v) and ethanol (50%, v/v), 25.0 oC temperature and treatment time of 12 min, which displayed enzyme activity of 1.71 IU /mg DW.

Keywords: β-galactosidase, optimization, permeabilization, response surface methodology, yeast

Procedia PDF Downloads 249

Authors: Jelena Filipović, Milenko Košutić, Vladimir Filipović

Abstract:

In the last decades, the urban population has been characterized by sedentary lifestyles, low physical activity, and "fast food". These changes in diet and physical nonactivity have been associated with an increase in chronic diseases. Bread is one of the most popular wheat products consumed worldwide. Spelt wheat has shown potential in various food applications, including bread, pasta, breakfast cereal, and other products of altered nutritional characteristics compared to conventional wheat products. It has very high protein content and even 30 to 60% higher concentration of mineral elements Fe, Zn, Cu, Mg and P compared to Triticum Aestivum. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and it is an old cultivar. So it can be used for organic and health-safe food. Changes in the formulation of bread with the aim of improving its nutritional and functional properties usually lead to changes in the dough's properties, which are related to the quality of the finished product. The aim of this paper is to research the impact of adding yeast extract to bread on sensory characteristics and consumer acceptance of a new product as a key factor for the successful marketing of a distinct product. The sensory analysis of bread with 5% yeast extract is as follows: the technological quality is very good (3.8), and the color of the product is excellent (4.85). Based on data, consumers' survey declared that they liked the taste of bread with 5% yeast extract (74%), consumers marked the product as likable (70%), and 75% of the total number of respondents would buy this new product. This paper is promoting a type of bread with 5% yeast extract (Z score 0.80) to improve diet and a product intended for consumers conscious about their health and diet.

Keywords: bread, yeast extract, sensory analysis, consumer survey, score analysis

Procedia PDF Downloads 51

Authors: Shivani Verma, Amit Agrawal, Pankaj Kumar Meena, Ashish B. Deoghare

Abstract:

Over the decade robotics has been a major area of interest among the researchers and scientists in reducing human efforts. The need for robots to replace human work in different dangerous fields such as underground mining, nuclear power station and war against terrorist attack has gained huge attention. Most of the robot design is based on human structure popularly known as humanoid robots. However, the problems encountered in humanoid robots includes low speed of movement, misbalancing in structure, poor load carrying capacity, etc. The simplification and adaptation of the fundamental design principles seen in animals have led to the creation of bio-inspired robots. But the major challenges observed in naturally inspired robot include complexity in structure, several degrees of freedom and energy storage problem. The present work focuses on design and fabrication of a bionic quadruped walking robot which is based on different joint of quadruped mammals like a dog, cheetah, etc. The design focuses on the structure of the robot body which consists of four legs having three degrees of freedom per leg and the electronics system involved in it. The robot is built using readily available plastics and metals. The proposed robot is simple in construction and is able to move through uneven terrain, detect and locate obstacles and take images while carrying additional loads which may include hardware and sensors. The robot will find possible application in the artificial intelligence sector.

Keywords: artificial intelligence, bionic, quadruped robot, degree of freedom

Procedia PDF Downloads 211

Authors: Avi Shrivastava

Abstract:

Our world continues to grapple with a lot of social issues. Economic growth and scientific advancements have not completely eradicated poverty, homelessness, discrimination and bias, gender inequality, health issues, mental illness, addiction, and other social issues. So, how do we improve the human condition in a world driven by advanced technology? The answer is simple: we will have to leverage technology to address some of the most important social challenges of the day. AI, or artificial intelligence, has emerged as a critical tool in the battle against issues that deprive marginalized and disadvantaged groups of the right to enjoy benefits that a society offers. Social work professionals can transform their lives by harnessing it. The lack of reliable data is one of the reasons why a lot of social work projects fail. Social work professionals continue to rely on expensive and time-consuming primary data collection methods, such as observation, surveys, questionnaires, and interviews, instead of tapping into AI-based technology to generate useful, real-time data and necessary insights. By leveraging AI’s data-mining ability, we can gain a deeper understanding of how to solve complex social problems and change lives of people. We can do the right work for the right people and at the right time. For example, AI can enable social work professionals to focus their humanitarian efforts on some of the world’s poorest regions, where there is extreme poverty. An interdisciplinary team of Stanford scientists, Marshall Burke, Stefano Ermon, David Lobell, Michael Xie, and Neal Jean, used AI to spot global poverty zones – identifying such zones is a key step in the fight against poverty. The scientists combined daytime and nighttime satellite imagery with machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda, and Malawi. In an article published by Stanford News, Stanford researchers use dark of night and machine learning, Ermon explained that they provided the machine-learning system, an application of AI, with the high-resolution satellite images and asked it to predict poverty in the African region. “The system essentially learned how to solve the problem by comparing those two sets of images [daytime and nighttime].” This is one example of how AI can be used by social work professionals to reach regions that need their aid the most. It can also help identify sources of inequality and conflict, which could reduce inequalities, according to Nature’s study, titled The role of artificial intelligence in achieving the Sustainable Development Goals, published in 2020. The report also notes that AI can help achieve 79 percent of the United Nation’s (UN) Sustainable Development Goals (SDG). AI is impacting our everyday lives in multiple amazing ways, yet some people do not know much about it. If someone is not familiar with this technology, they may be reluctant to use it to solve social issues. So, before we talk more about the use of AI to accomplish social work objectives, let’s put the spotlight on how AI and social work can complement each other.

Keywords: social work, artificial intelligence, AI based social work, machine learning, technology

Procedia PDF Downloads 97

Authors: Nuno Biga

Abstract:

Artificial Intelligence (AI) allows machines to interpret information and learn from context analysis, giving them the ability to make predictions adjusted to each specific situation. In addition to learning by performing deterministic and probabilistic calculations, the 'artificial brain' also learns through information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) that provides users with useful suggestions, namely to pursue the following operations: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time the bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed in a pilot project. Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of this information is materialised in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" that players can use during the Game. Each participant in the Virtual Assisted-BIGAMES permanently asks himself about the decisions he should make during the game in order to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, and as the participants gain a better understanding of the game, they will more easily dispense with the VA's recommendations and rely more on their own experience, capability, and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator (Serious Game Controller) is responsible for supporting the players with further analysis and the recommended action may be (or not) aligned with the previous recommendations of the VA. All the information should be jointly analysed and assessed by each player, who are expected to add “Emotional Intelligence”, a component absent from the machine learning process.

Keywords: artificial intelligence (AI), gamification, key performance indicators (KPI), machine learning, management simulators, serious games, virtual assistant

Procedia PDF Downloads 96

Authors: Muhammad Tanveer Altaf, Mehmet Bedir, Waqas Liaqat, Gönül Cömertpay, Volkan Çatalkaya, Celaluddin Barutçular, Nergiz Çoban, Ibrahim Cerit, Muhammad Azhar Nadeem, Tolga Karaköy, Faheem Shehzad Baloch

Abstract:

Food production needs to be almost double by 2050 in order to feed around 9 billion people around the Globe. Plant production mostly relies on fertilizers, which also have one of the main roles in environmental pollution. In addition to this, climatic conditions are unpredictable, and the earth is expected to face severe drought conditions in the future. Therefore, water and fertilizers, especially nitrogen are considered as main constraints for future food security. To face these challenges, developing integrative approaches for germplasm characterization and selecting the resilient genotypes performing under limiting conditions is very crucial for effective breeding to meet the food requirement under climatic change scenarios. This study is part of the European Research Area Network (ERANET) project for the characterization of the diversity panel of 172 sorghum accessions and six hybrids as control cultivars under limiting (+N/-H2O, -N/+H2O) and non-limiting conditions (+N+H2O). This study was planned to characterize the sorghum diversity in relation to resource Use Efficiency (RUE), with special attention on harnessing the interaction between genotype and environment (GxE) from a physiological and agronomic perspective. Experiments were conducted at Adana, a Mediterranean climate, with augmented design, and data on various agronomic and physiological parameters were recorded. Plentiful diversity was observed in the sorghum diversity panel and significant variations were seen among the limiting water and nitrogen conditions in comparison with the control experiment. Potential genotypes with the best performance are identified under limiting conditions. Whole genome resequencing was performed for whole germplasm under investigation for diversity analysis. GWAS analysis will be performed using genotypic and phenotypic data and linked markers will be identified. The results of this study will show the adaptation and improvement of sorghum under climate change conditions for future food security.

Keywords: germplasm, sorghum, drought, nitrogen, resources use efficiency, sequencing

Procedia PDF Downloads 70

Authors: Chih-Chieh Yang, Chung-Neng Huang

Abstract:

In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

Procedia PDF Downloads 130

Authors: Henry Lau, Dilupa Nakandala, Li Zhao

Abstract:

In today’s business, it is well-recognized that risk is an important factor that needs to be taken into consideration before a decision is made. Studies indicate that both the number of risks faced by organizations and their potential consequences are growing. Supply chain risk management has become one of the major concerns for practitioners and researchers. Supply chain leaders and scholars are now focusing on the importance of managing supply chain risk. In order to meet the challenge of managing and mitigating supply chain risk (SCR), we must first identify the different dimensions of SCR and assess its relevant probability and severity. SCR has been classified in many different ways, and there are no consistently accepted dimensions of SCRs and several different classifications are reported in the literature. Basically, supply chain risks can be classified into two dimensions namely disruption risk and operational risk. Disruption risks are those caused by events such as bankruptcy, natural disasters and terrorist attack. Operational risks are related to supply and demand coordination and uncertainty, such as uncertain demand and uncertain supply. Disruption risks are rare but severe and hard to manage, while operational risk can be reduced through effective SCM activities. Other SCRs include supply risk, process risk, demand risk and technology risk. In fact, the disorganized classification of SCR has created confusion for SCR scholars. Moreover, practitioners need to identify and assess SCR. As such, it is important to have an overarching framework tying all these SCR dimensions together for two reasons. First, it helps researchers use these terms for communication of ideas based on the same concept. Second, a shared understanding of the SCR dimensions will support the researchers to focus on the more important research objective: operationalization of SCR, which is very important for assessing SCR. In general, fresh food supply chain is subject to certain level of risks, such as supply risk (low quality, delivery failure, hot weather etc.) and demand risk (season food imbalance, new competitors). Effective strategies to mitigate fresh food supply chain risk are required to enhance operations. Before implementing effective mitigation strategies, we need to identify the risk sources and evaluate the risk level. However, assessing the supply chain risk is not an easy matter, and existing research mainly use qualitative method, such as risk assessment matrix. To address the relevant issues, this paper aims to analyze the risk factor of the fresh food supply chain using an approach comprising both fuzzy logic and hierarchical holographic modeling techniques. This novel approach is able to take advantage the benefits of both of these well-known techniques and at the same time offset their drawbacks in certain aspects. In order to develop this integrated approach, substantial research work is needed to effectively combine these two techniques in a seamless way, To validate the proposed integrated approach, a case study in a fresh food supply chain company was conducted to verify the feasibility of its functionality in a real environment.

Keywords: fresh food supply chain, fuzzy logic, hierarchical holographic modelling, operationalization, supply chain risk

Procedia PDF Downloads 235

Authors: So-Ra Lee, Myung-Min Oh

Abstract:

Generally, plants growing on the earth are under the influence of natural electric fields and may even require exposure of the electric field to survive. Electric signals have been observed within plants and seem to play an important role on various metabolic processes, but their role is not fully understood. In this study, we attempted to explore the response of plants under external electric fields in kale (Brassica oleracea var. acephala). The plants were hydroponically grown for 28 days in a plant factory. Electric currents at 10, 50 and 100 mA were supplied to nutrient solution for 3 weeks. Additionally, some of the plants were cultivated in a Faraday cage to remove the natural electric field. Kale plants exposed to electric fields had higher fresh weight than the control and plants in Faraday cage. Absence of electric field caused a significant decrease in shoot dry weight and root growth. Leaf area also showed a similar response with shoot fresh weight. Supplying weak electric stimulation enhanced nutritional quality including total phenolic content and antioxidant capacity. This work provides basic information on the effects of electric fields on plants and is a meaningful attempt for developing a new economical technology to increase crop productivity and quality by applying an electric field. This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Research Center Support Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (717001-07-02-HD240).

Keywords: electroculture, electric signal, faraday cage, electric field

Procedia PDF Downloads 286

Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

Abstract:

In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

Procedia PDF Downloads 267

Authors: Hela Gargouri, Nizar Moalla, Hassen Hadj Kacem

Abstract:

Meat adulteration affecting the safety and quality of food is becoming one of the main concerns of public interest across the world. The drastic consequences on the meat industry highlighted the urgent necessity to control the products' quality and to point out the complexity of both supply and processing circuits. Due to the expansion of this problem, the authentic testing of foods, particularly meat and its products, is deemed crucial to avoid unfair market competition and to protect consumers from fraudulent practices of meat adulteration. The adoption of authentication methods by the food quality-control laboratories is becoming a priority issue. However, in some developing countries, the number of food tests is still insignificant, although a variety of processed and traditional meat products are widely consumed. Little attention has been paid to provide an easy, fast, reproducible, and low-cost molecular test, which could be conducted in a basic laboratory. In the current study, the 359 bp fragment of the cytochrome-b gene was mapped by PCR-RFLP using firstly fresh biological supports (DNA and meat) and then turkey salami as an example of commercial processed meat. This technique has been established through several optimizations, namely: the selection of restriction enzymes. The digestion with BsmAI, SspI, and TaaI succeed to identify the seven included animal species when meat is formed by individual species and when the meat is a mixture of different origin. In this study, the PCR-RFLP technique using universal primer succeed to meet our needs by providing an indirect sequencing method identifying by restriction enzymes the specificities characterizing different species on the same amplicon reducing the number of potential tests.

Keywords: adulteration, animal species, authentication, meat, mtDNA, PCR-RFLP

Procedia PDF Downloads 107

Authors: Ian O'Loughlin

Abstract:

Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

Procedia PDF Downloads 265

Authors: Petre Voicu, Mircea Oaida, Radu Vasiu, Catalin Gheorghiu, Aurel Dumitru

Abstract:

The present work deals with the way in which food and tobacco waste can be used in agriculture. As a result of the lack of efficient technologies for their recycling, we are currently faced with the appearance of appreciable quantities of residual organic residues that find their use only very rarely and only after long storage in landfills. The main disadvantages of long storage of organic waste are the unpleasant smell, the high content of pathogenic agents, and the high content in the water. The release of these enormous amounts imperatively demands the finding of solutions to ensure the avoidance of environmental pollution. The measure practiced by us consists of the processing of this waste in special installations, testing in pilot experimental perimeters, and later administration on agricultural lands without harming the quality of the soil, agricultural crops, and the environment. The current crisis of raw materials and energy also raises special problems in the field of organic waste valorization, an activity that takes place with low energy consumption. At the same time, their composition recommends them as useful secondary sources in agriculture. The transformation of food scraps and other residues concentrated organics thus acquires a new orientation, in which these materials are seen as important secondary resources. The utilization of food and tobacco waste in agriculture is also stimulated by the increasing lack of chemical fertilizers and the continuous increase in their price, under the conditions that the soil requires increased amounts of fertilizers in order to obtain high, stable, and profitable production. The need to maintain and increase the humus content of the soil is also taken into account, as an essential factor of its fertility, as a source and reserve of nutrients and microelements, as an important factor in increasing the buffering capacity of the soil, and the more reserved use of chemical fertilizers, improving the structure and permeability for water with positive effects on the quality of agricultural works and preventing the excess and/or deficit of moisture in the soil.

Keywords: ecology, soil, organic waste, fertility

Procedia PDF Downloads 74

Authors: Jelena Filipović, Milenko Košutić, Vladimir Filipović

Abstract:

In the last decades urban population is characterized by sedentary lifestyles, low physical activity and "fast food". These changes in diet and physical non activity have been associated with the increase of chronic non diseases. Bread is one of the most popularly wheat products consumed worldwide. Spelt wheat has shown potential in various food applications, including bread, pasta, breakfast cereal and other products of altered nutritional characteristics compared to conventional wheat products. It has very high protein content and even 30 to 60% higher concentration of mineral elements Fe, Zn, Cu, Mg and P compared to Triticum Aestivum. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and it is an old cultivar. So it can be used for organic and health safe food. Changes in the formulation of bread with the aim to improve their nutritional and functional properties usually lead to changes in the dough properties which is related reflected to the quality of the finished product. The aim of this paper is researching the impact of adding yeast extract to bread on sensory characteristics and consumer acceptance of a new product as a key factor for successful marketing of a new product. The sensory analysis of bread with 5% yeast extract is as follows: the technological quality is very good (3.8) and the color of the product is excellent (4.85). Based on data consumers survey declared that they liked the taste of bread with 5% yeast extract (74%), consumers marked the product as likeable (70%), and 75% of the total number of respondents would buy this new product. This paper is promoting a new type of bread with 5% yeast extract (Z score 0.80) to improve diet and novel functional product which intended for consumers conscious about their health and diet.

Keywords: bread, yeast extract, sensory analysis, consumer survey, score analysis Z

Procedia PDF Downloads 49

Authors: Hany Elsaid Fawaz Abdallah

Abstract:

This study presents a new artificial neural network(ANN) model to predict the Nusselt Number and pressure drop for the turbulent flow in a triangular corrugated plate heat exchanger for forced air and turbulent water flow. An experimental investigation was performed to create a new dataset for the Nusselt Number and pressure drop values in the following range of dimensionless parameters: The plate corrugation angles (from 0° to 60°), the Reynolds number (from 10000 to 40000), pitch to height ratio (from 1 to 4), and Prandtl number (from 0.7 to 200). Based on the ANN performance graph, the three-layer structure with {12-8-6} hidden neurons has been chosen. The training procedure includes back-propagation with the biases and weight adjustment, the evaluation of the loss function for the training and validation dataset and feed-forward propagation of the input parameters. The linear function was used at the output layer as the activation function, while for the hidden layers, the rectified linear unit activation function was utilized. In order to accelerate the ANN training, the loss function minimization may be achieved by the adaptive moment estimation algorithm (ADAM). The ‘‘MinMax’’ normalization approach was utilized to avoid the increase in the training time due to drastic differences in the loss function gradients with respect to the values of weights. Since the test dataset is not being used for the ANN training, a cross-validation technique is applied to the ANN network using the new data. Such procedure was repeated until loss function convergence was achieved or for 4000 epochs with a batch size of 200 points. The program code was written in Python 3.0 using open-source ANN libraries such as Scikit learn, TensorFlow and Keras libraries. The mean average percent error values of 9.4% for the Nusselt number and 8.2% for pressure drop for the ANN model have been achieved. Therefore, higher accuracy compared to the generalized correlations was achieved. The performance validation of the obtained model was based on a comparison of predicted data with the experimental results yielding excellent accuracy.

Keywords: artificial neural networks, corrugated channel, heat transfer enhancement, Nusselt number, pressure drop, generalized correlations

Procedia PDF Downloads 79

Authors: Placide Poba-Nzaou, Anicet Tchibozo, Malatsi Galani, Ali Etkkali, Erwin Halim

Abstract:

Artificial intelligence (AI) is a general-purpose technology that is transforming many industries, working life and society by stimulating economic growth and innovation. Despite the huge potential of benefits to be generated, the adoption of AI varies from one organization to another, from one region to another, and from one industry to another, due in part to obstacles that can inhibit an organization or organizations located in a specific geographic region or operating in a specific industry from adopting AI technology. In this context, these obstacles and their implications for AI adoption from the perspective of configurational theory is important for at least three reasons: (1) understanding these obstacles is the first step in enabling policymakers and providers to make an informed decision in stimulating AI adoption (2) most studies have investigating obstacles or challenges of AI adoption in isolation with linear assumptions while configurational theory offers a holistic and multifaceted way of investigating the intricate interactions between perceived obstacles and barriers helping to assess their synergetic combination while holding assumptions of non-linearity leading to insights that would otherwise be out of the scope of studies investigating these obstacles in isolation. This study aims to pursue two objectives: (1) characterize organizations by uncovering the typical profiles of combinations of 15 internal and external obstacles that may prevent organizations from adopting AI technology, (2) assess the variation in terms of intensity of AI adoption associated with each configuration. We used data from a survey of AI adoption by organizations conducted throughout the EU27, Norway, Iceland and the UK (N=7549). Cluster analysis and discriminant analysis help uncover configurations of organizations based on the 15 obstacles, including eight external and seven internal. Second, we compared the clusters according to AI adoption intensity using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. The study uncovers three strongly separated clusters of organizations based on perceived obstacles to AI adoption. The clusters are labeled according to their magnitude of perceived obstacles to AI adoption: (1) Cluster I – High Level of perceived obstacles (N = 2449, 32.4%)(2) Cluster II – Low Level of perceived obstacles (N =1879, 24.9%) (3) Cluster III – Moderate Level of perceived obstacles (N =3221, 42.7%). The proposed taxonomy goes beyond the normative understanding of perceived obstacles to AI adoption and associated implications: it provides a well-structured and parsimonious lens that is useful for policymakers, AI technology providers, and researchers. Surprisingly, the ANOVAs revealed a “high level of perceived obstacles” cluster associated with a significantly high intensity of AI adoption.

Keywords: Artificial intelligence (AI), obstacles, adoption, taxonomy.

Procedia PDF Downloads 95

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

Abstract:

Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

Procedia PDF Downloads 174

Authors: Zh. Ghasemi, S. Nouri Saeedlou, A. Ghasemi, SL. Nasiri, P. Ayremlou, P. Mahasti

Abstract:

The use of nisin is successfully used as antibacterial agent in various food products. Although the conclusions of the previous studies were that nisin is not very effective in meat environments. The reduced antimicrobial efficacy of nisin when applied in food has been frequently observed. The aim of this study is to evaluate the potential of free and encapsulated nisin to inhibit the growth of staphylococcus aureus in minced beef. The minimum inhibitory concentration (MIC) of nisin is determined against S. aureus using the agar dilution method. Nisin is encapsulated by spray drying, and encapsulation efficiency, mass yield and total solids content values are 47.79%, 61%, and 96.41 respectively. The study in vitro release kinetics shows highest release of nisin from zein capsules is obtained after 72 hour. This work shows that an appropriate delivery system is necessary to obtain desirable effect of nisin in meat and meat product.

Keywords: nisin, encapsulation, Staphylococcus aureus, minced beef, antibacterial activity

Procedia PDF Downloads 287

Authors: Destin Kurniawati

Abstract:

Asthma is one of the deadly diseases in the world. According to the World Health Organization in 2012, 300 million people suffer from asthma of different races and classes. An estimated 250,000 people die because of asthma annually.As well as more than 57% of children and 51% of adults with asthma. There two risk factors for asthma. That risk factors are the host and environmental. One of the environmental factors that can bring asthma is allergens. When an allergen enters the body, the allergen binds to IgE and cause cell granulat- issued several mediators such as histamine, leukotrienes, bradykinin or something like that. This will cause localized edema effect on bronchial walls of small, thick mucous secretions in the bronchioles, and bronchial smooth muscle spasm. Then there will be inflammation of the airways. Methodology this research is by literature. Therefore, to anticipate and cope with asthma is to control environmental factors that serve to minimize allergens and controlling one's intake in the form of antioxidant-rich foods. Foods rich in antioxidants serve to improve lung function and decrease symptoms of the disease of the respiratory tract.

Keywords: asthma, deadly disease, allergen, environmental and food control

Procedia PDF Downloads 257

Authors: S. V. Ncube, A. I. O. Jideani, E. T. Gwata

Abstract:

The plight of food insecurity in developing countries has led to renewed interest in underutilized legumes. Their nutritional versatility, desirable functionality, pharmaceutical value and inherent bioactive compounds have drawn the attention of researchers. This has provoked the development of value added products with the aim of commercially exploiting their full potential. However processing of these legumes leads to changes in nutritional composition as affected by processing variables like pH, temperature and pressure. There is therefore a need for process control and quality assurance during production of the value added products. However, conventional methods for microbiological and biochemical identification are labour intensive and time-consuming. Biosensors offer rapid and affordable methods to assure the quality of the products. They may be used to quantify nutrients and anti-nutrients in the products while manipulating and monitoring variables such as pH, temperature, pressure and oxygen that affect the quality of the final product. This review gives an overview of the types of biosensors used in the food industry, their advantages and disadvantages and their possible application in processing of legumes.

Keywords: legume processing, biosensors, quality control, nutritional versatility

Procedia PDF Downloads 485

Authors: Magdalena Maria Rost-Roszkowska, Alina Chachulska-Żymełka, Monika Tarnawska, Maria Augustyniak, Alina Kafel, Agnieszka Babczyńska

Abstract:

Autophagy is a form of cell remodeling in which an internalization of organelles into vacuoles that are called autophagosomes occur. Autophagosomes are the targets of lysosomes, thus causing digestion of cytoplasmic components. Eventually, it can lead to the death of the entire cell. However, in response to several stress factors, e.g., starvation, heavy metals (e.g., cadmium) autophagy can also act as a pro-survival factor, protecting the cell against its death. The main aim of our studies was to check if the process of autophagy, which could appear in the midgut epithelium after Cd treatment, can be fixed during the following generations of insects. As a model animal, we chose the beet armyworm Spodoptera exigua Hübner (Lepidoptera: Noctuidae), a well-known polyphagous pest of many vegetable crops. We analyzed specimens at final larval stage (5th larval stage), due to its hyperfagy, resulting in great amount of cadmium assimilate. The culture consisted of two strains: a control strain (K) fed a standard diet, and a cadmium strain (Cd), fed on standard diet supplemented with cadmium (44 mg Cd per kg of dry weight of food) for 146 generations, both strains. In addition, the control insects were transferred to the Cd supplemented diet (5 mg Cd per kg of dry weight of food, 10 mg Cd per kg of dry weight of food, 20 mg Cd per kg of dry weight of food, 44 mg Cd per kg of dry weight of food). Therefore, we obtained Cd1, Cd2, Cd3 and KCd experimental groups. Autophagy has been examined using transmission electron microscope. During this process, degenerated organelles were surrounded by a membranous phagophore and enclosed in an autophagosome. Eventually, after the autophagosome fused with a lysosome, an autolysosome was formed and the process of the digestion of organelles began. During the 1st year of the experiment, we analyzed specimens of 6 generations in all the lines. The intensity of autophagy depends significantly on the generation, tissue and cadmium concentration in the insect rearing medium. In the Ist, IInd, IIIrd, IVth, Vth and VIth generation the intensity of autophagy in the midguts from cadmium-exposed strains decreased gradually according to the following order of strains: Cd1, Cd2, Cd3 and KCd. The higher amount of cells with autophagy was observed in Cd1 and Cd2. However, it was still higher than the percentage of cells with autophagy in the same tissues of the insects from the control and multigenerational cadmium strain. This may indicate that during 6-generational exposure to various Cd concentration, a preserved tolerance to cadmium was not maintained. The study has been financed by the National Science Centre Poland, grant no 2016/21/B/NZ8/00831.

Keywords: autophagy, cell death, digestive system, ultrastructure

Procedia PDF Downloads 230

Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

Abstract:

The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

Procedia PDF Downloads 93

Authors: Rama Shanker, Anil Kumar Sachan

Abstract:

Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

Procedia PDF Downloads 380

Authors: Madhuri Kanala, Geoffrey Savage

Abstract:

Worldwide CO₂ levels are slowly rising, and this may have effects on the growth and nutritional composition of many food plants. The production of secondary metabolites such as oxalates has not been investigated in depth. The oxalate content of many food plants are known to have adverse nutritional effects on humans and reduction in the oxalate contents of food plants is a very positive move. Recent studies had shown that the oxalate content of the leaves of spinach and silver beet reduced when the plants were grown in an environment where CO₂ was increased. The response of amaranth and strawberry spinach leaves to changes in the high CO₂ environment have not been understood though it is known that the plants do contain appreciable oxalate contents. A study was conducted where amaranth and strawberry spinach plants were grown in identical plant growth chambers with the same environmental conditions except that one chamber was supplied with ambient air (CO₂ 405 ppm) while the other chamber had the CO₂ level increased to 650 ppm. The total and soluble oxalate content of the leaves of raw and cooked amaranth and strawberry spinach were determined by HPLC and calcium levels were determined using ICP following 6 weeks of growth. The total oxalate content of the fresh leaves of amaranth and strawberry spinach were reduced by 29.5 % and 24.6% respectively in the leaves of the plants grown in increased CO₂ conditions compared to ambient levels. The soluble oxalate content of amaranth leaves grown under ambient and increased CO₂ conditions were future reduced by 42% and 26.8% respectively following cooking as the soluble oxalate was leached into the cooking water and discarded. The reduction of the oxalate and calcium levels of raw and cooked amaranth and strawberry spinach leaves following an increase in CO₂ content in the air is an interesting positive response to an otherwise significant environmental problem.

Keywords: amaranth, calcium oxalate, enriched CO₂, oxalates, strawberry spinach

Procedia PDF Downloads 184

Authors: Bandi Aruna

Abstract:

The increase of multidrug-resistant pathogens and the restriction on the use of antibiotics due to its side effects have drawn attention to the search for possible alternatives. Bacteriocins are ribosomally synthesized antimicrobial peptides that are active against Gram-positive and Gram-negative bacteria. The bacteriocins from lactic acid bacteria represent an important application of these peptides as clinical drugs or as food biopreservatives. The present study describes the isolation of bacteriocin producing lactic acid bacteria (LAB) from fermented batter of green gram and bengal gram using Man, Rogosa and Sharpe (MRS) media. The bacteriocin produced by these organisms inhibited the growth of Staphylococcus aureus, Escherichia coli, Klebsiella species, Pseudomonas aeruginosa, The isolates G1, G2 were isolated from green gram; B1 and B2 were isolated from fermented bengal gram batter. G1 and G2 were identified as Lactobacillus casie and B1 and B2 were identified as Streptococcus species. Antimicrobial activity of the bacteriocin produced by these strains was studied by agar well diffusion method. Bacteriocins produced by the Lactobacillus casie and Streptococcus secies retained their antagonistic property at pH of 5 and pH of 7. Exposure of bacteriocin to UV light for 4 min showed antibacterial activity. The antagonistic property was observed even at 100°C demonstrating stability at higher temperatures of the bacteriocin. The bacteriocins were stable for a period of 15 days at 27°C. The bacteriocins of G1, G2, and B2 exhibited highest antagonistic activity at pH of 5 and B1 at pH of 7. Therefore, the bacteriocins of the isolates may find important application in controlling the food-borne pathogens.

Keywords: Keywords: Antibacterial activity, Lactic acid bacteria, Bacteriocin

Procedia PDF Downloads 395

Authors: Wilfred A. Abiaa, Chibundu N. Ezekiel, Benedikt Warth, Michael Sulyok, Paul C. Turner, Rudolf Krska, Paul F. Moundipa

Abstract:

Mycotoxins are a wide range of toxic secondary metabolites of fungi that contaminate various food commodities worldwide especially in sub-Saharan Africa (SSA). Such contamination seriously compromises food safety and quality posing a serious problem for human health as well as to trade and the economy. Their concentrations depend on various factors, such as the commodity itself, climatic conditions, storage conditions, seasonal variances, and processing methods. When humans consume foods contaminated by mycotoxins, they exert toxic effects to their health through various modes of actions. Rural populations in sub-Saharan Africa, are exposed to dietary mycotoxins, but it is supposed that exposure levels and health risks associated with mycotoxins between SSA countries may vary. Dietary exposures and health risk assessment studies have been limited by lack of equipment for the proper assessment of the associated health implications on consumer populations when they eat contaminated agricultural products. As such, mycotoxin research is premature in several SSA nations with product evaluation for mycotoxin loads below/above legislative limits being inadequate. Few nations have health risk assessment reports mainly based on direct quantification of the toxins in foods ('external exposure') and linking food levels with data from food frequency questionnaires. Nonetheless, the assessment of the exposure and health risk to mycotoxins requires more than the traditional approaches. Only a fraction of the mycotoxins in contaminated foods reaches the blood stream and exert toxicity ('internal exposure'). Also, internal exposure is usually smaller than external exposure thus dependence on external exposure alone may induce confounders in risk assessment. Some studies from SSA earlier focused on biomarker analysis mainly on aflatoxins while a few recent studies have concentrated on the multi-biomarker analysis of exposures in urine providing probable associations between observed disease occurrences and dietary mycotoxins levels. As a result, new techniques that could assess the levels of exposures directly in body tissue or fluid, and possibly link them to the disease state of individuals became urgent.

Keywords: mycotoxins, biomarkers, exposure assessment, health risk assessment, sub-Saharan Africa

Procedia PDF Downloads 567

Authors: Gordana Ćetković, Vesna Tumbas Šaponjac, Jasna Čanadanović-Brunet, Sonja Đilas, Slađana Stajčić, Jelena Vulić, Mirjana Jakišić

Abstract:

Despite the recommended amount of daily intake of fruits, the consumption in modern age remains very low. Therefore there is a need for delivering valuable phytochemicals into the human body through different foods by developing functional food products fortified with natural bioactive compounds from plant sources. Recently, a growing interest rises in exploiting the fruit and vegetable by-products as sources of phytochemicals such as polyphenols, carotenoids, vitamins etc. Cherry contain high amounts of polyphenols, which are known to display a wide range of biological activities like antioxidant, anti-inflammatory, antimicrobial or anti-carcinogenic activities, improvement of vision, induction of apoptosis and neuroprotective effects. Also, cherry pomace, a by-product in juice processing, can also be promising source of phenolic compounds. However, the application of polyphenols as food additives is limited because after extraction these compounds are susceptible to degradation. Microencapsulation is one of the alternative approaches to protect bioactive compounds from degradation during processing and storage. Freeze-drying is one of the most used microencapsulation methods for the protection of thermosensitive and unstable molecules. In this study sour cherry pomace was extracted with food-grade solvent (50% ethanol) to be suitable for application in products for human use. Extracted polyphenols have been concentrated and stabilized on whey (WP) and soy (SP) proteins. Encapsulation efficiency in SP was higher (94.90%), however not significantly (p<0.05) from the one in WP (90.10%). Storage properties of WP and SP encapsulate in terms of total polyphenols, anthocyanins and antioxidant activity was tested for 6 weeks. It was found that the retention of polyphenols after 6 weeks in WP and SP (67.33 and 69.30%, respectively) was similar. The content of anthocyanins has increased in WP (for 47.97%), while their content in SP has very slightly decreased (for 1.45%) after 6-week storage period. In accordance with anthocyanins the decrease in antioxidant activity in WP (87.78%) was higher than in SP (43.02%). According to the results obtained in this study, the technique reported herewith can be used for obtaining quality encapsulates for their further use as functional food additives, and, on the other hand, for fruit waste valorization.

Keywords: cherry pomace, microencapsulation, polyphenols, storage

Procedia PDF Downloads 362

Authors: Rita Yuniatun, Dewi Fadlilah Firdausi, Anida Hanifah, Putrisuvi Nurjannah Zalqis, Erza Nur Afrilia, Akrima Fajrin Nurimani, Andrew Luis Krishna

Abstract:

Manganese (Mn) is one of the potential contaminants minerals geothermal water. Preliminary studies conducted in Ketenger village, the nearest village with Baturaden hot spring, showed that the concentration of Mn in water supply has exceeded the reference value. Mineral contamination problem in Ketenger village is not only Mn, but also other potential geothermal minerals, such as chromium (Cr), iron (Fe), sulfide (S2-), nickel (Ni), cobalt (Co), and zinc (Zn). It becomes a concern because generally the residents still use ground water as the water source for their daily needs, including drinking and cooking. Therefore, this study aimed to determine the distribution of mineral contamination in drinking water and food and to estimate the health risks possibility from the exposure. Four minerals (Mn, Fe, S2-, and Cr6+) were analyzed in drinking water, carbohydrate sources, vegetables, fishes, and fruits. The test results indicate that Mn concentration in drinking water is 0.35 mg/L, has exceeded the maximum contaminant level (MCL) according to the US EPA (MCL = 0.005 mg/L), whereas other minerals still comply with the standards. In addition, we found that the average of Mn concentration in the carbohydrate sources is quite high (1.87 mg/Kg). Measurement results in Chronic Daily Intake (CDI) and the Risk Quotient (RQ) found that exposure to manganese and other geothermal minerals in drinking water and food are safe from the non-carcinogenic effects in each age group (RQ<1). So, geothermal mineral concentrations in drinking water and food has no effect on non-carcinogenic risk in Ketenger’s residents because of CDI is also influenced by other parameters such as the duration of exposure and the rate of consumption. However, it was found that intake of essential minerals (Mn and Fe) are deficient in every age group. So that, the addition of Mn and Fe intake is recommended.

Keywords: CDI, contaminant, geothermal minerals, manganese, RQ

Procedia PDF Downloads 263