Journal articles on the topic 'Food sensors'
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Kryuk, Roman, Marina Kurbanova, Anastasia Kolbina, Konstantin Plotnikov, Igor Plotnikov, Andrey Petrov, and Mohammed El Amine Khelef. "Color Sensors “In Intelligent Food Packaging”." Food Processing: Techniques and Technology 52, no. 2 (July 6, 2022): 321–33. http://dx.doi.org/10.21603/2074-9414-2022-2-2366.
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The quality of food products depends not only on the technological parameters of production process, but also on storage conditions. Smart packaging controls storage conditions and tracks violations. The research objective was to review the use of sensors in food storage.
The study featured publically available information on sensor-equipped smart food packaging. The information was obtained from open sources published in 2016–2021.
The paper describes biosensors, chemical sensors, and indicators that determine some of the characteristics of food products and provide information to the producer, retailer, and consumer. Indicators proved to be the most promising type of sensors used in smart packaging. This type of sensor indicates the presence and concentration of various analytes through color change. The best indicators are those based on radio frequency (RFID), poison identifiers, compaction/leaks indicators, indicators of freshness/ripeness, etc. All the considered indicators visualize their data by changing color, which makes it possible to assess the quality of food products on the market.
Sensor-based smart packaging is a promising direction in food industry because they make it possible to monitor and control product quality. Smart packaging allows consumers to check the freshness of products by themselves.
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Ham, Mirim, Soohyun Kim, Wonmok Lee, and Hyunjung Lee. "Fabrication of Printable Colorimetric Food Sensor Based on Hydrogel for Low-Concentration Detection of Ammonia." Biosensors 13, no. 1 (December 23, 2022): 18. http://dx.doi.org/10.3390/bios13010018.
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With the increasing market share of ready-to-cook foods, accurate determination of the food freshness and thus food safety has emerged as a concern. To commercialize and popularize food sensing technologies, food sensors with diverse functionalities, low cost, and facile use must be developed. This paper proposes printable sensors based on a hydrogel-containing pH indicator to detect ammonia gas. The sensors were composed of biocompatible polymers such as 2-hydroxyethyl methacrylate (HEMA) and [2-(methacryloyloxy)ethyl] trimethylammonium chloride (MAETC). The p(HEMA-MAETC) hydrogel sensor with bromothymol blue (BTB) demonstrated visible color change as a function of ammonia concentration during food spoilage. Furthermore, polyacrylonitrile (PAN) was added to improve transport speed of ammonium ions as the matrix in the sensors and optimized the viscosity to enable successful printing. The color changed within 3 min at ammonia concentration of 300 ppb and 1 ppm, respectively. The sensor exhibited reproducibility over 10 cycles and selective exposure to various gases generated during the food spoilage process. In an experiment involving pork spoilage, the color change was significant before and after exposure to ammonia gas within 8 h in ambient conditions. The proposed sensor can be integrated in bar codes and QR codes that are easily mass produced.
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Pan, Mingfei, Zongjia Yin, Kaixin Liu, Xiaoling Du, Huilin Liu, and Shuo Wang. "Carbon-Based Nanomaterials in Sensors for Food Safety." Nanomaterials 9, no. 9 (September 17, 2019): 1330. http://dx.doi.org/10.3390/nano9091330.
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Food safety is one of the most important and widespread research topics worldwide. The development of relevant analytical methods or devices for detection of unsafe factors in foods is necessary to ensure food safety and an important aspect of the studies of food safety. In recent years, developing high-performance sensors used for food safety analysis has made remarkable progress. The combination of carbon-based nanomaterials with excellent properties is a specific type of sensor for enhancing the signal conversion and thus improving detection accuracy and sensitivity, thus reaching unprecedented levels and having good application potential. This review describes the roles and contributions of typical carbon-based nanomaterials, such as mesoporous carbon, single- or multi-walled carbon nanotubes, graphene and carbon quantum dots, in the construction and performance improvement of various chemo- and biosensors for various signals. Additionally, this review focuses on the progress of applications of this type of sensor in food safety inspection, especially for the analysis and detection of all types of toxic and harmful substances in foods.
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Zain, H. A., M. Batumalay, Z. Harith, H. R. A. Rahim, and S. W. Harun. "Surface plasmon resonance sensor for food safety." Journal of Physics: Conference Series 2411, no. 1 (December 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2411/1/012023.
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Abstract Surface plasmon resonance sensors have numerous applications in the discovery of poisonous gasses, water toxins, and the biomarkers of numerous infections. Surface plasmon sensors are a great candidate for future detecting stages due to their high sensitivity and fine resolution. A surface plasmon resonance sensor is also built for food safety using a Kretschmann setup with a gold coated prism. The setup was used to detect the analyte solution with concentrations 0%-3%. The sensor showed a good response and stability.
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Titova, Tanya, and Veselin Nachev. ""Electronic tongue" in the Food Industry." Food Science and Applied Biotechnology 3, no. 1 (March 19, 2020): 71. http://dx.doi.org/10.30721/fsab2020.v3.i1.74.
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“Electronic tongue” (e-tongue) is instrumental system are designed to crudely mimic human taste sensory organs and are composed of an array of sensors. Complex data sets from „e- tongue“ signals combined with multivariate statistics represent rapid and efficient tools for classification, recognition and identification of samples, also for the prediction of concentrations of different compounds. A wide variety of sensors can be employed into the design of these instrumental systems, especially that of „e-tongues“, offering numerous practical applications. In this study are review, characteristics of sensors and possibilities „e-tongue“ applications in the food industry.Practical applications: The “e-tongue” can be used in various applications, including on quality control in the food industry and pharmacy.
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MacAndrew, Alec, and Chris Harris. "SENSORS DETECT FOOD CONTAMINATION." Sensor Review 11, no. 4 (April 1991): 23–25. http://dx.doi.org/10.1108/eb007861.
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O' Connell, P. J., and G. G. Guilbault. "Sensors and Food Quality." Sensors Update 9, no. 1 (May 2001): 255–82. http://dx.doi.org/10.1002/1616-8984(200105)9:1<255::aid-seup255>3.0.co;2-v.
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CHAI, YATING, SUIQIONG LI, SHIN HORIKAWA, MI-KYUNG PARK, VITALY VODYANOY, and BRYAN A. CHIN. "Rapid and Sensitive Detection of Salmonella Typhimurium on Eggshells by Using Wireless Biosensors." Journal of Food Protection 75, no. 4 (April 1, 2012): 631–36. http://dx.doi.org/10.4315/0362-028x.jfp-11-339.
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This article presents rapid, sensitive, direct detection of Salmonella Typhimurium on eggshells by using wireless magnetoelastic (ME) biosensors. The biosensor consists of a freestanding, strip-shaped ME resonator as the signal transducer and the E2 phage as the biomolecular recognition element that selectively binds with Salmonella Typhimurium. This ME biosensor is a type of mass-sensitive biosensor that can be wirelessly actuated into mechanical resonance by an externally applied time-varying magnetic field. When the biosensor binds with Salmonella Typhimurium, the mass of the sensor increases, resulting in a decrease in the sensor's resonant frequency. Multiple E2 phage–coated biosensors (measurement sensors) were placed on eggshells spiked with Salmonella Typhimurium of various concentrations (1.6 to 1.6 × 107 CFU/cm2). Control sensors without phage were also used to compensate for environmental effects and nonspecific binding. After 20 min in a humidity-controlled chamber (95%) to allow binding of the bacteria to the sensors to occur, the resonant frequency of the sensors was wirelessly measured and compared with their initial resonant frequency. The resonant frequency change of the measurement sensors was found to be statistically different from that of the control sensors down to 1.6 × 102 CFU/cm2, the detection limit for this work. In addition, scanning electron microscopy imaging verified that the measured resonant frequency changes were directly related to the number of bound cells on the sensor surface. The total assay time of the presented methodology was approximately 30 min, facilitating rapid detection of Salmonella Typhimurium without any preceding sampling procedures.
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Rady, Ahmed, Joel Fischer, Stuart Reeves, Brian Logan, and Nicholas James Watson. "The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods." Sensors 20, no. 1 (December 31, 2019): 230. http://dx.doi.org/10.3390/s20010230.
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Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.
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Wu, Gan, Xilin Dou, Dapeng Li, Shihan Xu, Jicheng Zhang, Zhaoyang Ding, and Jing Xie. "Recent Progress of Fluorescence Sensors for Histamine in Foods." Biosensors 12, no. 3 (March 4, 2022): 161. http://dx.doi.org/10.3390/bios12030161.
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Biological amines are organic nitrogen compounds that can be produced by the decomposition of spoiled food. As an important biological amine, histamine has played an important role in food safety. Many methods have been used to detect histamine in foods. Compared with traditional analysis methods, fluorescence sensors as an adaptable detection tool for histamine in foods have the advantages of low cost, convenience, less operation, high sensitivity, and good visibility. In terms of food safety, fluorescence sensors have shown great utilization potential. In this review, we will introduce the applications and development of fluorescence sensors in food safety based on various types of materials. The performance and effectiveness of the fluorescence sensors are discussed in detail regarding their structure, luminescence mechanism, and recognition mechanism. This review may contribute to the exploration of the application of fluorescence sensors in food-related work.
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Li, Ming, Scott K. Cushing, and Nianqiang Wu. "Plasmon-enhanced optical sensors: a review." Analyst 140, no. 2 (2015): 386–406. http://dx.doi.org/10.1039/c4an01079e.
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This paper presents a critical review of recent research progress in plasmonic sensors, plasmon-enhanced fluorescence sensors, and surface-enhanced Raman scattering sensors. It places emphasis on the sensor design strategies, and highlights the applications of sensors in healthcare, homeland security, food safety and environmental monitoring.
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Tao, Hu, Mark A. Brenckle, Miaomiao Yang, Jingdi Zhang, Mengkun Liu, Sean M. Siebert, Richard D. Averitt, et al. "Food Sensors: Silk-Based Conformal, Adhesive, Edible Food Sensors (Adv. Mater. 8/2012)." Advanced Materials 24, no. 8 (February 14, 2012): 993. http://dx.doi.org/10.1002/adma.201290036.
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Shaalan, Nagih M., Faheem Ahmed, Osama Saber, and Shalendra Kumar. "Gases in Food Production and Monitoring: Recent Advances in Target Chemiresistive Gas Sensors." Chemosensors 10, no. 8 (August 17, 2022): 338. http://dx.doi.org/10.3390/chemosensors10080338.
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The rapid development of the human population has created demand for an increase in the production of food in various fields, such as vegetal, animal, aquaculture, and food processing. This causes an increment in the use of technology related to food production. An example of this technology is the use of gases in the many steps of food treatment, preservation, processing, and ripening. Additionally, gases are used across the value chain from production and packaging to storage and transportation in the food and beverage industry. Here, we focus on the long-standing and recent advances in gas-based food production. Although many studies have been conducted to identify chemicals and biological contaminants in foodstuffs, the use of gas sensors in food technology has a vital role. The development of sensors capable of detecting the presence of target gases such as ethylene (C2H4), ammonia (NH3), carbon dioxide (CO2), sulfur dioxide (SO2), and ethanol (C2H5OH) has received significant interest from researchers, as gases are not only used in food production but are also a vital indicator of the quality of food. Therefore, we also discuss the latest practical studies focused on these gases in terms of the sensor response, sensitivity, working temperatures, and limit of detection (LOD) to assess the relationship between the gases emitted from or used in foods and gas sensors. Greater interest has been given to heterostructured sensors working at low temperatures and flexible layers. Future perspectives on the use of sensing technology in food production and monitoring are eventually stated. We believe that this review article gathers valuable knowledge for researchers interested in food sciences and sensing development.
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Qiao, Xiujuan, Jingyi He, Ruixi Yang, Yanhui Li, Gengjia Chen, Sanxiong Xiao, Bo Huang, Yahong Yuan, Qinglin Sheng, and Tianli Yue. "Recent Advances in Nanomaterial-Based Sensing for Food Safety Analysis." Processes 10, no. 12 (December 3, 2022): 2576. http://dx.doi.org/10.3390/pr10122576.
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The increasing public attention on unceasing food safety incidents prompts the requirements of analytical techniques with high sensitivity, reliability, and reproducibility to timely prevent food safety incidents occurring. Food analysis is critically important for the health of both animals and human beings. Due to their unique physical and chemical properties, nanomaterials provide more opportunities for food quality and safety control. To date, nanomaterials have been widely used in the construction of sensors and biosensors to achieve more accurate, fast, and selective food safety detection. Here, various nanomaterial-based sensors for food analysis are outlined, including optical and electrochemical sensors. The discussion mainly involves the basic sensing principles, current strategies, and novel designs. Additionally, given the trend towards portable devices, various smartphone sensor-based point-of-care (POC) devices for home care testing are discussed.
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de Araujo, Ivan E., Mark Schatzker, and Dana M. Small. "Rethinking Food Reward." Annual Review of Psychology 71, no. 1 (January 4, 2020): 139–64. http://dx.doi.org/10.1146/annurev-psych-122216-011643.
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The conscious perception of the hedonic sensory properties of caloric foods is commonly believed to guide our dietary choices. Current and traditional models implicate the consciously perceived hedonic qualities of food as driving overeating, whereas subliminal signals arising from the gut would curb our uncontrolled desire for calories. Here we review recent animal and human studies that support a markedly different model for food reward. These findings reveal in particular the existence of subcortical body-to-brain neural pathways linking gastrointestinal nutrient sensors to the brain's reward regions. Unexpectedly, consciously perceptible hedonic qualities appear to play a less relevant, and mostly transient, role in food reinforcement. In this model, gut-brain reward pathways bypass cranial taste and aroma sensory receptors and the cortical networks that give rise to flavor perception. They instead reinforce behaviors independently of the cognitive processes that support overt insights into the nature of our dietary decisions.
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Elksne, Mārīte, Artūrs Solovjovs, and Artis Teilāns. "ENOSE FOR INTERNET OF THINGS." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 24 (April 22, 2020): 40–46. http://dx.doi.org/10.17770/het2020.24.6748.
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System “eNose” is developed within a project “eNose for Internet of things”, which is a part of a project contest “Research Grant of Rezekne Acadaemy of Technologies”. The aim of this work is to explore whether it is possible to detect spoiled food with help of sensors and a neural network. System “eNose” is intended to detect and classify spoiled food products within storages and notify related users. Detection and classification are performed by four gas sensors and a neural network. As a result, a web application was developed that performs such functions as storage and sensor registration, neural network training, spoiled food detection based on sensor data, and user permission control. It was concluded that sensors for this application must be very precise in order to receive best possible results.
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Weston, Max, Shu Geng, and Rona Chandrawati. "Food Sensors: Challenges and Opportunities." Advanced Materials Technologies 6, no. 5 (March 14, 2021): 2001242. http://dx.doi.org/10.1002/admt.202001242.
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Ohashi, E., and I. Karube. "Sensors for the food industry." Food Control 4, no. 4 (January 1993): 183–88. http://dx.doi.org/10.1016/0956-7135(93)90248-m.
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Duman, Elifcan, Can Altınelataman, and Adnan Tokaç. "The role and importance of photonic sensors in seafood safety applications." Ege Journal of Fisheries and Aquatic Sciences 37, no. 3 (September 15, 2020): 319–24. http://dx.doi.org/10.12714/egejfas.37.3.16.
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Microbiological, chemical, sensory analyses known as traditional methods are used for determination of fish quality including many concepts such as microbiological quality, sensory quality, nutritional properties, product specific properties, freshness, species-specific physical properties. With the developing technology; these time-consuming and error-free analyzes have been replaced by sensor technology, which is very suitable for quality measurements in order to achieve the expected speed and high standard and to be open to improvement. In this study, optical sensors and their applications are emphasized and a general evaluation is made about the usability of seafood processing technology in terms of food safety.
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Shahrul, Shahrul, Arie Budiansyah, Suryadi Suryadi, Rudi Arif Candra, and Dirja Nur Ilham. "IoT Based Paint Feed Process Monitoring System Implementation." Brilliance: Research of Artificial Intelligence 2, no. 1 (January 1, 2021): 7–12. http://dx.doi.org/10.47709/brilliance.v2i1.1492.
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The increasing level of human mobility causes pets to be abandoned because humans have activities that cannot be left behind or work that must always be done, with this, pets, one of which is a cat, are often hungry because the caregiver is busy working and does not have time to feed the cat. This research is about designing an automatic cat feeder with a periodic monitoring system with a Nodemcu control system with two sensors, namely an Ir sensor and an Ultrasonic sensor with a telegram notification output. the working principle of the Sensors Ir 1 and 2 will detect a cat, if it hits the cat, the place for giving food and drink will open automatically while ultrasonic sensors 1 and 2 are for monitoring food and drink if food and drink do not hit the ultrasonic sensor it will enter a notification that the food and drink had run out. the conclusion of making this tool is to make it easier for cat owners to automatically feed cats.
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Adamek, Martin, Jiri Matyas, Anna Adamkova, Jiri Mlcek, Martin Buran, Martina Cernekova, Veronika Sevcikova, Magdalena Zvonkova, Petr Slobodian, and Robert Olejnik. "A Study on the Applicability of Thermodynamic Sensors in Fermentation Processes in Selected Foods." Sensors 22, no. 5 (March 3, 2022): 1997. http://dx.doi.org/10.3390/s22051997.
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This study focuses on the use of thermodynamic sensors (TDS) in baking, brewing, and yogurt production at home. Using thermodynamic sensors, a change in the temperature flow between the two sensor elements during fermentation was observed for the final mixture (complete recipe for pizza dough production), showing the possibility of distinguishing some phases of the fermentation process. Even during the fermentation process in the preparation of wort and yogurt with non-traditional additives, the sensors were able to indicate significant parts of the process, including the end of the process. The research article also mentions as a new idea the use of trivial regulation at home in food production to determine the course of the fermentation process. The results presented in this article show the possibility of using TDS for more accurate characterization and adjustment of the production process of selected foods in the basic phase, which will be further applicable in the food industry, with the potential to reduce the cost of food production processes that involve a fermentation process.
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Vilela, Alice, Eunice Bacelar, Teresa Pinto, Rosário Anjos, Elisete Correia, Berta Gonçalves, and Fernanda Cosme. "Beverage and Food Fragrance Biotechnology, Novel Applications, Sensory and Sensor Techniques: An Overview." Foods 8, no. 12 (December 5, 2019): 643. http://dx.doi.org/10.3390/foods8120643.
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Flavours and fragrances are especially important for the beverage and food industries. Biosynthesis or extraction are the two main ways to obtain these important compounds that have many different chemical structures. Consequently, the search for new compounds is challenging for academic and industrial investigation. This overview aims to present the current state of art of beverage fragrance biotechnology, including recent advances in sensory and sensor methodologies and statistical techniques for data analysis. An overview of all the recent findings in beverage and food fragrance biotechnology, including those obtained from natural sources by extraction processes (natural plants as an important source of flavours) or using enzymatic precursor (hydrolytic enzymes), and those obtained by de novo synthesis (microorganisms’ respiration/fermentation of simple substrates such as glucose and sucrose), are reviewed. Recent advances have been made in what concerns “beverage fragrances construction” as also in their application products. Moreover, novel sensory and sensor methodologies, primarily used for fragrances quality evaluation, have been developed, as have statistical techniques for sensory and sensors data treatments, allowing a rapid and objective analysis.
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Kaya, Aydin, Ali Seydi Keçeli, Cagatay Catal, and Bedir Tekinerdogan. "Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model." Sensors 20, no. 11 (June 3, 2020): 3173. http://dx.doi.org/10.3390/s20113173.
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For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.
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Ghrissi, Hiba, Ana C. A. Veloso, Ítala M. G. Marx, Teresa Dias, and António M. Peres. "A Potentiometric Electronic Tongue as a Discrimination Tool of Water-Food Indicator/Contamination Bacteria." Chemosensors 9, no. 6 (June 16, 2021): 143. http://dx.doi.org/10.3390/chemosensors9060143.
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Microorganism assessment plays a key role in food quality and safety control but conventional techniques are costly and/or time consuming. Alternatively, electronic tongues (E-tongues) can fulfill this critical task. Thus, a potentiometric lab-made E-tongue (40 lipid sensor membranes) was used to differentiate four common food contamination bacteria, including two Gram positive (Enterococcus faecalis, Staphylococcus aureus) and two Gram negative (Escherichia coli, Pseudomonas aeruginosa). Principal component analysis and a linear discriminant analysis-simulated annealing algorithm (LDA-SA) showed that the potentiometric signal profiles acquired during the analysis of aqueous solutions containing known amounts of each studied bacteria allowed a satisfactory differentiation of the four bacterial strains. An E-tongue-LDA-SA model (12 non-redundant sensors) correctly classified 98 ± 5% of the samples (repeated K-fold-CV), the satisfactory performance of which can be attributed to the capability of the lipid membranes to establish electrostatic interactions/hydrogen bonds with hydroxyl, amine and/or carbonyl groups, which are comprised in the bacteria outer membranes. Furthermore, multiple linear regression models, based on selected subsets of E-tongue sensors (12–15 sensors), also allowed quantifying the bacteria contents in aqueous solutions (0.993 ± 0.011 ≤ R2 ≤ 0.998 ± 0.005, for repeated K-fold-CV). In conclusion, the E-tongue could be of great value as a preliminary food quality and safety diagnosis tool.
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Raju, Viprav B., Masudul H. Imtiaz, and Edward Sazonov. "Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data." Sensors 23, no. 2 (January 4, 2023): 560. http://dx.doi.org/10.3390/s23020560.
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Sensor-based food intake monitoring has become one of the fastest-growing fields in dietary assessment. Researchers are exploring imaging-sensor-based food detection, food recognition, and food portion size estimation. A major problem that is still being tackled in this field is the segmentation of regions of food when multiple food items are present, mainly when similar-looking foods (similar in color and/or texture) are present. Food image segmentation is a relatively under-explored area compared with other fields. This paper proposes a novel approach to food imaging consisting of two imaging sensors: color (Red–Green–Blue) and thermal. Furthermore, we propose a multi-modal four-Dimensional (RGB-T) image segmentation using a k-means clustering algorithm to segment regions of similar-looking food items in multiple combinations of hot, cold, and warm (at room temperature) foods. Six food combinations of two food items each were used to capture RGB and thermal image data. RGB and thermal data were superimposed to form a combined RGB-T image and three sets of data (RGB, thermal, and RGB-T) were tested. A bootstrapped optimization of within-cluster sum of squares (WSS) was employed to determine the optimal number of clusters for each case. The combined RGB-T data achieved better results compared with RGB and thermal data, used individually. The mean ± standard deviation (std. dev.) of the F1 score for RGB-T data was 0.87 ± 0.1 compared with 0.66 ± 0.13 and 0.64 ± 0.39, for RGB and Thermal data, respectively.
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Ilie-Mihai, Ruxandra-Maria, Bianca Cristina Ion, and Jacobus (Koos) Frederick van Staden. "Sodium Metabisulfite in Food and Biological Samples: A Rapid and Ultra-Sensitive Electrochemical Detection Method." Micromachines 13, no. 10 (October 10, 2022): 1707. http://dx.doi.org/10.3390/mi13101707.
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The primary benefit of using sulfites as a food additive is their antimicrobial and antioxidant properties, which stop fungi and bacteria from growing in a variety of foods. The application of analytical methods is necessary to ensure food quality control related to the presence of sulfites in a variety of foods. For the detection of sodium metabisulfite in food and urine samples, two sensors based on reduced graphene oxide doped with Pd paste and modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrin and 5,10,15,20-tetrakis (pentafluorophenyl chloride)-21H,23H-iron (III) porphyrin were proposed. The new sensors were evaluated and characterized using square wave voltammetry. The response characteristics showed that the detection limits for the sensors were 3.0 × 10−12 mol L−1 for TPP/rGO@Pd0 based sensors and 3.0 × 10−11 mol L−1 for Fe(TPFPP)Cl/rGO@Pd0 based sensors while the quantification limits were 1.0 × 10−11 mol L−1 for TPP/rGO@Pd0 based sensors and 1.0 × 10−10 mol L−1 for Fe(TPFPP)Cl/rGO@Pd0 based sensors. The sensors can be used to determine sodium metabisulfite in a concentration range between 1.0 × 10−11 and 1.0 × 10−7 mol L−1 for TPP/rGO@Pd0 based sensors and between 1.0 × 10−10 mol L−1 and 1.0 × 10−6 mol L−1 for Fe(TPFPP)Cl/rGO@Pd0 based sensors. A comparison between the proposed methods’ results and other analytical applications is also presented.
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da Costa, Tamíris Pacheco, James Gillespie, Xavier Cama-Moncunill, Shane Ward, Joan Condell, Ramakrishnan Ramanathan, and Fionnuala Murphy. "A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies." Sustainability 15, no. 1 (December 29, 2022): 614. http://dx.doi.org/10.3390/su15010614.
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Continuous monitoring of food loss and waste (FLW) is crucial for improving food security and mitigating climate change. By measuring quality parameters such as temperature and humidity, real-time sensors are technologies that can continuously monitor the quality of food and thereby help reduce FLW. While there is enough literature on sensors, there is still a lack of understanding on how, where and to what extent these sensors have been applied to monitor FLW. In this paper, a systematic review of 59 published studies focused on sensor technologies to reduce food waste in food supply chains was performed with a view to synthesising the experience and lessons learnt. This review examines two aspects of the field, namely, the type of IoT technologies applied and the characteristics of the supply chains in which it has been deployed. Supply chain characteristics according to the type of product, supply chain stage, and region were examined, while sensor technology explores the monitored parameters, communication protocols, data storage, and application layers. This article shows that, while due to their high perishability and short shelf lives, monitoring fruit and vegetables using a combination of temperature and humidity sensors is the most recurring goal of the research, there are many other applications and technologies being explored in the research space for the reduction of food waste. In addition, it was demonstrated that there is huge potential in the field, and that IoT technologies should be continually explored and applied to improve food production, management, transportation, and storage to support the cause of reducing FLW.
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S, Anil Subash, Manjunatha C, Ajit Khosla, R. Hari Krishna, and Ashoka S. "Current Progress in Materials, Device Fabrication, and Biomedical Applications of Potentiometric Sensor Devices: A Short Review." ECS Transactions 107, no. 1 (April 24, 2022): 6343–54. http://dx.doi.org/10.1149/10701.6343ecst.
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Potentiometric sensor devices are having a wide range of applications in environmental and biomedical fields. This short review aims to provide updates on recent innovations in various nanomaterials as sensing components used in potentiometric sensor devices. The review also covers the various methods and conditions used to develop these sensor nanomaterials with appropriately decorated by functional groups. Reduced graphene oxide along with traditional platinum electrodes is used to monitor algae growth in an aquatic ecosystem. Here, the addition of reduced-graphene increases the selectivity and precision of the potentiometric sensor. The review also describe the fabrication and the mechanism of sensing of carbon composite based glucose sensors, sweat sensors, and pH sensors, which are used for monitoring a human body. Sweat sensors are the ion-sensors which use carbon nanoparticles for high selectivity. Porous graphene oxide is also one of the highly used carbon nanomaterials which show high selectivity towards different types of chemicals under certain conditions. PANI/Graphene/CNT nanocomposite based potentiometric sensor is used to detect hazardous 4-aminophenol in the surrounding area. Using nanocomposite increases the selectivity and gives a high current response in the I-V graph. The granular nature of InVO4 is used in the fabrication of ammonia sensors. Formaldehyde is one of the commonly found adulterations in the food. A biosensor has been fabricated using CNTs-Fe3O4 nanocomposite to detect the formaldehyde in the foods. Finally the review summarizes the merits and limitations of various potentiometric sensors developed for different biomedical applications.
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Leite, Liliana, Inês Boticas, Miguel Navarro, Luís Nobre, João Bessa, Fernando Cunha, Pedro Neves, and Raúl Fangueiro. "Halochromic Inks Applied on Cardboard for Food Spoilage Monitorization." Materials 15, no. 18 (September 16, 2022): 6431. http://dx.doi.org/10.3390/ma15186431.
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Control of food spoilage is a critical concern in the current world scenario, not only to ensure the quality and safety of food but also to avoid the generation of food waste. This paper evaluates a dual-sensor strategy using six different pH indicators stamped on cardboard for the detection of spoilage in three different foods: beef, salmon, and strawberries. After function validation and formulation optimizations in the laboratory, the halochromic sensors methyl orange and bromocresol purple 2% (w/v) were stamped on cardboard and, in contact with the previously mentioned foods, were able to produce an easily perceptible signal for spoilage by changing color. Additionally, when it comes to mechanical characterization the inks showed high abrasion (>100 cycles) and adhesion resistance (>91%).
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Wang, Zhekang, Yutong Wang, and Zeping Wang. "MOFs-based Electrochemical Sensors and Biosensors for the Analysis of Food Contaminants." Highlights in Science, Engineering and Technology 21 (December 4, 2022): 64–70. http://dx.doi.org/10.54097/hset.v21i.3139.
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Nowadays, with the rapid increase of people's living standards, they paid more attention to food safety. a new method to detect food additives and bacteria is urgently needed. MOFs are a great material to be used to make a chemosensory of their high stability and porous structure. Compared to the MOFs sensor with any other sensors, it is more convenient, fast and sensitive. It shows excellent potential for the detection of food safety issues in the future. In this article, the important research significance of MOF in the field of food containers was introduced. Various methods of preparing MOFs, including the hydrothermal method, diffusion method and solvent-free method are introduced. Then present the influencing factors of MOFs materials from their fluorescence properties and heavy metals. Finally, the detection of coffeic acid, nitrite and carbendazim by MOF electrochemical sensor in the food safety field has been extended and evaluated. Aiming to connect the MOFs-based chemical sensors with the field of food safety.
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R Mohamed, Rajina, Razali Yaacob, Mohamad A Mohamed, Tengku Azahar Tengku Dir, and F. A Rahim. "Food Freshness Using Electronic Nose and Its Classification Method: A Review." International Journal of Engineering & Technology 7, no. 3.28 (August 17, 2018): 49. http://dx.doi.org/10.14419/ijet.v7i3.28.20964.
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Generally, E-nose mimics human olfactory sense to detect and distinguish an odor or gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to the odorant molecules (aroma). Each sensor will respond to their specific gas respectively. These sensors are a major part of the electronic nose to detect gas or odor contained in a volatile component. Information about the gas detected by sensors will be recorded and transmitted to the signal processing unit to perform the analysis of volatile organic compound (VOC) pattern and stored in the database classification, in order to determine the type of odor. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors in an electric signal form. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.
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Hussain, Ghulam, Mukesh Kumar Maheshwari, Mudasar Latif Memon, Muhammad Shahid Jabbar, and Kamran Javed. "A CNN Based Automated Activity and Food Recognition Using Wearable Sensor for Preventive Healthcare." Electronics 8, no. 12 (November 29, 2019): 1425. http://dx.doi.org/10.3390/electronics8121425.
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Recent developments in the field of preventive healthcare have received considerable attention due to the effective management of various chronic diseases including diabetes, heart stroke, obesity, and cancer. Various automated systems are being used for activity and food recognition in preventive healthcare. The automated systems lack sophisticated segmentation techniques and contain multiple sensors, which are inconvenient to be worn in real-life settings. To monitor activity and food together, our work presents a novel wearable system that employs the motion sensors in a smartwatch together with a piezoelectric sensor embedded in a necklace. The motion sensor generates distinct patterns for eight different physical activities including eating activity. The piezoelectric sensor generates different signal patterns for six different food types as the ingestion of each food is different from the others owing to their different characteristics: hardness, crunchiness, and tackiness. For effective representation of the signal patterns of the activities and foods, we employ dynamic segmentation. A novel algorithm called event similarity search (ESS) is developed to choose a segment with dynamic length, which represents signal patterns with different complexities equally well. Amplitude-based features and spectrogram-generated images from the segments of activity and food are fed to convolutional neural network (CNN)-based activity and food recognition networks, respectively. Extensive experimentation showed that the proposed system performs better than the state of the art methods for recognizing eight activity types and six food categories with an accuracy of 94.3% and 91.9% using support vector machine (SVM) and CNN, respectively.
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Neves, Paulo Alexandre, João Simões, Ricardo Costa, Luís Pimenta, Norberto Jorge Gonçalves, Carlos Albuquerque, Carlos Cunha, et al. "Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection." Sensors 22, no. 17 (August 26, 2022): 6443. http://dx.doi.org/10.3390/s22176443.
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Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
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Borawake, Dr M. P. "E-Gadget to Detect Food Freshness using IoT and ML." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 2072–76. http://dx.doi.org/10.22214/ijraset.2021.39615.
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Abstract: The food we consume plays an important role in our daily life. It provides us energy which is needed to work, grow, be active, and to learn and think. The healthy food is essential for good health and nutrition. Light, oxygen, heat, humidity, temperature and spoilage bacteria can all affect both safety and quality of perishable foods. Food kept at room temperature undergoes some chemical reactions after certain period of time, which affects the taste, texture and smell of a food. Consuming spoiled food is harmful for consumers as it can lead to foodborne diseases. This project aims at detecting spoiled food using appropriate sensors and monitoring gases released by the particular food item. Sensors will measure the different parameters of food such as pH, ammonia gas, oxygen level, moisture, etc. The microcontroller takes the readings from sensors and these readings then given as an input to a machine learning model which can decide whether the food is spoilt or not based on training data set. Also, we plan to implement a machine learning model which can calculate the lifespan of that food item. Index Terms: Arduino Uno, Food spoilage, IoT, Machine Learning, Sensors.
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Pirsa, Sajad, and Fardin Mohammad Nejad. "Simultaneous analysis of some volatile compounds in food samples by array gas sensors based on polypyrrole nano-composites." Sensor Review 37, no. 2 (March 20, 2017): 155–64. http://dx.doi.org/10.1108/sr-10-2016-0217.
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Purpose The purpose of this paper is to construct an array of sensors using polypyrrole–zinc oxide (PPy–ZnO) and PPy–vanadium (V; chemical formula: V2O5) fibers. To test responses of sensors, a central composite design (CCD) has been used. The results of the CCD technique revealed that the developed sensors are orthogonally sensitive to diacetyl, lactic acid and acetic acid. In total, 20 different mixtures of diacetyl, lactic acid and acetic acid were prepared, and the responses of the array sensors were recorded for each mixture. Design/methodology/approach A response surface regression analysis has been used for correlating the responses of the sensors to diacetyl, lactic acid and acetic acid concentrations during the gas phase in food samples. The developed multivariate model was used for simultaneous determination of diacetyl, lactic acid and acetic acid concentrations. Some food samples with unknown concentrations of diacetyl, lactic acid and acetic acid were provided, and the responses of array sensors to each were recorded. Findings The responses of each sensor were considered as target response in a response optimizer, and by an overall composite desirability, the concentration of each analyte was predicted. The present work suggests the applicability of the response surface regression analysis as a modeling technique for correlating the responses of sensor arrays to concentration profiles of diacetyl, lactic acid and acetic acid in food samples. Originality/value The PPy–ZnO and PPy–V2O5 nanocomposite fibers were synthesized by chemical polymerization. The provided conducting fibers, PPy–ZnO and PPy–V2O5, were used in an array gas sensor system for the analysis of volatile compounds (diacetyl, lactic acid and acetic acid) added to yogurt and milk samples.
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Mohamad Nor, Alif Syarafi, Mohd Amri Md. Yunus, Sophan Wahyudi, and Ibrahim Sallehhudin. "Low-Cost Sensors Array Based on Planar Electromagnetic Sensor Simulation for Environmental Monitoring." Advanced Materials Research 925 (April 2014): 614–18. http://dx.doi.org/10.4028/www.scientific.net/amr.925.614.
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Agriculture sectors have increased in sizes and numbers due to the expansion of human population. Human population growth has caused the demand in food, cloth, transportation, shelter etc. to increase tremendously. Essentially speaking, those surpluses of demands will adversely affect the production of agriculture sectors. In order to obtain high yield, the farmers might be overusing the fertilizer for their crops. These overused fertilizers will be dissolved in the nearest water resources such as river, pond and well. Hence, the natural water resources will be contaminated by these overused fertilizers. In this situation, there is a need for a sensor which could determine the contamination level in natural water resources. This project proposed low-cost sensor arrays based on planar electromagnetic sensors to monitor the contamination in the natural water resources. The sensors consist of three electromagnetic sensors that have been constructed in three types of configuration array namely, parallel, star, and delta. The modeling and simulation of the sensors were done by using COMSOL Multiphysics 4.2 software. Each sensor’s input was assign to 10 Volt peak to peak voltages and then the impedance of each sensor was obtained. In practical application, the impedance of these sensors will determine the contamination level of the water under test. The contamination condition was simulated by changing the electrical properties of the environment domain of the model that suit contamination condition. The impedance of each sensor was tabulated according to the increasing of frequency of the supply voltages. Hence, the best configuration of the planar electromagnetic sensors array for detection of contamination inside the natural water will be determined based on the sensitivity. This type of sensors will provide the in-situ measurement system which will save the cost and consequently the time required for each sample.
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Rebenklau, L., K. Irrgang, A. Wodtke, K. Augsburg, F. Bechtold, P. Gierth, H. Grießmann, L. Lippmann, and L. Niedermeyer. "Novel thermoelectric temperature sensors." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2015, CICMT (September 1, 2015): 000230–33. http://dx.doi.org/10.4071/cicmt-wp14.
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Nearly every industrial application needs temperature measurement. Typical temperature sensors are based on thermocouples or resistance elements. Nevertheless, these sensors are not always desired for every application. For example, temperature sensing of fluids or gases in pipes. A standard sensor inside such a material flow has an influence on the flow itself (flow resistance, turbulences) which would lead to incorrect temperature result. Additionally, application that need periodical cleaning of their pipe system (food or pharmaceutical production) can't use such sensors because of hygienically reasons. Novel thermoelectric temperature sensors, which could reduce the previously demonstrated problems have been developed as part of a research project. The basic idea of the novel sensor concept is to use thick film technology to enable novel sensor geometries. The typical use of thick film technology is realization of ceramic circuit boards, in which metal-based thick film pastes were screen printed and fired as conductive material. The sensor concept uses a combination of different commercially available metal-based pastes (platinum, silver, nickel, gold) to creates thermocouples based on the Seebeck effect.
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Kassim, Murizah, Muhammad Zulhelmi Zulkifli, Norsuzila Ya'acob, and Shahrani Shahbudin. "IoT System on Dynamic Fish Feeder Based on Fish Existence for Agriculture Aquaponic Breeders." Baghdad Science Journal 18, no. 4(Suppl.) (December 20, 2021): 1448. http://dx.doi.org/10.21123/bsj.2021.18.4(suppl.).1448.
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Maintaining and breeding fish in a pond are a crucial task for a large fish breeder. The main issues for fish breeders are pond management such as the production of food for fishes and to maintain the pond water quality. The dynamic or technological system for breeders has been invented and becomes important to get maximum profit return for aquaponic breeders in maintaining fishes. This research presents a developed prototype of a dynamic fish feeder based on fish existence. The dynamic fish feeder is programmed to feed where sensors detected the fish's existence. A microcontroller board NodeMCU ESP8266 is programmed for the developed hardware. The controller controls the feeding and feedback mechanism based on attached sensors. An ultrasonic sensor is programmed with the controller to detect the level of food and waterproof ultrasonic to detect existing fish. The humidity sensor was used to measure the humidity in the food container to control the food freshness. Two servo motors were used to move the waterproof sensor to attract the fish and to dispense the food to the fish when existed. The result presents four measured levels that are the temperature of the food container, the quality of food based on humidity measured, fish detection counter and level of fish food in the container. Data analytics on all the measured levels was presented on the ThingSpeak platform by using Blynk to get data collections from all sensors. This research is significant for fish breeders that support IR4.0 system connected online and mobile apps which also contribute to today’s agriculture.
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Li, Yue, Zhongxing Wang, Li Sun, Liqiang Liu, Chuanlai Xu, and Hua Kuang. "Nanoparticle-based sensors for food contaminants." TrAC Trends in Analytical Chemistry 113 (April 2019): 74–83. http://dx.doi.org/10.1016/j.trac.2019.01.012.
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McCauley, DJ. "Cover Crops, Sensors, and Food Security." CSA News 65, no. 12 (November 20, 2020): 3–9. http://dx.doi.org/10.1002/csan.20335.
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Bounegru, Alexandra, and Constantin Apetrei. "Voltammetric Sensors Based on Nanomaterials for Detection of Caffeic Acid in Food Supplements." Chemosensors 8, no. 2 (June 18, 2020): 41. http://dx.doi.org/10.3390/chemosensors8020041.
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Caffeic acid may be accurately detected in food supplements by using cyclic voltammetry and carbon screen-printed sensors modified with various nanomaterials. Sensor characterization by cyclic voltammetry in reference solutions has shown that carbon nanotubes or carbon nanofibers significantly improve the sensor response in terms of sensitivity and reversibility. Screen-printed sensors were then used in order to study the electrochemical behavior of caffeic acid in aqueous solution at pH 3.6. A redox process was observed in all cases, which corresponds to a reversible redox process involving the transfer of two electrons and two protons. The role of nanomaterials in the increment of sensor performance characteristics was evidenced. Calibration curves were developed for each sensor, and the detection (LOD) and quantification (LOQ) limits were calculated. Low LOD and LOQ values were obtained, in the 10−7 to 10−9 M range, which demonstrates that the method is feasible for quantification of caffeic acid in real samples. Caffeic acid was quantitatively determined in three food supplements using the most sensitive sensor, namely the carbon nanofiber sensor. The Folin–Ciocalteu spectrophotometric assay was used to validate the results obtained with the sensor. The results obtained by using the voltammetric method were consistent with those obtained by using the spectrophotometric method, with no statistically significant differences between the results obtained at 95% confidence level.
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Abu Bakar, Zahari, Muhammad Zairil Muhammad Nor, Kamaru Adzha Kadiran, Mohamad Farid Misnan, and Maisarah Noorezam. "Smart Plant Monitoring System Using Aquaponics Production Technological with Arduino Development Environment (IDE) and SMS Alert: A Prototype." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 22 (November 29, 2022): 32–47. http://dx.doi.org/10.3991/ijim.v16i22.34581.
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Aquaponics is one of the agricultural production technological advancements that should be publicized. Climate change, population expansion, water scarcity, soil degradation, and food security are just a few of the world's most pressing issues. Aquaponics, which is a closed-loop system that combines hydroponics and aquaculture, may be able to deal with issues like climate change, population expansion, water scarcity, soil degradation, and food security. Thus, this paper aims to design and construct an aquaponics system that combines fish farming with plant growth. The system used a variety of sensors, including temperature and humidity sensors, ultrasonic sensors, and pH sensors, as well as microcontrollers like Arduino, to monitor and manage the water quality, plant humidity, and other variables. When the sensor identifies any abnormal circumstance, early warnings in the form of SMS and push notifications are immediately given to the user to ensure a healthy growing environment for fish and plants. The Arduino Development Environment (IDE) software is used to write a programme that connects the microcontroller to various sensors and other devices. pH sensors, temperature and humidity sensors, ultrasonic sensors, liquid crystal displays (LCDs), and GSM circuits are all built and connected to the system. A GSM notification message is sent to a mobile phone when the pH, temperature, and ultrasonic sensor findings are out of range. The data from this system's monitoring reveals the values that have been taken on a daily basis. The graph demonstrated that the plant's growth is increasing every day.
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Zappa, Dario. "Low-Power Detection of Food Preservatives by a Novel Nanowire-Based Sensor Array." Foods 8, no. 6 (June 25, 2019): 226. http://dx.doi.org/10.3390/foods8060226.
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Food preservatives are compounds that are used for the treatment of food to improve the shelf life. In the food industry, it is necessary to monitor all processes for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. The present work reports the fabrication of a novel sensor array based on SnO2, CuO, and WO3 nanowires deposited on top of μHPs provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide, and ozone. Results are very promising; the sensors array was able to operate for a long time, consuming less than 50 mW for each single sensor, and principal component analysis (PCA) confirmed that the device was able to discriminate between different compounds.
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Van Duy, Lai, To Thi Nguyet, Dang Thi Thanh Le, Nguyen Van Duy, Hugo Nguyen, Franco Biasioli, Matteo Tonezzer, Corrado Di Natale, and Nguyen Duc Hoa. "Room Temperature Ammonia Gas Sensor Based on p-Type-like V2O5 Nanosheets towards Food Spoilage Monitoring." Nanomaterials 13, no. 1 (December 28, 2022): 146. http://dx.doi.org/10.3390/nano13010146.
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Gas sensors play an important role in many areas of human life, including the monitoring of production processes, occupational safety, food quality assessment, and air pollution monitoring. Therefore, the need for gas sensors to monitor hazardous gases, such as ammonia, at low operating temperatures has become increasingly important in many fields. Sensitivity, selectivity, low cost, and ease of production are crucial characteristics for creating a capillary network of sensors for the protection of the environment and human health. However, developing gas sensors that are not only efficient but also small and inexpensive and therefore integrable into everyday life is a difficult challenge. In this paper, we report on a resistive sensor for ammonia detection based on thin V2O5 nanosheets operating at room temperature. The small thickness and porosity of the V2O5 nanosheets give the sensors good performance for sensing ammonia at room temperature (RT), with a relative change of resistance of 9.4% to 5 ppm ammonia (NH3) and an estimated detection limit of 0.4 ppm. The sensor is selective with respect to the seven interferents tested; it is repeatable and stable over the long term (four months). Although V2O5 is generally an n-type semiconductor, in this case the nanosheets show a p-type semiconductor behavior, and thus a possible sensing mechanism is proposed. The device’s performance, along with its size, low cost, and low power consumption, makes it a good candidate for monitoring freshness and spoilage along the food supply chain.
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Gheorghe, Damaris-Cristina, Jacobus (Koos) Frederick van Staden, Raluca-Ioana Stefan-van Staden, and Paula Sfirloaga. "Gold Nanoparticles/Nanographene-Based 3D Sensors Integrated in Mini-Platforms for Thiamine Detection." Sensors 23, no. 1 (December 29, 2022): 344. http://dx.doi.org/10.3390/s23010344.
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Vitamins are essential for sustaining daily activities and perform crucial roles in metabolism, such as preventing vascular events and delaying the development of diabetic nephropathy. The ultrasensitive assessment of thiamine in foods is required for food quality evaluation. A mini-platform utilizing two 3D sensors based on nanographene and gold nanoparticles paste modified with protoporphyrin IX and protoporphyrin IX cobalt chloride is proposed for the detection of thiamine in blueberry syrup, multivitamin tablets, water, and a biological sample (urine). Differential pulse voltammetry was utilized for the characterization and validation of the suggested sensors. The sensor modified with protoporphyrin IX has a detection limit of 3.0 × 10−13 mol L−1 and a quantification limit of 1.0 × 10−12 mol L−1, whereas the sensor modified with protoporphyrin IX cobalt chloride has detection and quantification limits of 3.0 × 10−12 and 1.0 × 10−11 mol L−1, respectively. High recoveries (values greater than 95.00%) and low RSD (%) values (less than 5.00%) are recorded for both 3D sensors when used for the determination of thiamine in blueberry syrup, multivitamin tablets, water, and urine, demonstrating the 3D sensors’ and suggested method’s high reliability.
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Fahey, Thomas, Hai Pham, Alessandro Gardi, Roberto Sabatini, Dario Stefanelli, Ian Goodwin, and David William Lamb. "Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops." Sensors 21, no. 1 (December 29, 2020): 171. http://dx.doi.org/10.3390/s21010171.
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In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
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Akila, A., and P. Shalini. "Food grain storage management system." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 170. http://dx.doi.org/10.14419/ijet.v7i2.31.13433.
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Food grain Wastage cripples a country’s economy to a great extent. Food grain wastage is also associated with wastage of water, manpower during agricultural activities and electricity power used in food processing industries. It even causes deforestation. Adequate measures have to be taken to properly store the food grains so that they remain edible. The proposed storage management system uses the sensors to measure the levels of humidity, temperature and ammonia gas which will help us monitor quality of the food grains. The main idea is to identify the quality of the food grains using the sensors such as Temperature, Humidity and Ammonia Gas. The Quality of the food grain is measured using the factors like Humidity, Temperature and Ammonia gas sensors and sent through Wireless Communication to the server and the server makes the decision and alarms about the quality of the food grain to the maintenance people. Food grain Wastage cripples a country’s economy to a great extent. Food grain wastage is also associated with wastage of water, manpower during agricultural activities and electricity power used in food processing industries. It even causes deforestation. Adequate measures have to be taken to properly store the food grains so that they remain edible. The proposed storage management system uses the sensors to measure the levels of humidity, temperature and ammonia gas which will help us monitor quality of the food grains. The standard of identifying the food quality could be improved by using more sensors and can be implemented in the Food Storage Industry.
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Gull, Shazmina, Imran Sarwar Bajwa, Waheed Anwar, and Rubina Rashid. "Smart eNose Food Waste Management System." Journal of Sensors 2021 (July 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/9931228.
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The modern age is an era of fast-growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low-cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH4 gas while the MQ135 sensor detects CO2 and NH3 in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.
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Honeychurch, Kevin C., and Martina Piano. "Sensors for Environmental Monitoring and Food Safety." Biosensors 12, no. 6 (May 26, 2022): 366. http://dx.doi.org/10.3390/bios12060366.
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The aim of this Special Issue of the journal Biosensors, “Sensors for Environmental Monitoring and Food Safety”, was to report on the developments and advances in sensors and biosensors to meet the needs of environmental and food analysis [...]
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Yang, Zhongjie, Xiaofei Zhang, and Jun Guo. "Functionalized Carbon-Based Electrochemical Sensors for Food and Alcoholic Beverage Safety." Applied Sciences 12, no. 18 (September 9, 2022): 9082. http://dx.doi.org/10.3390/app12189082.
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Food is a necessity in people’s lives. Equally importantly, alcoholic beverages are also highly demanded globally due to the indispensable role they play in cultural, social, and ritual events. However, the production of food and alcoholic beverages suffers from a variety of contaminants, such as toxins, pesticides, antibiotic residues, and heavy metals, which are seriously harmful to human beings. These urgent threats have raised the awareness of the need to improve product quality and safety via developing effective, rapid, and economical monitoring and detecting methods. Fortunately, due to their numerous advantages, including high sensitivity, short response time, low cost, and easy portability, electrochemistry sensors have made huge contributions to ensuring the quality of food and alcoholic beverages. The purpose of this review is to introduce applications of electrochemical sensors to foods and alcoholic beverages, and to highlight the important role of carbon-based materials (i.e., carbon dots, carbon nanotubes, and graphene) as electrochemical sensors in detecting various contaminants. In addition, the preparation methods of these carbon-based electrochemical sensors and corresponding detection mechanisms are discussed in detail. It is hoped that this review can inspire more innovative detection technologies for ensuring the safety of food and alcoholic beverages.