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Advancements in real-time water monitoring technologies permit rapid detection of water quality, and threats from waste loads. Water Framework Directive mandating the establishment of Member States’ water resources monitoring, presence of hazardous contaminants in effluents, and perception of vulnerability of water distribution system to attacks, have spurred technical and economic interests. The paper aims to discuss these issues.

As alternative to traditional analyzers, chemosensors, operate according to physical principles, without sample collection (online), and are capable of supplying parameter values continuously and in real-time. Their low selectivity and stability issues have been overcome by technological developments. This review paper contains a comprehensive survey of existing and expected online monitoring technologies for measurement/detection of pollutants in water.

The state-of-the-art in online water monitoring is presented. Application examples are reported. Monitoring costs will become a lesser part of a water utility budget due to the fact that automation and technological simplification will abate human cost factors, and reduce the complexity of laboratory procedures.

An overview of applicable instrumentation, and forthcoming developments, is given. Technological development in this field is very rapid, and astonishing advances are anticipated in several areas (fingerprinting, optochemical sensors, biosensors, molecular techniques). Online monitoring is becoming an ever-important tool not only for compliance control or plant management purposes, but also as a useful approach to pollution control and reduction, minimizing the environmental impact of discharges.

A UK market of about £295 million over the next five years for advanced sensors and instruments in health and safety is envisaged in a new report commissioned by the Laboratory of the Government Chemist as part of the Government's Advanced Sensors Technology Transfer Programme (ASTTP). Good prospects are also seen in another new ASTTP report on advanced sensors in the healthcare sector. The two reports were introduced at the fourth annual seminar on Advanced Sensors organized by ERA Technology on 10 November 1992.

In this work, the sensing and actuating elements are designed with interdigitated capacitors away from the sensitive element on which the droplet is placed. This pattern helps to prevent interference of electrical elements with the droplet. Choosing shear resonance mode at this proposed structure minimizes the damping effect of droplet touch by the resonator structure. The glass-based standard fabrication method of the proposed biosensor is presented exactly.

Mechanical resonator sensors are extremely limited because of the high damping factor and the high electrical conductivity in the aqueous environment. In this work, a molecule detector biosensor is proposed for droplet analysis, which is possible to fabricate using micro-electro-mechanical systems (MEMS) technology. By electromechanical coupling of resonators as a mechanical resonator structure, a standing mechanical wave is formed at this structure by electrostatic actuating elements.

In this paper, a mechanical resonator structure as a biosensor is proposed for micro-droplet analysis that can be fabricated by MEMS technology. It is designed at a lower cost fabrication method using electrostatic technology and interdigitated capacitors. The response of the biosensor displacement frequency at the resonance frequency of the desired mode is reasonable for measuring the capacitive changes of its output. The mass sensitivity of the proposed biosensor is in the range of 1 ng, and it has a large sensitive area for capturing target molecules.

To evaluate the quality of the proposed design, the stimulated analysis is conducted by COMSOL and results are presented.

This paper aims to study different possibilities for implementing easy-to-use and cost-effective micro-systems to detect and trace expelled gases from rotten food. The paper covers various radio-frequency identification (RFID) technologies and gas sensors as the two promoting feasibilities for the tracing of packaged food. Monitoring and maintaining quality and safety of food in transport and storage from producer to consumer are the most important concerns in food industry. Many toxin gases, even in parts per billion ranges, are produced from corrupted and rotten food and can endanger the consumers’ health. To overcome the issues, intelligent traceability of food products, specifically the packaged ones, in terms of temperature, humidity, atmospheric conditions, etc., has been paid attention to by many researchers.

Food poisoning is a serious problem that affects thousands of people every year. Poisoning food must be recognized early to prevent a serious health problem.

Contaminated food is usually detectable by odor. A small gas sensors and low-cost tailored to the type of food packaging and a communication device for transmitting alarm output to the consumer are key factors in achieving intelligent packaging.

Conducting polymer composite, intrinsically conducting polymer and metal oxide conductivity gas sensors, metal–oxide–semiconductor field-effect transistor (MOSFET) gas sensors offer excellent discrimination and lead the way for a new generation of “smart sensors” which will mould the future commercial markets for gas sensors.

Small size, low power consumption, short response time, wide operating temperature, high efficiency and small area are most important features of introduced system for using in package food.

Describes the Amocut advanced monitoring system developed in the UK and Italy for the automatic monitoring of pH value, concentration, tramp oil, particulates (by abrasion), corrosivity and microbial activity of metalworking fluids.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Briefly describes the modes of microbial attack on meat stores in the presence of oxygen; reference is also made to citrus fruits. Describes simply the technology of two different types of vapour sensor, one optical and one electronic.

The provision of information about food to consumers is well recognized to be problematic and complex. Novel approaches tend to adopt app-based services as a way for consumers to get better food information. Potentially, these services could provide relevant information to consumers because they could address particular needs by leveraging on context-awareness, ubiquity and pervasiveness. Do current app-based services actualize these potentialities? The purpose of this paper is twofold: to propose a methodological approach that can be easily adapted to conduct reviews of app-based services and provide consumers with information about food and to answer this question on a specified food information domain (namely, olive oil).

A literature review was performed to investigate characteristics of food information services under a consumer’s value perspective. Then, a qualitative framework was built, which allowed a multidimensional analysis of the food information provided by a service through a mobile app. Moreover, an application of that framework on “olive oil” mobile apps was carried out, with the twofold objective of providing an example of application of the proposed framework in a real context and analyzing the role of mobile apps in reducing consumers’ information and knowledge gap in this particular domain. The review covers commercial applications on “olive oil,” available on the Apple App Store and Google Play Store.

A framework of multidimensional analysis of the two-way information flow in the interaction between a consumer/user and a food app-based service is proposed. The framework represents a useful tool to analyze the offer of mobile-based services in a given sector. The framework is then applied to perform a situation analysis of app-based services in the olive oil market. The analysis highlights that olive oil information providers do not seem to fully exploit recent advances in “Internet of food” technologies. Hence, results from our survey suggest some direction to conceive and develop innovative mobile olive oil apps that better exploit mobiquitous technology features.

Consumers perceive a growing need of information about food. This need is mainly due to recent cases of adulterations, allegations of fraud and subterfuges that have invested food sector (in particular, olive oil sector). This research provides a useful framework to analyze some aspects of management of food information provision through mobile apps. Such a framework represents a practical tool that provides fruitful insights for the design of a new generation of food-app based services within the so-called “internet of food” domain.

Although there are countless mobile apps aimed to assist consumers with their everyday food practices, little research has been devoted to investigate the impact of mobiquitous technologies on the management of food information provision to consumers. The available research lacks in defining an approach capable to analyze the relevance of information provided to targeted audiences by mobile food apps. This paper tries to fill this knowledge gap by proposing a framework that is based on a characterization of food information flows in terms of categorization, source and service features.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics.

Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products.

There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions.

The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars.

The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.