Potential use of electronic noses, electronic tongues and biosensors, as multisensor systems for spoilage examination in foods

Development and use of reliable and precise detecting systems in the food supply chain must be taken into account to ensure the maximum level of food safety and quality for consumers. Spoilage is a challenging concern in food safety considerations as it is a threat to public health and is seriously considered in food hygiene issues accordingly. Although some procedures and detection methods are already available for the determination ofspoilage in food products, these traditional methods have some limitations and drawbacks as they are time-consuming,labour intensive and relatively expensive. Therefore, there is an urgent need for the development of rapid, reliable, precise and non-expensive systems to be used in the food supply and production chain as monitoring devices to detect metabolic alterations in foodstuff. Attention to instrumental detection systems such as electronic noses, electronic tongues and biosensors coupled with chemometric approaches has greatly increased because they have been demonstrated as a promising alternative for the purpose of detecting and monitoring food spoilage. This paper mainly focuses on the recent developments and the application of such multisensor systems in the food industry. Furthermore, the most traditionally methods for food spoilage detection are introduced in this context as well. The challenges and future trends of the potential use of the systems are also discussed. Based on the published literature, encouraging reports demonstrate that such systems are indeed the most promising candidates for the detection and monitoring of spoilage microorganisms in different foodstuff

Evaluation of an electronic nose system for characterization of pomegranate varieties

The electronic nose (e-nose) would simulate the human sense (smell) to identify and realize the complex aromas by employing a chemical sensors array. One of the most common sensors used in electronic nose systems are metal oxide semiconductor (MOS) sensors. In this research, a low cost e-nose system based on six metal oxide semi-conductor (MOS) sensors as a non-destructive instrument for recognition pomegranate varieties is investigated. Principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for this purpose. The proposed e-nose has a capability of demonstrating a clear difference in aroma fingerprint of pomegranate by PCA and LDA analysis. Using LDA analysis, it is possible to identify and to categorize the difference between pomegranate varieties, and based on the results, the classification accuracy of 95.2% was obtained. Sensor array capabilities for classification of pomegranate varieties using loading analysis were investigated too. Results showed high ability of e-nose for distinguishing between the varieties of pomegranates

Energy Flows Modeling and Economic Evaluation of Watermelon Production in Fars Province of Iran

This study aimed to evaluate the efficiency of energy consumption and economic analysis of different watermelon cultivation systems in Fars Province of Iran. Watermelon production systems were classified into five systems, namely, custom tillage (group 1), conservation tillage (group 2), traditional planting (group3), semi mechanized planting (group 4), and mechanized planting (group 5). Data were collected from 317 watermelon producers from different parts of the province through face to face interviews. Multi-Layer Perceptron artificial neural networks were used to model the energy flows of watermelon production. The results showed that the greatest energy consumption belonged to mechanized planting system with the value of 81317.72 MJha-1 and with the productivity of 0.61 kgha-1 and energy use efficiency of 1.17. Clustering function with three inputs (human resources, machines and diesel fuel) showed that the difference between groups 2 and 4 is more than the other groups. The least energy consumption belonged to the conservative agriculture as78163.86 MJha-1 and the energy productivity and energy use efficiency about 0.64 kgha-1 and 1.22, respectively. The results of energy modeling showed that an ANN model with 9-10-1 structure was determined to be optimal for energy flow modeling of this system. Generally, it was concluded that the artificial neural network models can be applicable to prognosticate the energy flows of watermelon production. From an economic point of view, the least net profit belonged to traditional planting with the value of 2618.14$, and the most net return belonged to mechanized planting with the value of 2752.88$/ha

Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM)

Se ha estudiado la posible aplicación de una nariz electrónica basada en semiconductores de óxido metálico (e-nariz) como un instrumento que no sea destructivo para el seguimiento del cambio en la producción de volátiles de plátano durante el proceso de maduración. La propuesta de e-nariz no necesita ningún equipo de laboratorio avanzado o caro y resultó ser fiable en la grabación de las diferencias significativas entre las etapas de maduración. El Análisis de Componentes Principales (PCA), Análisis Discriminante Lineal (LDA), Modelado Suave Independiente de las Analogías de Clases (SIMCA) y Máquinas Soporte de Vectores (SVM) son técnicas utilizadas para este propósito. Los resultados mostraron que la dirección de la e-nariz distingue entre las diferentes etapas de maduración. La nariz electrónica fue capaz de detectar una clara diferencia en la huella digital de aroma de plátano cuando se utiliza el análisis de SVM en comparación con PCA o LDA y SIMCA. Utilizando el análisis de SVM, era posible diferenciar y clasificar las diferentes etapas de maduración de plátanos, y este método fue capaz de clasificar el 98,66% del total de muestras en su grupo respectivo. Las capacidades matrices de sensores en la clasificación de etapas de maduración usan el análisis de la carga y la SVM y SIMCA También se ha visto que conduce a desarrollar un sistema de e-nariz específico mediante la aplicación de los sensores más eficaces y a ignorar los sensores redundantesPotential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaning¬ful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.peerReviewe

Sensors and systems for environmental monitoring and control

Editorial titulado Sensors and systems for environmental monitoring and control, publicado en la Journal of sensors, en 2017. Se analiza el valor de controlar con monitores especializados los contaminantes de origen químico orgánico e inorgánico.Editorial entitled Sensors and systems for environmental monitoring and control, published in the Journal of sensors, in 2017. The value of controlling pollutants of organic and inorganic chemical origin with specialized monitors is analyzed.peerReviewe

Utilizing visible and near infrared spectroscopy based on multi-class support vector machines classification to characterize olive oil adulteration

Rapid and non-destructive adulteration detection is of particular importance to oil industries. This paper presents an application of visible and near-infrared spectroscopy (VNIR) for detection of adulteration levels in olive oil. Sunflower oil was used as an adulterant to olive oil and adulteration samples with different levels ranging from 0 to 40% were prepared and used for the experiments. The spectra were first considered in the range of 500-900 nm and then smoothened and normalized to reduce the light scattering effects. Principal component analysis (PCA) was performed on the spectra to have a primary data visualization and feature extraction. The extracted PCA scores were used to calculate the Mahalanobis distances of the adulterated samples from the pure sample. Further, the PCA scores were fed to the multi-class support vector machine (SVM) model to perform classification on the basis of different adulteration levels. The results showed that the spectral normalization highlighted different regions over the spectrum affected due to the adulteration. The PCA score biplots showed differences in the samples based on the different amounts of the adulteration. Moreover, the Mahalanobis distance provided a quantitative measure of the differences between the adulterated oil and the pure oil samples. The SVM modelling further supported the classification of the different levels of the adulteration. Consequently, the VNIRS in combination with the SVM could support the development of the classification protocols for detection of adulteration in olive oils

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process

On-line monitoring of food fermentation processes using electronic noses and electronic tongues: A review

Fermentation processes are often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, close follow-up of this type of processes is critical for detecting unfavorable deviations as early as possible in order to save downtime, materials and resources. Nevertheless the use of traditional analytical techniques is often hindered by the need for expensive instrumentation and experienced operators and complex sample preparation. In this sense, one of the most promising ways of developing rapid and relatively inexpensive methods for quality control in fermentation processes is the use of chemical multisensor systems. In this work we present an overview of the most important contributions dealing with the monitoring of fermentation processes using electronic noses and electronic tongues. After a brief description of the fundamentals of both types of devices, the different approaches are critically commented, their strengths and weaknesses being highlighted. Finally, future trends in this field are also mentioned in the last section of the article. (C) 2013 Elsevier B.V. All rights reserved.Peris Tortajada, M.; Escuder Gilabert, L. (2013). On-line monitoring of food fermentation processes using electronic noses and electronic tongues: A review. Analytica Chimica Acta. 804:29-36. doi:10.1016/j.aca.2013.09.048S293680

Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries

The electronic nose (e-nose) is designed to crudely mimic the mammalian nose in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern recognition is used to determine that one sample is similar or different from another based on headspace volatiles. There are different types of e-nose sensors including organic polymers, metal oxides, quartz crystal microbalance and even gas-chromatography (GC) or combined with mass spectroscopy (MS) can be used in a non-selective manner using chemical mass or patterns from a short GC column as an e-nose or “Z” nose. The electronic tongue reacts similarly to non-volatile compounds in a liquid. This review will concentrate on applications of e-nose and e-tongue technology for edible products and pharmaceutical uses

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling