On the temporal stability of analyte recognition with an e-nose based on a metal oxide sensor array in practical applications

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute of Technology (Germany). We consider the instability of e-nose operation at different time scales ranging from minutes to many years. To test the e-nose we employ open-air and headspace sampling of analyte odors. The multivariate recognition algorithm to process the multisensor array signals is based on the linear discriminant analysis method. Accounting for the received results, we argue that the stability of device operation is mostly affected by accidental changes in the ambient air composition. To overcome instabilities, we introduce the add-training procedure which is found to successfully manage both the temporal changes of ambient and the drift of multisensor array properties, even long-term. The method can be easily implemented in practical applications of e-noses and improve prospects for device marketing

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

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

Non-destructive Quality Monitoring of Fresh Fruits and Vegetables

Quality determines the shelf life as well as selling price of fresh fruit or vegetable and therefore, quality monitoring and testing of fresh commodities have paramount importance in their postharvest handling and supply chain management. Most of the methods used to assess fruits and vegetables quality are destructive in nature. Now-a-days, various mechanical, optical, electromagnetic, and dynamic non-destructive methods are gaining importance due to ease in operations, faster turn over and reliability. Some of the non-destructive techniques (NDT) are currently being used in laboratories, research institutions and food packaging and processing industries, whereas, some methods are still at developmental stage. Various NDT with respect to their principle and applications such as impact test, electronic nose, time-resolved reflectance spectrometry (TSR), near infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), X-Ray, ultra sonic, acoustic impulse response method, electrical conductivity methods etc., are discussed in this review

E-Nose Application to Food Industry Production

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic nose (e-nose) can be a useful tool to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of the e-nose in the food industry.Chilo, J.; Pelegrí Sebastiá, J.; Cupane, M.; Sogorb Devesa, TC. (2016). E-Nose Application to Food Industry Production. IEEE Instrumentation and Measurement Magazine. 19(1):27-33. doi:10.1109/MIM.2016.7384957S273319

A Novel Approach for Apple Freshness Prediction Based on Gas Sensor Array and Optimized Neural Network

Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [email protected] (W.Y.).Copyright © 2023 by the authors. Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.Shanxi Provincial Natural Science Foundation General Project, China, Project, grant number 202203021221117. Grantee: Wei Wang

New Trends in Beverage Packaging Systems: A Review

New trends in beverage packaging are focusing on the structure modification of packaging materials and the development of new active and/or intelligent systems, which can interact with the product or its environment, improving the conservation of beverages, such as wine, juice or beer, customer acceptability, and food security. In this paper, the main nutritional and organoleptic degradation processes of beverages, such as oxidative degradation or changes in the aromatic profiles, which influence their color and volatile composition are summarized. Finally, the description of the current situation of beverage packaging materials and new possible, emerging strategies to overcome some of the pending issues are discussed

New Trends in Beverage Packaging Systems: A Review

New trends in beverage packaging are focusing on the structure modification of packaging materials and the development of new active and/or intelligent systems, which can interact with the product or its environment, improving the conservation of beverages, such as wine, juice or beer, customer acceptability, and food security. In this paper, the main nutritional and organoleptic degradation processes of beverages, such as oxidative degradation or changes in the aromatic profiles, which influence their color and volatile composition are summarized. Finally, the description of the current situation of beverage packaging materials and new possible, emerging strategies to overcome some of the pending issues are discussed

GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation