Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution

Towards Drift Correction in Chemical Sensors Using an Evolutionary Strategy

Gas chemical sensors are strongly affected by the so-called drift, i.e., changes in sensors' response caused by poisoning and aging that may significantly spoil the measures gathered. The paper presents a mechanism able to correct drift, that is: delivering a correct unbiased fingerprint to the end user. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem

Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real in dustrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors

Candida milleri detected by Electronic Nose in tomato sauce

AbstractThe tomato sauce is a product of great importance for its massive production in Italy. Microbial contamination is a constant concern for the industries, causing severe economic losses, posing risks to consumers’ health and contributing to an enormous wasting of food. This work shows how the use of the Electronic Nose (EN) EOS 507C can be effective compare to the current procedures in the food production. EN composed of an array of thin film sensors, 6 Metal Oxide (MOX). All the samples were analyzed in parallel with classical chemical technique, like GC-MS with SPME

Rapid Screening of Alicyclobacillus acidoterrestris Spoilage of Fruit Juices by Electronic Nose: A Confirmation Study

Early screening of Alicyclobacillus spp. in fruit juices is a major applicative goal for the food industry, since juice contamination can lead to considerable loss of quality, and subsequently, to economic damages for juice producers. This paper presents an accurate study to assess and confirm the EOS507 electronic nose’s (EN) ability of diagnosing Alicyclobacillus acidoterrestris spoilage in artificially contaminated fruit juices. The authors experimental results have shown that the EOS507 can early identify, just after 24 hours from inoculation, contaminated orange and pear juices with an excellent classification rate close to 90% and with a detection threshold as low as 103 cfu/ml. In apple juice the detection threshold was about 105 cfu/ml, thus requiring longer incubation times (72 hours). PLS regression of EOS507 data can be also used to predict with fair accuracy the colony-forming units concentration of the bacteria. These results were supported by the GC/MS/MS measurements of specific chemical markers, such as guaiacol

Adsorption of Rhodamine B from Wastewater on the Arsenic- Hyperaccumulator Pteris Vittata Waste Roots

The Pteris vittata fern, which is a perennial plant known for hyper-accumulating Arsenic, can be grown in hydroponic cultures and is often used for phytoremediation of contaminated water. To reduce the cost of disposing As-contaminated biomass, this study examined the potential of using waste roots from Pteris vittata as a new and inexpensive bio-adsorbent for removing Rhodamine B (RB) dye, which is commonly used in industrial applications. Batch tests were performed at 25°C in order to observe both the rate and the equilibrium conditions of the system. The isotherm showed a typical Langmuir behavior exhibiting a maximum adsorption capacity of 42.7 mg/g. Kinetics tests were conducted at different solid-liquid ratios and fitted by a mathematical model. The maximum likelihood method was employed to estimate the effective diffusivity of RB in the solid which resulted 4.48 10-9 cm2/min. This study lays the groundwork for future investigations into the use of this material in continuous systems to determine its feasibility for application in industrial apparatus

Differential Detection of Potentially Hazardous Fusarium Species in Wheat Grains by an Electronic Nose

Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%