Array of conducting polymer sensors for the characterisation of wines

Application of an electronic nose instrument to fast classification of Polish honey types

The paper presents practical utilization of an electronic nose prototype, based on the FIGARO semiconductor sensors, in fast classification of Polish honey types-acacia flower, linden flower, rape, buckwheat and honeydew ones. A set of thermostating modules of the prototype provided gradient temperature characteristics of barbotage-prepared gas mixtures and stable measurement conditions. Three chemometric data analysis methods were employed for the honey samples classification: principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) with the furthest neighbour method. The investigation confirmed usefulness of this type of instrument in correct classification of all aforementioned honey types. In order to provide optimum measurement conditions during honey samples classification the following parameters were selected: volumetric flow rate of carrier gas-15 L/h, barbotage temperature-35 °C, time of sensor signal acquisition since barbotage process onset-60 s. Chemometric analysis allowed discrimination of three honey types using PCA and CA and all five honey types with LDA. The reproducibility of 96% of the results was within the range 4.9%-8.6% CV.

Application of Electronic Nose for Detection of Wine-Aging Methods

This study reports the application of an electronic nose for the identification and classification of red wines aged three different methods. The signals of the different wines detected by the 10 sensors present in the E-nose are significantly different from each other. The response to the signal generates a typical chemical fingerprint of the volatile compounds present in the wines. Principal Component Analysis can be applied for the dimensionality reduction of the collected signal. Since the total contribution rate of the first three principal components is up to 97.27%, different wines can be distinguished from each other by the three principal components. Euclidean distance, correlation analysis, Mahalanobis distance and linear discrimination analysis can offer 100% accuracy for known samples, and the accuracy rate can reach 88.9% for the 18 test samples. In addition, numerous advantages exist compared with sensory analysis in both authentication and quality control of wines.

Quality evaluation of agricultural distillates using an electronic nose

The paper presents the application of an electronic nose instrument to fast evaluation of agricultural distillates differing in quality. The investigations were carried out using a prototype of electronic nose equipped with a set of six semiconductor sensors by FIGARO Co., an electronic circuit converting signal into digital form and a set of thermostats able to provide gradient temperature characteristics to a gas mixture. A volatile fraction of the agricultural distillate samples differing in quality was obtained by barbotage. Interpretation of the results involved three data analysis techniques: principal component analysis, single-linkage cluster analysis and cluster analysis with spheres method. The investigations prove the usefulness of the presented technique in the quality control of agricultural distillates. Optimum measurements conditions were also defined, including volumetric flow rate of carrier gas (15 L/h), thermostat temperature during the barbotage process (15 °C) and time of sensor signal acquisition from the onset of the barbotage process (60 s).

Metal oxide sensors for electronic noses and their application to food analysis

Electronic noses (E-noses) use various types of electronic gas sensors that have partial specificity. This review focuses on commercial and experimental E-noses that use metal oxide semi-conductors. The review covers quality control applications to food and beverages, including determination of freshness and identification of contaminants or adulteration. Applications of E-noses to a wide range of foods and beverages are considered, including: meat, fish, grains, alcoholic drinks, non-alcoholic drinks, fruits, milk and dairy products, olive oils, nuts, fresh vegetables and eggs.

Applications of artificial neural networks (ANNs) in food science

Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

Direct integration of pervaporation as a sample preparation method for a dedicated "electronic nose"

The present study investigates the possibility of monitoring the bioproduction of a complex aroma profile with an analytical electronic aroma-sensing technique, the so-called "electronic nose", combined with a pervaporative sample enrichment method necessary to overcome the ethanol interference on the sensors' response. It presents in detail the development of a direct integrated pervaporation-electronic nose unit for a simple and fast analysis, which are key criteria for this technique to be broadly implemented. The system developed was investigated using model solutions simulating the muscatel wine must fermentation. It proved to be able to evaluate different relevant aroma compounds in solutions of varying degree of complexity, and also in the presence of ethanol, which is a major interference on the sensors' response to the aromas. The transient sensors' response was investigated in detail, revealing information for sample discrimination and reducing the analysis time. The system developed allowed a simple, fast, and selective analysis, therefore permitting a high sample throughput over time, with the possibility of fully automation.

Application of a headspace mass spectrometry system to the differentiation and classification of wines according to their origin, variety and ageing

The system based on coupling a headspace sampler to a mass spectrometer (HS-MS), considered a kind of electronic nose (e-nose), is an emerging technique in the field of food aroma analysis. The global mass spectrum this system provides is a fingerprint of each sample analysed that contains the information related to volatile composition of the sample. The use of chemometric techniques allows to compare the spectra of the samples and then, to classify them according to different properties. In this paper, we present the development of a method for wine analysis using a HS-MS system and multivariate analysis techniques. The method was successfully applied to differentiate and classify wines according to its origin, variety and ageing. The main advantages of the proposed methodology are the minimum sample preparation required and the speed of analysis (10 min/sample).