Tomasz Dymerski

Food Analysis Using Artificial Senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

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.

Chemical composition analysis and authentication of whisky

Whisky (whiskey) is one of the most popular spirit-based drinks made from malted or saccharified grains, which should mature for at least 3 years in wooden barrels. High popularity of products usually causes a potential risk of adulteration. Thus authenticity assessment is one of the key elements of food product marketing. Authentication of whisky is based on comparing the composition of this alcohol with other spirit drinks. The present review summarizes all information about the comparison of whisky and other alcoholic beverages, the identification of type of whisky or the assessment of its quality and finally the authentication of whisky. The article also presents the various techniques used for analyzing whisky, such as gas and liquid chromatography with different types of detectors (FID, AED, UV-Vis), electronic nose, atomic absorption spectroscopy and mass spectrometry. In some cases the application of chemometric methods is also described, namely PCA, DFA, LDA, ANOVA, SIMCA, PNN, k-NN and CA, as well as preparation techniques such SPME or SPE.

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).

Comprehensive two-dimensional gas chromatography and three-dimensional fluorometry for detection of volatile and bioactive substances in some berries

The volatile fractions of Cape gooseberry and blueberry were determined by headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME/GC×GC-TOFMS). The highest amount of alcohol (51.8%), ester (32.8%) and carboxylic acid (6.9%) was in blueberry in comparison with gooseberry and oppositely ketones (14.7%), aldehydes (9.9%) and terpenes (8%) were found in gooseberry. The bioactive compounds and antioxidant capacities were higher in blueberries than in gooseberries. Three dimensional fluorescence emission spectrometry (3D-FL) was applied to determine and to compare experimentally found binding parameters of berries extracts with human serum albumin (HSA). The fluorescence quenching of HSA by polyphenols from berries was a result of the formation of a polyphenol-HSA complex. The binding abilities of berries were highly correlated with the bioactivity of polyphenols and volatile substances. The cluster analysis (CA) and linear discriminant analysis (LDA) was applied to differentiate the berries samples according to their type.

Characteristics of odors emitted from municipal wastewater treatment plant and methods for their identification and deodorization techniques

Odors emitted from municipal wastewater treatment plants belong to a group of pollutants, which is the main cause of people complaining about atmospheric air quality. The limitation of emissions of unpleasant odors generated by wastewater treatment plants by using appropriate deodorization methods is omitted on numerous occasions. This can have a negative influence on public trust and the quality of atmospheric air. The article presents basic information on the characteristics of odors from wastewater treatment lines and wastewater processing and management lines in a model biological wastewater treatment plant conducting the biogas recovery process and also information is provided on deodorization methods, such as odor masking, biofiltration, thermal disposal and diffusion through activated sludge dedicated to neutralization of odors in biological treatment plants. The main focus is on the field olfactometry technique, which is one of the tools used in environmental protection. Its application facilitates performance of tests concerning the assessment of olfactory properties of odorants in polluted air.

Electronic noses in classification and quality control of edible oils: A review

The growing demand for wholesome and nutritious food leads to intensification of production, which in turn can have a detrimental effect on quality and well-being of consumers. For that reason, it is important to develop novel methods of food control which would be characterized by a short time of analysis, adequate sensitivity and relatively low cost. One such technique involves the use of multi-sensory devices called electronic noses. In recent years there has been a rapid development of this method, especially in the area of food control. Electronic olfaction can be successfully used in the analysis of edible oils, in particular in the determination of the products geographical origin, and in detection of adulteration as well as deterioration caused by external factors.

Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment

The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process.

Differentiation Between Spirits According to Their Botanical Origin

Agricultural distillates are used for the production of spirit beverages. As more and more products with a specific botanical composition specified on the label are produced (e.g. rye vodkas, wheat vodkas and Polish Vodka, which cannot be produced with the addition of maize distillate), it is necessary to look for quicker and more accurate methods allowing the determination of botanical origin of alcoholic beverages and materials used to their production. The aim of this study is to verify the usefulness of the electronic nose based on the ultra-fast gas chromatography for quick differentiation between agricultural distillates according to their botanical origin. Nineteen samples included distillates from rye, triticale, wheat and maize were used for the research. For data analysis, the following methods were used: PCA—Principal Component Analysis, DFA—Discriminant Function Analysis, SIMCA classification (SIMCA—Soft Independent Modeling of Class Analogies) and SQC—Statistical Quality Control. The use of the electronic nose with ultra-fast gas chromatography and the use of DFA and SIMCA analyses allowed differentiation between agricultural distillates according to their botanical origin.

The Analysis of Vodka: A Review Paper

Vodka is the most popular alcoholic beverage in Poland, Russia and other Eastern European countries, made from ethyl alcohol of agricultural origin that has been produced via fermentation of potatoes, grains or other agricultural products. Despite distillation and multiple filtering, it is not possible to produce 100 % ethanol. The solution with a minimum ethanol content of 96 %, which is used to produce vodkas, also contains trace amounts of other compounds such as, esters, aldehydes, higher alcohols, methanol, acetates, acetic acid and fusel oil. Regarding that fact, it is very important to carry on research on the analysis of the composition and verifying the authenticity of the produced vodkas. This paper summarizes the studies of vodka composition and verifying the authenticity and detection of falsified products. It also includes the methods for analysing vodkas, such as: using gas, ion and liquid chromatography coupled with different types of detectors, electronic nose, electronic tongue, conductivity measurements, isotope analysis, atomic absorption spectroscopy, near infrared spectroscopy, spectrofluorometry and mass spectrometry. In some cases, the use of chemometric methods and preparation techniques were also described.