Electronic nose based tea quality standardization

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

Electronic Sensor Technologies in Monitoring Quality of Tea: A Review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.

The Implications of Post-Harvest Storage Time and Temperature on the Phytochemical Composition and Quality of Japanese-Styled Green Tea Grown in Australia: A Food Loss and Waste Recovery Opportunity

The increases in consumer awareness of the potential health benefits of green tea have driven global demand for green tea products. This study investigated the effect of post-harvest processing and storage of Japanese-styled green tea (Camellia sinensis var. sinensis) grown in NSW, Australia. Harvested material underwent a processing delay of 6, 12, 18 or 24 h at temperatures of 0, 5 and 25 °C. Targeted green tea constituents: theanine, caffeine and catechins were determined using HPLC with UV detection. Product quality and commercial value were determined using the Quality Index (QI) Tool. Reductions in constituent levels were evident within all storage delays, with nominal quality preservation achieved by reducing the temperature. The green tea material stored at 25 °C for 24 h created the most commercially valued product, despite it having visual characteristics more akin to a semi-fermented tea. These visual characteristics are traditionally considered markers of green tea damage and are discarded; however, QI-Tool scoring suggests that this raw material presents as a commercially favourable source of food loss and waste (FLW). The findings of this study extend our understanding of post-harvest processing delays and storage on green tea quality and suggest the viability of a commercially valuable semi-fermented produced from FLW.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

Diagnosis of tuberculosis through breath test: A systematic review

Background

Breath tests may diagnose tuberculosis (TB) through detecting specific volatile organic compounds produced by Mycobacterium tuberculosis or the infected host.

Methods

To estimate the diagnostic accuracy of breath test with electronic-nose and other devices against culture or other tests for TB, we screened multiple databases until January 6, 2019.

Findings

We included fourteen studies, with 1715 subjects in the analysis. The pooled sensitivity and specificity of electronic-nose were 0.93 (95% CI 0.82–0.97) and 0.93 (95% CI 0.82–0.97), respectively, and no heterogeneity was found. The sensitivity and specificity of other breath test devices ranged from 0.62 to 1.00, and 0.11 to 0.84, respectively.

Interpretation

The low to moderate evidence of these studies shows that breath tests can diagnose TB accurately, however, to give a real-time test result, additional development is needed. Research should also focus on sputum smear negative TB, children, and the positioning of breath testing in the diagnostic work flow.

Funding

The authors received no specific funding for this work.

Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

OBJECTIVE

To investigate the potency of a hand-held point-of-care electronic-nose to diagnose pulmonary tuberculosis (PTB) among those suspected of PTB.

METHODS

Setting: Lung clinics and Dr. Sardjito Hospital, Yogyakarta, Indonesia. Participants: patients with suspected PTB and healthy controls. Sampling: 5 minutes exhaled breath. Sputum-smear-microscopy, culture, chest-radiography, and follow-up for 1.5-2.5 years, were used to classify patients with suspected PTB as active PTB, probably active PTB, probably no PTB, and no PTB. After building a breath model based on active PTB, no PTB, and healthy controls (Calibration phase), we validated the model in all patients with suspected PTB (Validation phase). In each variable (sex, age, Body Mass Index, co-morbidities, smoking status, consumption of alcohol, use of antibiotics, flu symptoms, stress, food and drink intake), one stratum's Receiver Operating Characteristic (ROC)-curve indicating sensitivity and specificity of the breath test was compared with another stratum's ROC-curve. Differences between Area-under-the-Curve between strata (p<0.05) indicated an association between the variable and sensitivity-specificity of the breath test. Statistical analysis was performed using STATA/SE 15.

RESULTS

Of 400 enrolled participants, 73 were excluded due to extra-pulmonary TB, incomplete data, previous TB, and cancer. Calibration phase involved 182 subjects, and the result was validated in 287 subjects. Sensitivity was 85% (95%CI: 75-92%) and 78% (95%CI: 70-85%), specificity was 55% (95%CI: 44-65%) and 42% (95%CI: 34-50%), in calibration and validation phases, respectively. Test sensitivity and specificity were lower in men.

CONCLUSION

The electronic-nose showed modest sensitivity and low specificity among patients with suspected PTB. To improve the sensitivity, a larger calibration group needs to be involved. With its portable form, it could be used for TB screening in remote rural areas and health care settings.

Odor characteristics of white croaker and small yellow croaker fish during refrigerated storage

White croaker and small yellow croaker both belong to the fish family Sciaenidae, but their economic value and odor characteristics are quite different. In this study, electronic nose and gas chromatography-mass spectrometry were utilized to explore the odor characteristics of the two stored for different refrigeration periods. The results showed that their odor profiles could be clearly distinguished by principal component analysis. Compounds associated with fresh white croaker were found to be more complex than smaller yellow croaker through the load graph, while the result was opposite in later cold storage. The absolute peak areas of compounds like trimethylamine and 3-methyl-butanol were 6.42 and 1.42, respectively, in the white croaker, which were higher than in the small yellow croaker at the first day of refrigeration. And compound such as indole was first produced in white croaker during late cold storage. However, there were more compounds related to spoilage in the small yellow croaker; compounds like phenylethyl alcohol and benzeneacetaldehyde were not detected in the white croaker. PRACTICAL APPLICATIONS: White croaker and small yellow croaker are almost indistinguishable in appearance, especially after being cooked. But there are vast differences in their meat quality and odor characteristics, which affect their commercial values. As a result, a lot of white croakers are dyed and sold as small yellow croakers, although this does not change their eating or odor qualities. Principal component analysis of the odor characteristics of the two species of fish stored for different periods of refrigeration might provide some scientific basis for exploring the causes of their economic value differences.

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

Analyte discrimination with chemically diverse sensor array based on electrocopolymerized pyrrole and vinyl derivatives

The development of chemically diverse arrays of sensing elements has gained the attention of researchers due to their anticipated capacity to mimic the function of olfactory receptors in the mammalian olfactory system.

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview

Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

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.

Quality control of Lonicera japonica stored for different months by electronic nose

The objective of this study was to investigate the potential of the electronic nose (E-nose) technique for monitoring the storage time and quality of L. japonica. An E-nose was used to detect odors of L. japonica samples during a storage period of 16 months. Linear discriminant analysis (LDA) and radial basis function (RBF) neural network were performed to differentiate L. japonica samples stored for different months. The content of chlorogenic acid of L. japonica was determined to confirm the quality changes and investigate its correlation with the odor response values. Results showed that L. japonica samples of different storage months could be classified correctly by LDA and RBF neural network. The change trends of the odor response and the content of chlorogenic acid had both declined along with the storage time. Also, there was a significant correlation (p=0.000) between the odor index and the content of chlorogenic acid. In conclusion, the odor intensity could reflect the quality of L. japonica to a certain degree. The E-nose technique could be used as a rapid, simple, sensitive and effective method for the quality control of L. japonica.

Facile route for the synthesis of a vertically aligned ZnO-PANI nanohybrid film for polyphenol sensing

Vertically aligned zinc oxide (ZnO) nanorods have been fabricated on a polyaniline (PANI) film template after electrochemical seeding and hydrothermal growth in a nutrient medium at a low temperature of 65 °C. Dense c-oriented [0001], hexagonal-shaped, vertically aligned ZnO nanorods are obtained on the PANI film surface, which is confirmed by X-ray diffraction and scanning electron microscopy studies. The nanohybrid film used as the working electrode has been characterized for sensing catechin polyphenol in different tea varieties through cyclic voltammetry. Principal component analysis shows enhancement in the classification ability of the nanohybrid film for various concentrations of catechin standard and tea infusions.

Rapid discrimination of Chinese red ginseng and Korean ginseng using an electronic nose coupled with chemometrics

Red ginseng is a precious and widely used traditional Chinese medicine. At present, Chinese red ginseng and Korean ginseng are both commonly found on the market. To rapidly and nondestructively discriminate between Chinese red ginseng and Korean ginseng, an electronic nose coupled with chemometrics was developed. Different red ginseng samples, including Chinese red ginseng (n=30) and Korean ginseng (South Korean red ginseng and North Korean red ginseng n=26), were collected. The metal oxide sensors on an electronic nose were used to measure the red ginseng samples. Multivariate statistical analyses, including principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modeling of class analogy (SIMCA), were employed. All of the samples were analyzed by PCA. Most of the samples were used to set up DFA and SIMCA models, and then the remaining samples (Nos. 9, 10, 17, 18, 29, 30, 34, 43, 44, 50, and 51) were projected onto the DFA and SIMCA models in the form of black dots to validate the models. The results indicated that Chinese red ginseng and Korean ginseng were successfully discriminated using the electronic nose coupled with PCA, DFA and SIMCA. The checking scores of the DFA and SIMCA models were 100. The samples projected onto the DFA and SIMCA models were all correctly discriminated. The DFA and SIMCA models were robust. Electronic nose technology is a rapid, accurate, sensitive and nondestructive method to discriminate between Chinese red ginseng and Korean ginseng.

Optimized Neural Network for Instant Coffee Classification through an Electronic Nose

Flavor is one of the most important features of food, especially of coffee. The evaluation of this sensory feature is complex yet indispensable in quality control of instant coffees. In this work, an artificial neural network (ANN) was developed for instant coffee classification based on an electronic nose (EN) aroma profile. To this purpose, a hybrid algorithm was developed, containing: bootstrap resample methodology; factorial design and sequential simplex optimization to tune network parameters; an ensemble multilayer perceptron (MLP) trained with backpropagation for coffee classification; and causal index procedure for knowledge extraction from the trained ANN. The produced neural network classifier correctly recognizes 100% of coffees studied. Furthermore, the causal index employment allowed inference of some rules on how the coffees were separated according to the sensors available in EN. The results indicate that the applied methodology is a promising tool for instant coffee quality control.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

Decoding complex chemical mixtures with a physical model of a sensor array

Combinatorial sensor arrays, such as the olfactory system, can detect a large number of analytes using a relatively small number of receptors. However, the complex pattern of receptor responses to even a single analyte, coupled with the non-linearity of responses to mixtures of analytes, makes quantitative prediction of compound concentrations in a mixture a challenging task. Here we develop a physical model that explicitly takes receptor-ligand interactions into account, and apply it to infer concentrations of highly related sugar nucleotides from the output of four engineered G-protein-coupled receptors. We also derive design principles that enable accurate mixture discrimination with cross-specific sensor arrays. The optimal sensor parameters exhibit relatively weak dependence on component concentrations, making a single designed array useful for analyzing a sizable range of mixtures. The maximum number of mixture components that can be successfully discriminated is twice the number of sensors in the array. Finally, antagonistic receptor responses, well-known to play an important role in natural olfactory systems, prove to be essential for the accurate prediction of component concentrations.

Mammalian and insect olfactory systems are combinatorial in nature - instead of activating a single specialized receptor, each analyte invokes a complex pattern of responses across the receptor array. The advantage of such systems lies in their ability to detect a large number of analytes with a relatively small number of receptors. However, the complexity of array responses to mixtures of analytes makes quantitative prediction of component concentrations a challenging task. Here we show that combinatorial output from an array of four engineered G-protein-coupled receptors can be used to predict the concentration of each component in mixtures of highly related sugar nucleotides. We employ a physical model of ligand-receptor interactions and carry out Bayesian analysis of the array output. Furthermore, our in silico designs of receptor arrays reveal that antagonistic responses, in which the receptor is bound by the ligand but there is no downstream reporter activity, are necessary for precise recognition of mixture components. This conclusion provides a rationale for the widespread inhibitory responses observed in olfactory systems. Our methodology can be employed with both biological systems and artificial receptor arrays (“electronic noses”) designed for various industrial needs.

Can odors of TCM be captured by electronic nose? The novel quality control method for musk by electronic nose coupled with chemometrics

Musk is a precious and wide applied material in traditional Chinese medicine, also, an important material for the perfume industry all over the world. To establish a rapid, cost-effective and relatively objective assessment for the quality of musk, different musk samples, including authentic, fake and adulterate, were collected. A oxide sensor based electronic nose (E-nose) was employed to measure the musk samples, the E-nose generated data were analyzed by principal component analysis (PCA), the responses of 18 sensors of E-nose were evaluated by loading analysis. Results showed that a rapid evaluation of complex response of the samples could be obtained, in combination with PCA and the perception level of the E-nose was given better results in the recognition values of the musk aroma. The authentic, fake and adulterate musk could be distinguished by E-nose coupled with PCA, sensor 2, 3, 5, 12, 15 and 17 were found to be able to better discriminate between musk samples, confirming the potential application of an electronic instrument coupled with chemometrics for a rapid and on-line quality control for the traditional medicines.

Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

Identification of tea storage times by linear discrimination analysis and back-propagation neural network techniques based on the eigenvalues of principal components analysis of e-nose sensor signals

An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.

Solid-phase micro-extraction (SPME-GC) and sensors as rapid methods for monitoring lipid oxidation in nuts

Dry foods with high fat content are susceptible to lipid oxidation, which involves a quality deterioration of the product, since this process is responsible for the generation of off-flavours. Hexanal is considered to be a good shelf-life indicator of such oxidation products. In addition, due to its high volatility, hexanal can be easily determined by fast headspace analytical techniques. For this reason an electronic nose comprising ten metal oxide semiconductors (MOS) and a solid-phase microextraction (SPME) coupled with gas chromatography and flame ionization detector (GC-FID) method were compared in order to determine hexanal formed in hazelnuts during storage under different conditions (room temperature, 40 degrees C, ultraviolet light, with and without oxygen scavenger). The results obtained by the two methods showed a good correlation, confirming the possibility of using a multi-sensor system as a screening tool for the monitoring of shelf-life and oxidation state of nuts.

Relative characterization of rosemary samples according to their geographical origins using microwave-accelerated distillation, solid-phase microextraction and Kohonen self-organizing maps

For centuries, rosemary (Rosmarinus officinalis L.) has been used to prepare essential oils which, even now, are highly valued due to their various biological activities. Nevertheless, it has been noted that these activities often depend on the origin of the rosemary plant and the method of extraction. Since both of these quality parameters can greatly influence the chemical composition of rosemary oil, an original analytical method was developed where "dry distillation" was coupled to headspace solid-phase microextraction (HS-SPME) and then a data mining technique using the Kohonen self-organizing map algorithm was applied to the data obtained. This original approach uses the newly described microwave-accelerated distillation technique (MAD) and HS-SPME; neither of these techniques require external solvent and so this approach provides a novel "green" chemistry sampling method in the field of biological matrix analysis. The large data set obtained was then treated with a rarely used chemometric technique based on nonclassical statistics. Applied to 32 rosemary samples collected at the same time from 12 different sites in the north of Algeria, this method highlighted a strong correlation between the volatile chemical compositions of the samples and their origins, and it therefore allowed the samples to be grouped according to geographical distribution. Moreover, the method allowed us to identify the constituents that exerted the most influence during classification.

Rapid headspace solid-phase microextraction-gas chromatographic-time-of-flight mass spectrometric method for qualitative profiling of ice wine volatile fraction. III. Relative characterization of Canadian and Czech ice wines using self-organizing maps

The determination of volatile and semi-volatile components of ice wine aroma was realized throughout the development of a rapid headspace solid-phase microextraction-gas chromatography-time-of-flight mass spectrometry (SPME-GC-TOF-MS) analytical method (Part I) and its application to the analysis of 137 samples produced in Canada and Czech Republic and collected directly from the producing wineries (Part II). In this Part III study, the complex matrix resulting from the analysis of the 58 compounds selected for each sample as described in Part II, was submitted to critical interpretation by using a self-organizing map (SOM) technique. Results were commented in terms of relative characterization of samples according to their geographical origin, grape varieties, and vintage years. When clear clustering was obtained, the most determinant compounds responsible for the observed differentiations were identified and further discussed.

Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years

In a previous work, the SPME-GC-MS method (chemical analysis) coupled with KSOM-ANN treatment of the results (statistical algorithm) has proved to be efficient to classify 70 strawberry samples harvested in the same year, through the 17 varieties to which they belonged, in a two-dimensional map. As an extension, the present study confirms that these results were not dependent on the year of strawberry production and discusses what effects were observed between results obtained in different years. Samples of different strawberry varieties were harvested during the three campaigns of 2000, 2001, and 2002 and analyzed independently. The chemical data matrix obtained in each case allowed the verification of the proposal that the same discriminative effect could be obtained independently of the year of production by using maps of different sizes. Therefore, 30 measures obtained from samples of 9 varieties in 2000, 54 measures from 13 varieties in 2001, and 80 measures from 20 varieties in 2002 were correctly classified by using 20, 35, and 56 hexagon maps, respectively. In a second analysis based on the 2002 production, the chemical differences between variety aromatic features were noted through the increasing size of the map used. Finally, results relative to 7 varieties cultivated in 2001 and 2002 and stored under exactly the same conditions were computed together for elaborating a single map. An interesting effect of double classification according to the year and the varieties was observed.