• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.38-48
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• 2020

The aroma pattern was analyzed using electronic nose to examine the possibility of origin discrimination according to the mixing ratio of Chinese and Korean red ginseng concentrates. The origin of Chinese red ginseng concentrate and Korea red ginseng concentrate could be distinguished and the pattern of aroma component detected decreased as the mixing ratio of Chinese red ginseng concentrate increased. Cultivar and habitat of Korean red ginseng concentrated was remarkably distinguished by the chromatogram of frequency pattern, derivative pattern and visual pattern using olfactory images known as vapor print^TM.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.856-860
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• 2005

Electronic-nose system was used to discriminate commercial sesame oils (A-F) extracted from imported seeds. Response (delta $R_{gas}/R_{air}$) of sensors gained from electronic nose was analyzed by principal component analysis (PCA). Flavor pattern of sesame oil A was similar to those of sesame oils B, C, and D. Sesame oils blended with corn oil at the ratio of 95:5, 90:10 and 80:20% (sesame oil/corn oil, w/w) could be discriminated from ouch genuine sesame oil.

• Korean Journal of Food Science and Technology
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• v.34 no.1
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• pp.13-17
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• 2002

The study was to analyze aroma patterns of Gastrodiae Rhizoma by the electronic nose with conducing polymer 32 sensors. Response by the electronic nose was analyzed by the principal component analysis (PCA). Sensory evaluation for the organoleptic characteristics of Gastrodiae Rhizoma was also performed. Raw Gastrodiae Rhizoma was very intensive in overall odor and taste. Hot air-dried $60^{\circ}C$ Gastrodiae Rhizoma was relatively weak in overall odor and taste. Intensity of aroma in the electronic nose was the highest in $60^{\circ}C$ hot air-dried Gastrodiae Rhizoma. As quality factor (QF) calcuated from PCA map of normalized pattern data by thirty two sensors showed less than 2, and so aroma pattern among raw, freezed-dried, and hot-air dried Gastrodiae Rhizoma had no difference. When PCA was performed for normalized pattern data by the selected sensitive ten sensors, QF value between raw and $60^{\circ}C$ hot air-dried Gastrodiae Rhizoma was 2.366. Thus aroma pattern of raw and $60^{\circ}C$ hot air-dried Gastrodiae Rhizoma was discriminated in electronic nose.

• Journal of Ginseng Research
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• v.41 no.1
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• pp.85-95
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• 2017

Background: American ginseng (Panax quinquefolius L.) and Asian ginseng (Panax ginseng Meyer) products, such as slices, have a similar appearance, but they have significantly different prices, leading to widespread adulteration in the commercial market. Their aroma characteristics are attracting increasing attention and are supposed to be effective and nondestructive markers to determine adulteration. Methods: The aroma characteristics of American and Asian ginseng were investigated using gas chromatography-mass spectrometry(GC-MS) and an electronic nose (E-nose). Their volatile organic compounds were separated, classified, compared, and analyzed with different pattern recognition. Results: The E-nose showed a good performance in grouping with a principle component analysis explaining 94.45% of variance. A total of 69 aroma components were identified by GC-MS, with 35.6% common components and 64.6% special ingredients between the two ginsengs. It was observed that the components and the number of terpenes and alcohols were markedly different, indicating possible reasons for their difference. The results of pattern recognition confirmed that the E-nose processing result is similar to that of GC-MS. The interrelation between aroma constituents and sensors indicated that special sensors were highly related to some terpenes and alcohols. Accordingly, the contents of selected constituents were accurately predicted by corresponding sensors with most $R^2$ reaching 90%. Conclusion: Combined with advanced chemometrics, the E-nose is capable of discriminating between American and Asian ginseng in both qualitative and quantitative angles, presenting an accurate, rapid, and nondestructive reference approach.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.69-77
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• 2005

The electronic nose (e-nose) has been used in food industry and quality controls in plastic packaging. Recently it finds its applications in medical diagnosis, specifically on detection of diabetes, pulmonary or gastrointestinal problem, or infections by examining odors in the breath or tissues with its odor characterizing ability. Moreover, the use of portable e-nose enables the on-site measurements and analysis of vapors without extra gas-sampling units. This is expected to widen the application of the e-nose in various fields including point-of-care-test or e-health. In this study, a PDA-based portable e-nose was developed using micro-machined gas sensor array and miniaturized electronic interfaces. The rich capacities of the PDA in its computing power and various interfaces are expected to provide the rapid and application specific development of the diagnostic devices, and easy connection to other facilities through information technology (IT) infra. For performance verification of the developed portable e-nose system, Six different vapors were measured using the system. Seven different carbon-black polymer composites were used for the sensor array. The results showed the reproducibility of the measured data and the distinguishable patterns between the vapor species. Additionally, the application of two typical pattern recognition algorithms verified the possibility of the automatic vapor recognition from the portable measurements. These validated the portable e-nose based on PDA developed in this study.

• Proceedings of the Ginseng society Conference
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• 2002.10a
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• pp.238-252
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• 2002

In order to screen out indicators for the discrimination of ginseng habitat, some physical and chemical characteristics of Korean red ginsengs (94 kinds) and Chinese red ginsengs (50 kinds) were analyzed by using a rheometer, an electronic nose system, a combined technique of solid phase micro-extraction (SPME) and gas chromatograph equipped with an electron capture detector (GC/ECD), an X-ray fluorescence spectrometer (XRF), an inductively coupled plasma mass spectrometer (ICP/MS), a near infrared spectrometer (NIRs) and high performance liquid chromatography equipped with evaporative light scattering detector (HPLC/ELSD). The results are summarized as follows: (i) The rhizome strengths of Korean red ginsengs were significantly higher than those of Chinese red ginsengs. (ii) The electronic nose patterns of Korean red ginsengs were significantly different from those of Chinese red ginsengs. (iii) Some unidentified peaks were detected not in the headspace of Korean red ginsengs but in the headspace of Chinese red ginsengs when the headspace volatiles prepared by the SPME technique were analyzed by GC/ECD. (iv) Either the content ratios of K to Ca or Mn to Fe were significantly different between Korean red ginsengs and Chinese red ginsengs. (v) The reflectance ratios of NIRs wavenumbers such as $904\;cm^{-1}\;to\;1088\;cm^{-1}$ for Korean red ginsengs were significantly different from those for Chinese red ginsengs. (vi) The content ratios of ginsenoside-Rg to ginsenoside-Re of Korean red ginsengs were significantly higher than those of Chinese red ginsengs. These results indicate that the rhizome strength, the electronic nose pattern, the occurrence of ECD-sensitive headspace volatile components, the content ratios of K to Ca and Mn to Fe, the NIRs pattern and the content ratio of ginsenoside-Rg to -Re may be indicators for the discrimination of ginseng habitat.

• Korean Journal of Food Science and Technology
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• v.32 no.1
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• pp.9-16
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• 2000

Aroma of various extracts of Agastache rugosa O. Kuntze was analyzed by electronic nose with 32 conducting polymer sensor arrays. The 57 extracts were prepared by extraction solvents (hot water, ethanol and NaCl solution), extraction temperatures $(100,\;80\;and\;60^{\circ}C)$, solvent mixture ratios of solvent (10 times 35 times) and parts of Agastache rugosa O. Kuntze(flower, leaf and stem). Aroma pattern of Agastache rugosa O. Kuntze extracts showed big difference in normalized pattern and odor intensity with extraction temperatures and parts, but showed no difference with extraction solvents. Especially in the case of ethanol extracts, because odor of ethanol itself was very strong, difference in aroma of extracts with extraction temperatures and parts did not show through the electronic nose. The organoleptic characteristics such as mint odor, grassy odor, mint taste, medicinal herb taste and sweetness for Agastache rugosa O. Kuntze extracts were determined by the profile test and the result of sensory evaluation by quantitative descriptive analysis was explained to QDA diagram. In correlation with the result of aroma analysis by electronic nose and the sensory evaluation, difference in aroma pattern among the extracts concretely brought to light definite characteristics such as mint odor and mint taste.

• Korean Journal of Food Science and Technology
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• v.48 no.2
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• pp.117-121
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• 2016

The objective of this study was to analyze the flavor patterns of commercial Korean distilled spirits using a mass spectrometry-based electronic nose and an electronic tongue. Thirteen types of domestic distilled spirits were used for analysis. The flavor patterns of the distilled spirits were clearly identified through discriminant function analysis of the raw materials. The obtained data of the electronic nose analysis showed that the flavor pattern of the distilled spirits aged in oak barrels was not significantly different from that of the general distilled spirits. According to the results of the electronic tongue, the distilled spirits made with rice had a stronger flavor than those made with foxtail millet. Compared to the sensitivity value of each taste sensor, the distilled spirits showed various intensities of sourness and umami. Therefore, an electronic nose and electronic tongue could be successfully used for quality evaluation and new product development of distilled spirits.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.329-334
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• 2004

The electronic nose (E-nose) recently finds its applications in medical diagnosis, specifically on detection of diabetes, pulmonary or gastrointestinal problem, or infections by examining odors in the breath or tissues with its odor characterizing ability. The odor recognition performance of E-nose can be improved by manipulating the sensor array responses of vapors in time-profile forms. The different chemical interactions between the sensor materials and the volatile organic compounds (VOC's) leave unique marks in the signal profiles giving more information than collection of the conventional piecemal features, i.e., maximum sensitivity, signal slopes, rising time. In this study, to use them in vapor recognition task conveniently, a novel time-profile method was proposed, which is adopted from digital image pattern matching. The degrees of matching between 8 different vapors were evaluated by using the proposed method. The test vapors are measured by the silicon-based gas sensor array with 16 CB-polymer composites installed in membrane structure. The results by the proposed method showed clear discrimination of vapor species than by the conventional method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.85-90
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• 2008

Recently, with the sensor technology, research about the electronic nose system which imitated the olfactory organ are being pushed actively. But, in case of general electronic nose system, an aroma is measured at the laboratory space where blocked external environment and is analyzed a part of measured data. In this paper, we propose the system which can measure and recognize an aroma in natural environment. We propose the Entropy algorithm which can detect the sensor reaction section among the continuous detection processing about an aroma. And we implement the aroma recognition system using the PCA(Principal Components Analysis) and K-NN(K-Nearest Neighbor) about the detected aroma. In order to evaluate the performance, we measured the aroma pattern, about 9 aroma oil, 50 times respectively. And we experimented the aroma detection and recognition using this. There was an error of 0.2s in the aroma detection and we get 84.3% recognition rate of the aroma recognition.