• Food Engineering Progress
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• v.13 no.4
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• pp.275-281
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• 2009

Sesame oils are partially mixed with other vegetable oils due to high price in a Korean market. To find out authentic sesame oil, a mass spectrometer-based electronic nose (MS-based E-nose) was used. Sesame oil (Se) was blended with soybean oil (So) or corn oil (Co) at the ratio (Se:So, Se:Co) of 97:3, 94:6, 91:9, 88:12 and 85:15, respectively. Intensities of each fragment from sesame oil by MS-based E-nose were completely different from those of soybean oil or corn oil. The obtained results were used for discriminant function analysis (DFA). Volatile organic components (VOC) of soybean oil or corn oil were similar to those of fresh air and DFA plot indicated a significant separation of pure sesame oil and pure other oil. The group of the mixed oil was seperated with that of sesame oil in DFA plot and the added amount of soybean oil to sesame oil was correlated with discriminant function first score (DF1). MS based E-nose system could be used as an efficient method to investigate the purity of sesame oil.

• Food Science of Animal Resources
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• v.31 no.2
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• pp.273-279
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• 2011

Various honeys from different sources were analyzed using an electronic nose based on a mass spectrometer. Various honeys were separated with different mixing ratios. Wild honey and artificial honey were blended at ratios of 100:0, 95:5, 90:10, 85:15, 80:20, 75:25, and 70:30, respectively. Data obtained from the electronic nose were used for discriminant function analysis (DFA). The DFA plot indicated a significant separation of honey from different sources. As the concentration of artificial honey increased, the first discriminant function score (DF1) moved from positive to negative (DF1: $r^2$=0.9962, F=490.6; DF2: $r^2$=0.9128, F=19.44). Furthermore, when acacia honey was mixed with artificial honey and separated with the mixing ratios, the DF scores were: DF1: $r^2$=0.9957, F=396.64; DF2: $r^2$=0.9447, F=29.3. When artificial honey was added to wild honey, it was possible to predict the following equation; DF1= -0.106${\times}$(concentration of artificial honey)+0.426 ($r^2$= 0.96). For acacia honey, the DF1= -0.112${\times}$(concentration of artificial honey)+0.434 ($r^2$=0.968).

• Korean Journal of Food Science and Technology
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• v.48 no.2
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• pp.187-191
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• 2016

The objective of this study was to analyze the volatile compounds in rice under various milling degrees using a mass spectrometry-based electronic nose and discriminant function analysis (DFA). Less volatile components were more frequently found in rice with a lower milling degree. Milling degree resulted in a shift of DF1 to the left side of the DFA plot. This indicated that the DF1 scores were correlated with the milling degree of rice. Brown rice was found to have more volatile components regardless of the milling degree. Thus, rice prepared at different milling degrees could be effectively discriminated with electronic nose analysis. Moreover, more volatile components were detected with an increase in storage time. A slight change in volatile components was found with an increase in the milling degree. The electronic nose could predict the milling degree and storage time of rice.

• Korean Journal of Food Science and Technology
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• v.43 no.1
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• pp.105-109
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• 2011

To determine mixing ratios for mixtures of rapeseed oil and other oils, an electronic nose (E-nose) based on a mass spectrometer system was used. Rapeseed oil was blended with soy bean oil or corn oil at ratios of 100:0, 97:3, 94:6, 91:9, 88:12, 85:15, and 80:20, respectively. The intensities of each fragment from the mixed rapeseed oil by E-nose based on MS were completely different from those of the soy bean oil and corn oil. The obtained data were used for discriminant function analysis (DFA). DFA plots indicated a significant separation of pure rapeseed oil and soy bean oil or corn oil and their mixtures. The added concentration of soy bean oil or corn oil to rapeseed oil was highly correlated to the first discriminant function score (DF1). When soy bean oil was added to rapeseed oil, it was possible to predict the following equation: DF1=-0.170*conc. of soy bean oil+0.431 ($r^2=0.989$). For corn oil the equation was: DF1=-0.1*conc. of corn oil+0.4 ($r^2=0.844$). The use of an E-nose based on a MS system is as an efficient method for the authentication of pure rapeseed oil.

• Korean Journal of Food Science and Technology
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• v.43 no.3
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• pp.291-302
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• 2011

The effects of roasting condition and storage time on rancidity of rapeseed oil were studied. Rapeseed oil from rapeseed roasted under different conditions were stored in the dark at $17^{\circ}C$. Volatile compounds of rapeseed oil were analyzed with an electronic nose (E-nose) and gas chromatography-mass spectrometry (GC-MS). The data from the E-nose were analyzed using discriminant function analysis (DFA). As roasting temperature increased from 150 to $240^{\circ}C$ over 20 min, the first discriminant function score (DF1) moved from positive to negative. DF1 decreased with storage time and changes in DF1 were higher between 0 and 2 days and between 20 and 24 days. Twenty-four compounds were identified in rapeseed oil, and hydrocarbons, furans, ketones, acids, benzene, and aldehydes were detected by GC-MS. The number of formed volatile compounds increased as storage time increased, but no increase in these compounds was detected by GC-MS.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.2
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• pp.98-105
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• 2018

The sale of brown rice batches composed of rice produced in different years is prohibited in Korea. Thus, new methods for the identification of the year of production are critical for maintaining the distribution of high quality brown rice. Here, we describe the exploitation of an enzyme that can be used to discriminate between freshly harvested and one-year-old brown rice. The degree of enzyme activity was visualized through freshness test with Guaiacol, Oxydol, and p-phenylenediamine reagents. With electronic eye equipment, we selected 29 color codes for identifying new brown rice and old brown rice. The discrimination power of selected color codes showed a minimum of 0.263 to a maximum of 0.922 and an average value of 0.62. The accuracy with which new brown rice and old brown rice could be identified was 100% in principal component analysis (PCA) and discriminant function analysis (DFA). The DFA analysis had greater discriminatory power than did the PCA analysis. A verification test using new brown rice, old brown rice, or a mixture of the two was then performed to validate our method. The accuracy of identification of new and old brown rice was 100% in both cases, whereas mixed brown rice samples were correctly classified at a rate of 96.9%. Additionally, in order to test whether the discriminant constructed in winter can be applied to samples collected in summer, new and old brown rice stored for 8 months were collected and tested. Both new and old brown rice collected in summer were classified as old brown rice and showed 50% identification accuracy. We were able to attribute these observations to changes in enzyme content over time, and therefore we conclude, it will be necessary to develop discriminants that are specific to distinct storage periods in the near future.

• Korean Journal of Food Science and Technology
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• v.41 no.6
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• pp.609-614
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• 2009

Sesame oil was sometimes replaced by mixed oil due to high price in Korean market. To find out authentic sesame oil, electronic nose (E-nose) based on mass spectrometer system was used. Sesame oil was blended with perilla oil at the ratio of 97:3, 94:6, 91:9, 88:12 and 85:15, respectively. Intensities of each fragment from sesame oil by E-nose based on MS were completely different from those of perilla oil. The obtained data was used for discriminant function analysis. For quantitative analysis, the partial least square algorithm was used. The added concentration of perilla oil to sesame oil was correlated with discriminant function first score (DF1) and second score (DF2). From this relationship it could be found out how much perilla oil added. DFA plot indicated a significant separation of pure sesame oil and pure perilla oil. The different geographical origin of sesame oil was used for blending with perilla oil were closed to that of sesame oil. Korean sesame oil mixture and Indian sesame oil one were well separated. And the correlation between mixing ratios and DF1 values was found at the ratio of 97:3, 91:9, and 85:15 (SE vs PE oil), respectively. But the added concentration of perilla oil to sesame oil was correlated with discriminant function first score (DF1). E-nose based on MS system could be used as an efficient method for purity of oil quality.

• Food Engineering Progress
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• v.14 no.1
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• pp.35-40
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• 2010

Electronic nose (E-nose) was assessed for grading pungency of powdered red pepper. Complex pretreatments are not required for flavor analysis unlike HPLC or Scoville tests. Mild and pungent taste of powdered red pepper were mixed at various concentrations of 0, 25, 50, 75, and 100%. Those were analyzed using mass spectrometer-based E-nose. Discriminant function analysis (DFA) was conducted on E-nose data. The $R^{2}$ and F-value of dicriminant function first score (DF1) were 0.9946 and 355.65, respectively, when the samples were separated by a relative degree of pungent taste. DF1 value decreased with increasing the amount of powdered red pepper with a pungent taste. It is similar to the increase in the concentration of capsaicin. Increasing the amount of red pepper powder, dicriminant function second score (DF2) values were moved from the negative position into the positive position. The $R^{2}$ and F-value of DF1 were 0.9890, 165.17 and DF2 were 0.9219, 21.64. Also, the results by MS based E-nose agreed to that by HPLC. There is the potential to grade pungent taste of powdered red pepper using the E-nose.

• FLOWER RESEARCH JOURNAL
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• v.19 no.4
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• pp.219-224
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• 2011

Phalaenopsis (P.) has various species, and some of them have strong fragrance. There are fragrant species such as P. bellina, P. violacea, P. schilleriana and used in breeding program for fragrant Phalaenopsis. This study was performed for establishment of fragrance analysis system using GC-MS and electronic nose in eight P. resources. We analyzed fragrant compound using the tissue of sepal, petal, column, and lip of P. '3010'. The percentage of the major compound was high in the petal and lip tissues. The main compound emitted from P. bellina was linalool (21.21%). It was possible that fragrance pattern could be analyzed among the resources using the electronic nose. Discriminant function analysis (DFA) was more useful than the principal component analysis (PCA) in statistics program. We utilized GC-MS method for the major compounds of flower from our breeding materials. This study would be useful to the fragrant analysis system for the fragrant orchid breeding in the future.