Improving the performance of an electronic nose by wine aroma training to distinguish between drip coffee and canned coffee.

Coffee aroma, with more than 600 components, is considered as one of the most complex food aromas. Although electronic noses have been successfully used for objective analysis and differentiation of total coffee aromas, it is difficult to use them to describe the specific features of coffee aroma (i.e., the type of smell). This is because data obtained by electronic noses are generally based on electrical resistance/current and samples are distinguished by principal component analysis. In this paper, we present an electronic nose that is capable of learning the wine related aromas using the aroma kit "Le Nez du Vin," and the potential to describe coffee aroma in a similar manner comparable to how wine experts describe wine aroma. The results of our investigation showed that the aromas of three drip coffees were more similar to those of pine and honey in the aroma kit than to the aromas of three canned coffees. Conversely, the aromas of canned coffees were more similar to the kit coffee aroma. In addition, the aromatic patterns of coffees were different from those of green tea and red wine. Although further study is required to fit the data to human olfaction, the presented method and the use of vocabularies in aroma kits promise to enhance objective discrimination and description of aromas by electronic noses.

Discrimination method of the volatiles from fresh mushrooms by an electronic nose using a trapping system and statistical standardization to reduce sensor value variation.

Electronic noses have the benefit of obtaining smell information in a simple and objective manner, therefore, many applications have been developed for broad analysis areas such as food, drinks, cosmetics, medicine, and agriculture. However, measurement values from electronic noses have a tendency to vary under humidity or alcohol exposure conditions, since several types of sensors in the devices are affected by such variables. Consequently, we show three techniques for reducing the variation of sensor values: (1) using a trapping system to reduce the infering components; (2) performing statistical standardization (calculation of z-score); and (3) selecting suitable sensors. With these techniques, we discriminated the volatiles of four types of fresh mushrooms: golden needle (Flammulina velutipes), white mushroom (Agaricus bisporus), shiitake (Lentinus edodes), and eryngii (Pleurotus eryngii) among six fresh mushrooms (hen of the woods (Grifola frondosa), shimeji (Hypsizygus marmoreus) plus the above mushrooms). Additionally, we succeeded in discrimination of white mushroom, only comparing with artificial mushroom flavors, such as champignon flavor and truffle flavor. In conclusion, our techniques will expand the options to reduce variations in sensor values.

[Angle-closure glaucoma: important points in the diagnosis and follow-up].

A 74-year-old woman visited her local physician complaining of fever, cough, headache, hyperemic left conjunctiva, and blurred vision. She was diagnosed as having common cold and medicated for it, and later, she visited our department. She had a shallow left anterior chamber with moderately dilated pupil. Gonioscopic examination of the left eye revealed a narrow angle corresponding to grade 1 by Shaffer grading system. Left intraocular pressure was 16 mmHg. She was diagnosed to have had a spontaneous recovery from a subacute attack of angle-closure glaucoma. Laser iridectomy was performed and she was put on a regular ophthalmological follow-up. Three years later, she experienced an attack of left angle-closure glaucoma secondary to lens intumescence. She was treated by phacoemulsification and aspitaion which resulted in the eventual cure.