Search

Essential oils are widely used in the food industry as natural food preservatives to extend product shelf life and as flavoring agents. However, not much has been done on their use in soymilk. The aim of the study is to determine the compounds of five essential oils by GC-MS and their effect on fungal growth inhibition and sensory acceptability of soymilk. The components of the essential oils of five spices, namely citronella, basil, cinnamon, eucalyptus and mint were analysed by gas chromatography-mass spectrometry (GC-MS). The minimum inhibitory concentration (MIC) of the essential oils was tested on the fungus Aspergillus flavus 3.4408 on PDA (agar dilution method). Sensory evaluation of soymilk flavored with the essential oils of citronella, basil and mint at different concentrations was done by ten member panelists using a 9-point hedonic scale. The main compound for basil was eugenol 83.26%. Cinnamon contained cinnamaldehyde (97.3%). The main compounds in citronella (Cymbopogon nardus) were limonene (38.51%), citronellal (30.29%). Eucalyptus (Eucalyptus globulus) essential oil mainly contained eucalyptol/cineole (76.70%), and Mint (Mentha arvensis): Menthol 42.72%, Menthone 25.72%. The MICs of citronella, basil, cinnamon, eucalyptus and mint were 5-10 µl/ml, 0.5-1 µl/ml, ≤0.1 µl/ml, >>10 µl/ml and 10-20 µl/ml, respectively. Thus, cinnamon was the most effective in inhibiting fungal growth, while eucalyptus was the least effective. These essential oils improved the soymilk flavor. Mint was the most preferred flavor, followed by citronella and basil. Thus, essential oils especially mint and citronella can be used for improving acceptability of soymilk at low concentration.

Urban vegetation is considered a crucial factor for realizing environmental quality goals and foreseeing sustainable local development in the framework of Local Agenda21 guiding environmental politics. High-resolution image offer abundance function in improving method of urban environment and guideline of evaluation. The method of Object-oriented classification, considered both spectral characters and structural information, can provide high-resolution images' classification precision and reduce large data redundancy compared with the traditional pixel-based classification methods. In this paper, we use object-oriented method to extract different green space based on various urban environmental land use, such as commonality green, street green and so on.. The QuickBird image is segmented to a set of different scale image, we have make many experiment in order to obtain the optimal segmentation of every level, so the image is segmented by scale 6, and then each object is classified into non-vegetation and 3 kinds of vegetation classes based on the nearest neighbour classifier (NN), Due to the effect of mixed pixels, the second segmentation level scale 5 is carried out into the non-vegetation class and several omitted vegetation objects are derived. Finally, we use a GIS mapping data combined with the results of vegetation classification to separate urban vegetation area into different usages (i.e. commonality green, street green...). Through analyse to the result of classification, the area of the vegetation is 597.63 hm2, the urban garden fraction is 25.38%. This method offers a new application approach for classifying the high-resolution remote sensing image and the result of accuracy can reach 98%.