文献一覧: 生物工学会誌 : seibutsu-kogaku kaishi (雑誌)

8 0 0 0 パンダから分離した耐熱性酵素群を産生する高温細菌による生ゴミ処理の試み

著者: 田口文章宗国冨張光磊
出版者: 公益社団法人日本生物工学会
雑誌: 生物工学会誌 : seibutsu-kogaku kaishi (ISSN:09193758)
巻号頁・発行日: vol.79, no.12, pp.463-469, 2001-12-15

To establish an efficient method for the microbial treatment of kitchen refuse, experiments were performed to isolate heat-stable multi-enzyme-producing thermophilic bacteria and to verify functional activities of the isolates for the complete decomposition of kitchen refuse. Five Bacillus strains-B.amyloliquefaciens FTP148, B.amyloliquefaciens FTP2414, B.subtilis FTP237, B.Licheniformis FTP136, and B.licheniformis FTP2530-were successfully isolated from feces of the Giant Panda. The strains showed efficient growth at incubation temperatures between 45℃ and 60℃ and produced amylase, protease, and lipase. The highest activities of the amylase and protease produced by the strains at the optimal growth temperature were obtained at reaction temperatures between 50℃ and 60℃. The amylase and protease activities were stable up to maximum temperatures of 75℃ and 70℃, respectively; after incubation at 60℃ for 3 h, 59% and 40%, respectively, of the initial activity remained. Using a commercial waste-treatment device, kitchen refuse was treated with the five strains with the following results. When 1 kg per day was treated for 4 weeks, a total of 24 kg of mixed refuse consisting of green vegetables and fish remains as well as raw and/or fried potatoes was reduced to only 0.98 kg and a final digestive rate of 96% was obtained. It is noteworthy that the internal temperature of the compost mass reached a peak of 72℃. These results indicate that the novel thermophilic bacterial strains isolated from Giant Panda feces may be useful for high-performance waste treatment at a high temperature of 65℃.

https://ci.nii.ac.jp/naid/110002933483

8 0 0 0 ニューラルネットワークと遺伝的アルゴリズムを用いた吟醸酒の品質モデリング

著者: 各務彰洋花井泰三本多裕之小林猛
出版者: 公益社団法人日本生物工学会
雑誌: 生物工学会誌 : seibutsu-kogaku kaishi (ISSN:09193758)
巻号頁・発行日: vol.73, no.5, pp.387-395, 1995-09-25
被引用文献数: 9

This paper deals with the quality modeling of Ginjo sake using a neural network (NN) and genetic algorithm (GA). A NN model was constructed to estimate 7 sensory evaluations concerning the quality of Ginjo sake from 18 chemical component analytical values. The performance index, J, of the NN model was significantly small compared with that obtained using multiple regression analysis (MRA). Using the model, analytical data on the chemical components was estimated from the 7 given sensory evaluation values by means of a genetic algorithm, which was employed as an optimizing method. It was found that almost all the estimated values coincided with the actual values within an error range of less than 0.3.