The adsorption of protein from model wine was investigated under different temperatures, pH values, contact times, and concentrations of ethanol, by certain bentonites. The results showed that ethanol molecules could ...The adsorption of protein from model wine was investigated under different temperatures, pH values, contact times, and concentrations of ethanol, by certain bentonites. The results showed that ethanol molecules could broaden the protein molecules＇ channel to the interlayer of bentonite, and the maximum protein adsorption amount occurred under an ethanol concentration of 12% （by volume） and a pH value of 3.56. The increased single point Brunauer-Emmitt-Teller （BET） surface area （SBET） and adsorption pore volume （VAds） suggested a larger amount of active adsorption sites of the bentonite surface and a wider protein channel from the surface to the inner adsorption sites of bentonite, respectively. At the same time, higher methylene blue test （MBT） and swelling index （Sw） indicated that it was easy for the entrance of water and the absorbance of protein. Higher temperature was found favorable to eliminate more proteins and it took about 20 to 40min to arrive at the maximum adsorption.展开更多
Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mod...Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.展开更多
基金Supported by the National Natural Science Foundation of China （No.20466002）, the Program of Ministry of Education for New Century Excellent Talents （NCET-04-0989）, the Doctor Funds of Xinjiang Bingtuan （04BSZJ04） and the Shihezi University＇s. Key Scientific and Technological Project （ZDGG2004-01）.
文摘The adsorption of protein from model wine was investigated under different temperatures, pH values, contact times, and concentrations of ethanol, by certain bentonites. The results showed that ethanol molecules could broaden the protein molecules＇ channel to the interlayer of bentonite, and the maximum protein adsorption amount occurred under an ethanol concentration of 12% （by volume） and a pH value of 3.56. The increased single point Brunauer-Emmitt-Teller （BET） surface area （SBET） and adsorption pore volume （VAds） suggested a larger amount of active adsorption sites of the bentonite surface and a wider protein channel from the surface to the inner adsorption sites of bentonite, respectively. At the same time, higher methylene blue test （MBT） and swelling index （Sw） indicated that it was easy for the entrance of water and the absorbance of protein. Higher temperature was found favorable to eliminate more proteins and it took about 20 to 40min to arrive at the maximum adsorption.
文摘Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.
