Studies on the Physicochemical Quality of Taiwan Local Wines and Rapid Analysis by Near Infrared Spectroscopy

• Main Authors: Shih-Lun Liu, 劉士綸
• Other Authors: Andi Shau-mei Ou, Ph. D
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/31140126193196110301

碩士 === 國立中興大學 === 食品科學系 === 89 === 貳、英文摘要 Abstract This study first surveyed the situation of wine making in Taiwan. Then totally 140 fruit wine and liquor samples (68 and 72, respectively) selected from Miaoli, Taichung, Nantou and Zhanghwa which in production and abundant variations of fruit wines and liquors (120) and supermarkets which the samples mainly produced by Taiwan Tobacco and Wine Board (20) were used as experimental materials to examine their physicochemical characteristics and near infrared spectroscopic scanning. Using principal component analysis, the correlations between their physicochemical characteristics and the differentiating ability for the types of fruit wine and liquor samples were studied. The feasibility of using near infrared spectroscopy to rapidly determine the physicochemical characteristics was evaluated. The results were summarized as follows: 1.From the results of the physicochemical analysis for all wine and liquor samples, the total soluble solids content ranged from 7.5 % to 35.78 %; the titratable acidity ranged from 0.04 % to 4.04 %; the pH content ranged from 2.83 to 5.5; the methanol content ranged from 0 ppm to 3769.1 ppm; the alcohol content ranged from 0 % to 56.46 %; the residual sugar content ranged from 0 ppm to 3401.7 ppm ; the total phenol content ranged from 0.69 ppm to 2583.16 ppm ;.the L value ranged from 4.82 to 100.72; the a value ranged from —1.04 to 59.32; the b value ranged from —0.17 to 38.35; the tartaric acid content ranged from 0 to 3233.31 mg/100 g; the malic acid content ranged from 0 to 1208.35 mg/100 g; the ascorbic acid content ranged from 0 to 310.78 mg/100 g; the citric acid content ranged from 0 to 3377.29 mg/100 g; the succinic acid content ranged from 0 to 1014.75 mg/100 g. The results showed the great variation in the samples, especially those private-make ones 2.Base on the physicochemical analysis results of all wine sample and liquor samples. The content of residual sugar, the total phenol , the titratable acidity and the organic acid in fruit liquor samples were extremely low while the content of methanol and ethanol were very high as compared to wine samples. The fruit liquor samples had higher Hunter L value and lower Hunter a and b valuethan the fruit wine samples. These physicochemical characteristics could be used for distinguish the fruit wine and liquor samples. 3.Based on the results of principal component analysis (PCA), PCA with wine-making-methods category had the best ability for explaining the variation. The first and second component could explain the sample variation up to 46% and 9%, respectively. PCA with kinds-of-fruit category was the best to distinguish the wine and liquor samples. 4.The R2 of NIRS calibration curves of 140 fruit wine and liquor samples for total soluble solids, titratable acidity, ethanol, residual sugar, L value, a value and b value were 0.99, 0.98, 0.99, 0.99, 0.98, 0.96 and 0.97, respectively. The correlation coefficients (r) for prediction of these 7 constituents were 0.99, 0.99, 0.99, 0.98, 0.97, 0.83 and 0.91, respectively. The R2 of NIRS calibration curves of 68 fruit wine samples for total soluble solids, titratable acidity, ethanol, residual sugar, L value and citric acid were 0.99, 0.96, 0.98, 0.96, 0.93 and 0.91, respectively. The correlation coefficients (r) for prediction of these 6 constituents were 0.99, 0.97, 0.93, 0.93, 0.93 and 0.75, respectively. The R2 of NIRS calibration curves of 72 fruit liquor samples for total soluble solids, ethanol and total phenol were 0.99, 0.95 and 0.94, respectively. The correlation coefficients (r) for prediction of these 3 constituents were 0.99, 0.85 and 0.93, respectively. It showed that these calibration curves could be used for rapidly determining these physicochemical characteristics. 5.Using PCA with category variables and those physicochemical characteristic items having good NIRS calibration curves, the different kinds of fruit for making those wine and liquor samples could be identified. It also indicates that NIRS help can help to identify those wine and liquor samples in this study.