针对传统茶叶感官评审中的不足,开发设计基于智能专家型名优茶审评系统,为茶叶品质控制与评鉴提供一种较为快捷、客观、公正的方法。该评审系统主要是利用数码相机和扫描仪,拍摄干茶与茶汤的外观图片,借助Photoshop 7.0软件,运用S-处理法对采集图片色泽进行分析,以样品色泽参数L、a均值,通过拟合方程T2=17.2+0.15（2L1+L2）–0.4（a1+3a2）,反映茶叶内在品质;为使系统更加客观、公正,增加人为感官易于判断的整体综合审评印象分T1修正指标,该指标仅审评茶叶的外形一致、老嫩均匀、异物异味及新旧真假等人为经验及专业技术要求相对较低的因子;基于智能专家型茶叶审评系统（IESFTE）数模为：TZF=17.2+0.25T1+0.15（2L1+L2）–0.4（a1+3a2）。将由该评审系统数模所得评审分值与系统样品库中同类茶样比对,给出茶叶评审报告。经检验,系统评审结果与名优茶审评专家的评审结果接近。
The Intelligent Expert System of Famous Tea Evaluation was developed, against the shortage of traditional tea evaluation, so as to provide a more efficient method of objective and fair for the quality control and evaluation of tea. This system is the use of digital cameras and scanners, capturing the appearance of dry tea and tea infusion, and then using S-acquisition image processing method for color analysis with Photoshop 7.0 software. By using the mean value of color parameters L of tea sample, with regression equation: T2=17.2+0.15(2L1+L2)–0.4(a1+3a2), to reflect the inherent quality of tea. To make the system more objective and impartial, adding the revised indicators, which characterized a comprehensive review of the overall impression of the tea sub-T1, and those factors that easy to judge through the human sensory senses. The index only contained those factors that the requirement of human experience and professional technique were relative low such as the consistent review of the shape and uniformity of tea, foreign matter and odour. The digital-model of The Intelligent Expert System of Famous Tea Evaluation(IESFTE) is: TZF=17.2+0.25T1+0.15(2L1+L2)–0.4(a1+3a2). The assessment result made by this Intelligent Expert System was close to the assessment results from experts.
[1] 周亦斌, 王俊. 新技术在茶叶品质评价中应用的现状及趋势[J]. 茶叶科学, 2004, 24(2): 82-85.
[2] 凌彩金, 王秋霜, 卓敏, 等. 茶叶审评技术研究进展[J]. 广东农业科学, 2010(3): 68-71.
[3] 冯花, 郭雅玲. 茶叶感官审评方法及其新发展[J]. 福建茶叶, 2010(7): 28-31.
[4] 张星海, 龚恕. 一种名优绿茶品质评鉴方法[P]. 中国: 201110088232.3, 2011-04-09.
[5] 李娇. 茶叶品质计算机视觉分级技术研究[D]. 杨凌: 西北农林科技大学, 2006: 17-23.
[6] 李晓丽, 何勇, 裘正军, 等. 基于多光谱图像的不同品种绿茶的纹理识别[J]. 浙江大学学报: 工学版, 2008, 42(12): 2133-2140.
[7] 汪建, 杜世平. 基于颜色和形状的茶叶计算机识别研究[J]. 茶叶科学, 2008, 28(6): 420-424.
[8] 李晓丽, 何勇. 基于多光谱图像及组合特征分析的茶叶等级区分[J]. 农业机械学报, 2009, 40(S):113-118.
[9] 蔡健荣. 利用计算机视觉定量描述茶叶色泽[J]. 农业机械学报, 2000, 31(4): 67-70.
[10] 陆建良, 梁月荣, 龚淑英, 等. 茶汤色差与茶叶感官品质相关性研究[J]. 茶叶科学, 2002, 22(1): 57-61.
[11] 王文杰, 罗守进, 黄建琴, 等. 电脑测定茶叶色泽的方法研究[J]. 茶叶科学, 2005, 25(1): 37-42.
[12] 李洁, 齐桂年. 利用计算机读取整茶色泽参数的方法研究[J]. 茶叶科学, 2007, 27(4): 328-334.
[13] 陈全胜, 赵杰文, 蔡健荣, 等. 基于近红外光谱和机器视觉多信息融合技术评判茶叶品质[J]. 农业工程学报, 2008, 24(3): 5-9.
[14] 于慧春, 王俊, 张红梅, 等. 龙井茶叶品质的电子鼻检测方法[J]. 农业机械学报, 2007, 38(7): 103-107.
[15] Bhattacharyya N, Seth S,T udu B. Monitoring of black tea fermentation process using electronic nose[J]. Journal of Food Engineering, 2007, 80: 1146-1156.
[16] 吴坚, 刘军, 傅敏, 等. 一种基于电子舌技术的绿茶分类方法[J]. 传感技术学报, 2006, 19(4): 963-966.
[17] 陈全胜, 江水泉, 王新宇. 基于电子舌技术和模式识别方法的茶叶质量等级评判[J]. 食品与机械, 2008, 24(1): 47-49.
[18] 童城. 伏安法电子舌对茶叶品质甄别的应用研究[D]. 合肥: 安徽农业大学, 2009: 13-29.
[19] Dutta R, Hines EL, Gardner JW.Tea quality prediction using a tin oxide-based electronic nose:an artificial intelligence approach[J]. Sensors and Actuators B, 2003, 94: 228-237.
[20] 罗文文. 近红外光谱分析技术对绿茶主要呈味物质定量分析的研究[D]. 杭州: 浙江大学, 2007:23-39.
[21] Sinija V R, H N Mishra. FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules[J]. LWT - Food Science and Technology, 2009, 42: 998-1002.
[22] Chen Q, J Zhao, C H Fang, et al. Feasibility study on identification of green, black and Oolong tea using near-infrared re?ectance spectroscopy based on support vector machine(SVM)[J]. Spectrochimica Acta Part A, 2007, 66: 568-574.
浙公网安备 33019902000101号 
