Qualitative discrimination of adulteration in matcha based on near infrared spectroscopy
-
摘要: 本实验采用近红外光谱技术与主成分分析法结合线性判别分析法（PCA-LDA）和K最邻近法,对抹茶中添加白砂糖、麦芽糊精、桑叶粉、大麦苗粉的现象进行定性判别分析。结果显示,PCA-LDA的定性判别结果优于K最邻近法,纯抹茶与掺伪抹茶、纯抹茶与掺糖抹茶、纯抹茶与掺糊精抹茶、纯抹茶与掺桑叶粉抹茶、纯抹茶与掺大麦苗粉抹茶、4种掺伪抹茶的定性分析模型的校正集识别率为98.3%、100%、91.7%、100%、100%、100%;预测集识别率为96.5%、100%、87.5%、95.8%、90.3%、95.3%。由此可知,通过PCA-LDA建立的定性判别模型准确度和识别率都很好,能够快速、准确的对抹茶中是否掺伪进行定性判别。Abstract: In this experiment, near infrared spectroscopy and principal component analysis combined with linear discriminant analysis method and K-nearest neighbors was used to qualify the qualitative discriminant analysis on the phenomenon of matcha added sugar, maltodextrin, mulberry leaf powder, barley seedling powder.The results showed that the qualitative judgment of PCA-LDA was superior to K nearest neighbor method. Pure matcha and fake matcha, pure matcha and matcha adulterated white sugar, pure matcha and matcha adulterated maltodextrin, pure matcha and matcha adulterated mulberry leaf powder, pure match and matcha adulterated barley flour, four kinds of fake matcha, the calibration set recognition rate of qualitative analysis model were respectively 98.3%, 100%, 91.7%, 100%, 100%, 100%, and the predictive set recognition rate were 96.5%, 100%, 87.5%, 95.8%, 90.3%, 95.3%, respectively. It can be seen that the models had very good precision and stabilization and they could quickly and accurately discriminate whether matha was adulterated.
-
[1] 刘东娜, 聂坤伦, 杜晓, 等.抹茶品质的感官审评与成分分析[J].食品科学, 2014, 35 (2) :168-172. [2] 尹春英, 刘乾刚.抹茶溯源及其利用[J].茶叶科学技术, 2008 (2) :13-15. [3] 李翔、许彦.抹茶制品开发研究进展[J].饮料工业, 2013, 16 (6) :13-15. [4] 徐睿瑶, 李宏远.日本抹茶的发展概况及其与中医茶疗的关系[J].世界中西医结合杂志, 2016, 11 (9) :1309-1310. [5] 李徽, 李春方, 任静, 等.遮阴时间对抹茶及其加工蛋糕品质的影响[J].上海师范大学学报:自然科学版, 2014, 43 (6) :573-577. [6] 张一洁.中国末茶文化研究[D].上海:华东师范大学, 2014 [7] 李雪明.近红外光谱分析技术在农产品食品品质在线无损检测中的应用研究进展[J].科学与财富, 2015 (26) :153-153. [8] 于英杰, 孙威江.近红外光谱及高光谱技术在茶叶上的应用[J].亚热带农业研究, 2014, 10 (4) :269-273. [9] 周小芬, 叶阳, 陈芃, 等.近红外光谱技术在茶叶品质评价中的研究与应用[J].食品工业科技, 2012, 33 (5) :413-417. [10] 王小燕, 王锡昌, 刘源, 等.近红外光谱技术在食品掺伪检测应用中的研究进展[J].食品科学, 2011, 32 (1) :265-269. [11] 张玉华, 孟一, 张应龙, 等.近红外光谱技术在食品安全领域应用研究进展[J].食品科技, 2012 (10) :283-286. [12] Ding H B, Xu R J.Near-infrared spectroscopic technique for detection of beef hamburger adulteration.[J].Journal of Agricultural&Food Chemistry, 2000, 48 (6) :2193-8.
[13] Mohanty B P, Barik S, Mahanty A.Food safety, labeling regulations and fish food authentication[J].National Academy Science Letters, 2013, 36 (3) :253-258.
[14] 彭星星, 陈文敏, 乔茜华, 等.近红外光谱技术鉴别核桃油中掺入菜籽油、大豆油及玉米油的研究[J].中国粮油学报, 2015 (12) :106-113. [15] 张亦婷, 刘翠玲, 位立娜.基于光谱技术的芝麻油掺假定性分析研究[J].食品科技, 2016 (9) :257-261. [16] 金垚, 杜斌, 智秀娟.NIR技术快速鉴定牛奶品牌与掺假识别[J].食品研究与开发, 2016, 37 (3) :178-181. [17] 倪力军, 钟霖, 张鑫, 等.近红外光谱结合非线性模式识别方法进行牛奶中掺假物质的判别[J].光谱学与光谱分析, 2014 (10) :2673-2678. [18] Shui-Fang L I, Shan Y, Zhu X R, et al.Detection of honey adulteration by addition of maltose syrup using near-infrared transflectance spectroscopy[J].Food Science&Technology, 2010.
[19] Huang L, Liu H, Zhang B, et al.Application of electronic nose with multivariate analysis and sensor selection for botanical origin identification and quality determination of honey[J].Food and Bioprocess Technology, 2015, 8 (2) :359-370.
[20] 徐玲玲, 李卫群, 朱慧, 等.近红外光谱法检测奶粉掺假[J].食品安全质量检测学报, 2016, 7 (8) :3133-3137. [21] 王宁宁, 申兵辉, 关建军, 等.近红外光谱分析技术识别奶粉中淀粉掺假的研究[J].光谱学与光谱分析, 2015 (8) :2141-2146. [22] 刘伟.基于光谱信息的模型辨识方法研究[D].北京:北京化工大学, 2014. [23] 严衍禄, 陈斌, 朱大洲.近红外光谱分析的原理、技术与应用[M].北京:中国轻工业出版社, 2013.83-84. [24] 杨志敏.应用近红外光谱技术快速检测原料肉新鲜度及掺假的研究[D].咸阳:西北农林科技大学, 2011. [25] 赵杰文, 林颢.食品、农产品检测中的数据处理和分析方法[M].北京:科学出版社, 2012.164-165.
计量
- 文章访问数: 222
- HTML全文浏览量: 38
- PDF下载量: 251
下载:
