| 基于近红外光谱技术的抹茶掺伪定性判别研究 |
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| 引用本文： | 赵开飞, 王敬涵, 张惠, 黄萧, 刘政权. 基于近红外光谱技术的抹茶掺伪定性判别研究[J]. 食品工业科技, 2017, (17): 241-244. DOI: 10.13386/j.issn1002-0306.2017.17.046 |
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| 作者姓名： | 赵开飞 王敬涵 张惠 黄萧 刘政权 |
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| 作者单位： | 1.安徽农业大学茶树生物学与资源利用国家重点实验室 |
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| 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。
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| 关 键 词： | 抹茶 近红外光谱 品质判别 主成分分析 线性判别分析 K最邻近法 |
| 收稿时间： | 2017-03-03 |
Qualitative discrimination of adulteration in matcha based on near infrared spectroscopy |
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| ZHAO Kai-fei, WANG Jing-han, ZHANG Hui, HUANG Xiao, LIU Zheng-quan. Qualitative discrimination of adulteration in matcha based on near infrared spectroscopy[J]. Science and Technology of Food Industry, 2017, (17): 241-244. DOI: 10.13386/j.issn1002-0306.2017.17.046 |
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| Authors: | ZHAO Kai-fei WANG Jing-han ZHANG Hui HUANG Xiao LIU Zheng-quan |
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| Affiliation: | 1.State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University |
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| 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. |
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| Keywords: | matcha near infrared spectroscopy quality identification principal component analysis linear discriminant analysis K-nearest neighbors |
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