光谱学与光谱分析, 2019, 39 (8): 2584, 网络出版: 2019-09-02  

一种基于近红外光谱与化学计量学的绿茶快速无损鉴别方法

Nondestructive Identification of Green Tea Based on Near Infrared Spectroscopy and Chemometric Methods
作者单位
1 湖南农业大学食品科学技术学院食品科学与生物技术湖南省重点实验室, 湖南 长沙 410128
2 湖南省农业科学院湖南省农产品加工研究所, 湖南 长沙 410125
3 湖南省农业科学院湖南省农产品加工研究所, 湖南 长沙 410125、
4 上海烟草集团有限责任公司技术中心北京工作站, 北京 101121
摘要
绿茶是我国饮用范围最广、 最受欢迎的一类茶叶。 不同品种绿茶叶外观上差别较小, 非专业人员难以直接用肉眼进行辨别。 传统化学方法操作复杂、 检测费用较高, 对样品具有破坏性, 无法实现快速无损分析。 近红外光谱技术是一种简便、 快速、 无损、 重现性好、 可直接用于在线定性定量分析的新型分析技术。 由于种植方式以及土壤、 气候等生长环境的差异, 不同品种绿茶叶中含氢基团有机物的种类和含量也不相同, 因此可以通过扫描样品的近红外光谱, 得到不同品种绿茶叶的特征信息, 实现对不同品种绿茶叶的快速鉴别。 研究提出了一种基于近红外光谱与化学计量学技术对不同品种绿茶的快速无损鉴别方法。 使用近红外光谱仪得到了八个品种绿茶样品的光谱图, 用主成分分析方法对不同品种绿茶样品数据进行了聚类分析。 使用连续小波变换方法消除了光谱信号中的基线干扰, 从而提升聚类效果。 利用基于标准偏差与相对标准偏差的变量筛选方法进一步提高了聚类结果的准确性。 结果表明: 主成分分析后样品的第一主成分和第二主成分的方差贡献率之和在90%以上, 可以选取前两个主成分进行聚类分析。 直接采用原始数据进行聚类分析的准确率较低, 难以满足应用需要; 连续小波变换可以有效地消除光谱信号中的基线干扰。 与直接使用原始光谱聚类结果相比, 采用连续小波变换后聚类效果有显著提升, 但依旧不能实现所有品种茶叶样品的准确鉴别。 为了进一步提高方法的稳健性和分类结果的准确性, 选取了标准偏差和相对标准偏差较大的波长数据进行聚类分析。 在符合平均值大于1%的波长范围内, 剔除标准偏差小于5‰的波长, 进一步选择较大相对标准偏差值对应的波长点进行聚类分析。 采用这种方式, 可以仅使用几十个甚至是几个波长即可实现绿茶样品品种的准确聚类分析。 波长筛选方法可以大大提高主成分分析结果的准确性, 采用近红外光谱分析技术与化学计量学方法可以实现对不同品种绿茶的快速鉴别。 经过对各个光谱吸收区域波长所对应官能团分析后, 初步得出多酚、 酰胺类以及氨基酸类物质的种类不同或含量差异是形成绿茶品种差异的重要原因。 所提出的基于近红外光谱与化学计量学技术的方法具有较强的鉴别能力, 为绿茶的快速无损分析提供了一种新手段。
Abstract
Green tea is the most popular type of tea in China. The differences of green tea leaves from different categories are very small, and it is hard to distinguish them for non-experts by appearances. Traditional chemical methods are complicated in operation and are destructive to samples and it is difficult to achieve fast and nondestructive analysis. Near infrared spectroscopy (NIR) is a new technology, which is simple, fast, non-destructive, good in reproducibility and can be used for on-line analysis. The differences in the composition and content of the organic components in tea samples would be formed due to different growing environments and panting patterns, which can be measured by the NIR spectra. With the help of NIR spectra, the characteristic information of hydrogen groups can be obtained. The difference information of green tea leaves from different categories can be obtained, and the identification of green tea samples can be achieved. In this study, NIR was applied for nondestructive analysis of green tea leaves from different categories with the aid of chemometric methods. The dataset consists of eight brands of green tea samples. A relation has been established between the spectra and the tea varieties. The data was analyzed with principal component analysis. Furthermore, baseline elimination by continuous wavelet transform was used for improving the accuracy of the method. The wavenumber selection based on standard deviation and relative standard deviation was used to further improve the accuracy. The results show that the total variance explained by the first two principal components in principal component analysis was over 90% and they were enough for further analysis. The result of classification analysis using the original data was poor and cannot be used for the real application. The baseline interference can be eliminated with continuous wavelet transform method and the classification results were improved. The wavenumber selection method based on standard deviation and relative standard deviation consists of two steps. At first, the wavenumbers with standard deviation below 0.005 and the average below 0.01 were removed. Then, the wavenumbers that have large value of relative standard deviation were selected as informative ones, because the larger value of the relative standard deviation, the more variation between the samples. It was found that acceptable classification results can be obtained when several or several tens informative wavenumbers are used. It was found that, the main differences between varieties of tea are polyphenols, amides and amino acids. The results show the classification of different brands of green tea samples can be achieved by the proposed method, which provides a new idea for the rapid analysis of tea samples.

李跑, 申汝佳, 李尚科, 单杨, 丁胜华, 蒋立文, 刘霞, 杜国荣. 一种基于近红外光谱与化学计量学的绿茶快速无损鉴别方法[J]. 光谱学与光谱分析, 2019, 39(8): 2584. LI Pao, SHEN Ru-jia, LI Shang-ke, SHAN Yang, DING Sheng-hua, JIANG Li-wen, LIU Xia, DU Guo-rong. Nondestructive Identification of Green Tea Based on Near Infrared Spectroscopy and Chemometric Methods[J]. Spectroscopy and Spectral Analysis, 2019, 39(8): 2584.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!