梁建华,郭嘉明,夏红玲,马成英,胡海涛,乔小燕.基于近红外光谱的英红九号红茶快速定级方法[J].包装工程,2023,44(13):157-165.
LIANG Jian-hua,GUO Jia-ming,XIA Hong-ling,MA Cheng-ying,HU Hai-tao,QIAO Xiao-yan.Rapid Grading Method of Black Tea 'Yinghong 9' Based on Near-infrared Spectroscopy[J].Packaging Engineering,2023,44(13):157-165. |
| 基于近红外光谱的英红九号红茶快速定级方法 |
| Rapid Grading Method of Black Tea 'Yinghong 9' Based on Near-infrared Spectroscopy |
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| DOI：10.19554/j.cnki.1001-3563.2023.13.019 |
| 中文关键词: 近红外光谱 红茶 定级 内质成分 |
| 英文关键词: near-infrared spectroscopy black tea grade biochemistry components |
| 基金项目:广东省乡村振兴战略专项资金(农业科技能力提升)(403?2018?XMZC?0002?90);广东省农业科学院“中青年学科带头人”培养项目(R2020PY?JX016) |
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| 中文摘要: |
| 目的 以英红九号红茶为研究对象，提出一种基于近红外光谱的红茶质量快速定级方法。方法 首先使用湿化学法对英红九号红茶进行内质成分含量检测，并通过感官审评对参试红茶进行定级，基于内质成分含量建立英红九号红茶定级模型，然后利用近红外光谱构建红茶内质成分的定量模型，以快速预测英红九号红茶的内质成分含量。将内质成分含量预测值输入定级模型，以预测英红九号红茶的质量等级。结果 建立了红茶茶多酚、可溶性糖、游离氨基酸和咖啡碱4个内质成分的偏最小二乘法定量模型，其测试集的决定系数分别为0.974 5、0.887 6、0.963 6、0.860 6，基于感官审评和内质成分的随机森林定级模型测试集的准确率为90.48%。结论 为红茶质量快速定级提供了一种可行方案，增强了基于近红外光谱的红茶定级方法的解释力。#$TAB |
| 英文摘要: |
| The tea production is developing towards large-scale currently, and the quality evaluation of tea after processing still relies on highly subjective artificial sensory evaluation, which is not suitable for the large-scale development of tea. Near-infrared spectroscopy (NIRS) has rich structural and compositional information, which is suitable for the detection of physicochemical parameters of hydrogen-containing organic substances. So it is widely used in the detection of biochemistry components of tea and the classification, such as authenticity discrimination and origin traceability. The work aims to take 'Yinghong 9' black tea as the research object, and propose a rapid grading method for tea quality based on NIRS. Firstly, a total of 42 samples of black tea processed from the fresh tea leaves of various grades were collected, a sub-sample was taken from each sample and ground into powder. A NIR spectrometer was used to scan tea powder to collect the spectrum of each sample. Secondly, quantitative models for the biochemistry components were constructed based on NIRS to gain the biochemistry component information of black tea. Thirdly, 5 professional tea tasters were invited to conduct sensory evaluation on all samples. Based on the opinions of the tea tasters, the quality grade of tea samples were determined. Finally, the relationship between sensory evaluation results and biochemistry components were established to achieve the quality grading of 'Yinghong 9' black tea. In particular, when establishing the grading model, only the black tea processed from the second grade fresh leaves was selected and divided into three grades according to the sensory evaluation results. The quantitative models of four biochemistry components including tea polyphenol, soluble sugar, free amino acid and caffeine in black tea were established while these four quantitative models were preprocessed by combination data correction and normalization to reduce noise, drift as well as other interference and improve the difference between samples. These quantitative models were uniformly built using Partial Least Squares algorithm after using Genetic Algorithm, Successive Projections Algorithm, Variable Combination Population Analysis combined with Genetic Algorithm and other algorithms respectively to extract features. In order to ensure the reliability and stability of the model, Kennard-Stone algorithm was used to divide the samples into calibration set and test set before modeling, and K-fold verification was used in the modeling process. The principal components of the four quantitative models were all less than 10. The coefficients of determination on calibration set were tea polyphenol 0.974 5, soluble sugar 0.887 6, free amino acid 0.963 6 and caffeine 0.860 6 and the Root Mean Squared Error were 0.630 0, 0.298 3, 0.045 6, 0.162 6, respectively. The grading model based on sensory evaluation and biochemistry components had an accuracy of over 85%, which was built using Random Forest algorithm with 35 trees. The research results provide a feasible scheme for rapid grading of processed black tea based on specially graded fresh tea leaves, and effectively improve the interpretability of black tea grading. |
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