基于电子鼻与GC-MS融合技术的优质红茶和缺陷红茶香气品质评价

doi:10.7506/spkx1002-6630-20230417-163

食品科学 ›› 2024, Vol. 45 ›› Issue (2): 274-282.doi: 10.7506/spkx1002-6630-20230417-163

基于电子鼻与GC-MS融合技术的优质红茶和缺陷红茶香气品质评价

王立磊，杨艳芹，谢佳灵，缪伊雯，王启威，江用文，邓余良，童华荣，袁海波

（1.西南大学食品科学学院，重庆 400715；2.中国农业科学院茶叶研究所农业农村部特种经济动植物生物学与遗传育种重点实验室，浙江杭州 310008）

出版日期:2024-01-25 发布日期:2024-02-05
基金资助:
中国农业科学院科技创新工程专项（CAAS-ASTIP-TRICAAS）；国家茶叶产业技术体系红茶加工岗位项目（CARS-19）

Aroma Quality Evaluation of High-Quality and Quality-Deficient Black Tea by Electronic Nose Coupled with Gas Chromatography-Mass Spectrometry

WANG Lilei, YANG Yanqin, XIE Jialing, MIAO Yiwen, WANG Qiwei, JIANG Yongwen, DENG Yuliang, TONG Huarong, YUAN Haibo

(1. College of Food Science, Southwest University, Chongqing 400715, China; 2. Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China)

Online:2024-01-25 Published:2024-02-05

摘要/Abstract

摘要： 依据专家感官审评结果将14 个红茶样本按香气品质的优劣划分为优质红茶与缺陷红茶2 组，基于快速气相电子鼻（fast gas chromatography-electronic-nose，GC-E-Nose）和气相色谱-质谱（gas chromatography-mass spectrometry，GC-MS）融合技术结合多元统计分析对2 组茶样进行判别分析，筛选影响两类茶样分类的关键差异组分。结果显示：GC-E-Nose（44 维）和GC-MS（73 维）相融合可以得到117 维融合数据集，用其建立的正交偏最小二乘判别分析模型可以实现两类红茶的准确分类，其模型解释能力和预测能力（R2Y＝0.976，Q2＝0.959）较单一的GC-E-Nose或GC-MS数据模型更优。基于变量投影重要性＞1.6和P＜0.05双变量原则，共筛选出二甲基硫醚（B3、B25）、β-紫罗酮（A59）、(3E)-4,8-二甲基壬-1,3,7-三烯（A20）、二氢猕猴桃内酯（A64）、芳樟醇（A17）、苯乙醇（A19）、δ-辛内酯（A41）和γ-壬内酯（A45）8 个关键香气组分对分类起重要作用。研究结果表明，GC-E-Nose与GC-MS融合技术可以实现缺陷红茶和优质红茶的快速、准确分类，该方法可作为传统感官审评方法的补充，为红茶品质控制和质量提升提供技术支撑。

关键词: 信息融合技术；红茶；香气；快速气相电子鼻；气相色谱-质谱

Abstract: According to the results of sensory evaluation performed by experts, 14 black tea samples were divided into two groups based on their aroma quality: high-quality and quality-deficient black tea. Using fast gas chromatography-electronic-nose (GC-E-Nose) and gas chromatography-mass spectrometry (GC-MS) combined with multivariate statistical analysis, discriminant analysis of the two groups were carried out, and the key differential components between these groups were selected. The results showed that 117-dimensional dataset was obtained by the fusion of the GC-E-Nose (44-dimensional) and GC-MS (73-dimensional) data and used to establish a model for accurate classification of the two types of black tea employing orthogonal partial least squares-discriminant analysis (OPLS-DA). The model’s explanatory and predictive capacity (R2Y = 0.976, Q2 = 0.959) were better than those of the model established based on the GC-E-Nose or GC-MS data. Based on variable important in projection (VIP) scores > 1.6 and P < 0.05, eight key aroma components including dimethyl sulfide (B3 and B25), β-ionone (A59), (3E)-4,8-dimethylnon-1,3,7-triene (A20), dihydroactinidiolide (A64), linalool (A17), phenylethyl alcohol (A19), δ-octyl lactone (A41) and γ-nonalatone (A45) were selected, which played an important role in the classification. These results showed that GC-E-Nose combined with GC-MS allows rapid and accurate discrimination between quality-deficient and high-quality black tea, which can be used as a supplement to traditional sensory evaluation, providing technical support for quality control and improvement of black tea.

Key words: data fusion technology; black tea; aroma; fast gas chromatography-electronic-nose; gas chromatography-mass spectrometry

中图分类号:

S571.1

王立磊，杨艳芹，谢佳灵，缪伊雯，王启威，江用文，邓余良，童华荣，袁海波. 基于电子鼻与GC-MS融合技术的优质红茶和缺陷红茶香气品质评价[J]. 食品科学, 2024, 45(2): 274-282.

WANG Lilei, YANG Yanqin, XIE Jialing, MIAO Yiwen, WANG Qiwei, JIANG Yongwen, DENG Yuliang, TONG Huarong, YUAN Haibo. Aroma Quality Evaluation of High-Quality and Quality-Deficient Black Tea by Electronic Nose Coupled with Gas Chromatography-Mass Spectrometry[J]. FOOD SCIENCE, 2024, 45(2): 274-282.

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基于电子鼻与GC-MS融合技术的优质红茶和缺陷红茶香气品质评价

Aroma Quality Evaluation of High-Quality and Quality-Deficient Black Tea by Electronic Nose Coupled with Gas Chromatography-Mass Spectrometry

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