Zuo Yamin, Tan Gaohao, Xiang Di, Chen Ling, Wang Jiao, Zhang Shengsheng, Bai Zhiwen, Wu Qing
School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China; Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China.
Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Sep 5;258:119847. doi: 10.1016/j.saa.2021.119847. Epub 2021 Apr 17.
Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.
近来，工业应用非常希望采用无损仪器鉴定绿茶品质，而非专业的人工品评测试。特殊风味是影响消费者偏好的关键品质特征。然而，在绿茶样品品质特征方面发挥关键作用的黄酮类化合物以及与感官相关的化合物，目前研究较少。在本研究中，我们提出一种客观准确的近红外光谱（NIRS）图谱，以支持整个绿茶感官评价链中的质量控制，通过两种互补方法对绿茶样品感官分析的复杂性进行了研究：标准计算以及结合化学计量学的新型NIRS路线图。分别测量了绿茶样品的物理品质、味觉指数和营养指数，同时考虑了对五种具有商业代表性的总体品质（“非常差”、“差”、“一般”、“好”和“优秀”）的绿茶进行味觉评价。我们的研究结果突出了NIRS在化学与感官关系中未被充分探索的作用及其在绿茶品质提升方面的广泛重要性和实用性。总体而言，对与感官相关属性的综合表征使得能够识别出大量光谱特征，这些特征大多与水分、可溶性固形物（SS）、茶多酚（TPP）、表没食子儿茶素没食子酸酯（EGCG）、表儿茶素（EC）和茶多糖（TPS）有关，它们可用作推定生物标志物，以快速评估与等级差异相关的绿茶风味变化。此外，NIRS数据被独立分为校准集（n = 80）和预测集（n = 40），其在物理品质和总杯品质方面的相关系数分别为0.9024和0.9020，相关性较高。在本研究中，我们证明NIRS是一种易于生成的策略，能够形成闭环反馈到先进过程控制流程中。然而，已建立的模型应通过更多来自不同地区的绿茶样品进行改进。
