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Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

机译:FT-NIR光谱法同时估算红茶的味道质量和味道相关化合物含量

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Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (R-p) & 0.76, and the root mean square error values of the prediction set (RMSEP) & 1.7% compared with Si-PLS models (0.71 ae 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.
机译:傅里叶变换近红外光谱(FT-NIR)耦合到化学计量算法,例如背部传播(BP) - adaBoost和协同间隔部分最小二乘(SI-PL),用于黑色的快速预测品质和味道相关的组件茶。通过化学分析和Pearson相关性确定八种主要的味道相关组分。基于来自不同国家的160种茶样品,预测了八种味道含有的味道相关成分的化学成分。预测结果揭示了BP-Adaboost模型给出了卓越的预测,所有相关系数的预测集(R-P)& 0.76,以及预测集(RMSEP)&amp的根均方误差值; LT;与SI-PLS型号相比,1.7%(0.71 AE 1.73%)。这意味着FT-NIR与BP-Adaboostis合并,能够随时部署红茶品质和味道相关组分的快速评估。

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