背景:多酚是中国红枣中最重要的植物化学物质,因为它们具有许多潜在的健康益处,例如避免癌症,降低冠状动脉疾病的风险,利尿活动,心肌兴奋剂,冠状动脉扩张器和肌肉松弛剂。
目的:利用近红外(NIR)和中红外(MIR)光谱数据融合方法对中国红枣中的多酚进行定量。
方法:共使用80个中国枣样品从NIR和MIR光谱中获取数据。利用协同区间偏最小二乘(Si-PLS)算法提取有效光谱区间作为NIR-MIR融合模型的输入变量。采用遗传算法构建了基于NIR-MIR融合的模型。使用校准(R2)和预测(R2)的相关系数评估开发的模型的性能,预测均方根误差(RMSEP),偏差和残差预测偏差(RPD)。
结果:基于GA的数据融合模型优于NIR和MIR构建模型。最佳GA融合模型的R2=0.9621,r2=0.9451,RPD=2.44,校准集偏差=0.004,预测集偏差=0.061,仅计算15个变量。
结论:这些发现表明,NIR和MIR的整合对于预测中国枣中总多酚含量是可能的。
BACKGROUND: Polyphenols are the foremost measure of phytochemicals in Chinese dates due to their many potential health benefits such as averting cancers, reducing the risk of coronary artery disease, diuretic activity, myocardial stimulant, coronary dilator and muscle relaxant.
OBJECTIVE: To quantitate the polyphenols in Chinese dates using a data fusion approach with near-infrared (NIR) and mid-infrared (MIR) spectroscopy.
METHODS: A total of 80 Chinese dates samples were used for data acquisition from both NIR and MIR spectroscopy. The efficient spectral intervals were extracted by the synergy interval partial least square (Si-PLS) algorithm as input variables for NIR-MIR fusion model. A genetic algorithm (GA) was used to construct the model based on NIR-MIR fusion. The performance of the developed models was evaluated using correlation coefficients of calibration (R2 ) and prediction (r2 ), root mean square error of prediction (RMSEP), bias and residual prediction deviation (RPD).
RESULTS: The data fusion model based on the GA was superior compared to NIR and MIR build model. The optimal GA-fusion model yielded R2 = 0.9621, r2 = 0.9451, RPD = 2.44, calibration set bias = 0.004 and prediction set bias = 0.061, computing only 15 variables.
CONCLUSIONS: These findings reveal that integration of NIR and MIR is possible for the prediction of total polyphenol content in Chinese dates.