基于近红外光谱技术快速检测椰汁品质

Rapid Detection of Coconut Juice Quality Based on the Near Infrared Spectroscopy

本研究应用近红外光谱技术结合主成分分析法(PCA)对3个不同品种的椰子,3个不同品牌成品椰子饮料及椰子粉进行定性分析。结果表明,对椰子3种不同形式的加工产品(椰子原汁、椰子饮料、椰子粉)进行定性分析的准确判别率均达到100%。采用近红外光谱技术结合偏最小二乘法(PLS)对椰汁饮料中原汁含量进行定量分析。为保证所建模型的稳健性、准确性,消除干扰,采用6种不同的预处理方法对近红外光谱技术进行优化,结果表明经过中心化预处理可得最佳模型,其Rp^2、RMSEP、Rc^2、RMSEC分别为0.9942、0.0435、0.9932、0.0519。本研究表明近红外光谱技术可为市售椰汁及椰子加工制品品质的快速、无损检测提供一种新思路。

The NIRs combined with principal component analysis(PCA)were firstly employed to conduct the qualitative analysis on three different kinds of coconuts,three brands of coconut drinks,and three brands of coconut powders in this work. The results revealed that the different coconut products could be clearly identified,and the accuracy rates for all of the above qualitative analyses were up to 100%. Moreover,the NIR spectra combined with partial least squares(PLS)was used to quantitative analysis of the content of raw juice in coconut beverage. To ensure the robustness and accuracy of the used model and eliminate the noise and baseline drift,six kinds of pretreatment methods were employed to optimize the NIR spectra. The results showed that the model after centralized preprocessing exhibited the best performance,in which the determination coefficient of prediction(Rp^2),the root mean square prediction error(RMSEP),determination coefficient of calibration(Rc^2),and root mean squared error of calibration(RMSEC)were 0.9942,0.0435,0.9932,and 0.0519,respectively. This study showed that the NIR spectroscopy could provide a new idea for the rapid and nondestructive detection of the qualities of commercially available coconut products.