摘要
文章分别在空气背景和蒸馏水背景下使用不同光程样品池(1,5mm),选择不同光谱分辨率(8,16,32cm-1)采集了83个不除气啤酒样品的近红外光谱,并应用偏最小二乘法(PLS)和逐步多元线性回归(SMLR)方法,对啤酒的真实浓度、原麦汁浓度以及酒精度三种主要成分进行了回归分析,并建立了相应的定标与预测模型。结果发现:不同背景、不同分辨率、不同光程条件下的定标预测结果相近,逐步多元线性回归方法定标预测结果好于偏最小二乘法。在实验室实现了应用近红外光谱对不除气的少量啤酒样品(约2mL)同时快速无损检测啤酒的三个重要指标。文章的结果为应用和进一步开发啤酒成分近红外在线分析仪奠定了基础。
Near infrared spectroscopy (NLR) has been used to determine important indicators of the quality of undeaired beers by a partial least squares (PLS) regression and stepwise multiple linear regression (SMLR). The indicators are original, real extract and alcohol contents. Absorbance spectra in transmission mode of 83 samples were obtained with 1 mm and 5 mm path- length quartz cell. The selected resolving powers are 8, 16 and 32 cm^-1. Air and water were used as background respectively. It was concluded that the calibration and prediction results are similar with different background, pathlength and resolving power. The SMLR method seems to be better than PLS method. The results of this paper provide a foundation for the application and further development of NIR on-line beer analyzer.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2008年第2期313-316,共4页
Spectroscopy and Spectral Analysis
基金
广东省科技厅项目(2006B36702002)资助