基于近红外光谱技术的瓶装醋定性检测

采用近红外光谱直接对四种瓶装醋进行检测,并与比色皿检测方法获得的谱图进行了比较。结果发现,两种检测方法获得的谱图在近红外区域有明显差别,瓶装醋在1 480和1 900nm附近无吸收峰,而在1 660nm附近有一吸收峰,明显区别于比色皿检测获得的谱图;瓶装醋检测获得的吸光度最大值不超过4,而用比色皿所测值约为6,说明玻璃包装对其检测有一定的影响。本研究采用一阶导数法来消除此影响,并用主成分-神经网络方法建立了瓶装醋的定性分析模型,模型预测精度达到100%,表明一阶导数可以消除玻璃包装对瓶装醋近红外检测的影响,实现定性分析的目的。

Qualitative detection of bottled vinegar based on NIR spectroscopy technique

To evaluate the applicability of non-invasive near infrared reflectance (NIR) spectroscopy for detection of bottled vinegar, transmission spectra of bottled vinegar samples were obtained and compared with the spectra obtained by using cell detection method, where samples were titrated in the fixed liquid cell with a 1.0 mm light path length. The result shows that, the spectra obtained by the two different methods have obvious differences in the near infrared region. Firstly, the spectra obtained by using bottled detection method have no absorption peak at 1 480 and 1 900 nm, and furthermore, it has absorption peak at 1 660 nm. Secondly, the max. absorbance value of bottled detection is less than 4, while with the cell detection it is near 6. It indicates that glass packaging has influence on the detection of bottled vinegar. The 1st derivative method was put forward to eliminate this influence, and then qualitative analysis model was obtained by using principal component analysis-artificial neural net work. The precision of prediction result achieved 100%. This research shows that 1st derivative can eliminate the influence of glass packaging, and realize the qualitative analysis of bottled vinegar.