Molecular structure and interaction of biopolymers as viewed by Fourier transform infrared spectroscopy: model studies on β-lactoglobulin

Fourier transform absorption infrared spectroscopy is a powerful and versatile tool used to determine the molecular structure of biomolecules. This technique is now widely used in biochemistry to study the conformation of biopolymers in aqueous solutions and complex systems. However, its enormous potential in the study of food biopolymers has yet to be reached. The aim of this paper is principally to provide information on biopolymers using Fourier transform infrared spectroscopy. β-Lactoglobulin (β-Lg), the major whey protein in the milk of ruminants, is chosen as a model. Emphasis will be put on the different structure levels of proteins in an aqueous solution, the protein–protein interactions, and the protein interactions with small host-molecules such as phospholipids.     

近红外光谱应用于高粱中总淀粉、直链淀粉与支链淀粉的定量分析

酱香型白酒以高粱为主要原料发酵而成,高粱中的淀粉,尤其是支链淀粉的含量对于酱香型白酒的产品质量起着至关重要的作用,因此针对其总淀粉、直链淀粉与支链淀粉的检测手段研究也应受到人们高度的重视。傅里叶变换近红外光谱法因其准确性高、稳定性好以及快速无损检测等特点,在建立总淀粉、直链淀粉和支链淀粉同时定量分析模型方面得到了较为成功的应用。结果表明,总淀粉、直链淀粉以及支链淀粉的定量分析模型相关系数分别达到0.954 9、0.923 6和0.940 1,交互验证均方根误差分别为1.027 1、0.088 5和1.264 6。可见,基于近红外光谱技术的定量分析方法可适用于高粱中总淀粉、直链淀粉与支链淀粉的同时定量分析。     

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages by Fourier transform infrared (FTIR) spectroscopy and chemometrics

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages was developed by Fourier transform infrared (FTIR) spectrometry combined with chemometrics. Transmittance spectra ranging from 400 to 4000cm(-1) of 73 Halal and 78 non-Halal Chinese ham sausages were measured. Sample preparation involved finely grinding of samples and formation of KBr disks (under 10MPa for 5min). The influence of data preprocessing methods including smoothing, taking derivatives and standard normal variate (SNV) on partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) was investigated. The results indicate removal of spectral background and baseline plays an important role in discrimination. Taking derivatives, SNV can improve classification accuracy and reduce the complexity of PLSDA. Possibly due to the loss of detailed high-frequency spectral information, smoothing degrades the model performance. For the best models, the sensitivity and specificity was 0.913 and 0.929 for PLSDA with SNV spectra, 0.957 and 0.929 for LS-SVM with second derivative spectra, respectively.