Amino acids are important nutrient substances for life, and many of them have several isomerides, while only L-type amino acids can be absorbed by body as nutrients. So it is certain worth to accurately classify and identify amino acids. In this paper, terahertz time-domain spectroscopy (THz-TDS) was used to detect isomers of various amino acids to obtain their absorption spectra, and their spectral characteristics were analyzed and compared. Results show that not all isomerides of amino acids have unique spectral characteristics, causing the difficulty of classification and identification. To solve this problem, partial least squares discriminant analysis (PLS-DA), firstly, was performed on extracting principal component of THz spectroscopy and classifying amino acids. Moreover, variable selection (VS) was employed to optimize spectral interval of feature extraction to improve analysis effect. As a result, the optimal classification model was determined and most samples can be accurately classified. Secondly, for each class of amino acids, PLS-DA combined with VS was also applied to identify isomerides. This work provides a suggestion for material classification and identification with THz spectroscopy.
Terahertz (THz) spectroscopy has fingerprint features for many bio-molecules with frequency between infrared and microwave covering the vibrational models of a great number of materials. In this study, THz-TDS was used to detect the preserved and bad meat. And the absorption coefficient indices of bad meat and preserved meat were measured in the range of 0.2–1.0 THz. The result shows that there are differences of pork tissue in both time domain and absorption coefficient in the process of deterioration. Then differences between preserved and bad meat were also presented. In order to investigate the relationship between the terahertz characteristics and meat quality, the changes of water content and material in the samples were also discussed. This work supplies reference for the application of THz technology in meat quality detection.
Recovering component spectra from terahertz measurements of unknown mixtures has been studied in this paper using nonnegative matrix factorization (NMF). NMF mathematically decomposes the spectra data into two nonnegative matrixes which describe the component spectra and the corresponding fractional abundance. Two basic algorithms in the class of this method, NMF and NMF with smoothness constraint (cNMF), were adopted to resolve the terahertz absorption spectra matrix obtained from a ternary mixture with varying compositions of Nitrofurantoin, L-Leucine and D-Tyrosine. The quality of the decomposition results was evaluated. The performance of the two algorithms on extracting component terahertz spectra was compared. The optimal result reached by cNMF in this study implies the capability of the NMF method for blind terahertz spectral unmixing. The attempt made in our work helps to further investigate unknown mixtures by terahertz spectroscopy.