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[Display omitted] • It proposed a detection method for the structural and quantitative analysis of GABA. • The limit of detection for GABA could reach up to 0.428% based on its THz spectrum. • The calculated results were in good agreement with their experimental results. • The vibrational motions of each THz and Raman peaks were quantitatively decomposed. γ-aminobutyric (GABA) is the most important inhibitory neurotransmitier in vertebrate central nervous systems. The content level of GABA is related to the different degree of malignancy gliomas. Thus, it can be considered a promising glioma biomarker. In this paper, the spectroscopic properties of GABA have been characterized by combining the THz spectroscopy with low-wavenumber Raman spectroscopy. The experimental results showed that, GABA exhibited three absorption peaks and three refractive index peaks in the range of 0.6–2.1 THz. The limit of detection can reach up to 0.428 % based on the absorption coefficient at the peak of 2.04 THz. Moreover, the low-wavenumber Raman spectrum of GABA showed seven characteristic peaks at 41.0, 50.8, 58.8, 77.2, 98.8, 115.6, 141.2 cm−1 in 0–150 cm−1 region. Moreover, the THz and low-wavenumber theoretical spectra of GABA were simulated with solid-state density function theory, respectively. The calculated results were in good agreement with the experimental observations. On the basis of calculated result, the vibrational motions of each THz and Raman characteristic modes were quantitatively decomposed by analytical mode-decoupling method, where the contribution percentages of external translation, external librations and intramolecular vibration of each vibration modes were analyzed Furthermore, the low-frequency characteristics of GABA was analyzed by combining the THz and low-wavenumber Raman spectroscopy. It is beneficial for the structural information analysis and quantitative identification of biomarker GABA in early stage diagnosis of glioma. [ABSTRACT FROM AUTHOR]

[Display omitted] • It provides Raman characteristics of the two specific brain tissues after bTBIs. • Four algorithms are performed to classify different bTBIs at different time points. • The highest accuracy is up to 95.3% for diagnosis of bTBIs at different time points. • It realizes diagnosis of bTBIs by combining Raman spectroscopy with machine learning. Blast-induced traumatic brain injury (bTBI) is a kind of nervous system disease, which results in a major health and economic problem to society. However, the rapid and label-free detection method with high sensitivity is still in great demand for the diagnosis of bTBI, especially for mild bTBI. In this paper, we report a new strategy for bTBI diagnosis through hippocampus and hypothalamus tissues based on Raman spectroscopy. The spectral characteristics of hippocampus and hypothalamus tissues of experimental bTBI in rats have been investigated for mild and moderate degrees at 3 h, 6 h, 24 h, 48 h, 72 h after blast exposure. The results show that the Raman spectra of mild and moderate bTBIs in 300–1700 cm−1 and 2800–3000 cm−1 regions exhibit significant differences at different time points compared with the control group. The main reason is the content change of proteins and lipids in hippocampus and hypothalamus tissues after bTBI. Moreover, four machine learning algorithms are used to automatically identify mild and moderate bTBIs at different time points (a total of 11 groups). The highest diagnostic accuracies are up to 95.3% and 88.5% based on Raman spectra of hippocampus and hypothalamus tissue, respectively. In addition, the classification performance of linear discriminant analysis classifier has been improved after data fusion. It is suggested that there has great potential as an alternative method for high-sensitive, rapid, label-free, economical diagnosis of bTBI. [ABSTRACT FROM AUTHOR]