ABSTRACT
3-Hydroxybenzaldehyde (3-HBA) was investigated in the range of 0.6-2.8 THz by terahertz time-domain spectroscopy (THz-TDS) and solid-state density functional theory (ss-DFT) with first-principles calculation. Four distinct peaks were found respectively, and among them, the intensity disparity between experiment and simulation spectra at 2.04 THz was recognized as the biggest inconsistency. Considering thermal behavior can be responsible for this, quasi-harmonic approximation (QHA) method was introduced to mimic the unit cell volume expansion. According to vibrational modes analysis, it was ascertained that the biggest vibrational modes discrepancy was also located at 2.04 THz. Molecules in 0% and 4% unit cell expansion exhibit an opposite rotational direction in a-b plane compared with 2% unit cell expansion. Noncovalent intermolecular interactions were investigated with independent gradient model (IGM), and the result indicates that hydrogen bonding is the dominating noncovalent interaction of 3-HBA. While calculating systematic potential energy to the displaced bonds stretching involving hydrogen atoms, it was found the anomalous potential energy variation to the bond stretching provides a possible explanation for the rotation direction divergence, that is, the rotation direction divergence can be related to some hydrogen atoms seeking lower overall potential energy around their equilibrium positions during bond stretching in response to the variational intermolecular van der Waals force. This research combined THz-TDS with the quasi-harmonic approximation method, elucidating the principle of vibrational characteristics in different volumes, which is beneficial to the investigation of the terahertz low-frequency vibration to thermal behavior as a reference in biochemistry and other fields.
ABSTRACT
The purpose of this paper is to use terahertz (THz) spectroscopy combined with manifold learning and improved support vector machine (SVM) model to identify the coumarin-based food additives. The 216 THz absorbance spectra (144 for calibration set and 72 for prediction set) of six coumarin-based food additives are measured by using THz time-domain spectroscopy (THz-TDS) in the range of 0.5-2.0 THz. The method (P-t-SNE) combined principal component analysis (PCA) with manifold learning t-distributed stochastic neighbor embedding (t-SNE) is used for feature extraction of the THz spectra. Then, an improved SVM using differential evolution (DE) to improve gray wolf optimization (GWO) to optimize parameters is proposed. Finally, the result shows that the prediction set accuracy of PCA-DEGWO-SVM, P-t-SNE-DEGWO-SVM, and P-t-SNE-GWO-SVM models are 97.22%, 98.61%, and 95.83%, respectively, indicating that the accuracy by P-t-SNE is increased by about 1.39% compared with that processed by PCA, and the accuracy by DEGWO is also increased by about 2.78% compared with that processed by GWO. In conclusion, the improved model (P-t-SNE-DEGWO-SVM) has the best identification effect, and it is proved to be an effective method to identify coumarin-based food additives. PRACTICAL APPLICATION: The method used in this paper can be applied in the field of food safety detection. When detecting coumarin-based food additives, the method proposed in this paper is more time-saving and efficient than traditional detection methods. Through some more tests and adjustments, it will be possible to achieve rapid and on-site identification of various food additives.
