Progress in application of terahertz time-domain spectroscopy for pharmaceutical analyses

Resonance features integration of multiple terahertz metamaterials sensors for qualification and quantification of trace fluoroquinolone antibiotics

BACKGROUND

The residual fluoroquinolone antibiotics (FQs) in the environment and food has raised public concerns over their potential impact on human health. Terahertz metamaterial sensors (TMSs) have garnered significant attention due to their capability to enhance the interaction between terahertz waves and antibiotic molecules, enabling the detection of trace antibiotics. However, conventional quantitative and qualitative methods based on TMSs suffer from low accuracy and cumbersome processes, respectively. Herein, this work proposed a novel approach that reconstructed optimal terahertz response features of different TMSs with machine learning algorithms, which allowed for analysis of three similar trace FQs with enhanced accuracy.

RESULTS

The prepared three patterned TMSs exhibited different resonance responses, which varied with changes in FQs types and concentrations. The resonance peak features of the three TMSs were fused to construct the resonance peak feature matrix (W0) and combined with the K-Nearest Neighbor (KNN) algorithm to build the W0-KNN classification model. The interval feature matrix was constructed by optimizing and expanding the resonance peak feature width. The optimal resonance peak interval feature matrix (Wt) was combined with Gaussian process regression (GPR) algorithms with different kernel functions to build the Wt-GPR quantitative model. The results showed that W0-KNN achieved 100 % classification accuracy for the three FQs. Wt-GPR exhibited high quantitative accuracy for all three FQs with the determination coefficient (R2) of 0.94-0.98, and root mean square error (RMSE) of 6.4085-10.6540. The results of Wt-GPR with different kernel functions had small fluctuations, demonstrating high stability in predictive performance.

SIGNIFICANCE

Reconstructing features from multi-TMSs in combination with machine learning algorithms enables rapid, precise, and reliable qualitative and quantitative analysis of trace FQs. Our research introduces innovative concepts and methodologies to detect trace FQs using TMS-based sensors, paving the way for future applications of TMS in the biomolecular sensing and detection.

Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS)

Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify three different gluten wheats (high gluten, medium gluten, and low gluten). After collecting the time-domain spectral information of the samples, the frequency-domain spectra, refractive index spectra and absorption coefficient spectra of the samples were obtained by calculating the optical parameters. The experimental results showed that there were differences in the refractive indices and absorption coefficients of wheat with different gluten levels. More importantly the differences in refractive index spectra were more significant. The Competitive Adaptive Reweighted Sampling (CARS) method was applied to select characteristic frequencies from the refractive index spectra within the frequency range of 0.1 to 1.5 THz, to establish a discrimination model for wheat gluten strength. We analysed and compared four discriminative models of Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), Improved Convolutional Neural Networks (Improved CNN) and Sparrow Algorithm Optimised Support Vector Machines (SSA-SVM). The final results indicated that the SSA-SVM model demonstrated the optimal discrimination performance, achieving an accuracy rate of 100% as reflected in the confusion matrix. In summary, this study provides an efficient, accurate, and non-destructive discrimination method for wheat gluten strength, offering a theoretical basis for differentiating wheat with varying gluten strengths in production processes. It holds practical significance for industrial production reference.

RS, S (+) - and R (-)-ibuprofen cocrystal polymorphs: Vibrational spectra, XRD measurement and DFT calculation studies

In this paper, cocrystal polymorphs of RS-ibuprofen (RS-IBU), S (+)-ibuprofen (S(+)-IBU), R (-)-ibuprofen (R(-)-IBU) with nicotinamide (NIC) were synthesized by different methods. RS-IBU is a chiral drug with only one chiral center in the molecule, which has two enantiomers (S (+)-IBU and R (-)-IBU). Due to the low solubility and bioavailability of IBU, its application is limited. The pharmaceutical cocrystal technology can improve the physicochemical properties of the drug. In this paper, we characterized RS-IBU, S (+)-IBU, R (-)-IBU, NIC, physical mixtures and cocrystal polymorphs by terahertz (THz), Raman and X-ray Diffraction (XRD), respectively. By observing the experimental results, we could clearly distinguish the cocrystal polymorphs. We found that the melt recrystallization method can generate cocrystal form A, while the solvent drop grinding method and solution evaporation method can generate cocrystal form B. In addition, in order to verify the successful preparation of them, we used density functional theory (DFT) to optimize and simulate the theoretical structures of the RS-IBU: NIC cocrystal polymorphs, and compared the simulated results with the experimental results. These research results provide a reference for the analysis and preparation of pharmaceutical cocrystal polymorphs and help to distinguish the cocrystal polymorphs.

Advances in the Identification Methods of Food-Medicine Homologous Herbal Materials

As a key component of both traditional medicine and modern healthcare, Food-Medicine Homologous Herbal Materials have attracted considerable attention in recent years. However, issues related to the quality and authenticity of medicinal materials on the market often arise, not only compromising their efficacy but also presenting potential risks to consumer health. Therefore, the establishment of accurate and efficient identification methods is crucial for ensuring the safety and quality of Food-Medicine Homologous Herbal Materials. This paper provides a systematic review of the research progress on the identification methods for Food-Medicine Homologous Herbal Materials, starting with traditional methods such as morphological and microscopic identification, and focusing on the applications of modern techniques, including biomimetic recognition, chromatography, mass spectrometry, chromatography-mass spectrometry coupling, hyperspectral imaging, near-infrared spectroscopy, terahertz spectroscopy, and DNA barcoding. Moreover, it provides a comprehensive analysis of the fundamental principles, advantages, and limitations of these methods. Finally, the paper outlines the current challenges faced by identification methods and suggests future directions for improvement, aiming to offer a comprehensive technical perspective on identifying Food-Medicine Homologous Herbal Materials and foster further development in this field.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification. However, most THz-based identification methods empirically intercept the low-frequency band of the THz absorption coefficients for analysis. In order to adaptively find out important intervals of the THz spectra, an interval-based sparse ensemble multi-class classifier (ISEMCC) for THz spectral data classification is proposed. In ISEMCC, the THz spectra are first divided into several small intervals through window sliding. Then the data of training samples in each interval are extracted to train some base classifiers. Finally, a final robust classifier is obtained through a nonnegative sparse combination of these trained base classifiers. With l 1 -norm, two objective functions that based on Mean Square Error (MSE) and Cross Entropy (CE) are established. For these two objective functions, two iterative algorithms based on the Alternating Direction Method of Multipliers (ADMM) and Gradient Descent (GD) are built respectively. ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse constraint ensures only a few important spectral segments are selected. In order to verify the performance of the proposed algorithm, comparative experiments on identifying the origin of Bupleurum and the harvesting year of Tangerine peel are carried out. The base classifiers used by ISEMCC are Support Vector Machine (SVM) and Decision Tree (DT). The experimental results demonstrate that the proposed algorithm outperforms six typical classifiers, including Random Forest (RF), AdaBoost, RUSBoost, ExtraTree, and the two base classifiers, in terms of classification accuracy.