Analysis of Flavonoid Compounds by Terahertz Spectroscopy Combined with Chemometrics

Characteristic fingerprint spectrum of α-synuclein mutants on terahertz time-domain spectroscopy

α-Synuclein, a presynaptic neuronal protein encoded by the SNCA gene, is involved in the pathogenesis of Parkinson's disease. Point mutations and multiplications of α-synuclein (A30P and A53T) are correlated with early-onset Parkinson's disease characterized by rapid progression and poor prognosis. Currently, the clinical identification of SNCA variants, especially disease-related A30P and A53T mutants, remains challenging and also time consuming. This study aimed to develop a novel label-free detection method for distinguishing the SNCA mutants using transmission terahertz (THz) time-domain spectroscopy. The protein was spin-coated onto the quartz to form a thin film, which was measured using THz time-domain spectroscopy. The spectral characteristics of THz broadband pulse waves of α-synuclein protein variants (SNCA wild type, A30P, and A53T) at different frequencies were analyzed via Fourier transform. The amplitude A intensity (AWT, AA30P, and AA53T) and peak occurrence time in THz time-domain spectroscopy sensitively distinguished the three protein variants. The phase φ difference in THz frequency domain followed the trend of φWT > φA30P > φA53T. There was a significant difference in THz frequency amplitude A' corresponding to the frequency ranging from 0.4 to 0.66 THz (A'A53T > A'A30P > A'WT). At a frequency of 0.4-0.6 THz, the transmission T of THz waves distinguished three variants (TA53T > TA30P > TWT), whereas there was no difference in the transmission T at 0.66 THz. The SNCA wild-type protein and two mutant variants (A30P and A53T) had distinct characteristic fingerprint spectra on THz time-domain spectroscopy. This novel label-free detection method has great potential for the differential diagnosis of Parkinson's disease subtypes.

Applications of some advanced sequencing, analytical, and computational approaches in medicinal plant research: a review

The potential active chemicals found in medicinal plants, which have long been employed as natural medicines, are abundant. Exploring the genes responsible for producing these compounds has given new insights into medicinal plant research. Previously, the authentication of medicinal plants was done via DNA marker sequencing. With the advancement of sequencing technology, several new techniques like next-generation sequencing, single molecule sequencing, and fourth-generation sequencing have emerged. These techniques enshrined the role of molecular approaches for medicinal plants because all the genes involved in the biosynthesis of medicinal compound(s) could be identified through RNA-seq analysis. In several research insights, transcriptome data have also been used for the identification of biosynthesis pathways. miRNAs in several medicinal plants and their role in the biosynthesis pathway as well as regulation of the disease-causing genes were also identified. In several research articles, an in silico study was also found to be effective in identifying the inhibitory effect of medicinal plant-based compounds against virus' gene(s). The use of advanced analytical methods like spectroscopy and chromatography in metabolite proofing of secondary metabolites has also been reported in several recent research findings. Furthermore, advancement in molecular and analytic methods will give new insight into studying the traditionally important medicinal plants that are still unexplored.

Terahertz Spectroscopy and Imaging Techniques for Herbal Medicinal Plants Detection: A Comprehensive Review

Herbal medicine (HM), derived from various therapeutic plants, has garnered considerable attention for its remarkable effectiveness in treating diseases. However, numerous issues including improved varieties selection, hazardous residue detection, and concoction management affect herb quality throughout the manufacturing process. Therefore, a practical, rapid, nondestructive detection technology is necessary. Terahertz (THz) spectroscopy, with low energy, penetration, and fingerprint features, becomes preferable method for herb quality appraisal. There are three parts in our review. THz techniques, data processing, and modeling methods were introduced in Part I. Three primary applications (authenticity, composition and active ingredients, and origin detection) of THz in medicinal plants quality detection in industrial processing and marketing were detailed in Part II. A thorough investigation and outlook on the well-known applications and advancements of this field were presented in Part III. This review aims to bring new enlightenment to the in-depth THz application research in herbal medicinal plants.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy

Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.

Quantitative Analysis of Routine Chemical Constituents of Tobacco Based on Thermogravimetric Analysis

As the most basic indexes to evaluate the quality of tobacco, the contents of routine chemical constituents in tobacco are mainly detected by continuous-flow analysis at present. However, this method suffers from complex operation, time consumption, and environmental pollution. Thus, it is necessary to establish a rapid accurate detection method. Herein, different from the ongoing research studies that mainly chose near-infrared spectroscopy as the information source for quantitative analysis of chemical components in tobacco, we proposed for the first time to use the thermogravimetric (TG) curve to characterize the chemical composition of tobacco. The quantitative analysis models of six routine chemical constituents in tobacco, including total sugar, reducing sugar, total nitrogen, total alkaloids, chlorine, and potassium, were established by the combination of TG curve and partial least squares algorithm. The accuracy of the model was confirmed by the value of root mean square error for prediction. The models can be used for the rapid accurate analysis of compound contents. Moreover, we performed an in-depth analysis of the chemical mechanism revealed by the result of the quantitative model, namely, the regression coefficient, which reflected the correlation degree between the six chemicals and different stages of the tobacco thermal decomposition process.

To Estimate Performance of Artificial Neural Network Model Based on Terahertz Spectrum: Gelatin Identification as an Example

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

Designing luminescent diimine-Cu (I)-phosphine complexes by tuning N-ligand and counteranions: correlation of weak interactions, luminescence and THz absorption spectra

Herein, six new [Cu(N^N)(P^P)]+/0 complexes with different N-ligand and counteranions [Cu2(dmp)2(bdppmapy)I2] (1), [Cu2(dmp)2(bdppmapy)(CN)2]·3CH3OH (2), [Cu(dmp)(bdppmapy)](BF4) (3), [Cu(dmp)(bdppmapy)](ClO4) (4), [Cu(phen)(bdppmapy)](BF4) (5), [Cu(phen)(bdppmapy)](ClO4) (6) have been synthesized and characterized (bdppmapy = N,N-bis[(diphenylphosphino)methyl]-2-pyridinamine,...

Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information

Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC₂), Cd-PC₂ mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC₂ chelates Cd²⁺ in a 2:1 ratio to form Cd(PC₂)₂ complex; Cd connected to thoils of PC₂ and changed β-turn and random coil of PC₂ peptide chain to β-Sheet which presented as terahertz vibrations of PC₂ around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC₂ as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.

Application of Identification and Evaluation Techniques for Edible Mushrooms: A Review

Edible mushrooms are healthy food with high nutritional value, which is popular with consumers. With the increase of the problem of mushrooms being confused with the real and pollution in the market, people pay more and more attention to food safety. More than 167 articles of edible mushroom published in the past 20 years were reviewed in this paper. The analysis tools and data analysis methods of identification and quality evaluation of edible mushroom species, origin, mineral elements were reviewed. Five techniques for identification and evaluation of edible mushrooms were introduced and summarized. The macroscopic, microscopic and molecular identification techniques can be used to identify species. Chromatography, spectroscopy technology combined with chemometrics can be used for qualitative and quantitative study of mushroom and evaluation of mushroom quality. In addition, multiple supervised pattern-recognition techniques have good classification ability. Deep learning is more and more widely used in edible mushroom, which shows its advantages in image recognition and prediction. These techniques and analytical methods can provide strong support and guarantee for the identification and evaluation of mushroom, which is of great significance to the development and utilization of edible mushroom.

Novel Terahertz Spectroscopy Technology for Crystallinity and Crystal Structure Analysis of Cellulose

Crystallinity is an essential indicator for evaluating the quality of fiber materials. Terahertz spectroscopy technology has excellent penetrability, no harmful substances, and commendable detection capability of absorption characteristics. The terahertz spectroscopy technology has great application potential in the field of fiber material research, especially for the characterization of the crystallinity of cellulose. In this work, the absorption peak of wood cellulose, microcrystalline cellulose, wood nano cellulose, and cotton nano cellulose were probed in the terahertz band to calculate the crystallinity, and the result compared with XRD and FT-IR analysis. The vibration model of cellulose molecular motion was obtained by density functional theory. The results showed that the average length of wood cellulose (WC) single fiber was 300 μm. The microcrystalline cellulose (MCC) was bar-like, and the average length was 20 μm. The cotton cellulose nanofiber (C-CNF) was a single fibrous substance with a length of 50 μm, while the wood cellulose nanofiber (W-CNF) was with a length of 250 μm. The crystallinity of cellulose samples in THz was calculated as follows: 73% for WC, 78% for MCC, 85% for W-CNF, and 90% for C-CNF. The crystallinity values were obtained by the three methods which were different to some extent. The absorption peak of the terahertz spectra was most obvious when the samples thickness was 1 mm and mixed mass ratio of the polyethylene and cellulose was 1:1. The degree of crystallinity was proportional to the terahertz absorption coefficients of cellulose, the five-movement models of cellulose molecules corresponded to the five absorption peak positions of cellulose.

Anti-Inflammatory Effects of Heritiera littoralis Fruits on Dextran Sulfate Sodium- (DSS-) Induced Ulcerative Colitis in Mice by Regulating Gut Microbiota and Suppressing NF-κB Pathway

Materials and Methods

The chemical compositions of EFH were identified using LC-ESI-MS. The mice with 3% DSS-induced UC were administered EFH (200, 400, and 800 mg/kg), sulfasalazine (SASP, 200 mg/kg), and azathioprine (AZA, 13 mg/kg) for 10 days via daily gavage. The colonic inflammation was evaluated by the disease activity index (DAI), colonic length, histological scores, and levels of inflammatory mediators. The gut microbiota was characterized by 16S rRNA gene sequencing and analysis.

Results

LC-ESI-MS analysis showed that EFH was rich in alkaloids and flavones. The results indicated that EFH significantly improved the DAI score, relieved colon shortening, and repaired pathological colonic variations in colitis. In addition, proteins in the NF-κB pathway were significantly inhibited by EFH. Furthermore, EFH recovered the diversity and balance of the gut microbiota.

Conclusions

EFH has protective effects against DSS-induced colitis by keeping the balance of the gut microbiota and suppressing the NF-κB pathway.