Quantification of active ingredients in Potentilla tormentilla by Raman and infrared spectroscopy

Selective metabolism of tormentil rhizome constituents by human gut microbiota and its impact on biodiversity ex vivo

Tormentil rhizome was used for centuries in traditional medicine to treat gastrointestinal disorders. Its anti-inflammatory, antioxidant, and antibacterial properties, and unique taste, suggest its potential as a food additive for functional foods. The research aimed to determine gut metabolites of the extract, their impact on microbiota biodiversity, and interactions between them. Gut metabolites were obtained by ex vivo incubation of extract with fecal samples. Among the compounds, only catechin and its oligomers were metabolized by gut microbiota. Triterpenes remained unchanged, while ellagic acid derivatives were undetected in metabolized and unmetabolized forms. The extract also promoted bacteria growth from the Ruminococcaceae family (producers of short-chain fatty acids) and other families, increasing microbiota biodiversity. A mutual interaction occurred between Ruminococcaceae and metabolites, with bacteria influencing metabolite production and metabolites enhancing bacterial growth. The selective metabolism of tormentil rhizome and its interaction with gut microbiota may offer new strategies to improve gut health.

Application of vibrational and fluorescence spectroscopy to the compositional analysis of colored-flesh potatoes

BACKGROUND

Determination of composition and physicochemical parameters of natural products requires dedicated, often laborious and expensive, analytical protocols. Different spectroscopic techniques, in conjunction with chemometrics, seem to have a considerable potential in direct analysis of raw plant material and foods, without any chemical treatment.

RESULTS

Fluorescence spectroscopy and three vibrational spectroscopy techniques were applied to determine total polyphenol content, antioxidant activity and macronutrient levels in red- and purple-fleshed potato varieties. Excitation-emission matrix fluorescence, Fourier transform Raman, attenuated total reflection Fourier transform infrared and near-infrared spectra were recorded for the freeze-dried samples. Combining spectral data and the results of reference analyses, partial least squares regression models were constructed for each parameter studied. For polyphenols and antioxidant activity, quantification errors found for validation samples amounted to 3.74-5.04% and 4.75-6.35%, respectively, whereas macronutrient analysis gave errors in the 3.45-4.55%, 3.09-5.30% and 5.10-8.58% ranges for starch, protein and sugar determinations, respectively.

CONCLUSION

The obtained results demonstrate that different spectroscopic techniques in combination with multivariate modeling allow simultaneous determination of various parameters of plant samples based on a single sample spectrum. They can effectively replace commonly used protocols of food product analysis requiring sample dissolving and extraction of the compounds of interest. © 2023 Society of Chemical Industry.

Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning

Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Raman Spectroscopic and Sensory Evaluation of Cocoa Liquor Prepared with Ecuadorian Cocoa Beans Treated with Gamma Irradiation or Induced Electromagnetic Field Fermentation

Cocoa liquor is the primary precursor of the worldwide highly appreciated commodity chocolate. Its quality depends on several factors, such as the type of cocoa, the fermentation process, and the control of the contaminants in the fermented beans. This study aims to evaluate whether the induced magnetic field treatment during the fermentation process or the pathogen reduction with gamma irradiation after the fermentation affect the characteristics of the cocoa liquor obtained from Ecuadorian cocoa beans. For this purpose, liquor samples from controls (standard process), from beans treated with an induced magnetic field up to 80 mT, and from beans irradiated with nominal doses up to 3 kGy were characterized through Raman spectroscopic analysis and sensorial evaluation. The most relevant bands of the cocoa liquor were assigned according to reports from the literature, spectroscopic data, and chemometrics. The spectra corresponding to different treatments and doses were visually very similar, but they could be discriminated using OPLS-DA models, where the most intense Raman signals were attributed to the lipid components. The sensorial evaluation rated the presence of floral, fruity, almondy, acid, and bitter flavors, along with astringency and intense aroma, and these attributes exhibited variable behavior depending on the dose of the irradiation or magnetic treatment. Therefore, both treatments may exert an influence on cocoa beans and, therefore, on the cocoa liquor quality.

A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy

A novel method based on GC-MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.

Quantitative Determination of Polyphenols and Flavonoids in Cistus × incanus on the Basis of IR, NIR and Raman Spectra

Cistus is a plant that has been used in natural medicine for hundreds of years; it works primarily as an antioxidant and cleansing agent. Cistus × incanus leaves or herb can be an attractive source of polyphenols and flavonoids. The official protocols of active compound analysis relies on the extraction of compounds of interest from plant matter, which makes their determination long and costly. An analysis of plant material in its native state can be performed using vibrational spectroscopy. This paper presents a comparison of Raman spectroscopy, attenuated total reflection in mid-infrared and diffuse reflectance technique in the near-infrared region for the simultaneous quantification of total polyphenols (TPC) and flavonoids (TF) content, as well as the determination of FRAP antioxidant activity of C. incanus material. Utilizing vibrational spectra and using partial least squares algorithm, TPC and TF were quantified with the RSEP_VAL errors in the 2.7-5.4% range, while FRAP antioxidant activity for validation sets was determined with relative errors ranged from 5.2 to 9.3%. For the analyzed parameters, the lowest errors of predictions were computed for models constructed using Raman data. The developed models allow for fast and precise quantification of the studied active compounds in C. incanus material without any chemical sample treatment.

Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra

Poplar bark and leaves can be an attractive source of salicylates and other biologically active compounds used in medicine. However, the biochemical variability of poplar material requires a standardization prior to processing. The official analytical protocols used in the pharmaceutical industry rely on the extraction of active compounds, which makes their determination long and costly. An analysis of plant materials in their native state can be performed using vibrational spectroscopy. This paper presents for the first time a comparison of diffuse reflectance in the near- and mid-infrared regions, attenuated total reflection, and Raman spectroscopy used for the simultaneous determination of salicylates and flavonoids in poplar bark and leaves. Based on 185 spectra of various poplar species and hybrid powdered samples, partial least squares regression models, characterized by the relative standard errors of prediction in the 4.5-9.9% range for both calibration and validation sets, were developed. These models allow for fast and precise quantification of the studied active compounds in poplar bark and leaves without any chemical sample treatment.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.

UV spectroscopy and HPLC combined with chemometrics for rapid discrimination and quantification of Curcumae Rhizoma from three botanical origins

Curcumae Rhizoma (Ezhu in Chinese) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Ezhu from three botanical origins (Curcuma kwangsiensis, Curcuma phaeocaulis, and Curcuma wenyujin), ultra-violet (UV) spectroscopy and high performance liquid chromatography (HPLC) combined with chemometric tools were employed in this study. Firstly, the analysis method for the simultaneous determination of eleven compounds in Ezhu was developed by HPLC, and the UV spectra of thirty-eight batches of Ezhu were acquired. Then, principal component analysis (PCA), an unsupervised pattern recognition method, was applied on the HPLC and UV spectral data. PCA did not show a clear separation between C. phaeocaulis and C. wenyujin samples with HPLC data. By contrast, the supervised techniques, decision tree (DT) and linear discriminant analysis (LDA), achieved the complete discrimination for the three species of Ezhu with 100 % correct classification rate (CCR), showing excellent performance. Based on UV spectral data, PCA presented good performance for discriminating the three species of Ezhu. LDA, support vector machine (SVM) and k-nearest neighbors (KNN) models provided 96.3 % CCR for the calibration set and 100 % CCR for the validation set. Moreover, the partial least squares (PLS) and back propagation-artificial neural network (BP-ANN) quantitative models established on UV spectral data were satisfactory in predicting the contents of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane. The residual predictive deviation (RPD) for zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane of PLS models were 3.169, 1.502 and 1.735, and that of BP-ANN models were 3.467, 2.481 and 2.370, respectively. The present work proposed a rapid and reliable method for the discrimination of Ezhu from three botanical origins and the prediction of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane contents in Ezhu, which will help a lot in the quality control of Ezhu and other multi-origin herbal medicines.

Preventive Applications of Polyphenols in Dentistry-A Review

Polyphenols are natural substances that have been shown to provide various health benefits. Antioxidant, anti-inflammatory, and anti-carcinogenic effects have been described. At the same time, they inhibit the actions of bacteria, viruses, and fungi. Thus, studies have also examined their effects within the oral cavity. This review provides an overview on the different polyphenols, and their structure and interactions with the tooth surface and the pellicle. In particular, the effects of various tea polyphenols on bioadhesion and erosion have been reviewed. The current research confirms that polyphenols can reduce the growth of cariogenic bacteria. Furthermore, they can decrease the adherence of bacteria to the tooth surface and improve the erosion-protective properties of the acquired enamel pellicle. Tea polyphenols, especially, have the potential to contribute to an oral health-related diet. However, in vitro studies have mainly been conducted. In situ studies and clinical studies need to be extended and supplemented in order to significantly contribute to additive prevention measures in caries prophylaxis.

Electrochemical and Spectrophotometric Methods for Polyphenol and Ascorbic Acid Determination in Fruit and Vegetable Extracts

RESEARCH BACKGROUND

Fresh-cut fruits and vegetables are considered sources of antioxidant compounds. However, their shelf life is limited due to nutritional, quality and safety deterioration. Therefore, in recent decades, various methods have been reported for food processing and preservation, as well as for the determination of antioxidant compounds, due to their many benefits when consumed. The aim of the present work is to compare the performance of electrochemical and spectrophotometric methods in the analysis of the content of polyphenolic compounds and ascorbic acid in extracts from fruits (eggplant), edible roots (carrot) and leaves (rocket, lettuce and chard), and evaluate their capability to detect small changes in the antioxidant content in the eggplant extracts previously irradiated with different UV-C light intensities.

EXPERIMENTAL APPROACH

Polyphenolic compounds and ascorbic acid were determined by electrochemical and spectrophotometric methods. An enzymatic biosensor and a nanocomposite sensor were used for polyphenolic compounds and ascorbic acid, respectively, in electrochemical measurements, while Folin-Ciocalteu and Kampfenkel methods were used for spectrophotometric measurements.

RESULTS AND CONCLUSION

Results obtained through the different methodologies were comparable and consistent with each other. Both methods allowed determining the content of ascorbic acid and polyphenolic compounds in the fruit and vegetable extracts. Moreover, both techniques enable the detection of the analyte concentration changes in samples exposed to different UV-C intensities and storage days. Finally, it was observed that the antioxidant capacity depends on the type of food, treatment and storage period.

NOVELTY AND SCIENTIFIC CONTRIBUTION

Both methodologies were suitable for the quantification of analytes; however, the electrochemical sensors provided higher specificity and selectivity, applicable to different fruit and vegetable matrices, obtaining results with higher precision, in shorter time and with a smaller sample volume, minimizing the economic costs because of the lower consumption of reagents.

The Role of medicinal herbs in treatment of insulin resistance in patients with Polycystic Ovary Syndrome: A literature review

Background Polycystic Ovary Syndrome (PCOS) is one of the most common endocrine abnormalities in women. Due to the side effects of drugs, the tendency to use natural antioxidants and anti-inflammatory agents to regulate metabolism, hyperinsulinemia, and hyperlipidemia in PCOS patients has been increased. This review aimed to investigate the role of herbal substances on the treatment of PCOS. Methods The present review was carried out using keywords such as polycystic ovary syndrome and/or PCOS and/or herb. Databases including Web of Science, PubMed, and Science Direct were used to collect all related articles published from 1990 to 2019. We excluded studies unrelated to the PCOS and medical herbs. Results Overall, 361 records were identified through database searching. After primary screening and the full-texts assessment, 323 records were excluded, and 38 articles were finally included. The results indicate that some medicinal herbs may have a key role in treating PCOS. The compounds in these medical herbs can affect lipid profiles (Aloe vera, chamomile, and cinnamon), insulin resistance (cinnamon, chamomile, Aloe vera, and Camellia sinensis), blood glucose (Aloe vera, cinnamon, and Camellia sinensis), hormones (Aloe vera, silymarin, chamomile, fenugreek, Camellia sinensis, Heracleum persicum, Potentilla, Mentha spicata, Foeniculum vulgar, licorice, and Marrubium), and ovarian tissue (Aloe vera, chamomile, Camellia sinensis, Mentha spicata, and silymarin). Conclusion Natural substances such as Aloe vera, cinnamon, green tea, fenugreek, and silymarin can be used as a new supportive care for PCOS. Further clinical trials are warranted to confirm their benefits and safety.