COVID-19 impedimetric biosensor based on polypyrrole nanotubes, nickel hydroxide and VHH antibody fragment: specific, sensitive, and rapid viral detection in saliva samples.

SARS-CoV-2 rapid spread required urgent, accurate, and prompt diagnosis to control the virus dissemination and pandemic management. Several sensors were developed using different biorecognition elements to obtain high specificity and sensitivity. However, the task to achieve these parameters in combination with fast detection, simplicity, and portability to identify the biorecognition element even in low concentration remains a challenge. Therefore, we developed an electrochemical biosensor based on polypyrrole nanotubes coupled via Ni(OH)2 ligation to an engineered antigen-binding fragment of heavy chain-only antibodies (VHH) termed Sb#15. Herein we report Sb#15-His6 expression, purification, and characterization of its interaction with the receptor-binding domain (RBD) of SARS-CoV-2 in addition to the construction and validation of a biosensor. The recombinant Sb#15 is correctly folded and interacts with the RBD with a dissociation constant (KD) of 27.1 ± 6.4 nmol/L. The biosensing platform was developed using polypyrrole nanotubes and Ni(OH)2, which can properly orientate the immobilization of Sb#15-His6 at the electrode surface through His-tag interaction for the sensitive SARS-CoV-2 antigen detection. The quantification limit was determined as 0.01 pg/mL using recombinant RBD, which was expressively lower than commercial monoclonal antibodies. In pre-characterized saliva, both Omicron and Delta SARS-CoV-2 were accurately detected only in positive samples, meeting all the requirements recommended by the World Health Organization for in vitro diagnostics. A low sample volume of saliva is needed to perform the detection, providing results within 15 min without further sample preparations. In summary, a new perspective allying recombinant VHHs with biosensor development and real sample detection was explored, addressing the need for accurate, rapid, and sensitive biosensors.

Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics

There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infrared (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-IR (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-IR spectroscopy (NIRS) and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long- and short-term experiments. The spectra were preprocessed and analyzed by partial least-squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus-type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on- site, and low-cost virus prescreening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.

Authentication of Tinospora cordifolia derived herbal supplements using high resolution mass spectrometry-based metabolomics approach – A pilot study

Tinospora cordifolia herbal supplements have recently gained prominence due to their promising immunomodulatory and anti-viral effects against SARS-CoV-2. Mislabelling or diluting Tinospora supplements for profit may harm public health. Thus, validating the label claim of these supplements in markets is critical. This study investigated how high resolution mass spectrometry-based metabolomics and chemometrics can be used to distinguish Tinospora cordifolia from two other closely related species (T. crispa and T. sinensis). The Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) and PLS-DA based chemometric models predicted the species identity of Tinospora with 94.44% accuracy. These classification models were trained using 54 T. cordifolia, 21 T. crispa, and 21 T. sinensis samples. We identified 7 biomarkers, including corydine, malabarolide, ecdysterone, and reticuline, which discriminated Tinospora cordifolia from the two other species. The label claim of 25 commercial Tinospora samples collected from different parts of India was verified based on the relative abundance of the biomarker compounds, of which 20 were found authentic. The relative abundance of biomarkers significantly varied in the 5 suspicious market samples. This pilot study demonstrates a robust metabolomic approach for authenticating Tinospora species, which can further be used in other herbal matrices for product authentication and securing quality. © 2023 Elsevier B.V.

Preparation and FTIR-ATR combined with chemometrics analysis of self-emulsifying loaded sungkai extract from Peronema canecens

The use of immunomodulators is one strategy in maintaining the immune system during the Covid-19 pandemic. Sungkai leaf extract from Peronema canecens keeps the immune system in good shape. Therefore, in this study, we formulated a self-emulsifying loaded sungkai leaves extract (SE-SLE) with oleic acid and virgin coconut oil (VCO) oil phases, span 80 and tween 80 as surfactants and co-surfactants in the form of PEG-400 and PG. Chemometric analysis was conducted by observing the typical pattern in each FTIR-ATR spectra. The pattern is divided into several groups based on the wavenumber and analyzed using principal component analysis (PCA) to identify the compounds contained therein. Grouping based on chemical properties via IR spectra on SE-SLE resulted in two large groups. The results obtained are beneficial as initial information in developing and optimizing the self-nano emulsifying drug delivery system formula.Copyright © RJPT All right reserved.

Review: Recent Development in Food Adulteration Analytical Techniques

Food adulteration is an enduring concern to date, and its detection and food authentication are some of the major approaches which can eradicate adulteration. This article reviews modern analytical techniques which are currently used for optimization and identification of adulterants. The Analytical techniques reviewed are distributed in three sections- Computer vision, Spectral and spectral Imaging techniques, and Electrical Techniques. Computer vision is a very advanced technique, allowing the analysis of several parameters with accurate and precise results. The hazardous effect of adulteration and future challenges and impacts are briefly discussed. The Previous decade is considered to be a massive success for food adulterant detection as it involved a chemometrics study. The use of chemometrics with analytical techniques is briefly discussed. Chemometrics is the science of extracting information from the chemical system by data-driven means. The inevitability of food in perspective to the current COVID-19 pandemic is very strong and authenticated food is a prior requirement. Copyright © 2022 Society of Pharmaceutical Sciences and Research. All rights reserved.

Chemical profile of anti-epidemic sachet based on multiple sample preparation coupled with gas chromatography-mass spectrometry analysis combined with an embedded peaks resolution method and their action mechanisms.

The anti-epidemic sachet (Fang Yi Xiang Nang, FYXN) in traditional Chinese medicine (TCM) can prevent COVID-19 through volatile compounds that can play the role of fragrant and dampness, heat-clearing and detoxifying, warding off filth and pathogenic factors. Nevertheless, the anti-(mutant) SARS-CoV-2 compounds and the compounds related to the mechanism in vivo, and the mechanism of FYXN are still vague. In this study, the volatile compound set of FYXN was constructed by gas chromatography-mass spectrometry (GC-MS) based on multiple sample preparation methods, which include headspace (HS), headspace solid phase microextraction (HS-SPME) and pressurized liquid extraction (PLE). In addition, selective ion analysis (SIA) was used to resolve embedded chromatographic peaks present in HS-SPME results. Preliminary analysis of active compounds and mechanism of FYXN by network pharmacology combined with disease pathway information based on GC-MS results. A total of 96 volatile compounds in FYXN were collected by GC-MS analysis. 39 potential anti-viral compounds were screened by molecular docking. 13 key pathways were obtained by KEGG pathway analysis (PI3K-Akt signaling pathway, HIF-1 signaling pathway, etc.) for FYXN to prevent COVID-19. 16 anti-viral compounds (C95, C91, etc.), 10 core targets (RELA, MAPK1, etc.), and 16 key compounds related to the mechanism in vivo (C56, C30, etc.) were obtained by network analysis. The relevant pharmacological effects of key pathways and key compounds were verified by the literature. Finally, molecular docking was used to verify the relationship between core targets and key compounds, which are related to the mechanism in vivo. A variety of sample preparation methods coupled with GC-MS analysis combined with an embedded peaks resolution method and integrated with network pharmacology can not only comprehensively characterize the volatile compounds in FYXN, but also expand the network pharmacology research ideas, and help to discover the active compounds and mechanisms in FYXN.

Interactive Python Notebooks for Physical Chemistry

Chemistry simulations using interactive graphic user interfaces (GUIs) represent uniquely effective and safe tools to support multidimensional learning. Computer literacy and coding skills have become increasingly important in the chemical sciences. In response to both of these facts, a series of Jupyter notebooks hosted on Google Colaboratory were developed for undergraduate students enrolled in physical chemistry. These modules were developed for use during the COVID-19 pandemic when Millsaps College courses were virtual and only virtual or online laboratories could be used. These interactive exercises employ the Python programming language to explore a variety of chemical problems related to kinetics, the Maxwell-Boltzmann distribution, numerical versus analytical solutions, and real-world application of concepts. All of the modules are available for download from GitHub (https://github.com/Abravene/Python-Notebooks-for-Physical-Chemistry). Accessibility was prioritized, and students were assumed to have no prior programming experience;the notebooks are cost-free and browser-based. Students were guided to use widgets to build interactive GUIs that provide dynamic representations, immediate access to multiple investigations, and interaction with key variables. To evaluate the perceived effectiveness of this introduction to Python programming, participants were surveyed at the beginning and end of the course to gauge their interest in pursuing programming and data analysis skills and how they viewed the importance of programming and data analysis for their future careers. Student reactions were generally positive and showed increased interest in programming and its importance in their futures, so these notebooks will be incorporated into the in-person laboratory in the future.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives.

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

FTIR-ATR based fingerprinting and chemometrics analysis of metabolites profile of Phyllanthus niruri L. affected by fertilization with NPK-chitosan nanopolymer and harvesting age

Summary: Introduction: Phyllanthus niruri L. (PnL) is a herbaceous plant containing flavonoid quercetin and can be used as an immunomodulator to prevent Covid-19. However, the flavonoid content and yield of herbs extract were not maximized. Therefore, PnL herbs were planted in various harvest periods and application of NPK-chitosan nanopolymer fertilizer to estimate these parameters. Objectives: Determine the effect of NPK-chitosan nanopolymer fertilizer and harvesting age on herb extracts also determine the grouping pattern and correlation between responses based on FTIR-ATR spectral pattern using a chemometric approach. Methods: Each group consisted of 50 plants. The formulation of NPK-chitosan nanopolymer fertilizer based on the dose of NPK consisting of the first dose is 15.5 grams/group, the second dose is 31 grams/group, and the third dose is 7.5 grams/group. Grouping of differences in harvesting age for plants consisted of 4, 6, and 8 weeks after the plant (WAP). Extraction used ultrasound-Assisted extraction, and data were analyzed using a chemometric approach. Results: Extract with the highest yield was found in second harvest time and third doses of fertilizer (W2D3) which is 9.73 %, and the highest TFC obtained in an extract with second harvest time and first doses of fertilizer (W2D1) is 17.34 mg QE/g. Total flavonoid content and extract yield were influenced by functional groups at wavenumbers 3486.77-3157.12 cm-1 (1);1740.96-1670.34 cm-1 (3);1425.02-1272.62 cm-1 (5);1257,753-1138,81 cm-1 (6);1131.38-945.53 cm-1 (7);711.36-529.23 cm-1 (8). Conclusions: The results showed that harvest time and fertilizer dose affected the growth parameters of PnL, total flavonoid content, and yield of extract. Functional groups in IR spectra also have positive and negative correlations with total flavonoid and yield extract responses. © 2022 Shaum Shiyan et al., published by Sciendo.

Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-infrared Spectroscopy and Chemometrics

There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infra-red (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-infrared (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-infrared spectroscopy and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco’s modified eagle medium (DMEM), were collected in long-term and short-term experiments. The spectra were pre-processed, and analysed by partial least squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on-site and low-cost virus pre-screening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2. IEEE

Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue.

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14-88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.

Classification of Polish Natural Bee Honeys Based on Their Chemical Composition.

The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.

Cuttlefish bone powder as an efficient solid-phase extraction sorbent of anti-SARS-CoV-2 drugs in environmental water.

Many antiviral drugs were developed to counteract coronavirus disease, 2019 (COVID-19) with severe acute respiratory syndrome. Therefore, the scientific community's efforts have focused on the detection and quantification of antiviral compounds currently being tested for COVID-19 treatment. Cuttlefish bone powder (CFBP) has been used for the first time as solid-phase extraction (SPE) sorbent for the extraction of SARS CoV-2 antiviral drugs (chloroquine, ritonavir and indomethacin) from water samples. An effective and sensitive method was developed by combining SPE and liquid chromatography- UV detection (LC-UV). An experimental design was applied for the optimization of extraction process. Experimental variables were optimized using Doehlert matrix. The developed method included 50 mg of CFBP sorbent, 20 mL of water sample at pH = 9 and 5 mL of ACN/KH2PO4 buffer solution (80:20, v/v) in the elution step. For validation of the method, selectivity, linearity precision, and sensitivity were evaluated. Extraction recovery percentage of all Sars cov-2 antivirals were above 98.2%. The detection and quantification limits were between 0.1 and 0.5 µg L-1 and 0.6 and 2 µg L-1, respectively. The current study suggested that CFBP has the application potential for the enhanced SPE of SARS CoV-2 antiviral drugs from water samples.

Volatile and semi-volatile compounds in flavoured hard seltzer beverages: comparison of high-capacity sorptive extraction (HiSorb) methods

The change in consumer behaviour towards healthier lifestyles since the Covid-19 pandemic has seen a steep rise in popularity of low-calorie, low-sugar food and beverage alternatives, like flavoured hard seltzers. In this study, a fully automated, high-capacity sorptive extraction (HiSorb) technique, coupled with gas chromatography–mass spectrometry (GC–MS), was developed to investigate volatile and semi-volatile organic compounds (VOCs and SVOCs) used for flavouring of hard seltzers. As part of methos optimisation we trialled various sample preparation protocols and compared extraction via direct immersion vs. extraction from the headspace. The best headspace and immersive techniques were then further analysed in a ‘stacked’ extraction, whereby extracts from both were collected onto a focusing trap and fired to the GC to produce a single chromatogram. HiSorb probes with 4 alternate phases were compared: Polydimethylsiloxane (PDMS), divinylbenzene/PDMS (DVB/PDMS), carbon wide range/ PDMS (CWR/PDMS) and a triple phase (DVB/CWR/PDMS), with the DVB/PDMS phase proving to extract the highest number of compounds. The DVB/PDMS probe was further applied to a study of four berry/cherry flavour hard seltzer drinks, produced by 4 different leading commercial brands, with 64 compounds extracted and identified. Chemometrics were able to distinguish each brand's flavour profile by detection of unique compounds, these having potential for use as quality and authenticity markers.

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective.

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.

Characterization of Carotenoids from Pineapples: An Integrated andModular Experiment for Practical Learning of UV-vis Spectroscopy,Chromatography, Mass Spectrometry, and Chemometrics br

In the past few decades, chemistry has evolved to interact withvarious disciplines to synergistically help tackle global challenges. This, in turn,requires that newer generations of chemistry students are trained to be moreflexible in accepting and coordinating new concepts. In this experiment, pineapplewas used as a key model to allow for the incorporation of multiple importantconcepts into the laboratory. Using carotenoid separation as a main goal, columnchromatography, UV-vis spectroscopy, thin-layer chromatography, high-perform-ance liquid chromatography, mass spectrometry, and principal component analysiscan be included in a cohesive laboratory experiment. On the other hand, theseactivities were also designed to be modular, thus allowing instructors to add,remove, or modify the contents in a highly customizable manner. This makes ithighly versatile and amenable to uncertain situations like unexpected universityclosure due to COVID-19 related lockdown. Overall, this laboratory experimentserves as a practical example of how chemistry can help solve real-world problems while also allowing highflexibility in teaching management

Post-COVID syndrome screening through breath analysis using electronic nose technology.

There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.Trial registrationBreath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.

Effects of environmental parameters and their interactions on the spreading of SARS-CoV-2 in North Italy under different social restrictions. A new approach based on multivariate analysis.

In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May-December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection ("free/summer period"); b) increasing incidence of disease, social restrictions and use of personal protections ("confined/autumn period"). The "hospitalized people in medical area wards/100000 residents" was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions. The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function.

A two-tiered system of analysis to tackle rice fraud: The Indian Basmati study.

Demand for high quality Basmati rice has increased significantly in the last decade. This commodity is highly vulnerable to fraud, especially in the post COVID-19 era. A unique two-tiered analytical system comprised of rapid on-site screening of samples using handheld portable Near-infrared NIR and laboratory confirmatory technique using a Head space gas chromatography mass spectrometry (HS-GC-MS) strategy for untargeted analysis was developed. Chemometric models built using NIR data correctly predicted nearly 100% of Pusa 1121 and Taraori, two high value types of Basmati, from potential adulterants. Furthermore, rice VOC profile fingerprints showed very good classification (R2 >0.9, Q2 > 0.9, Accuracy > 0.99) for these high quality Basmati varieties from potential adulterant varieties with aldehydes identified as key VOC marker compounds. Using a two-tiered system of a rapid method for on-site screening of many samples alongside a laboratory-based confirmatory method can classify Basmati rice varieties, protecting the supply chain from fraud.