Automatic Classification of Antimalarial Herbal Drugs Exposed to Ultraviolet Radiation from Unexposed Ones Using Laser-Induced Autofluorescence with Chemometric Techniques.

Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.

Chemical characterization and speciation of the soluble fraction of Arctic PM10.

The chemical composition of the soluble fraction of atmospheric particulate matter (PM) and how these components can combine with each other to form different species affect the chemistry of the aqueous phase dispersed in the atmosphere: raindrops, clouds, fog, and ice particles. The study was focused on the analysis of the soluble fraction of Arctic PM10 samples collected at Ny-Ålesund (Svalbard Islands, Norwegian Arctic) during the year 2012. The concentration values of Na+, K+, NH4+, Ca2+, Mg2+, Mn2+, Cu2+, Zn2+, Fe3+, Al3+, Cl-, NO2-, NO3-, SO42-, PO43-, formate, acetate, malonate, and oxalate in the water-soluble fraction of PM10 were determined by atomic spectroscopy and ion chromatography. Speciation models were applied to define the major species that would occur in aqueous solution as a function of pH (2-10). The model highlights that (i) the main cations such as Na+, K+, Mg2+, and Ca2+ occur in the form of aquoions in the whole investigated pH range; (ii) Cu2+, Zn2+, and, in particular, Fe3+ and Al3+ are mostly present in their hydrolytic forms; and (iii) Al3+, Fe3+, and Cu2+ form solid hydrolytic species that precipitate at pH values slightly higher than neutrality. These latter metals show interesting interactions with oxalate and sulfate ions, too. The speciation models were also calculated considering the seasonal variability of the concentration of the components and at higher concentration levels than those found in water PM extracts, to better simulate concentrations actually found in the atmospheric aqueous phase. The results highlight the role of oxalate as the main organic ligand in solution.

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques.

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

Antioxidant Activities of Different Peanut (Arachis hypogaea L.) Market Types by Spectrophotometric Techniques Combined with Chemometrics.

Peanut is rich in oil and protein and has a large content of bioactive constituents consisting of tocopherols, phytosterols, and so on. Generally, Virginia, Spanish, Valencia and Runner market types are grown of peanut. In this study, it is aimed to determine the antioxidant activity, total phenolic content and total flavonoid content of peanuts from four different market types, for the first time, and group them with principal component analysis (PCA) and hierarchical cluster analysis (HCA). For PCA, PC1 and PC2 explained 87.655 % of the total variation and, according to the HCA of peanut samples, two main groups were determined. The total phenolic content changed 1.556 to 2.899 mg GAE/g. The lowest value have seen at Spanish merket type to determine the antioxidant activities of peanut samples were maked FRAP and DPPH assay, the lowest FRAP value (8.136 µmol FeSO47H2O/g sample) was seen at Valencia market type, the highest (14.004 µmol FeSO47H2O/g sample) was seen at Virginia market type. It was determined that the total flavonoid, total phenolic content, and antioxidant activities of the Virginia, Valencia, Spanish, and Runner market types included in the study were different from each other, and the Virginia market type showed superior characteristics compared to the others. The results obtained suggest that Virginia market type may be preferred more especially in peanut cultivation for food uses. It is thought that this study can be a source for future studies by eliminating a deficiency in the literature.

Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods.

In this research, the total phenolic and flavonoid amounts, phenolic compositions, in vitro antioxidant, antibacterial and antidiabetic properties of the methanol extracts obtained from Scabiosa L. (Caprifoliaceae) species distributed in the flora of Türkiye were investigated using chemometric methods. For this purpose, principal component (PCA) and agglomerative hierarchical clustering analysis were performed as chemometric methods. Chlorogenic acid, quinic acid and cyranoside were determined in the extracts. According to chemometric analysis, S. columbaria subsp. ochroleuca var. ochroleuca and S. triniifolia species were found to be valuable in terms of methanol extract yields, total phenolic and flavonoid contents, antioxidant and antidiabetic activities while S. columbaria subsp. ochroleuca var. webbiana species were found to be valuable in terms of phenolic composition. The methanol extracts of Scabiosa species showed high antioxidant activity, with high phenolic and flavonoid contents. Among the tested 13 bacteria, Scabiosa extracts showed only low activity against Klebsiella pneumoniae, Streptococcus pneumoniae and Pseudomonas aeruginosa. The extracts showed high α-amylase and α-glucosidase inhibitory activity. The results show that Scabiosa methanol extracts may be a source of alternative antioxidants that may be beneficial in slowing or preventing the progression of various oxidative stress-related diseases.

Analysis of Antioxidant Properties and Compounds in Different Rosa Taxa Fruits Using UV-Vis and UPLC-TQ-MS Techniques.

This comprehensive analysis of the fruits of Rosa spp. (FR) evaluates their chemical components and antioxidant activity. The study quantified total flavonoids and polyphenols using aluminum trichloride colorimetric assay and Folin-Ciocalteu methods, with the fruit of Rosa. laxa Rtez. var. mollis Yü et Ku. sample exhibiting the highest concentrations of 59.21 mg/g and 81.13 mg/g, respectively. Ultra-High-Performance Liquid Chromatography-Triple Quadrupole Mass Spectrometry (UPLC-TQ-MS) assessed seven primary components, with notable levels of euscaphic acid, ursolic acid, and gallic acid. Antioxidant activities were tested using DPPH and ABTS methods, showing strong activities in samples the fruits of Rosa. persica Mickx ex Juss. and Rosa. laxa Rtez. var. kaschgarica (Rupr.) Y. L. Han. Chemometric analyses, including similarity, cluster, principal component, and grey relational analyses, were used to explore relationships between FR varieties and their antioxidant properties. The study provides a vital basis for future FR quality assessments.

SERS-based Ag NCs@PDMS flexible substrate combined with chemometrics for rapid detection of foodborne pathogens on egg surface.

An innovative method is introduced based on the combination of label-free surface-enhanced Raman scattering with advanced multivariate analysis. This technique allows both quantitative and qualitative assessment of Salmonella typhimurium and Escherichia coli on eggshells. Using silver nanocubes embedded in polydimethylsiloxane, we consistently achieved Raman spectra of bacteria. The stability of the Ag NCs@PDMS substrate is confirmed using rhodamine 6G over 30 days under standard conditions. Principal component analysis (PCA) effectively distinguishes between S. typhimurium and E. coli spectra. Partial least squares regression (PLS) models were developed for quantitative determination of bacteria on egg surfaces, yielding accurate results with minimal error. The S. typhimurium model achieves Rc2 = 0.9563 and RMSEC = 0.601 in calibration, and Rv2 = 0.9113 and RMSEV = 0.907 in validation. Similarly, the E. coli model achieves Rc2 = 0.9877 and RMSEC = 0.322 in calibration, and Rv2 = 0.9606 and RMSEV = 0.579 in validation. Recoveries validate PLS predictions by inoculating egg surfaces with varying bacterial amounts. Our study demonstrates the feasibility of SERS-PLS for quantitative determination of S. typhimurium and E. coli on eggshells, promising enhanced food safety protocols.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques.

INTRODUCTION: Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools. OBJECTIVES: The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources. METHODS: The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar." RESULTS: An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds. CONCLUSION: Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Exploring the impact of polyphenolic compounds on the chromatic characteristics in flowers of Rhododendron arboreum Sm. collected from different altitudinal locations.

INTRODUCTION: Rhododendron arboreum Sm. flowers grow in the Himalayan region and have traditionally been used in beverages and food. These wild edible Himalayan flowers are known for their sweet-sour flavor and beautiful scarlet red color. The primary pigments responsible for the scarlet red color of these flowers are anthocyanins. OBJECTIVE: In the present study, we conducted chemo-profiling and elucidated the chromatic characteristics of R. arboreum flower petals growing in the wild in different altitudinal areas. METHODOLOGY: The content of anthocyanins, phenolics, and other flavonoids was determined in R. arboreum flower petals collected from 38 different locations in two provinces in India (Himachal Pradesh and Uttarakhand) to obtain a distinguishable chemical index. A UHPLC method has also been developed and validated for the quantitative analysis. Besides, the color characteristics of each collected floral sample were also analyzed. RESULTS: Chemometric analysis (principal component analysis [PCA] and heatmap analysis) revealed that floral samples collected from different altitudes exhibited similar chemical diversity, whereas statistical analysis (bivariate linear correlation) revealed a positive correlation between the color parameter a*/b* and cyanidin glycosides. Besides, non-targeted metabolomics analysis was carried out, which resulted in the tentative identification of 150 metabolites. CONCLUSION: The results revealed that there is a direct influence of accumulated anthocyanins to color parameter a*/b* values in the floral samples irrespective of altitude.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Diffuse reflectance spectroscopy and digital soil mapping for assessing soil-associated off-road vehicle mobility risk.

Soil attributes such as granulometric fractions and Atterberg limits (LL: liquid limit, PL: plastic limit, and PI: plasticity index) are needed to assess off-road vehicle mobility (OVM) risks. Parameters describing these attributes are generally measured in soil samples collected from a few locations through cumbersome laboratory methods. Although diffuse reflectance spectroscopy (DRS) can rapidly yield estimates for soil attributes in samples collected from specific locations and digital soil mapping (DSM) can transform such discrete measurements into spatially-continuous inference systems, these two technologies are rarely used for assessing OVM risks. In this study, we combined the DRS and DSM approaches for deriving spatially-continuous estimates for the key vehicle mobility parameters (gravel, sand, and fine particles; Cu: coefficients of uniformity; Cc: coefficient of curvature; LL; and PI) and classified soils using the Unified Soil Classification System (USCS). A total of 204 soil samples were collected from the north-eastern Himalayan state of Sikkim for measuring these parameters along with spectral reflectance over the visible and near-infrared region. Results of the chemometric models in the DRS approach showed that the USCS parameters may be estimated with the coefficient of determination (R2) values as high as 0.72. The fine (<2 mm diameter) fraction spectra provided the best estimates for the Atterberg limits while a combination of spectra collected from fine and coarse (>2 mm diameter) fractions was effective in estimating other granulometric fractions except for sand, which was best estimated using the coarse fraction spectra. With the DSM approach allowing effective mapping of these parameters, a spatially-continuous framework to quantify soil-associated OVM risks was developed for Sikkim for the first time.

Traceability Research on Geographic Erigeron breviscapus Based on High-Resolution Mass Spectrometry and Chemometric Analysis.

A method was developed to identify and trace the geographic sources of Erigeron breviscapus using high-resolution mass spectrometry and chemometrics. The representative samples were collected from the geographic area of Honghe Dengzhanhua and other areas in Yunnan province and Guizhou province. The data points could be determined well using the PCA and PLS-DA diagram. A total of 46 characteristic compounds were identified from Honghe Dengzhanhua and within Guizhou province, but 37 compounds were different from Honghe Dengzhanhua and other counties in Yunnan province. Two biomarkers were found from three regions. Their structures were inferred as 8-amino-7-oxononanoic acid and 8-hydroxyquinoline, and they had the same molecular composition. This may suggest that a possible synthesis pathway can be proven in the future.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

DSC, TGA-FTIR and FTIR Assisted by Chemometric Factor Analysis and PXRD in Assessing the Incompatibility of the Antiviral Drug Arbidol Hydrochloride with Pharmaceutical Excipients.

Arbidol hydrochloride is an antiviral product widely used in Russia and China for the treatment of, among other diseases, influenza. In recent years, it has turned out to be highly effective against COVID-19. However, there is little knowledge about its physicochemical properties and its behavior in the presence of various pharmaceutical excipients, which could be useful in the development of new preparations by increasing its solubility and bioavailability. For this reason, binary mixtures composed of arbidol hydrochloride and selected pharmaceutical excipients such as chitosan, polyvinylpyrrolione K-30 and magnesium stearate were prepared and subjected to differential scanning calorimetry (DSC), thermogravimetry combined with Fourier transform infrared spectrometry (TGA-FTIR) and Fourier transform infrared spectrometry (FTIR) analyses. In order to obtain clarity in the interpretation of the outcomes, chemometric calculations with factor analysis (FA) were used. Additionally, a powder X-ray diffraction (PXRD) and an intrinsic dissolution rate study were performed for arbidol hydrochloride itself and in the presence of excipients. As a result of the study, it was revealed that arbidol hydrochloride may undergo polymorphic transformations and be incompatible with chitosan and magnesium stearate. However, mixing arbidol hydrochloride with polyvinylpyrrolidone K-30 guarantees the obtaining of durable and safe pharmaceutical preparations.

Determination of heavy metals in edible oils by a novel voltammetry taste sensor array.

Herein, a novel voltammetry taste sensor array (VTSA) using pencil graphite electrode, screen-printed electrode, and glassy carbon electrode was used to identify heavy metals (HM) including Cad, Pb, Sn and Ni in soybean and rapeseed oils. HMs were added to edible oils at three concentrations of 0.05, 0.1 and 0.25 ppm, and then, the output of the device was classified using a chemometric classification method. According to the principal component analysis results, PG electrode explains 96% and 81% of the variance between the data in rapeseed and soybean edible oils, respectively. Additionally, the SP electrode explains 91% of the variance between the data in rapeseed and soybean oils. Moreover, the GC electrode explains 100% and 99% of the variance between the data in rapeseed and soybean edible oils, respectively. K-nearest neighbor exhibited high capability in classifying HMs in edible oils. In addition, partial least squares in the combine of VTSA shows a predict 99% in rapeseed oil. The best electrode for soybean edible oil was GC.

Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques.

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

Metabolomic Profile of Volatile Organic Compounds from Leaves of Cashew Clones by HS-SPME/GC-MS for the Identification of Candidates for Anthracnose Resistance Markers.

Anthracnose caused by Colletotrichum gloeosporioides affects the leaves, inflorescences, nuts, and peduncles of cashew trees (Anacardium occidentale). The use of genetically improved plants and the insertion of dwarf cashew clones that are more resistant to phytopathogens are strategies to minimize the impact of anthracnose on cashew production. However, resistance mechanisms related to the biosynthesis of secondary metabolites remain unknown. Thus, this study promoted the investigation of the profile of volatile organic compounds of resistant cashew clone leaves ('CCP 76', 'BRS 226' and 'BRS 189') and susceptible ('BRS 265') to C. gloeosporioides, in the periods of non-infection and infection of the pathogen in the field (July-December 2019 - Brazil). Seventy-eight compounds were provisionally identified. Chemometric analyses, such as Principal Component Analysis (PCA), Discriminating Partial Least Squares Analysis (PLS-DA), Discriminating Analysis of Orthogonal Partial Least Squares (OPLS-DA), and Hierarchical Cluster Analysis (HCA), separated the samples into different groups, highlighting hexanal, (E)-hex-2-enal, (Z)-hex-2-en-1-ol, (E)-hex-3-en-1-ol, in addition to α-pinene, α-terpinene, γ-terpinene, ß-pinene, and δ-3-carene, in the samples of the resistant clones in comparison to the susceptible clone. According to the literature, these metabolites have antimicrobial activity and are therefore chemical marker candidates for resistance to C. gloeosporioides in cashew trees.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region.

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

Quantification and holistic quality evaluation of Wulingzhi extract by UHPLC-Q-Orbitrap-HRMS coupled with chemometric approaches.

The excreta of Trogopterus xanthipes ("Wulingzhi" in Chinese, WLZ) is a well-known traditional Chinese medicine. It has been used for centuries to treat amenorrhea, menstruation and postpartum abdominal pain. However, a systematic quality study on WLZ chemical markers has yet to be conducted. This study aimed to establish an ultra-high-performance liquid chromatography coupled with a hybrid quadruple extraction Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) method for the simultaneous quantitative determination of 20 compounds in 53 batches of WLZ; the method rapidly and sensitively determined the 20 plant- or animal-derived compounds. Firstly, the proposed approach was validated to satisfy the method's linearity, detection limits, precision, repeatability, stability and accuracy. Subsequently, multivariate analysis was used to identify correlations between the samples and feed, processing and regions. Finally, this method was used to further identify chemical markers for quality control in combination with chemometrics. This is the first report on pinusolide, betaine, hippuric acid, 4-oxorentinoic acid, 15-methoxypinusolidic acid and 4-oxoisotrentinoin in WLZ; the quality of WLZ became homogeneous after processing with vinegar (V-WLZ). Moreover, we screened for potential component markers, including uridine, allantoin, amentoflavone, hippuric acid, 3,4-dihydroxybenzoic acid, pinusolide, quercetin and kaempferol. These results were practical and efficient for the chemical clarification of WLZ and V-WLZ.