jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Krishnan | S. Aruna | K. Karthick | Ellappan Venugopal
Source: Scientific Programming (article, 2023-04-21)
Field: Chemistry — Analytical Chemistry

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. Multiclass classification ; Machine learning ; Artificial intelligence ; Undersampling ; Identification (biology) ; Computer science ; Random forest ; Algorithm ; Mathematics ; Support vector machine ; Botany Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding ; Soybean genetics and cultivation
Authors: Xiaoming Xue | Zhenan Chen | Haoqi Wu | Handong Gao | Jiajie Nie | Xinyang Li
Source: Forests (article, 2023-06-17)
Field: Chemistry — Analytical Chemistry

Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) Pterocarpus santalinus is considered among the finest luxury woods in the world and has potential commercial and medicinal value. Due to its rich hue and high price, Pterocarpus santalinus has often been substituted and mislabeled with other woods of lower economic value. To maintain the order of the timber market and the interests of consumers, it is necessary to establish a fast and reliable method for Pterocarpus species identification. In this study, wood samples of Pterocarpus santalinus and nine other wood samples commonly used for counterfeiting were analyzed by visible light/near-infrared (Vis/NIR) hyperspectral imaging (HSI). The spectral data were preprocessed with different algorithms. Principal component analysis (PCA) was applied in different spectral ranges: 400~2500 nm, 400~800 nm, and 800~2500 nm. Partial least squares discriminant analysis (PLS-DA) and square support vector machine (SVM) modeling methods were performed for effective discrimination. The best classification model was SVM combined with a normalization preprocessing method in whole spectral range (400~2500 nm), with prediction accuracy higher than 99.8%. The results suggest that the use of Vis/NIR-HSI in combination with chemometric approaches can be used as an effective tool for the discrimination of Pterocarpus santalinus. Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; Near-infrared spectroscopy ; Artificial intelligence ; Preprocessor ; Normalization (sociology) ; Mathematics ; Remote sensing ; Computer science ; Geography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Essential Oils and Antimicrobial Activity
Authors: Mohamed M. Salim | Aya Saad Radwan | Ghada M. Hadad | Fathalla Belal | Mahmoud M. Elkhoudary
Source: Scientific Reports (article, 2023-03-02)
Field: Chemistry — Analytical Chemistry

Green fluorometric strategy for simultaneous determination of the antihypertensive drug telmisartan (A tentative therapeutic for COVID-19) with Nebivolol in human plasma Telmisartan (TEL) and Nebivolol (NEB) are frequently co-formulated in a single dosage form that is frequently prescribed for the treatment of hypertension, moreover, telmisartan is currently proposed to be used to treat COVID19-induced lung inflammation. Green rapid, simple, and sensitive synchronous spectrofluorimetric techniques for simultaneous estimation of TEL and NEB in their co-formulated pharmaceutical preparations and human plasma were developed and validated. Synchronous fluorescence intensity at 335 nm was used for TEL determination (Method I). For the mixture, the first derivative synchronous peak amplitudes (D<sup>1</sup>) at 296.3 and 320.5 nm were used for simultaneous estimation of NEB and TEL, respectively (Method II). The calibration plots were rectilinear over the concentration ranges of 30-550 ng/mL, and 50-800 ng/mL for NEB and TEL, respectively. The high sensitivity of the developed methods allowed for their analysis in human plasma samples. NEB`s Quantum yield was estimated by applying the single-point method. The greenness of the proposed approaches was evaluated using the Eco-scale, National Environmental Method Index (NEMI), and Green Analytical Procedure Index (GAPI) methods. Telmisartan ; Nebivolol ; Human plasma ; Coronavirus disease 2019 (COVID-19) ; Chromatography ; Chemistry ; Pharmacology ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Antibiotics Pharmacokinetics and Efficacy ; Electrochemical sensors and biosensors
Authors: Qiang Guan | Dongxue Zhao | Shuai Feng | Tongyu Xu | Haoriqin Wang | Kai Song
Source: Agronomy (article, 2023-04-18)
Field: Chemistry — Analytical Chemistry

Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops. Hyperspectral imaging ; Principal component analysis ; Support vector machine ; Leaf spot ; Pattern recognition (psychology) ; Artificial intelligence ; Feature selection ; Feature (linguistics) ; Computer science ; Mathematics ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Peanut Plant Research Studies
Authors: Roger Schurch | Osvaldo Muñoz | Jorge Alfredo Ardila‐Rey | Pablo Donoso | Vidyadhar Peesapati
Source: Polymers (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously-one to capture the sequence of PD pulses and another to acquire PD pulse waveforms-and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation. Epoxy ; Partial discharge ; Materials science ; Identification (biology) ; Waveform ; Composite material ; Electrical treeing ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Dielectric materials and actuators
Authors: J. Brewer | Sachin Kulkarni | Aaswath P. Raman
Source: Nano Letters (article, 2023-09-21)
Field: Materials Science — Surfaces, Coatings and Films

Resonant Anti-Reflection Metasurfaces for Infrared Transmission Optics A fundamental capability needed for any transmissive optical component is anti-reflection, yet this capability can be challenging to achieve in a cost-effective manner over longer infrared wavelengths. We demonstrate that Mie-resonant photonic structures can enable high transmission through a high-index optical component, allowing it to function effectively over long-wavelength infrared wavelengths. Using silicon as a model system, we demonstrate a resonant metasurface that enables a window optic with transmission up to 40% greater than that of unpatterned Si. Imaging comparisons with unpatterned Si and off-the-shelf germanium optics are shown as well as modulation transfer function measurements, showing excellent performance and suitability for imaging applications. Our results show how resonant photonic structures can be used to improve optical transmission through high-index optical components and highlight their possible use in infrared imaging applications. Optics ; Transmission (telecommunications) ; Infrared ; Reflection (computer programming) ; Photonics ; Optoelectronics ; Wavelength ; Materials science ; Refractive index ; Optical transfer function ; Physics ; Computer science ; Telecommunications Optical Coatings and Gratings ; Photonic and Optical Devices ; Metamaterials and Metasurfaces Applications
Authors: Brittany L. Bonnett | Dina Sheyfer | Pubudu N. Wimalasiri | Srikanth Nayak | Jyotsana Lal | Qingteng Zhang | Söenke Seifert | G. B. Stephenson | Michael J. Servis
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Critical fluctuations in liquid–liquid extraction organic phases controlled by extractant and diluent molecular structure Extractant aggregation in liquid-liquid extraction organic phases impacts extraction energetics and is related to the deleterious efficiency-limiting liquid-liquid phase transition known as third phase formation. Using small angle X-ray scattering, we find that structural heterogeneities across a wide range of compositions in binary mixtures of malonamide extractants and alkane diluents are well described by Ornstein-Zernike scattering. This suggests that structure in these simplified organic phases originates from the critical point associated with the liquid-liquid phase transition. To confirm this, we measure the temperature dependence of the organic phase structure, finding critical exponents consistent with the 3D Ising model. Molecular dynamics simulations were also consistent with this mechanism for extractant aggregation. Due to the absence of water or any other polar solutes required to form reverse-micellar-like nanostructures, these fluctuations are inherent to the binary extractant/diluent mixture. We also show how the molecular structure of the extractant and diluent modulate these critical concentration fluctuations by shifting the critical temperature: critical fluctuations are suppressed by increasing extractant alkyl tail lengths or decreasing diluent alkyl chain lengths. This is consistent with how extractant and diluent molecular structure are known to impact metal and acid loading capacity in many-component LLE organic phases, suggesting phase behavior of practical systems may be effectively studied in simplified organic phases. Overall, the explicit connection between molecular structure, aggregation and phase behavior demonstrated here will enable the design of more efficient separations processes. Diluent ; Extraction (chemistry) ; Liquid liquid ; Liquid–liquid extraction ; Chemistry ; Limiting ; Liquid phase ; Phase (matter) ; Chromatography ; Organic chemistry ; Thermodynamics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Crystallization and Solubility Studies
Authors: Yanjun Chen | Tiantian Liang | Shunda Qiao | Yufei Ma
Source: Sensors (article, 2023-04-17)
Field: Chemistry — Spectroscopy

A Miniaturized 3D-Printed Quartz-Enhanced Photoacoustic Spectroscopy Sensor for Methane Detection with a High-Power Diode Laser In this invited paper, a highly sensitive methane (CH<sub>4</sub>) trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) technique using a high-power diode laser and a miniaturized 3D-printed acoustic detection unit (ADU) is demonstrated for the first time. A high-power diode laser emitting at 6057.10 cm<sup>-1</sup> (1650.96 nm), with the optical power up to 38 mW, was selected as the excitation source to provide a strong excitation. A 3D-printed ADU, including the optical and photoacoustic detection elements, had a dimension of 42 mm, 27 mm, and 8 mm in length, width, and height, respectively. The total weight of this 3D-printed ADU, including all elements, was 6 g. A quartz tuning fork (QTF) with a resonant frequency and Q factor of 32.749 kHz and 10,598, respectively, was used as an acoustic transducer. The performance of the high-power diode laser-based CH<sub>4</sub>-QEPAS sensor, with 3D-printed ADU, was investigated in detail. The optimum laser wavelength modulation depth was found to be 0.302 cm<sup>-1</sup>. The concentration response of this CH<sub>4</sub>-QEPAS sensor was researched when the CH<sub>4</sub> gas sample, with different concentration samples, was adopted. The obtained results showed that this CH<sub>4</sub>-QEPAS sensor had an outstanding linear concentration response. The minimum detection limit (MDL) was found to be 14.93 ppm. The normalized noise equivalent absorption (NNEA) coefficient was obtained as 2.20 × 10<sup>-7</sup> cm<sup>-1</sup>W/Hz<sup>-1/2</sup>. A highly sensitive CH<sub>4</sub>-QEPAS sensor, with a small volume and light weight of ADU, is advantageous for the real applications. It can be portable and carried on some platforms, such as an unmanned aerial vehicle (UAV) and a balloon. Photoacoustic spectroscopy ; Materials science ; Laser ; Diode ; Wavelength ; Laser diode ; Optoelectronics ; Detection limit ; Absorption (acoustics) ; Optics ; Analytical Chemistry (journal) ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Chemical Sensor Technologies
Authors: Daniel Schorn‐García | Barbara Giussani | María Jesús García‐Casas | Daniel Rico | Ana Belén Martín‐Diana | Laura Aceña | Olga Busto | Ricard Boqué | Montserrat Mestres
Source: Foods (article, 2023-02-24)
Field: Chemistry — Analytical Chemistry

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest. Ripening ; Ripeness ; Viticulture ; Berry ; Vine ; Mathematics ; Horticulture ; Chemistry ; Food science ; Wine ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Meat and Animal Product Quality
Authors: Heidi R. Abd El-Hadi | Maya S. Eissa | Hala E. Zaazaa | Basma M. Eltanany
Source: BMC Chemistry (article, 2023-05-13)
Field: Chemistry — Analytical Chemistry

Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D<sup>0</sup>) and Fourier self-deconvolution (FSD), as TRI was directly determined by D<sup>0</sup> at 367.0 nm in the range (2.00-10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00-8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00-7.00 µg/mL) for TRI and (2.00-6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied. Univariate ; Multivariate statistics ; Chemometrics ; Deconvolution ; Partial least squares regression ; Principal component analysis ; Statistics ; Principal component regression ; Mathematics ; Mean squared error ; Triamterene ; Calibration ; Chemistry ; Chromatography Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety
Authors: Anton Vladyka | Christoph J. Sahle | Johannes Niskanen
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Towards structural reconstruction from X-ray spectra We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO<sub>2</sub> at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the K<i>β</i>'' and K<i>β</i><sub>2</sub> peaks in the spectrum from the Coulomb matrix descriptor with a training set of ∼ 10<sup>4</sup> samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices. When applying this to the moments of the ensemble-mean spectrum, we obtain distances from the active site that match closely to those of the ensemble mean and which moreover reproduce the pressure-induced coordination change in amorphous GeO<sub>2</sub>. With this approach utilizing emulator-based component analysis, we are able to filter out the artificially complete structural information available from simulated snapshots, and quantitatively analyse structural changes that can be inferred from the changes in the K<i>β</i> emission spectrum alone. Spectral line ; Crystallography ; Materials science ; Physics ; Chemistry ; Astronomy Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography