jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing An | Xiuzhi Luo | Lijian Xiong | Xiuying Tang | Haipeng Lan
Source: Foods (article, 2023-04-26)
Field: Chemistry — Analytical Chemistry

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters Inner-injury fragrant pears are easily prone to rot during storage. Discriminating inner injury in the Korla fragrant pear from the normal pear is difficult as the flesh may be injured while the peel of the fruit remains intact. This study demonstrated the recognition of inner-injury pears based on their electric characteristics to pick out the inner-injury pears before storage. The electrical parameters parallel equivalent capacitance, quality factor, parallel equivalent inductance, parallel equivalent resistance, complex impedance, and phase angle were measured using the fruit electrical characteristic detection instrument. Principal component analysis and correlation analysis were used to determine the characteristic parameters, connected with the qualitative value of the fragrant pear to establish three discrimination models. When the measurement frequency was 100 kHz, compared with the Naïve Bayes and K-nearest neighbor models, the Support Vector Machine model with the characteristic parameters of quality factor, parallel equivalent resistance, and phase angle performed best. The recognition accuracy of the test set was 92.00%, the precision was 92.41%, the recall was 97.33%, and the F<sub>1</sub> score was 0.95. Therefore, the electrical characteristic technique effectively detected the inner injury of fragrant pears and provided a new way to distinguish the inner injury of fruits. PEAR ; Principal component analysis ; Mathematics ; Materials science ; Biological system ; Artificial intelligence ; Horticulture ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Xuejing Wang | Michael T. Pettes | Yongqiang Wang | Jian‐Xin Zhu | Rohan Dhall | Chengyu Song | Andrew C. Jones | Jim Ciston | Jinkyoung Yoo
Source: Nano Letters (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Enhanced Exciton-to-Trion Conversion by Proton Irradiation of Atomically Thin WS<sub>2</sub> Defect engineering of van der Waals semiconductors has been demonstrated as an effective approach to manipulate the structural and functional characteristics toward dynamic device controls, yet correlations between physical properties with defect evolution remain underexplored. Using proton irradiation, we observe an enhanced exciton-to-trion conversion of the atomically thin WS<sub>2</sub>. The altered excitonic states are closely correlated with nanopore induced atomic displacement, W nanoclusters, and zigzag edge terminations, verified by scanning transmission electron microscopy, photoluminescence, and Raman spectroscopy. Density functional theory calculation suggests that nanopores facilitate formation of in-gap states that act as sinks for free electrons to couple with excitons. The ion energy loss simulation predicts a dominating electron ionization effect upon proton irradiation, providing further evidence on band perturbations and nanopore formation without destroying the overall crystallinity. This study provides a route in tuning the excitonic properties of van der Waals semiconductors using an irradiation-based defect engineering approach. Trion ; Exciton ; Density functional theory ; Materials science ; van der Waals force ; Semiconductor ; Band gap ; Proton ; Nanoclusters ; Molecular physics ; Photoluminescence ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Han Qing | Joseph Peller | Sara W. Erasmus | Christopher T. Elliott | Saskia M. van Ruth
Source: Food Research International (article, 2023-05-22)
Field: Chemistry — Analytical Chemistry

Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques. Particle size ; Spice ; Wavelength ; Particle (ecology) ; Materials science ; Biological system ; Food science ; Chemistry ; Optoelectronics ; Biology ; Geology ; Ecology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Angeline W. Maina | Erich-Christian Oerke
Source: Plant Disease (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

Characterization of Rice–<i>Magnaporthe oryzae</i> Interactions by Hyperspectral Imaging Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three <i>Magnaporthe oryzae</i> isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × <i>M. oryzae</i> genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × <i>M. oryzae</i> interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to <i>M. oryzae</i> infection. Hyperspectral imaging ; Biology ; Genotype ; Magnaporthe ; Plant disease resistance ; Virulence ; Xanthomonas oryzae ; Pathogen ; Spectral imaging ; Gene ; Magnaporthe grisea ; Botany ; Genetics ; Oryza sativa ; Remote sensing Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nihar Pradeep Khandave | Ashok Sekhar | Pramodh Vallurupalli
Source: Journal of Biomolecular NMR (article, 2023-06-10)
Field: Chemistry — Spectroscopy

Studying micro to millisecond protein dynamics using simple amide 15N CEST experiments supplemented with major-state R2 and visible peak-position constraints Chemistry ; Spurious relationship ; Minor (academic) ; Maxima and minima ; Millisecond ; Position (finance) ; Relaxation (psychology) ; Amplitude ; Chemical physics ; Analytical Chemistry (journal) ; Physics ; Optics ; Statistics ; Quantum mechanics ; Chromatography Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Xueying Liu | Yinong Xie | Jinlin Qiu | Wei Chen | Yineng Liu | Jinfeng Zhu
Source: Nanophotonics (article, 2023-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic honeycomb stack metamaterials of graphene for ultrawideband terahertz absorption Graphene aerogels have implied great potential for electromagnetic wave absorption. However, the investigation of their design for broadband absorption in the terahertz (THz) range remains insufficient. Here, we propose an anisotropic honeycomb stack metamaterial (AHSM) based on graphene to achieve ultrawideband THz absorption. The absorption mechanism is elucidated using the effective medium method, offering deeper physics insights. At low THz frequencies, the impedance matching from the air to the AHSM can be improved by reducing the chemical potential of graphene for high absorption. There is a suppression of absorption at the intermediate frequencies due to constructive interference, which can be avoided by shortening the sizes of honeycomb edges. With the aim to elevate absorption at high frequencies, one can increase the stack layer number to enhance multiple reflections and destructive interference within the metastructure. Based on the above principles, we design an AHSM that achieves a broadband absorbance of over 90 % from 1 THz to 10 THz. This absorption can tolerate a wide range of incident angles for both TE and TM wave excitations. Our research will provide a theoretical guide to future experimental exploration of graphene aerogels for THz metamaterial absorber applications. Terahertz radiation ; Graphene ; Metamaterial ; Materials science ; Absorption (acoustics) ; Metamaterial absorber ; Stack (abstract data type) ; Optoelectronics ; Impedance matching ; Cloak ; Broadband ; Optics ; Electrical impedance ; Nanotechnology ; Physics ; Tunable metamaterials ; Composite material ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Kas J. Houthuijs | M. J. Horn | Dennis Vughs | Jonathan Martens | Andrea M. Brunner | Jos Oomens | Giel Berden
Source: Chemosphere (article, 2023-09-01)
Field: Chemistry — Spectroscopy

Identification of organic micro-pollutants in surface water using MS-based infrared ion spectroscopy Comprehensive monitoring of organic micro-pollutants (OMPs) in drinking water sources relies on non-target screening (NTS) using liquid-chromatography and high-resolution mass spectrometry (LC-HRMS). Identification of OMPs is typically based on accurate mass and tandem mass spectrometry (MS/MS) data by matching against entries in compound databases and MS/MS spectral libraries. MS/MS spectra are, however, not always diagnostic for the full molecular structure and, moreover, emerging OMPs or OMP transformation products may not be present in libraries. Here we demonstrate how infrared ion spectroscopy (IRIS), an emerging MS-based method for structural elucidation, can aid in the identification of OMPs. IRIS measures the IR spectrum of an m/z-isolated ion in a mass spectrometer, providing an orthogonal diagnostic for molecular identification. Here, we demonstrate the workflow for identification of OMPs in river water and show how quantum-chemically predicted IR spectra can be used to screen potential candidates and suggest structural assignments. A crucial step herein is to define a set of candidate structures, presumably including the actual OMP, for which we present several strategies based on domain knowledge, the IR spectrum and MS/MS spectrum. Mass spectrometry ; Chemistry ; Mass spectrum ; Infrared spectroscopy ; Tandem mass spectrometry ; Hazardous air pollutants ; Spectroscopy ; Analytical Chemistry (journal) ; Environmental chemistry ; Chromatography ; Pollutant ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Liru Zhang | Mingsheng Chen | R.Q. Zhang | Hong Cheng | Chuan Shen | Sui Wei
Source: New Journal of Physics (article, 2023-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband achromatic metalens design based on predictive neural network and particle swarm optimization-genetic algorithm Abstract The main challenge in designing broadband achromatic metalenses is to achieve the desired phase distribution at different wavelengths and positions. Finding the exact relationship between phase modulation and the size or shape of individual nanopillars is a critical but time-consuming step. This paper presents a novel joint design framework predictive neural networks and particle swarm optimization-genetic algorithms, which combines predictive neural network (PNN) and particle swarm optimization-genetic algorithm (PSO-GA). The proposed framework aims to accurately predict the phase response of nanopillars using PNN, increase the number of phase data points to establish a one-to-one correspondence between the phase and nanopillar parameters in the design of broadband achromatic metalenses, and optimize the parameters of an individual nanostructure of the metalens using PSO-GA. To validate the efficacy of the proposed method, a broadband achromatic metalens for line polarization light in the range of 260–350 nm is designed. Numerical simulations demonstrate that the designed metalens exhibits achromatic focusing. The method proposed in this paper may find wider application in the design of more complex metasurface devices. Achromatic lens ; Broadband ; Particle swarm optimization ; Nanopillar ; Physics ; Optics ; Genetic algorithm ; Artificial neural network ; Algorithm ; Phase (matter) ; Computer science ; Artificial intelligence ; Nanostructure ; Machine learning Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Neural Networks and Reservoir Computing
Authors: Yu Kitamura | Hiroki Toshima | Akihiro Inokuchi | Daisuke Tanaka
Source: Molecular Systems Design & Engineering (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Bayesian optimization of the composition of the lanthanide metal–organic framework MIL-103 for white-light emission To realize lanthanide metal–organic frameworks (Ln-MOFs) with white light emission, it is necessary to adjust their RGB composition. We adopted the Bayesian optimization technique to optimize the stoichiometric ratio of metal-salts in Ln-MOFs. Lanthanide ; Metal-organic framework ; Composition (language) ; Stoichiometry ; Metal ; Materials science ; White light ; Bayesian optimization ; Computer science ; Chemistry ; Optoelectronics ; Artificial intelligence ; Physical chemistry ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Machine Learning in Materials Science
Authors: Shihao Guan | Yuqian Shang | Chao Zhao
Source: Sustainability (article, 2023-05-09)
Field: Chemistry — Analytical Chemistry

Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times. Principal component analysis ; Linear discriminant analysis ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Kernel (algebra) ; Identification (biology) ; Computer science ; Mathematics ; Botany Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Meat and Animal Product Quality
Authors: Jiangning Song | Lukasz Kurgan
Source: Bioinformatics Advances (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction We show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools. Citation ; Server ; Web server ; Computer science ; Resource (disambiguation) ; Function (biology) ; Source code ; Key (lock) ; Empirical research ; World Wide Web ; Data science ; The Internet ; Biology ; Statistics ; Mathematics ; Operating system Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Samuel Ortega | Stein‐Kato Lindberg | Stein Harris Olsen | Kathryn E. Anderssen | Karsten Heia
Source: LWT (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Early identification of mushy Halibut syndrome with hyperspectral image analysis Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not carefully investigated this condition, nor novel technologies for MHS detection have been proposed. In this research work, we propose using hyperspectral imaging to detect MHS. After collecting a dataset of hyperspectral images of halibut affected by MHS, two different goals were targeted. Firstly, the estimation of the chemical composition of the samples (specifically fat and water content) from their spectral data by using constrained spectral unmixing. Secondly, supervised classification using partial least squares discriminant analysis (PLS-DA) was evaluated to identify specimens affected by MHS. The outcomes of our study suggest that the prediction of fat from the spectral data is possible, but the prediction of the water content was not found to be accurate. However, the detection of MHS using PLS-DA was precise for hyperspectral images from both fillets and whole fish, with lower bounds of 75% and 83% for precision and recall, respectively. Our findings suggest hyperspectral imaging as a suitable technology for the early screening of MHS. Hyperspectral imaging ; Halibut ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial intelligence ; Identification (biology) ; Mathematics ; Linear discriminant analysis ; Fish <Actinopterygii> ; Computer science ; Food science ; Biology ; Statistics ; Fishery ; Ecology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Pedro Compais | J. Arroyo | Miguel Ángel Castán-Lascorz | Jorge Barrio | Antonia Gil
Source: Engineering Applications of Artificial Intelligence (article, 2023-07-20)
Field: Chemistry — Analytical Chemistry

Detection of slight variations in combustion conditions with machine learning and computer vision When monitoring combustion conditions, detecting minor variations, which may be complex even for the human eye, is critical for providing a fast response and correcting deviations. The aim of this study is to detect slight variations in combustion conditions by developing a flame monitoring system using machine learning and computer vision techniques applied to color images. Predictive models are developed for fuel blends with different heating values. The predictive models classify the combustion equivalence ratio based on multiple conditions, using a mean step size of 0.10 between states, a lower value than previously reported in related studies. Three machine learning algorithms are used for each fuel blend: logistic regression, support vector machine, and artificial neural network (multilayer perceptron). These models are fed the statistical, geometrical, and textural features extracted from the color images of the flames. The classification achieves accuracies from 0.78 to 0.97 in the detection of slight variations in the combustion conditions for all heating values. Thus, the monitoring system developed in this study is a promising alternative for implementation on an industrial scale and quick detection of changes in combustion conditions. Combustion ; Computer science ; Artificial intelligence ; Multilayer perceptron ; Artificial neural network ; Support vector machine ; Machine learning ; Perceptron ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Thermography and Photoacoustic Techniques
Authors: Hongwei Yu | Sara W. Erasmus | Qiang Wang | Hongzhi Liu | Saskia M. van Ruth
Source: Journal of Food Composition and Analysis (article, 2023-04-17)
Field: Chemistry — Analytical Chemistry

Rapid classification of peanut varieties for their processing into peanut butters based on near‐infrared spectroscopy combined with machine learning Principal component analysis ; Linear discriminant analysis ; Feature selection ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Mathematics ; Random forest ; Partial least squares regression ; Near-infrared spectroscopy ; Computer science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research