jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Paola Cucuzza | Silvia Serranti | Andrea Uzzo
Source: Detritus (article, 2022-03-31)
Field: Chemistry — Analytical Chemistry

Recycling-oriented characterization of PET waste stream by SWIR hyperspectral imaging and variable selection methods The proposed study was carried out to develop a fast and efficient strategy for plastic waste sensor-based sorting in recycling plants, based on hyperspectral imaging (HSI), combined with variable selection methods, to produce a high-quality recycled polyethylene terephthalate (PET) flakes stream. Variable selection techniques were applied in order to identify a limited number of spectral bands useful to recognize the presence of other plastic materials, considered as contaminant, inside a stream of recycled PET flakes, reducing processing time as requested by sorting online applications. Post-consumer plastic samples were acquired by HSI device working in the short-wave infrared (SWIR) range (1000 - 2500 nm). As a first step, the hypercubes were processed applying chemometric logics to build a partial least squares discriminant analysis (PLSDA) classification model using the full investigated spectral range, able to identify PET and contaminant classes. As a second step, two different variable selection methods were then applied, i.e., interval PLSDA (I-PLSDA) and variable importance in projection (VIP) scores, in order to identify a limited number of spectral bands useful to recognize the two classes and to evaluate the best method, showing efficiency values close to those obtained by the full spectrum model. The best result was achieved by the VIP score method with an average efficiency value of 0.98. The obtained results suggested that the variables selection method can represent a powerful approach for the sensor-based sorting-online, decreasing the amount of data to be processed and thus enabling faster recognition compared to the full spectrum model. Hyperspectral imaging ; Sorting ; Computer science ; Feature selection ; Pattern recognition (psychology) ; Polyethylene terephthalate ; Artificial intelligence ; Partial least squares regression ; Selection (genetic algorithm) ; Variable (mathematics) ; Spectral imaging ; Range (aeronautics) ; Biological system ; Remote sensing ; Mathematics ; Materials science ; Machine learning ; Algorithm Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhiyong Zou | Jie CHEN | Li Wang | Weijia Wu | Tingjiang YU | Yuchao WANG | Yongpeng Zhao | Peng Huang | Bi LIU | Man Zhou | Ping Lin | Lijia Xu
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of peanuts mildew based on hyperspectral image technology and machine learning algorithm In order to realize the rapid nondestructive detection of mildew peanut in the process of peanuts storage, hyperspectral imaging technology was proposed to detect mildew peanut. A total of 200 peanuts were selected from 5 kinds of peanuts purchased in the market for moldy treatment, and the remaining 400 peanuts were kept sterile. After completion, samples were collected with a hyperspectral instrument to obtain spectral data of the samples. According to the characteristics of the data, 10 pre-processing algorithms were used to de-noise the data, and Median Filtering (MF) had the best effect, with the recognition accuracy reaching 97.7%. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to extract important feature bands in the spectral data pre-processed by MF. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to model the extracted feature bands. The results showed that LightGBM is the best algorithm with a detection rate of 99.10%. Optuna algorithm was used to tune its parameters. Compared with the previous model, the running time of the optimized model was improved by about 0.25 s. The results showed that hyperspectral imaging provides an efficient and nondestructive method for detecting mildew in peanut storage. Hyperspectral imaging ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Noise (video) ; Pattern recognition (psychology) ; Algorithm ; Image processing ; Feature extraction ; Mathematics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Maria Merin Antony | C. S. Suchand Sandeep | Hoong-Ta Lim | Murukeshan Vadakke Matham
Source: IEEE Transactions on Instrumentation and Measurement (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Hyperspectral Vision Based Probe for In Situ Corrosion Monitoring in Saline Environments Corrosion is a major concern in various engineering fields. Early detection of corrosion helps in reducing the risk associated with the failure of metal structures invaded by corrosion. Though several techniques exist for monitoring corrosion, most of these techniques are contact-based and tend to be destructive in nature. In addition, only a few of these techniques can provide fast, point-by-point analysis of corrosion. In this paper, we propose and demonstrate a novel corrosion inspection probe with integrated illumination for the sensitive monitoring of corrosion along each point of an area under inspection based on snapshot hyperspectral vision. Being a small and flexible probe, it can be used for monitoring corrosion in difficult-to-access locations. The experimental results correlate well with the standard corrosion prediction model and are also validated by spectroscopic Raman analysis. The possibility of incorporating automated corrosion monitoring based on machine vision as the future development of the probe is also highlighted. Corrosion ; Corrosion monitoring ; Hyperspectral imaging ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence Corrosion Behavior and Inhibition ; Thermography and Photoacoustic Techniques ; Non-Destructive Testing Techniques
Authors: Emre Yıldız | Nursel Altan Özbek
Source: International Journal of 3D Printing Technologies and Digital Industry (article, 2022-04-17)
Field: Materials Science — Materials Chemistry

EFFECT OF CRYOGENIC TREATMENT AND TEMPERING TEMPERATURE ON MECHANICAL AND MICROSTRUCTURAL PROPERTIES OF AISI 431 STEEL This study investigated the effects of cryogenic treatment and tempering temperature applied after cryogenic treatment on the mechanical and microstructural properties of AISI 431 martensitic stainless steel. After conventional heat treatment (CHT), the steel samples were cryogenically treated at -180 °C for 6 hours and then tempered at 200 °C (CT200) and 300 °C (CT300) for 2 hours. After these processes, hardness measurement, tensile test, and abrasion test were carried out to determine the mechanical properties of the steel samples. In addition, microstructure photographs were taken to determine the microstructural properties. As a result of the study, it was observed that the cryogenic treatment applied after the conventional heat treatment was effective on the mechanical properties of AISI 431 martensitic stainless steel. Cryogenic treatment and tempering temperature showed the greatest effect on wear resistance. CT200 and CT300 samples were 62% and 56% less worn than the CHT sample. Compared to the CHT sample, the yield strength of CT200 and CT300 samples increased by 6.95% and 7.03%, while the hardness increased by 3.89% and 3.52%. Tempering ; Cryogenic treatment ; Materials science ; Microstructure ; Metallurgy ; Ultimate tensile strength ; Martensite ; Abrasion (mechanical) ; Martensitic stainless steel ; Tensile testing ; Cryogenic temperature ; Composite material Metal Alloys Wear and Properties ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Thomas A. Manz
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Apples to apples comparison of standardized to unstandardized principal component analysis of methods that assign partial atomic charges in molecules Articles by Cho <i>et al.</i> (<i>ChemPhysChem</i>, 2020, <b>21</b>, 688-696) and Manz (<i>RSC Adv.</i>, 2020, <b>10</b>, 44121-44148) performed unstandardized and standardized, respectively, principal component analysis (PCA) to study atomic charge assignment methods for molecular systems. Both articles used subsets of atomic charges computed by Cho <i>et al.</i>; however, the data subsets employed were not strictly identical. Herein, an element by element analysis of this dataset is first performed to compare the spread of charge values across individual chemical elements and charge assignment methods. This reveals an underlying problem with the reported Becke partial atomic charges in this dataset. Due to their unphysical values, these Becke charges were not included in the subsequent PCA. Standardized and unstandardized PCA are performed across two datasets: (i) 19 charge assignment methods having a complete basis set limit and (ii) all 25 charge assignment methods (excluding Becke) for which Cho <i>et al.</i> computed atomic charges. The dataset contained ∼2000 molecules having a total of 29 907 atoms in materials. The following five methods (listed here in alphabetical order) showed the greatest correlation to the first principal component in standardized and unstandardized PCA: DDEC6, Hirshfeld-I, ISA, MBIS, and MBSBickelhaupt (note: MBSBickelhaupt does not appear in the 19 methods dataset). For standardized PCA, the DDEC6 method ranked first followed closely by MBIS. For unstandardized PCA, Hirshfeld-I (19 methods) or MBSBickelhaupt (25 methods) ranked first followed by DDEC6 in second place (both 19 and 25 methods). Principal component analysis ; Chemistry ; Molecule ; Component (thermodynamics) ; Partial charge ; Physics ; Thermodynamics ; Statistics ; Mathematics ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Gertrud E. Morlock
Source: Phytochemistry Reviews (article, 2022-11-01)
Field: Chemistry — Spectroscopy

Planar chromatographic super-hyphenations for rapid dereplication Abstract Analytical chemistry offers important tools that provide insight and step-by-step understanding. However, natural samples containing many thousands of different compounds are subject to natural variance which makes it complex for artificial intelligence and human understanding. Instead of the prevailing comprehensive separation/detection approach, a prioritization approach is discussed for dereplication that focuses on the most necessary to discover. It is extremely helpful to combine on the same surface two disciplines, i.e. chemistry for separation of mixtures and biology for detection of biological effects, to filter out and prioritize the important compounds that then need to be identified. Complex mixtures are separated in parallel with imaging high-performance thin-layer chromatography, detected non-targeted with planar multiplex bioassays to prioritize compounds and elute the important ones to orthogonal column chromatography−diode array detection−high-resolution mass spectrometry for further characterization. Super-hyphenations reduce thousands of compounds to a manageable number of important active compounds. Its potential lies not only in the prioritization of compounds, but also detection of unknown compounds that were not previously the focus of analysts. An image is worth a thousand words and an effect image even more so. On-surface multiplex bioassays enable the differentiation of opposing signals/effects of compounds in a complex natural sample, which only makes understanding possible. On-surface metabolization enables the study of any changes in the effects through digestion or de-/toxification (S9 liver system). By these new tools applied on the same surface, the mechanisms of action of complex mixtures can be better understood. The latest open-source 2LabsToGo system combines the chemistry and biology laboratory in a miniaturized system developed for wider use of this innovative technique. Multiplex ; Elution ; Resolution (logic) ; Chromatography ; Chemistry ; Prioritization ; Bioassay ; Biochemical engineering ; Computer science ; Artificial intelligence ; Biology ; Bioinformatics ; Engineering Analytical Chemistry and Chromatography ; Biochemical Analysis and Sensing Techniques ; Advanced Chemical Sensor Technologies
Authors: Yurong Zhang | Guanqiang Lu | Xianqing Zhou | Jun‐Hu Cheng
Source: Molecules (article, 2022-12-07)
Field: Chemistry — Analytical Chemistry

Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations (1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R<sup>2</sup> = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R<sup>2</sup> = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively. Hyperspectral imaging ; Support vector machine ; Ageing ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biological system ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Katiuscia Mannaro | Matteo Baire | Alessandro Fanti | Matteo Bruno Lodi | Luca Didaci | Alessandro Fedeli | Luisanna Cocco | Andrea Randazzo | Giuseppe Mazzarella | Giorgio Fumera
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread In this paper, we address the problem of automatic image segmentation methods applied to the partial automation of the production process of a traditional Sardinian flatbread called pane Carasau for assuring quality control. The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study the shape variations undergone by the sheet depending on some environmental and production variables. The knowledge can thus be used to create a system capable of predicting the quality of the shape of the dough produced and empower the production process. We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread. Experiments demonstrated that the method can successfully achieve accurate segmentation of bread sheets images, ensuring that the dimensions extracted are representative of the sheets coming from the production process. The algorithm proved to be fast and accurate in estimating the size of the bread sheets in various scenarios that occurred over a year of acquisitions. The maximum error committed by the algorithm is equal to the 2.2% of the pixel size in the worst scenario and to 1.2% elsewhere. Computer science ; Automation ; Process (computing) ; Image segmentation ; Segmentation ; Production (economics) ; Artificial intelligence ; Pixel ; Support vector machine ; Quality (philosophy) ; Computer vision ; Algorithm ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Takuro Tsutsumi | Y. Ôno | Tetsuya Taketsugu
Source: Journal of Chemical Theory and Computation (article, 2022-11-09)
Field: Chemistry — Physical and Theoretical Chemistry

Multi-state Energy Landscape for Photoreaction of Stilbene and Dimethyl-stilbene We have recently developed the reaction space projector (ReSPer) method, which constructs a reduced-dimensionality reaction space uniquely determined from reference reaction paths for a polyatomic molecular system and projects classical trajectories into the same reaction space. In this paper, we extend ReSPer to the analysis of photoreaction dynamics and relaxation processes of stilbene and present the concept of a "multi-state energy landscape," incorporating the ground- and excited-state reaction subspaces. The multi-state energy landscape successfully explains the previously established photoreaction processes of <i>cis</i>-stilbene, such as the <i>cis</i>-<i>trans</i> photoisomerization and photocyclization. In addition, we discuss the difference in the excited-state reaction dynamics between stilbene and 1,1'-dimethyl stilbene based on a common reaction subspace determined from the framework part of reference structures with different number of atoms. This approach allows us to target any molecule with a common framework, greatly expanding the applicability of the ReSPer analysis. The multi-state energy landscape provides fruitful insight into photochemical reactions, exploring the excited- and ground-state potential energy surfaces, as well as comprehensive reaction processes with nonradiative transitions between adiabatic states, within the stage of a reduced-dimensionality reaction space. Excited state ; Azulene ; Reaction dynamics ; Potential energy surface ; Photoisomerization ; Energy landscape ; Curse of dimensionality ; Ground state ; Photochemistry ; Complete active space ; Chemistry ; Computational chemistry ; Relaxation (psychology) ; Linear subspace ; Potential energy ; Chemical physics ; Molecule ; Atomic physics ; Computer science ; Physics ; Isomerization ; Molecular orbital ; Organic chemistry ; Mathematics Photochemistry and Electron Transfer Studies ; Advanced Chemical Physics Studies ; Radical Photochemical Reactions
Authors: Carlo Matera | Pablo Calvé | Verònica Casadó-Anguera | Rosalba Sortino | Alexandre M. J. Gomila | Estefanía Moreno | Thomas Gener | Cristina Delgado-Sallent | Pau Nebot | Davide Costazza | Sara Conde-Berriozabal | Mercè Masana | Jordi Hernando | Vicent Casadó | M. Victoria Puig | Pau Gorostiza
Source: International Journal of Molecular Sciences (article, 2022-09-04)
Field: Materials Science — Materials Chemistry

Reversible Photocontrol of Dopaminergic Transmission in Wild-Type Animals Understanding the dopaminergic system is a priority in neurobiology and neuropharmacology. Dopamine receptors are involved in the modulation of fundamental physiological functions, and dysregulation of dopaminergic transmission is associated with major neurological disorders. However, the available tools to dissect the endogenous dopaminergic circuits have limited specificity, reversibility, resolution, or require genetic manipulation. Here, we introduce azodopa, a novel photoswitchable ligand that enables reversible spatiotemporal control of dopaminergic transmission. We demonstrate that azodopa activates D<sub>1</sub>-like receptors in vitro in a light-dependent manner. Moreover, it enables reversibly photocontrolling zebrafish motility on a timescale of seconds and allows separating the retinal component of dopaminergic neurotransmission. Azodopa increases the overall neural activity in the cortex of anesthetized mice and displays illumination-dependent activity in individual cells. Azodopa is the first photoswitchable dopamine agonist with demonstrated efficacy in wild-type animals and opens the way to remotely controlling dopaminergic neurotransmission for fundamental and therapeutic purposes. Dopaminergic ; Neuroscience ; Dopamine ; Neurotransmission ; Neuropharmacology ; Dopamine receptor ; Dopaminergic pathways ; Biology ; Zebrafish ; Agonist ; Receptor ; Biochemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Retinal Development and Disorders
Authors: Shengpeng WANG | Panpan Liu | Feng Lin | Jing Teng | Fei YE | Anhui Gui | Xueping WANG | Lin Zheng | Shiwei Gao | Pengcheng Zheng
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp2) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and Rp2 of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm-1-4751.7 cm-1, 4755.6 cm-1-5129.7 cm-1, 6262.7 cm-1-6633.9 cm-1 and 7386 cm-1-7756.3 cm-1), whose RMSEP and Rp2 were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp2=0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly. Partial least squares regression ; Polyphenol ; Near-infrared spectroscopy ; Content (measure theory) ; Mean squared error ; Chemistry ; Mathematics ; Calibration ; Green tea ; Biological system ; Food science ; Statistics ; Physics ; Biochemistry ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects