jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. I. Fedyanina | O. V. Karastoyanova | Н. В. Коровкина
Source: Food systems (review, 2022-01-05)
Field: Chemistry — Analytical Chemistry

Methods for determining color characteristics of vegetable raw materials. A review Food product quality defines a complex of food product properties such size, shape, texture, color and others, and determines acceptability of these products for consumers. It is possible to detect defects in plant raw materials by color and classify them by color characteristics, texture, shape, a degree of maturity and so on. Currently, the work on modernization of color control systems has been carried out for rapid and objective measuring information about color of plant raw materials during their harvesting, processing and storage. The aim of the work is to analyze existing methods for determining color characteristics of plant raw materials described in foreign and domestic studies. Also, this paper presents the results of the experimental studies that describe the practical use of methods for measuring food product color. At present, the following methods for determining color characteristics by the sensor analysis principle are used: sensory, spectrophotometric and photometric. These methods have several disadvantages. Therefore, computer vision has found wide application as an automated method for food control. It is distinguished by high confidence and reliability in the process of determining freshness, safety, a degree of maturity and other parameters of plant raw materials that are heterogeneous in terms of the abovementioned indicators. The computer vision method is realized in the following systems: conventional, hyperspectral and multispectral. Each subsequent system is a component of the preceding one. Materials presented in the paper allow making a conclusion about the effectiveness of the computer vision systems with the aim of automatic sorting and determining quality of plant raw materials in the food industry. Computer science ; Raw material ; Multispectral image ; Artificial intelligence ; Process (computing) ; Quality (philosophy) ; Sorting ; Color analysis ; Computer vision ; Process engineering ; Engineering Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology ; Advanced Scientific Research Methods
Authors: Carlo Matera | Pablo Calvé | Verònica Casadó-Anguera | Rosalba Sortino | Alexandre M. J. Gomila | Estefanía Moreno | Thomas Gener | Cristina Delgado | Pau Nebot | Davide Costazza | Sara Conde-Berriozabal | Mercè Masana | Jordi Hernando | Vicent Casadó | M. Victoria Puig | Pau Gorostiza
Source: ChemRxiv (preprint, 2022-05-17)
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 D1-like receptors in vitro in a light-dependent manner. Moreover, it enables reversibly photocontrolling zebrafish motility on a time scale 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 wildtype animals and opens the way to remotely controlling dopaminergic neurotransmission for fundamental and therapeutic purposes. Dopaminergic ; Neuroscience ; Dopamine ; Neurotransmission ; Dopamine receptor ; Neuropharmacology ; Zebrafish ; Biology ; Dopaminergic pathways ; Agonist ; Receptor Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Zebrafish Biomedical Research Applications
Authors: Jasurbek Gulomov | Р. Алиев | N. Mirzaalimov | B. Rashidov | J. Alieva
Source: Journal of Nano- and Electronic Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Study of Mono- and Polycrystalline Silicon Solar Cells with Various Shapes for Photovoltaic Devices in 3D Format: Experiment and Simulation Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Monocrystalline silicon ; Polycrystalline silicon ; Photovoltaic system ; Materials science ; Solar cell ; Prism ; Optoelectronics ; Crystalline silicon ; Silicon ; Open-circuit voltage ; Photoelectric effect ; Solar simulator ; Voltage ; Optics ; Electrical engineering ; Nanotechnology ; Engineering ; Physics Silicon Nanostructures and Photoluminescence ; Thermal Radiation and Cooling Technologies ; Thin-Film Transistor Technologies
Authors: Siti Mariatul Hazwa Mohd Huzir | Aqib Fawwaz Mohd Amidon | Anis Hazirah ‘Izzati Hasnu Al-Hadi | Nurlaila Ismail | Zakiah Mohd Yusoff | Saıful Nizam Tajuddin | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2022-11-18)
Field: Chemistry — Organic Chemistry

Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation &lt;span&gt;This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.&lt;/span&gt; Agarwood ; Stepwise regression ; Regression analysis ; Grading (engineering) ; Mathematics ; Linear regression ; Regression ; Correlation coefficient ; Statistics ; Essential oil ; Coefficient of determination ; Engineering ; Chemistry ; Food science ; Medicine Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Forensic Fingerprint Detection Methods
Authors: Ji‐zhe Yin | Lei Geng | Yongmin Guo | Yalong Huang | Ye Song | Heng Zhao
Source: Cognitive Computation and Systems (article, 2022-04-18)
Field: Chemistry — Analytical Chemistry

Apple appearance quality classification method based on double branch feature fusion network Abstract This paper carried out a classification study on the quality of different types of apples. In order to achieve a high‐precision classification of high‐quality apples in various dimensions, shorten the classification time, improve efficiency, and make it feasible for practical applications. This paper proposes an automatic recognition and classification model OB‐Net (for apple appearance quality) based on a dual‐branch structure. The model consists of two branches, an O branch and a B branch. The O branch extracts the shape and contour features of the apple surface defect by decomposing the feature map into high and low frequencies. The B branch extracts the shape size and texture characteristics of the apple surface defect by fusing channel attention and spatial attention mechanisms; this is done to further increase the feature distance between different types of defects. Experimental results show that the classification accuracy rates of rot, insect bites, russeting, scratches, and intact apples are 95.65%, 98.17%, 94.62%, 92.02% and 97.67%, respectively; the overall classification and recognition accuracy rate reaches 95.64%. Finally, the three aspects of the feature map, heat map and category probability statistics map extracted from the OB‐Net network model prove the validity of the network model in the classification of apple appearance quality. Pattern recognition (psychology) ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Texture (cosmology) ; Feature extraction ; Quality (philosophy) ; Mathematics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Aqib Fawwaz Mohd Amidon | Siti Mariatul Hazwa Mohd Huzir | Zakiah Mohd Yusoff | Nurlaila Ismail | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

The significance of artificial intelligent technique in classifying various grades of agarwood oil Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification. Agarwood ; Preprocessor ; Categorization ; Artificial intelligence ; Computer science ; Quality (philosophy) ; Pattern recognition (psychology) ; Machine learning Wood and Agarwood Research ; Forensic Fingerprint Detection Methods ; Identification and Quantification in Food
Authors: Aininu Nafiunisa | Nita Aryanti | Dyah Hesti Wardhani
Source: Acta Scientiarum Polonorum Technologia Alimentaria (article, 2022-06-30)
Field: Chemistry — Analytical Chemistry

Ultrasonic preparation of olive oil pickering emulsion stabilized by modified starch Background. In order to obtain a stable emulsion system for food products fortified by bioactive compounds, it is necessary to add an emulsifier and stabilizer. Because food has a close relationship with health, it would be better if the emulsifier and stabilizer came from natural ingredients. One natural compound which acts as a stabilizer is polysaccharides. This research involved an investigation of the stabilization of oil in water emulsion using modified polysaccharides and its complex with a surfactant. Materials and methods. Food grade extra virgin olive oil was used as the oil phase. Pickering emulsion was prepared by adding starch particles as an emulsion stabilizer. Corn starch was used as the starch source and modified using gelatinization methods without any addition of chemicals. As for the polysaccharidesurfactant complex, the modified starch was combined with a food-grade nonionic surfactant -Tween-80. Results. The results show that the addition of the stabilizer did not have any specific impact on the density or viscosity of the emulsion product. However, it somehow helped to stabilise the emulsion, as proven by the turbidity profile and the microscopic morphology investigation. Olive oil ; Starch ; Emulsion ; Pickering emulsion ; Ultrasonic sensor ; Modified starch ; Food science ; Engineering ; Materials science ; Chemical engineering ; Polymer science ; Chemistry ; Acoustics ; Physics Spectroscopy and Chemometric Analyses
Authors: Jousiane Alves Martins | Alcinei Mí­stico Azevedo | Anna Christina de Almeida | Luana Cristina Rodrigues da Silva | Ana Clara Gonçalves Fernandes | Nermy Ribeiro Valadares | Ignácio Aspiazú
Source: Acta Scientiarum. Technology/Acta scientiarum. Technology (article, 2022-05-24)
Field: Chemistry — Analytical Chemistry

Fuzzy logic is a powerful tool for the automation of milk classification The demand for the consumption of milk and dairy products by the consumer market is very high. This makes it difficult to analyze the large number of milk samples for quality. In addition to the requirement to consider many quality attributes, there are usually large number of producers, who need daily milk evaluations. The aim of the study was to evaluate the efficiency of fuzzy logic in decision making for the classification of milk. In the fuzzification stage, physical and chemical characteristics of the milk were considered as input linguistic variables. For each linguistic variable, pertinence functions were created, and these were made considering the trapezoidal forms. In the inference stage, rules were established for the association of linguistic variables and output variables (adulterated, inadequate and adequate). To verify the efficiency of the modeled system, 1,000 adulterated, inadequate and adequate milk samples were computationally simulated. Precision was verified when automating decision making in the classification of milk by the fuzzy logic, totaling 100% of correctness. Therefore, the fuzzy system is an efficient tool for the classification of milk and can be used advantageously by professionals in the field in order to reduce human and financial resources. Correctness ; Fuzzy logic ; Automation ; Quality (philosophy) ; Variable (mathematics) ; Computer science ; Field (mathematics) ; Artificial intelligence ; Fuzzy control system ; Fuzzy set ; Data mining ; Mathematics ; Machine learning ; Engineering ; Algorithm Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Advanced Chemical Sensor Technologies
Authors: Berrak Özer | Martin A. Karlsen | Zachary Thatcher | Ling Lan | Brian T. McMahon | Peter Strickland | Simon P. Westrip | Koh S. Sang | D.G. Billing | Dorthe Bomholdt Ravnsbæk | Simon J. L. Billinge
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges. Identifier ; Computer science ; Upload ; Information retrieval ; Rank (graph theory) ; Set (abstract data type) ; Object (grammar) ; Similarity (geometry) ; Data mining ; Database ; World Wide Web ; Programming language ; Image (mathematics) ; Artificial intelligence X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Kun Liang | Jinpeng Song | Shiyu Tang | Dezhou Wang | Pengfei Jiang | Bin Cao
Source: Research Square (preprint, 2022-08-18)
Field: Chemistry — Analytical Chemistry

Mid-level data fusion combined with the fingerprint region in near-infrared and Fourier transform mid-infrared spectroscopy for classification DON levels defect of Fusarium head blight wheat <title>Abstract</title> In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. Experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples. Fingerprint (computing) ; Fusion ; Sensor fusion ; Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Spectroscopy ; Fourier transform infrared spectroscopy ; Near-infrared spectroscopy ; Infrared ; Fourier transform ; Computer science ; Materials science ; Mathematics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mycotoxins in Agriculture and Food
Authors: Nickolay Shchurov | Igor Isaev | Sergey Burikov | Tatiana A. Dolenko | Kirill Laptinskiy | S. A. Dolenko
Source:  (article, 2022-11-15)
Field: Chemistry — Analytical Chemistry

Taking into Account Mutual Correlations during Selection of Significant Input Features in Neural Network Solution of Inverse Problems of Spectroscopy In the neural network solution of many physical problems, it becomes necessary to reduce the dimension of the input data in order to achieve a more accurate and stable solution while reducing computational complexity. When solving the inverse problem of spectroscopy, high multicollinearity between input features is often observed, as spectral lines may be much wider than the spectral channel width. This leads to the need to use a feature selection method that takes into account this characteristic. The method discussed in this article is based on iterative selection of input features with the highest Pearson correlation with the target variable and elimination of input features with high cross-correlation. This study compares the quality of the neural network solution to the problem of determining the concentration of heavy metal ions in water by Raman and absorption spectra on the full feature set and on its subsets produced by the considered feature selection method and by conventional methods of selection of significant input features. Feature selection ; Artificial neural network ; Computer science ; Multicollinearity ; Inverse problem ; Dimension (graph theory) ; Artificial intelligence ; Selection (genetic algorithm) ; Feature (linguistics) ; Mathematical optimization ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: A.L. Thomson | Simone Vassiliadis | Amy Copland | Danielle Stayches | J. L. Jacobs | Elizabeth Morse‐McNabb
Source: Precision Agriculture (article, 2022-05-31)
Field: Chemistry — Analytical Chemistry

Comparing how accurately four different proximal spectrometers can estimate pasture nutritive characteristics: effects of spectral range and data type Abstract The nutrition of grazing ruminants can be optimized by allocating pasture according to its nutritive characteristics, provided that nutritive concentrations are determined in near-real time. Current proximal spectrometers can provide accurate predictive results but are bulky and expensive. This study compared an industry standard, ‘control’, proximal spectrometer, often used for scientific estimation of pasture nutrient concentrations in situ (350–2500 nm spectral range), with three lower-cost, ‘next-generation’, handheld spectrometers. The candidate sensors included a hyperspectral camera (397–1004 nm), and two handheld spectrometers (908–1676 nm and 1345–2555 nm respectively). Pasture samples ( n = 145) collected from two paddocks on a working Australian dairy farm, over three timepoints, were scanned in situ by each instrument and then analysed for eight nutritive parameters. Chemometric models were then developed for each nutrient using data from each sensor (split into 80:20 calibration and validation sets). According to Lin’s Concordance Correlation Coefficient (LCCC) from independent validation ( n = 29), the hyperspectral camera was the best candidate instrument (LCCC from 0.31 to 0.85, and 0.67 on average), rivalling the control sensor (LCCC from 0.41 to 0.84, and 0.67 on average). Consideration was given to whether the hyperspectral camera’s success was due to spectral range or data type/capture method. It was found that the 400–920 nm (trimmed) spectral region was slightly less sensitive in principle to nutrient concentrations than higher spectral ranges. Therefore, the predictive performance of the camera was attributed to the advantage of gathering data as hyperspectral images as opposed to single spectra. Hyperspectral imaging ; Spectrometer ; Pasture ; Calibration ; Imaging spectrometer ; Remote sensing ; Range (aeronautics) ; Environmental science ; Mathematics ; Materials science ; Statistics ; Optics ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Soil Geostatistics and Mapping