jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Agus Dharmawan | Rudiati Evi Masithoh | Hanim Zuhrotul Amanah
Source: Foods (article, 2023-05-24)
Field: Chemistry — Analytical Chemistry

Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins Arabica coffee, one of Indonesia's economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis-NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee. Principal component analysis ; Artificial intelligence ; Preprocessor ; Coffea arabica ; Multilayer perceptron ; Pattern recognition (psychology) ; Computer science ; Linear discriminant analysis ; Artificial neural network ; Arabica coffee ; Machine learning ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Ebrahim Taghinezhad | Antoni Szumny | Adam Figiel
Source: Molecules (review, 2023-03-24)
Field: Chemistry — Analytical Chemistry

The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement. Hyperspectral imaging ; Process engineering ; Process analytical technology ; Water content ; Process (computing) ; Computer science ; Environmental science ; Precision agriculture ; Remote sensing ; Agricultural engineering ; Artificial intelligence ; Agriculture ; Work in process ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Anam Abbas | Gary G. Wells | Glen McHale | Khellil Sefiane | Daniel Orejón
Source: ACS Applied Materials & Interfaces (article, 2023-02-15)
Field: Materials Science — Surfaces, Coatings and Films

Silicone Oil-Grafted Low-Hysteresis Water-Repellent Surfaces Wetting plays a major role in the close interactions between liquids and solid surfaces, which can be tailored by modifying the chemistry as well as the structures of the surfaces' outermost layer. Several methodologies, such as chemical vapor deposition, physical vapor deposition, electroplating, and chemical reactions, among others, have been adopted for the alteration/modification of such interactions suitable for various applications. However, the fabrication of low-contact line-pinning hydrophobic surfaces via simple and easy methods remains an open challenge. In this work, we exploit one-step and multiple-step silicone oil (5-100 cSt) grafting on smooth silicon substrates (although the technique is suitable for other substrates), looking closely at the effect of viscosity as well as the volume and layers (one to five) of oil grafted as a function of the deposition method. Remarkably, the optimization of grafting of silicone oil fabrication results in non-wetting surfaces with extremely low contact angle hysteresis (CAH) below 1° and high contact angles (CAs) of ∼108° after a single grafting step, which is an order of magnitude smaller than the reported values of previous works on silicone oil-grafted surfaces. Moreover, the different droplet-surface interactions and pinning behavior can additionally be tailored to the specific application with CAH ranging from 1 to 20° and sliding angles between 1.5 and 60° (for droplet volumes of 3 μL), depending on the fabrication parameters adopted. In terms of roughness, all the samples (independent of the grafting parameters) showed small changes in the root-mean-square roughness below 20 nm. Lastly, stability analysis of the grafting method reported here under various conditions shows that the coating is quite stable under mechanical vibrations (bath ultrasonication) and in a chemical environment (ultrasonication in a bath of ethanol) but loses its low-pinning characteristics when exposed to saturated steam at <i>T</i> ∼ 99 °C. The findings presented here provide a basis for selecting the most appropriate and suitable method and parameters for silicone oil grafting aimed at low pinning and low hysteresis surfaces for specific applications. Materials science ; Wetting ; Contact angle ; Silicone oil ; Fabrication ; Chemical vapor deposition ; Silicone ; Surface finish ; Surface roughness ; Hysteresis ; Composite material ; Deposition (geology) ; Chemical engineering ; Grafting ; Nanotechnology ; Polymer Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Fluid Dynamics and Thin Films
Authors: Camelia-Elena Tincu | Călin Vasile Andrițoiu | Marcel Popa | Lăcrămioara Ochiuz
Source: Polymers (review, 2023-10-02)
Field: Materials Science — Biomaterials

Recent Advancements and Strategies for Overcoming the Blood–Brain Barrier Using Albumin-Based Drug Delivery Systems to Treat Brain Cancer, with a Focus on Glioblastoma Glioblastoma multiforme (GBM) is a highly aggressive malignant tumor, and the most prevalent primary malignant tumor affecting the brain and central nervous system. Recent research indicates that the genetic profile of GBM makes it resistant to drugs and radiation. However, the main obstacle in treating GBM is transporting drugs through the blood-brain barrier (BBB). Albumin is a versatile biomaterial for the synthesis of nanoparticles. The efficiency of albumin-based delivery systems is determined by their ability to improve tumor targeting and accumulation. In this review, we will discuss the prevalence of human glioblastoma and the currently adopted treatment, as well as the structure and some essential functions of the BBB, to transport drugs through this barrier. We will also mention some aspects related to the blood-tumor brain barrier (BTBB) that lead to poor treatment efficacy. The properties and structure of serum albumin were highlighted, such as its role in targeting brain tumors, as well as the progress made until now regarding the techniques for obtaining albumin nanoparticles and their functionalization, in order to overcome the BBB and treat cancer, especially human glioblastoma. The albumin drug delivery nanosystems mentioned in this paper have improved properties and can overcome the BBB to target brain tumors. Blood–brain barrier ; Drug delivery ; Glioblastoma ; Albumin ; Medicine ; Brain tumor ; Cancer ; Drug ; Brain cancer ; Targeted drug delivery ; Central nervous system ; Cancer research ; Pharmacology ; Pathology ; Nanotechnology ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Drug Transport and Resistance Mechanisms ; Glioma Diagnosis and Treatment
Authors: Akarsh Verma | Oliver K. Johnson | Gregory B. Thompson | Ian Chesser | Shigenobu Ogata | Eric R. Homer
Source: Acta Materialia (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Insights into factors that affect non-Arrhenius migration of a simulated incoherent Σ3 grain boundary Grain boundary ; Arrhenius equation ; Materials science ; Nucleation ; Grain boundary diffusion coefficient ; Molecular dynamics ; Condensed matter physics ; Arrhenius plot ; Chemical physics ; Thermodynamics ; Activation energy ; Microstructure ; Chemistry ; Composite material ; Physics ; Physical chemistry ; Computational chemistry Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; High Temperature Alloys and Creep
Authors: Khaled Younes | Yahya Kharboutly | Mayssara Antar | Hamdi Chaouk | Emil Obeid | Omar Mouhtady | Mahmoud Abu-samha | Jalal Halwani | Nimer Murshid
Source: Gels (article, 2023-04-04)
Field: Chemistry — Spectroscopy

Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of "Green Aerogels" in water treatment's ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a "Principal Component Analysis" (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization. Aerogel ; Nanocellulose ; Principal component analysis ; Adsorption ; Water scarcity ; Materials science ; Sustainability ; Nanotechnology ; Process engineering ; Computer science ; Environmental science ; Chemical engineering ; Agriculture ; Artificial intelligence ; Chemistry ; Cellulose ; Engineering ; Ecology Aerogels and thermal insulation ; Solar-Powered Water Purification Methods
Authors: Dário Passos | Puneet Mishra
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-11-05)
Field: Chemistry — Analytical Chemistry

Deep Tutti Frutti: Exploring CNN architectures for dry matter prediction in fruit from multi-fruit near-infrared spectra Convolutional Neural Networks (CNNs) have proven to be a valuable Deep Learning (DL) algorithm to model near-infrared spectral data in Chemometrics. However, optimizing CNN architectures and their associated hyperparameters for specific tasks is challenging. In this study, we explore the development of 1D-CNN architectures for the task of fruit dry matter (DM) estimation, testing various designs and optimization strategies to achieve a generic DL model that is robust against data fluctuations. The models are built using a multi-fruit data set (n = 2397) that includes NIR spectra of apples, kiwis, mangoes, and pears. The obtained CNN models are compared with PLS (taken as baseline), and to Locally Weighted PLS (LW-PLS) models. In general, the optimized CNN architectures obtained lower RMSEs (best RMSE = 0.605 %) than PLS (RMSE = 0.892 %) and LW-PLS (RMSE = 0.687 %) on a holdout test set. For this specific task, CNNs start outperforming PLS when the number of training samples is around 500. Furthermore, it is also shown how a global CNN model, trained on multi-fruit data, performs against PLS models of individual fruits in the sub-tasks of individual fruit DM prediction and generalization to an external mango data set. Overall, with proper architecture optimization, CNNs show strong performance and generalization for NIR-based dry matter estimation across diverse fruits. Convolutional neural network ; Hyperparameter ; Mean squared error ; Test set ; Computer science ; Artificial intelligence ; Generalization ; Pattern recognition (psychology) ; Set (abstract data type) ; Data set ; Deep learning ; Dry matter ; Machine learning ; Mathematics ; Statistics ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Jianwu Lin | Dianzhi Yu | Renyong Pan | Jitong Cai | Jiaming Liu | Licai Zhang | Xingtian Wen | Xishun Peng | Tomislav Cernava | Safa Oufensou | Quirico Migheli | Xiaoyulong Chen | Xin Zhang
Source: Frontiers in Plant Science (article, 2023-02-14)
Field: Chemistry — Analytical Chemistry

Improved YOLOX-Tiny network for detection of tobacco brown spot disease Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants. Hot spot (computer programming) ; Sweet spot ; Leaf spot ; Biology ; Computer science ; Botany ; Computer network Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Yong Hao | Xiyan Li | Chengxiang Zhang | Zuxiang Lei
Source: Biosensors (article, 2023-01-29)
Field: Chemistry — Analytical Chemistry

Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears. Browning ; Near-infrared spectroscopy ; Linear discriminant analysis ; Artificial intelligence ; Computer science ; Chemistry ; Pattern recognition (psychology) ; Food science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: João Medeiros‐Silva | Aurelio J. Dregni | Noah H. Somberg | Pu Duan | Mei Hong
Source: Science Advances (article, 2023-10-13)
Field: Chemistry — Spectroscopy

Atomic structure of the open SARS-CoV-2 E viroporin The envelope (E) protein of the SARS-CoV-2 virus forms cation-conducting channels in the endoplasmic reticulum Golgi intermediate compartment (ERGIC) of infected cells. The calcium channel activity of E is associated with the inflammatory responses of COVID-19. Using solid-state NMR (ssNMR) spectroscopy, we have determined the open-state structure of E's transmembrane domain (ETM) in lipid bilayers. Compared to the closed state, open ETM has an expansive water-filled amino-terminal chamber capped by key glutamate and threonine residues, a loose phenylalanine aromatic belt in the middle, and a constricted polar carboxyl-terminal pore filled with an arginine and a threonine residue. This structure gives insights into how protons and calcium ions are selected by ETM and how they permeate across the hydrophobic gate of this viroporin. Endoplasmic reticulum ; Biophysics ; Transmembrane domain ; Threonine ; Lipid bilayer ; Transmembrane protein ; Amino acid ; Chemistry ; Golgi apparatus ; Membrane ; Ion channel ; Biochemistry ; Biology ; Phosphorylation ; Serine Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Atomic and Subatomic Physics Research
Authors: Nannan Zhang | Xiao Zhang | Peng Shang | Rui Ma | Xintao Yuan | Li Li | Tiecheng Bai
Source: Remote Sensing (article, 2023-07-01)
Field: Chemistry — Analytical Chemistry

Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease. Support vector machine ; Particle swarm optimization ; Pattern recognition (psychology) ; Macro ; Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Jiaping Zhang | Jiaqi Hou | Lei Zhou | Wuqing Ding | Qiangang Fu
Source: Journal of the American Ceramic Society (article, 2023-08-27)
Field: Materials Science — Ceramics and Composites

TaSi <sub>2</sub> ‐modified HfB <sub>2</sub> –SiC coating: Preparation and ablation behavior Abstract HfB 2 –SiC coatings modified with TaSi 2 were prepared on carbon/carbon composites by slurry‐dipping combined with gaseous silicon infiltration. The effects of TaSi 2 content on the microstructure and ablation performance were investigated. With 20 wt.% addition of TaSi 2 , the HfB 2 –SiC coating demonstrated the highest resistance to ablation under an oxyacetylene torch. The good ablation resistance was attributed to the formation of the Hf–Ta–Si–O compound phase, the Hf–Ta–O skeleton and the “sheet‐like” clusters. During ablation, the Hf–Ta–Si–O compound phase healed the defects and inhibited penetration of oxygen. Meanwhile, both the Hf–Ta–O skeleton and the “sheet‐like” clusters with good stability pinned the compound phase. They jointly enhanced the ablation performance of the coating. Coating ; Materials science ; Ablation ; Microstructure ; Slurry ; Composite material ; Penetration (warfare) ; Silicon ; Oxygen ; Phase (matter) ; Carbon fibers ; Chemical engineering ; Chemistry ; Metallurgy ; Composite number ; Organic chemistry Advanced ceramic materials synthesis ; MXene and MAX Phase Materials ; Advanced materials and composites