jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raquel Martínez‐Peña | Salvador Castillo‐Gironés | Sara Álvarez | Sergio Vélez
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Tracing pistachio nuts’ origin and irrigation practices through hyperspectral imaging Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis. The results demonstrated high accuracy in classifying pistachios based on origin, with accuracies exceeding 94%, and in assessing water content and colour pigments, where both PLS and SVM models achieved 99% accuracy. The research highlighted distinct spectral signatures associated with different irrigation treatments, particularly in the Near-Infrared (NIR) region, with PLS showing an accuracy of 92%. However, challenges were noted in predicting fruit orientation, while predicting height location within the tree was more successful, reflecting clearer spectral distinctions. Regression models also showed promise, particularly in predicting yield (R<sup>2</sup> = 0.89 with PLS) and percentage of blank nuts (R<sup>2</sup> = 0.71 with PLS). The correlation analysis revealed key insights, such as an inverse relationship between blank nuts and yield, and a strong correlation between yield and split nuts. Despite challenges in predicting fruit orientation, the research showed promising results in forecasting yield and commercial quality factors, indicating the effectiveness of spectral analysis in optimising pistachio production and sustainability. Hyperspectral imaging ; Support vector machine ; Partial least squares regression ; Mathematics ; Spectral signature ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Remote sensing ; Environmental science ; Geography ; Statistics Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Horticultural and Viticultural Research
Authors: Christopher J. Kingsbury | Mathias O. Senge
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Quantifying near-symmetric molecular distortion using symmetry-coordinate structural decomposition We imagine molecules to be perfect, but rigidified units can be designed to bend from their ideal shape, discarding their symmetric elements as they progress through vibrations and larger, more permanent distortions. The shape of molecules is either simulated or measured by crystallography and strongly affects chemical properties but, beyond an image or tabulation of atom-to-atom distances, little is often discussed of the accessed conformation. We have simplified the process of shape quantification across multiple molecular types with a new web-accessible program - SCSD - through which a molecular subunit possessing near-symmetry can be dissected into symmetry coordinates with ease. This parameterization allows a common set of numbers for comparing and understanding molecular shape, and is a simple method for database analysis; this program is available at https://www.kingsbury.id.au/scsd. Distortion (music) ; Decomposition ; Symmetry (geometry) ; Physics ; Biological system ; Crystallography ; Theoretical physics ; Computational chemistry ; Chemistry ; Mathematics ; Geometry ; Biology Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Cristian Cruz-Carrasco | Josefa Díaz‐Álvarez | Francisco P. Chávez | Abel Sánchez-Venegas | Juan Villegas-Cortéz
Source: AgriEngineering (article, 2024-10-28)
Field: Chemistry — Analytical Chemistry

Detection of Aspergillus flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption. Hyperspectral imaging ; Aspergillus flavus ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Computer science ; Biology ; Microbiology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Valentina Erastova | Ivana Radosavljević Evans | William Nelson Glossop | Songül Güryel | Paul Hodgkinson | Hannah E. Kerr | Vasily S. Oganesyan | Lorna K. Softley | Helen M. Wickins | Mark R. Wilson
Source: Journal of the American Chemical Society (article, 2024-06-27)
Field: Chemistry — Spectroscopy

Unravelling Guest Dynamics in Crystalline Molecular Organics Using <sup>2</sup> H Solid-State NMR and Molecular Dynamics Simulation 2H solid-state NMR and atomistic molecular dynamics (MD) simulations are used to understand the disorder of guest solvent molecules in two cocrystal solvates of the pharmaceutical furosemide. Traditional approaches to interpreting the NMR data fail to provide a coherent model of molecular behavior and indeed give misleading kinetic data. In contrast, the direct prediction of the NMR properties from MD simulation trajectories allows the NMR data to be correctly interpreted in terms of combined jump-type and libration-type motions. Time-independent component analysis of the MD trajectories provides additional insights, particularly for motions that are invisible to NMR. This allows a coherent picture of the dynamics of molecules restricted in molecular-sized cavities to be determined. Chemistry ; Molecular dynamics ; Solid-state ; Solid-state nuclear magnetic resonance ; Dynamics (music) ; Computational chemistry ; Nuclear magnetic resonance spectroscopy ; Physical chemistry ; Chemical physics ; Nuclear magnetic resonance ; Stereochemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Abdelhak El Kharki | Jamila Mechbouh | Miriam Wahbi | Otmane Yazidi Alaoui | Hakim Boulaassal | Mustapha Maâtouk | Omar El Kharki
Source: Revista de Teledetección (article, 2024-11-04)
Field: Chemistry — Analytical Chemistry

Optimizing SVM for argan tree classification using Sentinel-2 data: A case study in the Sous-Massa Region, Morocco The development of efficient classifiers for land cover remains challenging due to the presence of hyperparameters in the model. Conventional approaches rely on manual tuning, which is both time-consuming and impractical, often leading to suboptimal results. This study aimed to optimize the hyperparameters of the Support Vector Machine (SVM) algorithm using the grid search method to map the distribution of the Argan forest in the Souss-Massa region of Morocco from Sentinel-2 satellite image. To achieve this, we examined the C parameter for the linear function, as well as the C and gamma parameters for the radial RBF and sigmoid functions. Similarly, we explored the C, gamma, and degree parameters for the polynomial function chosen using the grid search method. These parameters are compared with the default hyperparameters of each SVM function. The results are validated using the cross-validation method and by the following scores: accuracy, precision, recall, F1 score, and Cohen’s Kappa. The experiments were conducted using the Earth Engine Python API in Google Colab (Google Collaboratory). In addition, experimental results indicate that the hyperparameters selected by grid search yield higher scores than the default hyperparameters. The best results were achieved using the hyperparameters of the polynomial base kernel, specifically with C = 10, degree = 2, and gamma = 10. Accuracy = 96.61%. Support vector machine ; Tree (set theory) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Combinatorics Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Identification and Quantification in Food
Authors: Aruna Prakash
Source: Journal of Materials Science (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

Atomistic modeling of idealized equal channel angular pressing process Abstract Severe plastic deformation (SPD) processes are forming techniques that impose large plastic strains to achieve ultra-fine grained and nanocrystalline microstructures. The refined microstructure results in a significant increase in strength and in many cases without a considerable loss in ductility. Despite their long existence, a comprehensive understanding of the deformation and grain refinement mechanisms during SPD is still lacking. Atomistic simulations that are regularly used to obtain insights into material behavior have rarely been used to study SPD processes. In this work, we use large scale atomistic simulations of the molecular statics/dynamics kind to model the equal channel angular pressing (ECAP) process, a specific SPD process. Three different materials—Al, Ni and Cu—are used to model high, medium and low stacking fault energy materials. Large simulation cells are used to facilitate grain refinement at the nanoscale. The simulations are carefully analyzed in terms of stress–strain behavior, dislocation activity and grain refinement. All samples show the formation of deformation twins at the nanoscale, which generally detwin at increased strains via dislocation twin interactions. A significant number of stair-rod dislocations form via dislocation–dislocation interactions and are present at low angle grain boundaries in the early stages of deformation. Such stair-rod dislocations also result in the formation of stacking fault tetrahedra which are present in significant numbers in all samples. Finally, difficulties in studying grain refinement at very large strains in atomistic simulations are discussed. Materials science ; Stacking-fault energy ; Dislocation ; Microstructure ; Severe plastic deformation ; Pressing ; Deformation (meteorology) ; Molecular dynamics ; Ductility (Earth science) ; Embedded atom model ; Nanocrystalline material ; Deformation mechanism ; Partial dislocations ; Crystallography ; Composite material ; Nanotechnology ; Creep ; Computational chemistry Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Metallurgy and Material Forming
Authors: Daniel Cozzolino | Pooja Sanal | Jana Schreuder | Paul J. Williams | Elham Assadi Soumeh | Milou Helene Dekkers | Molly Anderson | Sheree Boisen | Louwrens C. Hoffman
Source: Foods (article, 2024-01-09)
Field: Chemistry — Analytical Chemistry

Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R<sup>2</sup><sub>CV</sub>) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R<sup>2</sup><sub>CV</sub> of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R<sup>2</sup><sub>CV</sub> of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R<sup>2</sup><sub>CV</sub> of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness. Partial least squares regression ; Chemometrics ; Principal component analysis ; Coefficient of determination ; Standard deviation ; Near-infrared spectroscopy ; Chemistry ; Mathematics ; Analytical Chemistry (journal) ; Chromatography ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Henan Liu | Sijia Ma | Ni Liang | Xin Wang
Source: Foods (article, 2024-12-23)
Field: Chemistry — Analytical Chemistry

Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance. Partial least squares regression ; Rice bran oil ; Camellia ; Artificial intelligence ; Mathematics ; Algorithm ; Bran ; Biological system ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Statistics ; Raw material ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Emrah Dönmez
Source: European Food Research and Technology (article, 2024-02-20)
Field: Chemistry — Analytical Chemistry

Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+ Abstract Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated. Convolutional neural network ; Transformer ; Winter wheat ; Artificial intelligence ; Computer science ; Multilayer perceptron ; Artificial neural network ; Machine learning ; Pattern recognition (psychology) ; Agronomy ; Engineering ; Biology ; Voltage Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Samuel Okyem | Jonathan V. Sweedler
Source: Mass Spectrometry Reviews (review, 2024-11-18)
Field: Chemistry — Spectroscopy

Recent Advancements in the Characterization of D‐Amino Acid and Isoaspartate Post‐Translational Modifications One of the great triumphs of mass spectrometry-based peptide and protein characterization is the characterization of their modifications as most modifications have a characteristic mass shift. What happens when the modification does not change the mass of the peptide? Here, the characterization of several peptide and proteins modifications that do not involve a mass shift are highlighted. Protein and peptide synthesis on ribosomes involves L-amino acids; however, posttranslational modifications (PTMs) can convert these L-amino acids into their D-isomers. As another example, nonenzymatic PTM of aspartate leads to the formation of three different isomers, with isoaspartate being the most prevalent. Both modifications do not alter the mass of the peptide and yet can have profound impact on the physicochemical characteristics of the peptide. Several MS and ion mobility techniques are highlighted, as are other methods such as chromatography, enzymatic enrichment, and labeling. The challenges inherent to these analytical methods and prospective developments in bioinformatics and computational strategies are discussed for these zero-dalton PTMs. Chemistry ; Posttranslational modification ; Peptide ; Mass spectrometry ; Characterization (materials science) ; Amino acid ; Combinatorial chemistry ; Biochemistry ; Chromatography ; Enzyme ; Nanotechnology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Amino Acid Enzymes and Metabolism
Authors: Sarada Prasad Parida | Debashish Mishra | Rajiv Lochan Padhy | Pankaj Charan Jena | Sudhansu Ranjan Das | Ali Basem | Debabrata Dhupal | Ammar H. Elsheikh
Source: ES Materials & Manufacturing (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Effect of Alkali Treatment on Mechanical and Buckling Behaviour of Natural Fiber Reinforced Composite Cylinder The novelty of the work is that not only a cylindrical pipe is fabricated from natural fiber and flyash, but the cylinder is also fabricated in a functional gradation of the natural fiber and flyash in an epoxy base. Materials science ; Composite material ; Composite number ; Fiber ; Gradation ; Epoxy ; Natural fiber ; Cylinder ; Buckling ; Structural engineering ; Mechanical engineering ; Engineering ; Computer science Natural Fiber Reinforced Composites ; Material Properties and Applications ; Innovations in Concrete and Construction Materials
Authors: Xuerong Zhao | Yuanyuan Liu | Zongbao Huang | Gangao Li | Zilin Zhang | Xiuhan He | Huiling Du | Meiqin Wang | Zhiwei Li
Source: Scientific Reports (article, 2024-08-30)
Field: Chemistry — Analytical Chemistry

Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes. Hyperspectral imaging ; Principal component analysis ; Near-infrared spectroscopy ; Greenhouse ; Sensor fusion ; Multispectral image ; Pattern recognition (psychology) ; Remote sensing ; Computer science ; Horticulture ; Artificial intelligence ; Biology ; Physics ; Optics ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture