jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bing Fu | Gen Kaneko | Jun Xie | Zhifei Li | Jingjing Tian | Wangbao Gong | Kai Zhang | Yun Xia | Ermeng Yu | Guangjun Wang
Source: Foods (article, 2020-11-06)
Field: Chemistry — Analytical Chemistry

Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes Crisp grass carp products from China are becoming more prevalent in the worldwide fish market because muscle hardness is the primary desirable characteristic for consumer satisfaction of fish fillet products. Unfortunately, current instrumental methods to evaluate muscle hardness are expensive, time-consuming, and wasteful. This study sought to develop classification models for differentiating the muscle hardness of crisp grass carp on the basis of blood analysis. Out of the total 264 grass carp samples, 12 outliers from crisp grass carp group were removed based on muscle hardness (<9 N), and the remaining 252 samples were used for the analysis of seven blood indexes including hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), glucose 6-phosphate dehydrogenase (G6PD), malondialdehyde (MDA), glutathione (GSH/GSSH), red blood cells (RBC), platelet count (PLT), and lymphocytes (LY). Furthermore, six machine learning models were applied to predict the muscle hardness of grass carp based on the training (152) and testing (100) datasets obtained from the blood analysis: random forest (RF), naïve Bayes (NB), gradient boosting decision tree (GBDT), support vector machine (SVM), partial least squares regression (PLSR), and artificial neural network (ANN). The RF model exhibited the best prediction performance with a classification accuracy of 100%, specificity of 93.08%, and sensitivity of 100% for discriminating crisp grass carp muscle hardness, followed by the NB model (93.75% accuracy, 91.83% specificity, and 94% sensitivity), whereas the ANN model had the lowest prediction performance (85.42% accuracy, 81.05% specificity, and 85% sensitivity). These machine learning methods provided objective, cheap, fast, and reliable classification for in vivo crisp grass carp and also prove useful for muscle quality evaluation of other freshwater fish. Grass carp ; Carp ; Value (mathematics) ; Class (philosophy) ; Mathematics ; Fishery ; Statistics ; Artificial intelligence ; Fish <Actinopterygii> ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Evolutionary Algorithms and Applications ; Meat and Animal Product Quality
Authors: Michal Levin | Marion Scheibe | Falk Butter
Source: BMC Genomics (article, 2020-10-06)
Field: Materials Science — Biomaterials

Proteotranscriptomics assisted gene annotation and spatial proteomics of Bombyx mori BmN4 cell line We show that proteotranscriptomics is an efficient, cost-effective and accurate approach to improve previous annotations or generate new gene models. As this technique is based on de-novo transcriptome assembly, it provides the possibility to study any species also in the absence of genome sequence information for which proteogenomics would be impossible. Proteogenomics ; Biology ; Computational biology ; Proteomics ; Genome project ; Genome ; Gene ; Annotation ; Gene Annotation ; Transcriptome ; Bombyx ; Reference genome ; Genetics ; Genomics ; DNA microarray ; Bombyx mori ; Gene expression Silk-based biomaterials and applications ; Genomics and Phylogenetic Studies ; Insect Resistance and Genetics
Authors: Federico Stilo | Chiara Cordero | Carlo Bicchi | Daniela Peroni | Qingping Tao | Stephen E. Reichenbach
Source: Journal of Visualized Experiments (article, 2020-09-02)
Field: Chemistry — Spectroscopy

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography Data processing and evaluation are critical steps of comprehensive two-dimensional gas chromatography (GCxGC), particularly when coupled to mass spectrometry. The rich information encrypted in the data may be highly valuable but difficult to access efficiently. Data density and complexity can lead to long elaboration times and require laborious, analyst-dependent procedures. Effective yet accessible data processing tools, therefore, are key to enabling the spread and acceptance of this advanced multidimensional technique in laboratories for daily use. The data analysis protocol presented in this work uses chromatographic fingerprinting and template matching to achieve the goal of highly automated deconstruction of complex two-dimensional chromatograms into individual chemical features for advanced recognition of informative patterns within individual chromatograms and across sets of chromatograms. The protocol delivers high consistency and reliability with little intervention. At the same time, analyst supervision is possible in a variety of settings and constraint functions that can be customized to provide flexibility and capacity to adapt to different needs and goals. Template matching is shown here to be a powerful approach to explore extra-virgin olive oil volatilome. Cross-alignment of peaks is performed not only for known targets, but also for untargeted compounds, which significantly increases the characterization power for a wide range of applications. Examples are presented to evidence the performance for the classification and comparison of chromatographic patterns from sample sets analyzed under similar conditions. Computer science ; Protocol (science) ; Data mining ; Consistency (knowledge bases) ; Flexibility (engineering) ; Matching (statistics) ; Reliability (semiconductor) ; Pattern recognition (psychology) ; Artificial intelligence ; Power (physics) ; Mathematics Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Li‐Ming Zhao | K-jiro Watanabe | Naoki Nakatani | Akira Nakayama | Xin Xu | Jun‐ya Hasegawa
Source: The Journal of Chemical Physics (article, 2020-10-06)
Field: Materials Science — Materials Chemistry

Extending nudged elastic band method to reaction pathways involving multiple spin states There are diverse reactions including spin-state crossing, especially the reactions catalyzed by transition metal compounds. To figure out the mechanisms of such reactions, the discussion of minimum energy intersystem crossing (MEISC) points cannot be avoided. These points may be the bottleneck of the reaction or inversely accelerate the reactions by providing a better pathway. It is of great importance to reveal their role in the reactions by computationally locating the position of the MEISC points together with the reaction pathway. However, providing a proper initial guess for the structure of the MEISC point is not as easy as that of the transition state. In this work, we extended the nudged elastic band (NEB) method for multiple spin systems, which is named the multiple spin-state NEB method, and it is successfully applied to find the MEISC points while optimizing the reaction pathway. For more precisely locating the MEISC point, a revised approach was adopted. Meanwhile, our examples also suggest that special attention should be paid to the criterion to define an image optimized as the MEISC point. Intersystem crossing ; Bottleneck ; Point (geometry) ; Spin (aerodynamics) ; Position (finance) ; Spin states ; State (computer science) ; Computer science ; Work (physics) ; Chemistry ; Statistical physics ; Computational chemistry ; Physics ; Algorithm ; Excited state ; Atomic physics ; Mathematics ; Quantum mechanics ; Thermodynamics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Jens Oppliger | Fabian Donat Natterer
Source: Physical Review Research (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Sparse sampling for fast quasiparticle-interference mapping The authors use an analogy to the salesman problem to multiply the throughput of a scanning tunneling microscope (STM) for quantum materials discovery. The results show efficient STM trip-planning and advocates the use of compressed sensing to focus on the most relevant local density of states measurements. Quasiparticle ; Focus (optics) ; Throughput ; Interference (communication) ; Scanning tunneling microscope ; Analogy ; Computer science ; Travelling salesman problem ; Sampling (signal processing) ; Quantum ; Artificial intelligence ; Physics ; Nanotechnology ; Algorithm ; Materials science ; Optics ; Computer vision ; Telecommunications ; Quantum mechanics ; Wireless Electronic and Structural Properties of Oxides ; Surface and Thin Film Phenomena ; Force Microscopy Techniques and Applications
Authors: Tong Ben | Fangyuan Chen | Long Chen | Ahmed Abu‐Siada | Libing Jing | Rongge Yan
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Vibration Analysis of Magnetically Controlled Reactor Considering DC Magnetic Flux The main factors of stress distribution in MCRs are the magnetostriction effect of the core materials and the magnetic force between gaps under DC bias excitation. This article developed a coupling model for MCRs considering Maxwell magnetic force and magnetostriction under DC flux density biases. The constitutive equations of the magnetic field and strain field are constructed based on the magnetic property curves with a new measured and analyzed method to consider DC magnetic flux biases, which is the key contributions of this study. Then, the electromagnetic vibration properties of the MCR model are calculated and analyzed. To prove the validity of the proposed method, the vibration of a 4.4 kVar-220 V MCR is tested and analyzed. Magnetostriction ; Magnetic field ; Magnetic flux ; Vibration ; Coupling (piping) ; Excitation ; Physics ; Magnetostatics ; Electromagnetic field ; Mechanics ; Condensed matter physics ; Materials science ; Acoustics ; Composite material Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Electromagnetic Simulation and Numerical Methods
Authors: Xiwei Wang | Xiaoyang XING | Maocheng Zhao | Junrong Yang
Source: Food Science and Technology (article, 2020-12-18)
Field: Chemistry — Analytical Chemistry

Comparison of multispectral modeling of physiochemical attributes of greengage: Brix and pH values Chemometric modeling concerns both accuracy and computational expense for the prediction of quality-indicating attributes of food materials. Modeling approaches were explored with the hyperspectral images with pH and Brix values of greengages. A two-phase architecture was applied for modeling. Firstly, waveband selection was performed using two approaches, i.e., succession projection algorithm (SPA) and its combination with genetic algorithm (SPA+GA). Secondly, multispectral models based on the two feature sets of wavebands were built via a total of six different modeling methods, i.e., partial least squares regression (PLSR) and extreme learning machine (ELM) in their respective stand-alone versions, their applications combined with genetic algorithm (GA), and their ensemble enhancements with modified Adaboost.RT (MAdaboost.RT). Analysis of accuracy and computational expense showed that supervised feature selection with SPA+GA was superior to unsupervised SPA for better modeling accuracy. MAdaboost.RT-ELM showed high accuracy at low computational expense. ELM models were the better base models than the PLSR ones, for being more randomized and diverse. It indicates that MAdaboost.RT-ELM on SPA is the best choice for a quick test on a newly available dataset, while switching the dimensionality reduction from SPA to SPA+GA may yield more accurate models with added, but well worthy, computational expense. Multispectral image ; Computer science ; Environmental science ; Biological system ; Chemistry ; Environmental chemistry ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Animal Nutrition and Physiology
Authors: Qianqian Li | Zhisheng Wu | Ling Lin | Jingqi Zeng | Jixiong Zhang | Hongyuan Yan | Shungeng Min
Source: Scientific Reports (article, 2020-03-25)
Field: Chemistry — Analytical Chemistry

High-level Fusion Coupled with Mahalanobis Distance Weighted (MDW) Method for Multivariate Calibration Abstract Near infrared spectra (NIR) technology is a widespread detection method with high signal to noise ratio (SNR) while has poor modeling interpretation due to the overlapped features. Alternatively, mid-infrared spectra (MIR) technology demonstrates more chemical features and gives a better explanation of the model. Yet, it has the defects of low SNR. With the purpose of developing a model with plenty of characteristics as well as with higher SNR, NIR and MIR technologies are combined to perform high-level fusion strategy for quantitative analysis. A novel chemometrical method named as Mahalanobis distance weighted (MDW) is proposed to integrate NIR and MIR techniques comprehensively. Mahalanobis distance (MD) based on the principle of spectral similarity is obtained to calculate the weight of each sample. Specifically, the weight is assigned to the inverse ratio of the corresponding MD. Besides, the proposed MDW method is applied to NIR and MIR spectra of active ingredients in deltamethrin and emamectin benzoate formulations for quantitative analysis. As a consequence, the overall results show that the MDW method is promising with noticeable improvement of predictive performance than individual methods when executing high-level fusion for quantitative analysis. Mahalanobis distance ; Computer science ; Calibration ; Pattern recognition (psychology) ; Artificial intelligence ; Similarity (geometry) ; Fusion ; Near-infrared spectroscopy ; Inverse ; Mathematics ; Statistics ; Biological system ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shuqiang Lyu | Yiyi Liu | Miaole Hou | Qinli Yin | Wang‐Ting Wu | Xueyun Yang
Source: Heritage Science (article, 2020-03-30)
Field: Chemistry — Analytical Chemistry

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode Abstract Background Mixed pigment analysis is an important and complex subject in preserving and restoring Chinese paintings since the colors observed by our naked eyes or instruments such as hyperspectral cameras are usually a mixture of several kinds of pigments. The purpose of this study was to explore a more effective method to confirm the type of every pure pigment and their proportion in pigment mixtures on the surface of paintings. Methods Two endmember extraction algorithms were adopted to identify the types of pigments and an improved method of ratio spectra derivative spectrophotometry was used to determine their proportion. Main works (1) Extracting the pure pigment components from mixed spectrum by adopting two blind source separation algorithms: Independent Component Analysis and Non-negative Matrix Factorization; (2) matching the separated pure spectrum with the pigment spectral library built in our laboratory to determine the pigment type; and (3) calculating the proportions of mixed pigments using the Ratio Spectra Derivative Spectrophotometry based on Mode, which is improved based on the original algorithm. Finally, a comparison was made with two abundance inversion algorithms: Least Squares Algorithm and Minimum Volume Simplex Analysis. The correlation coefficient and root mean square error were used to provide evidence for accuracy evaluation. Conclusions (1) Non-negative matrix factorization is more suitable for endmember extraction; and (2) Ratio spectra derivative spectrophotometry based on mode is more suitable for abundance inversion. Endmember ; Spectrophotometry ; Pigment ; Mean squared error ; Mathematics ; Derivative (finance) ; Analytical Chemistry (journal) ; Hyperspectral imaging ; Chemistry ; Computer science ; Chromatography ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Color Science and Applications ; Remote-Sensing Image Classification
Authors: Yuanyuan Shao | Chong Gao | Guantao Xuan | Xuemei Gao | Youqing Chen | Zhichao Hu
Source: International journal of agricultural and biological engineering (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Determination of damaged wheat kernels with hyperspectral imaging analysis Hyperspectral imaging was applied to classify the damaged wheat kernels and healthy kernels. The spectral information was extracted from damaged wheat kernels and healthy kernels samples. The effective wavelengths were obtained from spectral of 865-1711 nm by X-loadings of principal component analysis (PCA) and successive projection algorithm (SPA) method, respectively. Partial least square method (PLS) and least square-support vector machine (LS-SVM) were then used to build classification models on full spectral data and effective wavelengths dataset, respectively. The results showed that the classification accuracy of every LS-SVM model was the best, being 100%. While the accuracy of the PLS model was slightly lower, still over 97%. The confusion matrix showed that several damaged wheat kernels samples were misclassified as healthy samples, while all healthy samples were correctly classified. The overall results indicated that hyperspectral imaging could be used for discriminating the damaged wheat kernels and could provide a reference for detecting other grain kernels grading degrees. Further, this study can provide a research basis for the development of online or portable detectors on grain damaged kernels recognition, which will be beneficial for grain grading or post-harvest quality processing of other grains. Keywords: hyperspectral image, damaged wheat kernels, determination, PCA, SPA, LS-SVM DOI: 10.25165/j.ijabe.20201305.4413 Citation: Shao Y Y, Gao C, Xuan G T, Gao X M, Chen Y Q, Hu Z C. Determination of damaged wheat kernels with hyperspectral imaging analysis. Int J Agric & Biol Eng, 2020; 13(5): 194–198. Hyperspectral imaging ; Principal component analysis ; Mathematics ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Kernel (algebra) ; Grading (engineering) ; Projection (relational algebra) ; Computer science ; Statistics ; Algorithm ; Biology ; Combinatorics Spectroscopy and Chemometric Analyses
Authors: Ryo Tamura | Koji Hukushima | Akira Matsuo | Koichi Kindo | Masashi Hase
Source: Physical review. B./Physical review. B (article, 2020-06-29)
Field: Materials Science — Materials Chemistry

Data-driven determination of the spin Hamiltonian parameters and their uncertainties: The case of the zigzag-chain compound <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>KCu</mml:mi></mml:mrow><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">P</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>12</mml:mn></mml:msub></mml:math> We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty, from multiple physical quantities. Using our technique, an effective model of ${\mathrm{KCu}}_{4}{\mathrm{P}}_{3}{\mathrm{O}}_{12}$ is determined from the experimentally observed magnetic susceptibility and magnetization curves with various temperatures under high magnetic fields. An effective model, which is the quantum Heisenberg model on a zigzag chain with eight spins having ${J}_{1}=\ensuremath{-}8.54\ifmmode\pm\else\textpm\fi{}0.51\phantom{\rule{4pt}{0ex}}\mathrm{meV}$, ${J}_{2}=\ensuremath{-}2.67\ifmmode\pm\else\textpm\fi{}1.13\phantom{\rule{4pt}{0ex}}\mathrm{meV}$, ${J}_{3}=\ensuremath{-}3.90\ifmmode\pm\else\textpm\fi{}0.15\phantom{\rule{4pt}{0ex}}\mathrm{meV}$, and ${J}_{4}=6.24\ifmmode\pm\else\textpm\fi{}0.95\phantom{\rule{4pt}{0ex}}\mathrm{meV}$, describes these measured results well. These uncertainties are successfully determined by the noise estimation. The relations among the estimated magnetic interactions or physical quantities are also discussed. The obtained effective model is useful to predict hard-to-measure properties such as spin gap, spin configuration at the ground state, magnetic specific heat, and magnetic entropy. Zigzag ; Hamiltonian (control theory) ; Chain (unit) ; Physics ; Statistical physics ; Mathematical physics ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Geometry Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Crystal Structures and Properties
Authors: Hasan Ali Gamal Al-Kaf | Nayef Abdulwahab Mohammed Alduais | Abdul‐Malik H. Y. Saad | Kim Seng Chia | Abdulqader M. Mohsen | Hitham Alhussian | Ammar Abdo Mohammed Haidar Mahdi | Wan Saiful-Islam Wan Salim
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

A Bootstrapping Soft Shrinkage Approach and Interval Random Variables Selection Hybrid Model for Variable Selection in Near-Infrared Spectroscopy High dimensionality problem in spectra datasets is a significant challenge to researchers and requires the design of effective methods that can extract the optimal variable subset that can improve the accuracy of predictions or classifications. In this study, a hybrid variable selection method, based on the incremental number of variables using bootstrapping soft shrinkage method (BOSS) and interval random variable selection (IRVS) method is proposed and named BOSS-IRVS. The BOSS method is used to determine the informative intervals, while the IRVS method is used to search for informative variables in the informative interval determined by BOSS method. The proposed BOSS-IRVS method was tested using seven different public accessible near-infrared (NIR) spectroscopic datasets of corn, diesel fuel, soy, wheat protein, and hemoglobin types. The performance of the proposed method was compared with that of two outstanding variable selection methods i.e. BOSS and hybrid variable selection strategy based on continuous shrinkage of variable space (VCPA-IRIV). The experimental results showed clearly that the proposed method BOSS-IRVS outperforms VCPA-IRIV and BOSS methods in all tested datasets and improved the percentage of the prediction accuracy, by 15.4 and 15.3 for corn moisture,13.4 and 49.8 for corn oil, 41.5 and 50.6 for corn protein, 12.6 and 5.6 for soy moisture, 0.6 and 6.3 for total diesel fuel, 19.9 and 14.3 for wheat protein, and 5.8 and 20.3 for hemoglobin. Boss ; Bootstrapping (finance) ; Feature selection ; Shrinkage ; Selection (genetic algorithm) ; Computer science ; Variable (mathematics) ; Statistics ; Mathematics ; Interval (graph theory) ; Artificial intelligence ; Pattern recognition (psychology) ; Engineering ; Econometrics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research