jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Praveen Naik | Bhawana Rudra
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Arecanut X-Ray Images for Quality Assessment Using Adaptive Genetic Algorithm and Deep Learning The traditional approach for analyzing the quality of arecanuts is based on their external appearance. However, using machine learning and deep learning techniques, automated classifications were performed. But the true quality can only be analyzed when the internal structure of the arecanut is analyzed. Therefore, we use the X-ray imaging technique to determine the internal quality of arecanuts. We prepared a novel dataset of arecanut X-ray images and used a YOLOv5 based deep learning architecture for classification. The present study employs an adaptive genetic algorithm based approach for hyperparameter optimization to enhance the mean average precision (mAP) using a light weight model that is generated using a ghost network and a feature pyramid network (FPN). We achieved the highest mAP of 97.84% using our method with a lower model size of 15 MB. Our method has excelled in detection performance when compared to cutting-edge object detection algorithms such as YOLOv3, YOLOv4, Detetron, YOLOv6, YOLOv8, and YOLOX. We also acknowledged the performance enhancement using the adaptive genetic algorithm on the Pascal VOC 2007 image dataset. Despite significant computational requirements for executing genetic algorithms, we showed that genetic algorithms can be used to boost mAP. Additionally, the methodology developed in this investigation produced multiple models with the best mAP featuring optimized hyperparameters. This methodical strategy is useful for the design of an automatic, non-destructive, integrated X-ray image based classification system. This system has the potential to revolutionize the quality assessment of arecanuts by offering a more efficient method of evaluation. Computer science ; Artificial intelligence ; Genetic algorithm ; Hyperparameter ; Pascal (unit) ; Pattern recognition (psychology) ; Artificial neural network ; Object detection ; Feature (linguistics) ; Deep learning ; Pyramid (geometry) ; Machine learning ; Algorithm ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Coconut Research and Applications
Authors: Razieh Pourdarbani | Sajad Sabzi | Mohammad Nadimi | Jitendra Paliwal
Source: Horticulturae (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

Interpretation of Hyperspectral Images Using Integrated Gradients to Detect Bruising in Lemons Lemons are a popular citrus fruit known for their medicinal and nutritional properties. However, fresh lemons are vulnerable to mechanical damage during transportation, with bruising being a common issue. Bruising reduces the fruit’s shelf life and increases the risk of bacterial and fungal contamination, leading to economic losses. Furthermore, discoloration typically occurs after 24 h, so it is crucial to detect bruised fruits promptly. This paper proposes a novel method for detecting bruising in lemons using hyperspectral imaging and integrated gradients. A dataset of hyperspectral images was captured in the wavelength range of 400–1100 nm for lemons that were sound and artificially bruised (8 and 16 h after bruising), with three distinct classes of images corresponding to these conditions. The dataset was divided into three subsets i.e., training (70%), validation (20%), and testing (10%). Spatial–spectral data were analyzed using three 3D-convolutional neural networks: ResNetV2, PreActResNet, and MobileNetV2 with parameter sizes of 242, 176, and 9, respectively. ResNetV2 achieved the highest classification accuracy of 92.85%, followed by PreActResNet at 85.71% and MobileNetV2 at 83.33%. Our results demonstrate that the proposed method effectively detects bruising in lemons by analyzing darker pixels in the images, subsequently confirming the presence of bruised areas through their spatial distribution and accumulation. Overall, this study highlights the potential of hyperspectral imaging and integrated gradients for detecting bruised fruits, which could help reduce food waste and economic losses. Hyperspectral imaging ; Artificial intelligence ; Pixel ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Environmental science ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Xiuzhen Fu | Mengjie Bai | Yawen Xu | Tao Wang | Zhenning Hui | Xiaowen Hu
Source: Frontiers in Plant Science (article, 2023-02-07)
Field: Chemistry — Analytical Chemistry

Cultivars identification of oat (Avena sativa L.) seed via multispectral imaging analysis Cultivar identification plays an important role in ensuring the quality of oat production and the interests of producers. However, the traditional methods for discrimination of oat cultivars are generally destructive, time-consuming and complex. In this study, the feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis. The principal component analysis (PCA), linear discrimination analysis (LDA) and support vector machines (SVM) were applied to classify seeds of 16 oat cultivars according to their morphological features, spectral traits or a combination thereof. The results demonstrate that clear differences among cultivars of oat seeds could be easily visualized using the multispectral imaging technique and an excellent discrimination could be achieved by combining data of the morphological and spectral features. The average classification accuracy of the testing sets was 89.69% for LDA, and 92.71% for SVM model. Therefore, the potential of a new method for rapid and nondestructive identification of oat cultivars was provided by multispectral imaging combined with multivariate analysis. Avena ; Multispectral image ; Cultivar ; Principal component analysis ; Support vector machine ; Identification (biology) ; Artificial intelligence ; Agronomy ; Pattern recognition (psychology) ; Mathematics ; Biology ; Botany ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Seed and Plant Biochemistry
Authors: Baishao Zhan | Peng Li | Ming Li | Wei Luo | Hailiang Zhang
Source: Agriculture (article, 2023-07-27)
Field: Chemistry — Analytical Chemistry

Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears. Partial least squares regression ; Wavelength ; Chemistry ; Analyte ; PEAR ; Biological system ; Near-infrared spectroscopy ; Chromatography ; Analytical Chemistry (journal) ; Mathematics ; Physics ; Optics ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Y. Bhargav Kumar | Nandan Kumar | Lijo John | Hridoy Jyoti Mahanta | S. Vaikundamani | Selvaraman Nagamani | G. Madhavi Sastry | G. Narahari Sastry
Source: Proteins Structure Function and Bioinformatics (article, 2023-10-03)
Field: Chemistry — Physical and Theoretical Chemistry

Analyzing the cation‐aromatic interactions in proteins: Cation‐aromatic database V2.0 The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation-π or cation-σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation-aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS-HIS, PHE-ARG, and TRP-ARG pairs exhibited a preferential geometry. The motif pair HIS-HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR-LYS at 10.17%. Conversely, the motif pair TRP-HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation-aromatic interactions in biological molecules. The updated version of CAD (Cation-Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2. Aromaticity ; Chemistry ; Aromatic amino acids ; Structural motif ; Amino acid residue ; Protein Data Bank ; Molecule ; Crystallography ; Database ; Stereochemistry ; Amino acid ; Protein structure ; Peptide sequence ; Organic chemistry ; Biochemistry ; Computer science Crystallography and molecular interactions ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kingdow Chanachot | Wanphut Saechua | Jetsada Posom | Panmanas Sirisomboon
Source: Foods (article, 2023-10-20)
Field: Chemistry — Analytical Chemistry

A Geographical Origin Classification of Durian (cv. Monthong) Using Near-Infrared Diffuse Reflectance Spectroscopy The objective of this research was to classify the geographical origin of durians (cv. Monthong) based on geographical identification (GI) and regions (R) using near infrared (NIR). The samples were scanned with an FT-NIR spectrometer (12,500 to 4000 cm<sup>-1</sup>). The NIR absorbance differences among samples that were collected from different parts of the fruit, including intact peel with thorns (I-form), cut-thorn peel (C-form), stem (S-form), and the applied synthetic minority over-sampling technique (SMOTE), were also investigated. Models were developed across several classification algorithms by the classification learner app in MATLAB. The models were optimized using a featured wavenumber selected by a genetic algorithm (GA). An effective model based on GI was developed using SMOTE-I-spectra with a neural network; accuracy was provided as 95.60% and 95.00% in cross-validation and training sets. The test model was provided with a testing set value of %accuracy, and 94.70% by the testing set was obtained. Likewise, the model based on the regions was developed from SMOTE-ICS-form spectra, with the ensemble classifier showing the best result. The best result, 88.00FF% accuracy by cross validation, 86.50% by training set, and 64.90% by testing set, indicates the classification model of East (E-region), Northeast (NE-region), and South (S-region) regions could be applied for rough screening. In summary, NIR spectroscopy could be used as a rapid and nondestructive method for the accurate GI classification of durians. Classifier (UML) ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Absorbance ; Test set ; Spectrometer ; MATLAB ; Computer science ; Set (abstract data type) ; Artificial neural network ; Sampling (signal processing) ; Cross-validation ; Mathematics ; Biological system ; Remote sensing ; Filter (signal processing) ; Chemistry ; Geology ; Optics ; Physics ; Chromatography ; Computer vision ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Daniel Dinculescu | Cristiana Luminița Gîjiu | Manuela Rossemary Apetroaei | Raluca Isopescu | Ileana Rău | Verginica Schröder
Source: Materials (article, 2023-01-05)
Field: Materials Science — Biomaterials

Optimization of Chitosan Extraction Process from Rapana venosa Egg Capsules Waste Using Experimental Design New green and sustainable sources were chosen to obtain chitosan, an important material, with many applications in different fields. The present study is focused on egg capsules of <i>Rapana venosa</i> waste as raw material for chitosan oligomers. As previous studies revealed that chitosan extraction from this material takes place with a low yield, the present research aimed to optimize this step. A 2<sup>2</sup> experimental plan, with three replicates in the center, was proposed to investigate the influence of NaOH concentration and temperature on the yield extraction. After a primary analysis of the experimental data, a favorable temperature value was selected (90 °C) at which the total dissolution of the egg capsules was obtained. Then, at this temperature, the experimental plan was extended exploring the influence of the NaOH concentration on three levels (5, 6, and 7%) and the extraction duration on two levels (60 and 85 min). Based on all experimental data, a neural model was obtained and validated. The neural model was used to maximize the yield, applying Genetic Algorithm (GA) implemented in Matlab<sup>®</sup>. The resulting optimal solution is: NaOH concentration 6.47%, temperature 90 °C, duration 120 min, with a yield value of 7.05%. Chitosan ; Extraction (chemistry) ; Raw material ; Yield (engineering) ; Materials science ; Pulp and paper industry ; Chromatography ; Process engineering ; Chemical engineering ; Chemistry ; Composite material ; Engineering ; Organic chemistry Nanocomposite Films for Food Packaging
Authors: Stephanie Stransky | Yan Sun | Xuyan Shi | Simone Sidoli
Source: Frontiers in Molecular Biosciences (article, 2023-11-23)
Field: Chemistry — Spectroscopy

Ten questions to AI regarding the present and future of proteomics The role of a scientist is at first not so different from a philosopher. They both need to question common thinking and evaluate whether reality is not as we always thought. Based on this, we need to design hypotheses, experiments, and analyses to prove our alternative vision. Artificial Intelligence (AI) is rapidly moving from an "assistant" into a proper "colleague" for literature mining, data analysis and interpretation, and literally having (almost) real scientific conversations. However, being AI based on existing information, if we rely on it excessively will we still be able to question the <i>status quo</i>? In this article, we are particularly interested in discussing the future of proteomics and mass spectrometry with our new electronic collaborator. We leave to the reader the judgement whether the answers we received are satisfactory or superficial. What we were mostly interested in was laying down what we think are critical questions that the proteomics community should occasionally ask to itself. Proteomics has been around for more than 30 years, but it is still missing a few critical steps to fully address its promises as being the new genomics for clinical diagnostics and fundamental science, while becoming a user-friendly tool for every lab. Will we get there with the help of AI? And will these answers change in a short period, as AI continues to advance? Judgement ; Status quo ; Data science ; Computer science ; Interpretation (philosophy) ; Engineering ethics ; Psychology ; Artificial intelligence ; Epistemology ; Political science ; Engineering Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Genetics, Bioinformatics, and Biomedical Research
Authors: Longjie Li | Junxian Guo | Qian Wang | Jun Wang | Ya Liu | Yong Shi
Source: Sensors (article, 2023-10-19)
Field: Chemistry — Analytical Chemistry

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSE<sub>c</sub> was 0.3429, and R<sub>c</sub><sup>2</sup> was 0.9905. In the prediction set, RMSE<sub>p</sub> was 1.5670, and R<sub>p</sub><sup>2</sup> was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSE<sub>c</sub> was 0.8721, and R<sub>c</sub><sup>2</sup> was 0.9429. In the prediction set, RMSE<sub>p</sub> was 1.1810, and R<sub>p</sub><sup>2</sup> was 0.8683. Computer science ; Remote sensing ; Data acquisition ; Software portability ; Preprocessor ; Data pre-processing ; Data set ; Data collection ; Support vector machine ; Data mining ; Artificial intelligence ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Julia Głowacka | Łukasz Derpeński | Miłosz Frydrych | Bogna Sztorch | Błażej Bartoszewicz | Robert E. Przekop
Source: Materials (article, 2023-10-28)
Field: Materials Science — Biomaterials

Robotization of Three-Point Bending Mechanical Tests Using PLA/TPU Blends as an Example in the 0–100% Range This article presents the development of an automated three-point bending testing system using a robot to increase the efficiency and precision of measurements for PLA/TPU polymer blends as implementation high-throughput measurement methods. The system operates continuously and characterizes the flexural properties of PLA/TPU blends with varying TPU concentrations. This study aimed to determine the effect of TPU concentration on the strength and flexural stiffness, surface properties (WCA), thermal properties (TGA, DSC), and microscopic characterization of the studied blends. Flexural strength ; Materials science ; Bending ; Composite material ; Three point flexural test ; Stiffness ; Characterization (materials science) ; Thermal ; Nanotechnology biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials