Validated green and white RP-HPLC method for the estimation of Zolmitriptan in marketed dosage form.

OBJECTIVE: - To develop and validate a rapid, accurate, economical, effective and greenery RP-HPLC method for the determination of Zolmitriptan in tablet dosage form. MATERIAL AND METHOD: - RP-HPLC method was developed using Luna (C18) (4.6 x 250 mm, 5 µm) column and Sodium phosphate buffer (pH 4.7): Methanol [75: 25, v/v] was used as mobile phase at a flow rate of 1.0 mL/min. The detection was carried out at 227 nm. Further, eco-friendliness, productivity and performance of the optimized analytical method were assessed by green and white tools. RESULTS: - The retention time of Zolmitriptan was found to be 3.25 min with acceptable chromatographic parameters. The optimized RP-HPLC method was more eco-friendly, efficient, throughput and practicable than the reported methods as confirmed by AES, AGREE, GAPI and RGB tools. Further, the proposed analytical method showed all the validation parameters within the acceptance limit of ICH Q2 R1 guidelines. The linear regression analysis indicated a good linear response in the 10 to 120 µg/mL concentration range with R2 of 0.99998. The percentage content and percentage assay of Zolmitriptan in Zomig-5mg tablet was found to be 103.36 ± 0.356 % and 97.86 ± 0.693 %. CONCLUSION: - The developed and validated method has several advantages compared to the reported HPLC methods and is useful in the systematic analysis of Zolmitriptan in its dosage form.

PIAA: Pre-imaging all-round assistant for digital radiography.

BACKGROUND: In radiography procedures, radiographers' suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources. OBJECTIVE: We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR. METHODS: PIAA consists of an RGB-Depth (RGB-D) multi-camera array, an embedded computing platform, and multiple software components. It features an Adaptive RGB-D Image Acquisition (ARDIA) module that automatically selects the appropriate RGB camera based on the distance between the cameras and patients. It includes a 2.5D Selective Skeletal Keypoints Estimation (2.5D-SSKE) module that fuses depth information with 2D keypoints to estimate the pose of target body parts. Thirdly, it also uses a Domain expertise (DE) embedded Full-body Exposure Parameter Estimation (DFEPE) module that combines 2.5D-SSKE and DE to accurately estimate parameters for full-body DR views. RESULTS: Optimizes DR workflow, significantly enhancing operational efficiency. The average time required for positioning patients and preparing exposure parameters was reduced from 73 seconds to 8 seconds. CONCLUSIONS: PIAA shows significant promise for extension to full-body examinations.

A smartphone-assisted portable dual-mode immunoassay of gentamicin based on curcumin nanoparticles and carbon dots.

A smartphone-assisted portable dual-mode immunoassay was constructed based on curcumin nanoparticles (CNPs) and carbon dots (CDs) for gentamicin (GEN) detection. CNPs were labeled with goat anti-mouse IgG (Ab2) to create a conjugation that coupled dual signals to concentrations of GEN antigens. CNPs were introduced to pH 7.4 water and showed insignificant color and optical responses. When exposed to the high pH environment, the structure of CNPs changed and color and optical properties were restored. Because of the inner filter effect (IFE) between CNPs and CDs, the fluorescence of CNPs at 550 nm quenched the fluorescence of CDs at 450 nm. Colorimetry and ratiometric fluorescence (F550 nm/F450 nm) dual-mode immunoassay linearly correlated with GEN ranged from 10-4 to 100 µg/mL with a detection limit (LOD) of 8.98 × 10-5 µg/mL and 4.66 × 10-5 µg/mL, respectively. This work supplied a portable, sensitive, and specific platform to detect GEN.

Nonintrusive and Effective Volume Reconstruction Model of Swimming Sturgeon Based on RGB-D Sensor.

The sturgeon is an important commercial aquaculture species in China. The measurement of sturgeon mass plays a remarkable role in aquaculture management. Furthermore, the measurement of sturgeon mass serves as a key phenotype, offering crucial information for enhancing growth traits through genetic improvement. Until now, the measurement of sturgeon mass is usually conducted by manual sampling, which is work intensive and time consuming for farmers and invasive and stressful for the fish. Therefore, a noninvasive volume reconstruction model for estimating the mass of swimming sturgeon based on RGB-D sensor was proposed in this paper. The volume of individual sturgeon was reconstructed by integrating the thickness of the upper surface of the sturgeon, where the difference in depth between the surface and the bottom was used as the thickness measurement. To verify feasibility, three experimental groups were conducted, achieving prediction accuracies of 0.897, 0.861, and 0.883, which indicated that the method can obtain the reliable, accurate mass of the sturgeon. The strategy requires no special hardware or intensive calculation, and it provides a key to uncovering noncontact, high-throughput, and highly sensitive mass evaluation of sturgeon while holding potential for evaluating the mass of other cultured fishes.

Ratiometric fluorescence probe based on carbon dots and p-phenylenediamine-derived nanoparticles for the sensitive detection of manganese ions.

A ratiometric fluorescence probe based on carbon quantum dots with 420 nm emission (bCQDs) and a p-phenylenediamine-derived fluorescence probe with 550 nm emission (yprobe) is constructed for the detection of Mn2+. The presence of Mn2+ results in the enhanced absorption band at 400 nm of yprobe, and the fluorescence of yprobe is significantly enhanced based on the chelation-enhanced fluorescence mechanism. The fluorescence of bCQDs is then quenched based on the inner filtration effect. The ratio (I550/I420) linearly increases with the increase of Mn2+ concentration within 2.00 × 10-7-1.50 × 10-6 M, and the limit of detection is 1.76 × 10-9 M. Given the fluorescence color changing from blue to yellow, the visual sensing of Mn2+ is feasible based on bCQDs/yprobe coupled with RGB value analysis. The practicability of the proposed method has been verified in tap water, lake water, and sparkling water beverage, indicating that bCQDs/yprobe has promising application in Mn2+ monitoring.

An Analytical Study on the Utility of RGB and Multispectral Imagery with Band Selection for Automated Tumor Grading.

The implementation of tumor grading tasks with image processing and machine learning techniques has progressed immensely over the past several years. Multispectral imaging enabled us to capture the sample as a set of image bands corresponding to different wavelengths in the visible and infrared spectrums. The higher dimensional image data can be well exploited to deliver a range of discriminative features to support the tumor grading application. This paper compares the classification accuracy of RGB and multispectral images, using a case study on colorectal tumor grading with the QU-Al Ahli Dataset (dataset I). Rotation-invariant local phase quantization (LPQ) features with an SVM classifier resulted in 80% accuracy for the RGB images compared to 86% accuracy with the multispectral images in dataset I. However, the higher dimensionality elevates the processing time. We propose a band-selection strategy using mutual information between image bands. This process eliminates redundant bands and increases classification accuracy. The results show that our band-selection method provides better results than normal RGB and multispectral methods. The band-selection algorithm was also tested on another colorectal tumor dataset, the Texas University Dataset (dataset II), to further validate the results. The proposed method demonstrates an accuracy of more than 94% with 10 bands, compared to using the whole set of 16 multispectral bands. Our research emphasizes the advantages of multispectral imaging over the RGB imaging approach and proposes a band-selection method to address the higher computational demands of multispectral imaging.

Synthesis of a Multi-Stimulus Responsive RGB Fluorescent Organic Molecule based on Dark Through-Bond Energy Transfer Mechanism.

In this study, a novel multi-stimulus responsive RGB fluorescent organic molecule, RTPE-NH2, was designed and synthesized based on the combination of aggregation-induced emission tetraphenylethylene (TPE) luminophore and acid-responsive fluorescent molecular switch Rhodamine B. RTPE-NH2 exhibits aggregation-induced emission behavior, as well as UV irradiation-stimulus and acid-stimulus responsive fluorescence properties. It could emit orange-red (R), green(G), and blue(B) light in both solution and PMMA film under 365 nm excitation. The dark through-bond energy transfer (DTBET) mechanism was proposed and supported by control experiments and TD-DFT calculations. The synthesis and application of RTPE-NH2 could accelerate the development of organic smart materials with high sensitivity and excellent optical properties.

Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV.

BACKGROUND: Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV-RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI. RESULTS: The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = -0.783). Furthermore, the visible atmospheric resistance index, the green-red vegetation index, the modified green-red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables. CONCLUSION: This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer-level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

Concurrent Validity of Motion Parameters Measured With an RGB-D Camera-Based Markerless 3D Motion Tracking Method in Children and Young Adults.

OBJECTIVE: Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. METHODS: Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. RESULTS: Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. CONCLUSION: Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. CLINICAL IMPACT: The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.

Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium-infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium-infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.

SiamEFT: adaptive-time feature extraction hybrid network for RGBE multi-domain object tracking.

Integrating RGB and Event (RGBE) multi-domain information obtained by high-dynamic-range and temporal-resolution event cameras has been considered an effective scheme for robust object tracking. However, existing RGBE tracking methods have overlooked the unique spatio-temporal features over different domains, leading to object tracking failure and inefficiency, especally for objects against complex backgrounds. To address this problem, we propose a novel tracker based on adaptive-time feature extraction hybrid networks, namely Siamese Event Frame Tracker (SiamEFT), which focuses on the effective representation and utilization of the diverse spatio-temporal features of RGBE. We first design an adaptive-time attention module to aggregate event data into frames based on adaptive-time weights to enhance information representation. Subsequently, the SiamEF module and cross-network fusion module combining artificial neural networks and spiking neural networks hybrid network are designed to effectively extract and fuse the spatio-temporal features of RGBE. Extensive experiments on two RGBE datasets (VisEvent and COESOT) show that the SiamEFT achieves a success rate of 0.456 and 0.574, outperforming the state-of-the-art competing methods and exhibiting a 2.3-fold enhancement in efficiency. These results validate the superior accuracy and efficiency of SiamEFT in diverse and challenging scenes.

Synergistic integration of a rhodamine-labelled tripeptide into AIE-active fluorogenic probe: Enabling nanomolar detection of Al3+ ions through test strips, thin films, and Arduino-assisted optosensing platform.

Peptide-fluorophore conjugates (PFCs) have been expeditiously utilized for metal ion recognition owing to their distinctive characteristics. Selective detection and quantification of aluminum is essential to minimize health and environmental risks. Herein, we report the synthesis and characterization of a new chemoprobe with aggregation-induced emission characteristics by chemically conjugating rhodamine-B fluorophore with a tripeptide. The probe revealed ß-sheet secondary conformation in both solid and solution states, as confirmed by FT-IR, PXRD, and CD experiments. AIE characteristics of the probe in water-MeCN mixtures revealed the formation of spherically shaped nanoaggregates with an average size of 353 ± 7 nm, as confirmed by SEM, TEM, and DLS studies. The probe exhibited a large stokes shift (175 nm) and displayed selective colorimetric and fluorometric responses towards Al3+ ions with an extremely low detection limit (51 nm) and a fast response time (≤15 s). Comparative NMR studies confirmed the cleavage of spirolactam ring upon aluminum binding. The probe's practicality was enhanced through integration into test strips and thin films, allowing solid-phase detection of Al3+ ions. Furthermore, an RGB-Arduino enabled optosensing device has been developed to enable instant quantifiable analysis of aluminum concentrations in real-time conditions.

A full reference quality assessment method with fused monocular and binocular features for stereo images.

Aiming to automatically monitor and improve stereoscopic image and video processing systems, stereoscopic image quality assessment approaches are becoming more and more important as 3D technology gains popularity. We propose a full-reference stereoscopic image quality assessment method that incorporate monocular and binocular features based on binocular competition and binocular integration. To start, we create a three-channel RGB fused view by fusing Gabor filter bank responses and disparity maps. Then, using the monocular view and the RGB fusion view, respectively, we extract monocular and binocular features. To alter the local features in the binocular features, we simultaneously estimate the saliency of the RGB fusion image. Finally, the monocular and binocular quality scores are calculated based on the monocular and binocular features, and the quality scores of the stereo image prediction are obtained by fusion. Performance testing in the LIVE 3D IQA database Phase I and Phase II. The results of the proposed method are compared with newer methods. The experimental results show good consistency and robustness.

Penalization and Color Code-Technical Approaches for Methods' Greenness and Whiteness Appraisal in Veterinary Medication: Assay of Toltrazuril Suspension.

BACKGROUND: Intestinal coccidiosis is a debilitating disease in poultry and livestock, leading to economic impact worldwide. Coccidiosis is prevented and treated in broilers by the inclusion of anticoccidials in feed. Toltrazuril is administered in potable water to treat coccidiosis. OBJECTIVE: Three robust analytical methods for quantitation of toltrazuril in pure and pharmaceutical formulations are developed. Furthermore, ecological metrics; either penalization- or color-code-based techniques are applied for the appraisal of assays. METHODS: Firstly, Second-Derivative (Δλ; 5 nm) spectrophotometric method; Toltrazuril is measured from peak to peak at 244-260 nm within a linearity range of 5-25 µg/mL. The second one is a high-performance thin-layer chromatography (HPTLC) analysis performed on an aluminum sheet of silica gel using ethyl acetate, methanol, ammonium chloride buffer, and water (8:1:0.5:0.5) (%V/V) as the elution phase. Toltrazuril, at a retardation factor of 0.66 ± 0.01, is linearly determined in the range of 1-9 µg/spot at 243 nm. The third one is Reversed Phase-HPLC-diode array detection, using Agilent column C18 (5 µm, 4.6 x 150 mm) in isocratic elution mode with a mobile phase of acetonitrile and water in a ratio of 80:20 (v/v), respectively, at 1 mL/min flow rate. Toltrazuril elutes at a retention time of 2.58 ± 0.1 min and is linearly determined at 243 nm in the range of 0.25-25 µg/mL. RESULTS: Calculated 2D-values and peak areas are highly correlated to their corresponding drug concentrations at coefficients; r > 0.999. All methods were ICH validated and applied to dosage form with satisfactory % recoveries (97-103%). Statistical comparisons reported one using t-test and F-test disclose insignificant variation. Examining greenness and whiteness norms, proposed methods were evaluated and ranked alongside four different reported methods. CONCLUSION: The proposed methods are green, accurate, and can be applied in routine quality control for the determination of toltrazuril in pharmaceutical formulations.

High-performance colorimetric sensor based on PtRu bimetallic nanozyme for xanthine analysis.

The identification and quantification of xanthine are crucial for assessing the freshness and quality of food products, particularly in the seafood industry. Herein, a new approach was developed, involving the in-situ controllable growth of Pt91Ru9 nanoparticles on graphitic carbon nitride to yield Pt91Ru9@C3N4 catalytic materials. By integrating Pt91Ru9@C3N4 with the xanthine/xanthine oxidase (XOD) enzyme catalytic system, a nanozyme-enzyme tandem platform was obtained for the quantification analysis of xanthine. Under the catalytic oxidation of xanthine by XOD in the presence O2, H2O2 was generated. Upon the addition of peroxidase-like activity of Pt91Ru9@C3N4, H2O2 can be decomposed into •OH and 1O2, which can further catalyze the oxidation of TMB to its oxidation product oxTMB with an absorption peak at 652 nm. This smartphone-assisted portable colorimetric sensor for visual monitoring xanthine with a low detection limit of 8.92 nmol L-1, and successfully applied to detect xanthine in grass carp and serum samples.

ML-Based Edge Node for Monitoring Peoples' Frailty Status.

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%.

3D object reconstruction: A comprehensive view-dependent dataset.

The dataset contains RGB, depth, segmentation images of the scenes and information about the camera poses that can be used to create a full 3D model of the scene and develop methods that reconstruct objects from a single RGB-D camera view. Data were collected in the custom simulator that loads random graspable objects and random tables from the ShapeNet dataset. The graspable object is placed above the table in a random position. Then, the scene is simulated using the PhysX engine to make sure that the scene is physically plausible. The simulator captures images of the scene from a random pose and then takes the second image from the camera pose that is on the opposite side of the scene. The second subset was created using Kinect Azure and a set of real objects located on the ArUco board that was used to estimate the camera pose.

Chemical nature evolution of solid supports used in electromembrane extraction procedures: A comparative analysis based on metric tools.

BACKGROUND: In recent decades, green chemistry has been focusing on the adaptation of different chemical methods towards environmental friendliness. Sample preparation procedures, which constitute a fundamental step in analytical methodology, have also been modified and implemented in this direction. In particular, electromembrane extraction (EME) procedures, which have traditionally used plastic supports, have been optimized towards greener approaches through the emergence of alternative materials. In this regard, biopolymer-based membranes (such as agarose or chitosan) have become versatile and very promising substitutes to perform these processes. RESULTS: Different green metric tools (Analytical Eco-Scale, ComplexGAPI and AGREEprep have been applied to study the evolution of solid supports used in EME from nanostructured tissues and polymer inclusion membranes to agar films and chitosan flat membranes. The main goal is to evaluate the usage of these new biomaterials in the analytical procedure to quantify their environmental impact in the frame of Green Analytical Chemistry (GAC). In addition, both RGB model and BAGI metrics have been employed to study the sustainability of the whole procedure, including not only greenness, but also analytical performance and feasibility aspects. Results obtained after the performance of the mentioned metrics have demonstrated that the most efficient and environmentally friendly analytical methods are based on the use of chitosan supports. This improvement is mainly due to the chemical nature of this biopolymer as well as to the removal of organic solvents. SIGNIFICANCE: This work highlights the advantages of biodegradable materials employment in EME procedures to achieve green analytical methodologies. These materials also contribute to raise the figure of merits regarding to the quantification parameters in a wide range of applications compared to classical supports employed in EME, thus enhancing sustainability of procedures.

Colorimetric enzymatic rapid test for the determination of atropine in baby food using a smartphone.

A method for the enzymatic determination of atropine has been developed, which is based on a sequence of reactions involving (1) the hydrolysis of atropine to give tropine; (2) the enzymatic oxidation of tropine with NAD (catalysed by tropinone reductase); and (3) an indicator reaction, in which the NADH previously formed reduces the dye iodonitrotetrazolium chloride (INT) to a reddish species, the reaction catalysed by diaphorase. The method was first developed in solution (linear response range from 2.4 × 10-6 M to 1.0 × 10-4 M). It was then implemented in cellulose platforms to develop a rapid test where the determination is made by measuring the RGB coordinates of the platforms using a smartphone-based device. The device is based on the integrating sphere concept and contains a light source to avoid external illumination effects. The smartphone is controlled by an app that allows a calibration line to be generated and the atropine concentration to be quantified; moreover, since the app normalizes the CCD response of the smartphone, the results and calibrations obtained with different smartphones are similar and can be shared. Using the G coordinate, the results were shown to have a linear response with the concentration of atropine ranging from 1.2 × 10-5 M to 3.0 × 10-4 M with an RSD of 1.4% (n = 5). The method has been applied to the determination of atropine in baby food and buckwheat samples with good results.

Analyzing Medicago spp. seed morphology using GWAS and machine learning.

Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 other Medicago spp., for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness & red-green-blue (RGB) intensities. The results revealed phenotypic diversity and correlations among the tested accessions. Based on the phenotypic data of M. sativa subsp. sativa, a genome-wide association study (GWAS) was conducted using single nucleotide polymorphisms (SNPs) called against the Medicago truncatula genome. Genes in proximity to associated markers were detected, including CPR1, MON1, a PPR protein, and Wun1(threshold of 1E-04). Machine learning models were utilized to validate GWAS, and identify additional marker-trait associations for potentially complex traits. Marker S7_33375673, upstream of Wun1, was the most important predictor variable for red color intensity and highly important for brightness. Fifty-two markers were identified in coding regions. Along with strong correlations observed between seed morphology traits, these genes will facilitate the process of understanding the genetic basis of seed morphology in Medicago spp.