High Precision Research Articles

The egg-counter: a novel microfluidic platform for characterization of Caenorhabditis elegans egg-laying.

Reproduction is a fundamental process that shapes the demography of every living organism yet is often difficult to assess with high precision in animals that produce large numbers of offspring. Here, we present a novel microfluidic research platform for studying Caenorhabditis elegans' egg-laying. The platform provides higher throughput than traditional solid-media behavioral assays while providing a very high degree of temporal resolution. Additionally, the environmental control enabled by microfluidic animal husbandry allows for experimental perturbations difficult to achieve with solid-media assays. We demonstrate the platform's utility by characterizing C. elegans egg-laying behavior at two commonly used temperatures, 15 and 20 °C. As expected, we observed a delayed onset of egg-laying at 15 °C degrees, consistent with published temperature effects on development rate. Additionally, as seen in solid media studies, egg laying output was higher under the canonical 20 °C conditions. While we validated the Egg-Counter with a study of temperature effects in wild-type animals, the platform is highly adaptable to any nematode egg-laying research where throughput or environmental control needs to be maximized without sacrificing temporal resolution.

Q-switched temperature fiber sensing device based on graded index and D-shaped multimode fiber

In the paper, a Q-switched temperature sensing device is proposed and verified, which consists of a modulator based on a large core multimode fiber and a graded index multimode optical fiber (LMF-GIMF), and a sensing unit formed by sandwiching a D-shaped multimode fiber (MMF) between two single mode fibers (SMF). Utilizing the modulator to transform continuous light into pulsed laser, a stable Er3+-doped Q-switched laser with a repetition frequency of 23.04 kHz and a modulation depth of 7.47 % has been achieved. Under its pumping, the SMF-MMF-SMF sensing structure performed thermal detection with a sensitivity of −81.5 pm/℃ as temperature varies in the range of 25 °C to 65 °C. The temperature sensing device based on Q-switching can be used as an independent pulse laser to realize the switch between the light source and the sensing device. At the same time, the sensing device can provide real-time feedback according to the change of the central wavelength, which allows for immediate adjustments in the operational state, enhancing the flexibility and responsiveness. In addition, the characteristics of the sensor based on Q-switched laser can reduce thermal damage and extends the service life of the sensor. As far as we know, this is the first report on Q-switched temperature sensing based on the LMF-GIMF modulator. The proposed sensing device has the advantages of high signal-to-noise ratio, low operating threshold, minimal thermal damage, and serves as an independent Q-switched laser source, which can be applied to pulse laser measurement with high precision and time resolution in the future.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner’s reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Probing quantum decoherence at Belle II and LHCb

With the advent of Belle II and the LHCb upgrade, the precision measurements of various B-Physics observables are on cards. This holds significant potential for delving into physics beyond the standard model of electroweak interactions. These measurements can also serve as means to establish limits on phenomena occurring at much finer length scales, such as quantum decoherence, which may arise due to potential discreteness in space-time or non-trivial topological effects. In this work, we set up the formalism to investigate the impact of quantum decoherence on several potential observables in B meson systems. The approach employs the trace-preserving Kraus operator formalism, extending unitary evolution to non-unitary dynamics while maintaining complete positivity. In this formalism, the decoherence effects are parametrized in terms of a single parameter. Through the analysis of purely leptonic, semileptonic, and non-leptonic decays of B mesons, we identify observables that could, in principle, be influenced by decoherence. The theoretical expressions are provided without neglecting the impact of decay width difference (∆Γ) and CP violation in mixing. Considering that many of these observables can be measured with high precision using the abundant data collected by LHCb and Belle II, our formalism can be applied to establish constraints on the decoherence parameter through multiple decay channels. This offers an alternative set-up for such studies, which, at present, are predominantly conducted in the neutrino sector.

Real-time deep-learning-based object detection and unsupervised statistical analysis for quantitative evaluation of defect length direction on magnetooptical faraday effect

Magnetooptical nondestructive inspection (MONDI) is widely used in various industries to detect defects in metal including ferromagnetic materials. Despite its high precision and sensitivity, its inspection accuracy is highly dependent on the direction of the magnetizer. In addition, the irregular shapes of defects make the assessment of their severity challenging. To address these limitations, we propose an efficient real-time deep learning (DL)-based object detection model called the MONDI detector. This model is designed to integrate a DL-based object detection model with statistical analysis based on regression model to classify and quantify the magnetizer direction. To comprehensively assess algorithm performance, this study investigates variations in the excitation field, defect shape, and defect depth. This analysis is expected to contribute to the state-of-the-art object detection paradigm in MONDI system.

Full inference for the anisotropic fractional Brownian field

The anisotropic fractional Brownian field (AFBF) is a non-stationary Gaussian random field which has been used for the modeling of textured images. In this paper, we address the open issue of estimating the functional parameters of this field, namely the topothesy and Hurst functions. We propose an original method which fits the empirical semi-variogram of an image to the semi-variogram of a turning-band field that approximates the AFBF. Expressing the fitting criterion in terms of a separable non-linear least square criterion, we design a minimization algorithm inspired by the variable projection approach. This algorithm also includes a coarse-to-fine multigrid strategy based on approximations of functional parameters. Compared to existing methods, the new method enables to estimate both functional parameters on their whole definition domain. On simulated textures, we show that it has a low estimation error, even when the parameters are approximated with a high precision. We also apply the method to characterize mammograms and sample images with synthetic parenchymal patterns.

Light-weight Fusion Network for UAV Visible-light and Infrared Images based on Real-time Brain-like Intelligent Computing

Abstract. Multiple sensors equipped on the Unmanned Aerial Vehicle (UAV) enables the acquisition of multi-modal and multi-source remote sensing data. UAV remote sensing usually faces with real-time or near-real-time tasks in complex and highly dynamic environments, such as disaster monitoring, traffic management, border patrol and so on. Under these conditions, the image fusion algorithm needs to be high efficiency, precision and reliability. In this paper, we proposed an intelligent real-time fusion network for UAV multi-source remote sensing data based on AI brain-like chips, and deployed the algorithm on the UAV platform to achieve online high-efficiency computing. Firstly, we have developed a novel image fusion algorithm named SFNet for infrared and visible image fusion based on ShuffleNetv2. Then, we use ZCA and l1-norm to process the remodeled deep feature. The weight maps are generated by bi-cubic interpolation and soft-max operation. Finally, the fused image is reconstructed by weighted-average operation. The proposed SFNet is deployed on the Lynxi KA200 brain computing chip, and a comprehensive inference test is carried out with UAV remote sensing data. Several State-Of-The-Art (SOTA) data fusion algorithms are deployed on the same chip for experimental comparison. The proposed SFNet is proved to have faster inference speed and better feature extraction results on brain-like chips. It is more suitable for real-time UAV remote sensing image fusion tasks.

Comprehensive Vibration Analysis and Modeling for ZY-3 Satellite's Lifecycle

Abstract. A stable platform environment is essential for achieving high-quality imaging and accurate attitude measurement of the ZY-3's stereo camera. It serves as the fundamental guarantee for the satellite's high geometric precision. Throughout its orbital operation, the platform encounters periodic vibrational occurrences resulting from internal load mechanics and significant external environmental variations, thereby impeding the high-fidelity collection of information by the satellite payload. The detection and modeling of vibrations are crucial tasks for the satellite ground system, as they provide reliable parameter inputs for optimizing attitude accuracy and image quality. This paper presents a method for developing a comprehensive frequency variation vibrational model of the ZY-3 satellite platform throughout its lifecycle, utilizing the inertial gyroscopes (gyros) data of the ZY-3 satellite. The instrument has identified vibrations at three specific frequencies: 0.21 Hz, 0.56 Hz, and 1.12 Hz, each with amplitudes of 0.07″, 0.04″, and 0.04″, respectively. Additionally, it has detected two fluctuating frequencies around 0.3 Hz and 0.6Hz. The period of frequency variation is approximately one year, with vibration ranges of (0.267, 0.299) and (0.603, 0.695). Following a malfunction of the solar panels on the satellite, there has been a shift in the vibration range. Moreover, as the satellite's on-orbit duration increases, this range is also undergoing gradual changes.

Synchrotron x-ray spectra characterisation for radiation therapy applications at the ESRF - ID17 biomedical beamline

Objective. Radiation therapy requires reliable dosimetry protocols to deliver successful treatments with high accuracy and precision. In this context, accurate knowledge of the beam’s energy spectra is mandatory. The goal of this study was to validate the synchrotron x-ray spectrum of the ID17 beamline at the European Synchrotron Radiation Facility (ESRF). The modification of the synchrotron storage ring and beamline in recent years necessitates a new characterisation of the radiation spectra of the ID17 beamline. The validated spectra will be a starting point for possible future clinical applications. Approach. The half value layer method was used to measure the attenuation of the x-ray spectrum in Al and Cu. Experimental data was validated against theoretical data produced using OASYS; an in-house developed software for calculating beamline spectra. Two different spectral configurations, ‘conventional’ and ‘clinical’, were investigated. The characterised spectra were used to perform dosimetric validation of depth dose profiles measured in a water-equivalent phantom. The dose profile was measured using two different detectors and compared with calculations generated using two different Monte Carlo algorithms. Main results. The results showed good agreement between measured and predicted half value layers, with differences of less than 1% in most cases. Excellent dosimetric agreement to within 3% was obtained, an agreement that satisfies the requirements in conventional radiotherapy for approvable treatment planning. Significance. Accurate spectra have been defined and validated for the ESRF—ID17 Biomedical beamline. The validated spectra can be used as input for future dosimetric studies and treatment planning systems in the context of preclinical studies and possible future clinical trials.

CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space

Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain.Scientific contributionTo the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

CRTED: Few-Shot Object Detection via Correlation-RPN and Transformer Encoder–Decoder

Few-shot object detection (FSOD) aims to address the challenge of requiring a substantial number of annotations for training in conventional object detection, which is very labor-intensive. However, the existing few-shot methods achieve high precision with the sacrifice of time for exhaustive fine-tuning or have poor performance in novel-class adaptation. We presume the major reason is that the valuable correlation feature among different categories is insufficiently exploited, hindering the generalization of knowledge from base to novel categories for object detection. In this paper, we propose few-shot object detection via Correlation-RPN and transformer encoder–decoder (CRTED), a novel training network to learn object-relevant features of inter-class correlation and intra-class compactness while suppressing object-agnostic features in the background with limited annotated samples. And we also introduce a four-way tuple-contrast training strategy to positively activate the training progress of our object detector. Experiments over two few-shot benchmarks (Pascal VOC, MS-COCO) demonstrate that our proposed CRTED without further fine-tuning can achieve comparable performance with current state-of-the-art fine-tuned works. The codes and pre-trained models will be released.

Impact of Real-Time Blind Slat Angle Control on Reducing Total Energy Consumption of a Library Building Using an Optimized Artificial Neural Network Model: A Case Study of the Six Moroccan Thermal Zones

Abstract The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

Toward Selective Transport of Monovalent Metal Ions with High Permeability Based on Crown Ether-Encapsulated Metal-Organic Framework Sub-Nanochannels.

Achieving selective transport of monovalent metal ions with high precision and permeability analogues to biological protein ion channels has long been explored for fundamental research and various applications, such as ion sieving, mineral extraction, and energy harvesting and conversion. However, it still remains a significant challenge to construct artificial nanofluidic devices to realize the trade-off effects between selective ion transportation and high ion permeability. In this work, we report a bioinspired functional micropipet with in situ growth of crown ether-encapsulated metal-organic frameworks (MOFs) inside the tip and realize selective transport of monovalent metal ions. The functional ion-selective micropipet with sub-nanochannels was constructed by the interfacial growth method with the formation of composite MOFs consisting of ZIF-8 and 15-crown-5. The resulting micropipet device exhibited obvious monovalent ion selectivity and high flux of Li+ due to the synergistic effects of size sieving in subnanoconfined space and specific coordination of 15-crown-5 toward Na+. The selectivity of Li+/Na+, Li+/K+, Li+/Ca2+, and Li+/Mg2+ with 15-crown-5@ZIF-8-functionalized micropipet reached 3.9, 5.2, 105.8, and 122.4, respectively, which had an obvious enhancement compared to that with ZIF-8. Notably, the ion flux of Li+ can reach up to 93.8 ± 3.6 mol h-1·m-2 that is much higher than previously reported values. Furthermore, the functional micropipet with 15-crown-5@ZIF-8 sub-nanochannels exhibited stable Li+ selectivity under various conditions, such as different ion concentrations, pH values, and mixed ion solutions. This work not only provides new opportunities for the development of MOF-based nanofluidic devices for selective ion transport but also facilitates the promising practical applications in lithium extraction from salt-like brines, sewage treatment, and other related aspects.

Single shot point-diffraction interferometer by a plate beamsplitter

Abstract A single shot point-diffraction interferometer is proposed in this paper, in which one plate beamsplitter is assembled with one mirror and one pinhole array to duplicate an object beam and generate one object beam and one reference beam. One mirror is placed perpendicularly at the rear of the optical 4f system, while one thin plate beamsplitter is placed ahead of the mirror with an inclined angle. With the help of the half-transmission and half-reflection of the plate beamsplitter, one object beam is duplicated into to copies. Owning to the different angles of the plate beamsplitter and the mirror with respect to the optical axis, after Fourier transformed by the second Lens of the 4f system and reflected by the cube beamsplitter, the two copies are separated in the spectral domain. Then, one object beam and one reference beam are generated by the pinhole array placed at the spectral plane. The object and reference beams are Fourier transformed by the third Lens and interferes to generate an off-axis interferogram at the camera. By adjusting the inclined angle of the plate beamsplitter, the carrier frequency of the interferogram can be modulated easily. Therefore, this interferometer has a simple optical setup, easy optical implementation and outstanding measurement ability with high precision, measurement efficiency and stability. Several experimental results will be provided to demonstrate the effectiveness of the proposed system.

Inkjet Printing of Long-Range Ordering Two-Dimensional Magnetic Ti0.8Co0.2O2 Film

The value of two-dimensional (2D) materials in printed electronics has been gradually explored, and the rheological properties of 2D material dispersions are very different for various printing technologies. Understanding the rheological properties of 2D material dispersions plays a vital role in selecting the optimal manufacturing technology. Inkjet printing is suitable for small nanosheet sizes and low solution viscosity, and it has a significant advantage in developing nanosheet inks because of its masklessness, high efficiency, and high precision. In this work, we selected 2D Ti0.8Co0.2O2 nanosheets, which can be synthesized in large quantities by the liquid phase exfoliation technique; investigated the effects of nanosheet particle size, solution concentration on the rheological properties of the dispersion; and obtained the optimal printing processing method of the dispersion as inkjet printing. The ultrathin Ti0.8Co0.2O2 nanosheet films were prepared by inkjet printing, and their magnetic characteristics were compared with those of Ti0.8Co0.2O2 powder. The films prepared by inkjet printing exhibited long-range ordering, maintaining the nanosheet powders’ paramagnetic characteristics. Our work underscored the potential of inkjet printing as a promising method for fabricating precisely controlled thin films using 2D materials, with applications spanning electronics, sensors, and catalysis.

Land Cover Classification Based on Multimodal Remote Sensing Fusion

Abstract. Global high-precision and high timeliness land cover data is a fundamental and strategic resource for global strategic interest maintenance, global environmental change research, and sustainable development planning. However, due to difficulties in obtaining control and reference information from overseas, a single data source cannot effectively cover, and surface coverage classification faces significant challenges in information extraction. Based on this, this article proposes an intelligent interpretation method for typical elements based on multimodal fusion, starting from the characteristics of domestic remote sensing images. It also develops an optical SAR data conversion and complementarity strategy based on convolutional translation networks, as well as a typical element extraction algorithm. This solves the problems of sparse remote sensing images, limited effective observations, and difficult information recognition, thereby achieving automation of typical element information under dense observation time series High precision extraction and analysis.

Backstepping Control Design in Conjunction with an EKF-based Sensorless Field-Oriented Control of an IPMSM

The collector and brushless electronic commutation machines have gained widespread utilization in industrial applications. Among these machines, internal permanent magnet synchronous motors (IPMSMs) have become increasingly popular due to their numerous advantages, including high torque/current and torque/inertia ratios, robust construction, high efficiency, and reliability. However, the incorporation of position sensors in IPMSMs has introduced challenges concerning their application, performance, mass production, and cost. Consequently, the implementation of sensorless control techniques has become essential in drive systems and various applications. This paper proposes a backstepping control approach for achieving speed sensorless control of IPMSMs, employing an extended Kalman filter (EKF). The first step involves developing a comprehensive nonlinear dynamical model of the IPMSM in the direct and quadrature (d-q) rotor frame and obtaining its corresponding state-space representation. Subsequently, backstepping controllers for rotor speed and current tracking are designed to ensure precise tracking and anti-disturbance performance. These controllers are integrated into the field-oriented control (FOC) scheme. The Lyapunov stability theorem is employed to guarantee the asymptotic stability condition of the backstepping controller. To address the challenge of estimating immeasurable mechanical parameters of the IPMSM and accurately tracking the system states, an EKF is designed. This filter enables the estimation of mechanical parameters and achieves high steady-state precision within a finite time. The effectiveness of the proposed methodology is demonstrated through simulations conducted under various dynamic operating conditions, including sudden torque load changes, command speed changes, and parameter variations.

A voltammetric method coupled with chemometrics for determination of a ternary antiparkinson mixture in its dosage form: greenness assessment

An electroanalytical methodology was developed by direct differential pulse voltammetric (DPV) measurement of Levodopa (LD), Carbidopa (CD) and Entacapone (ENT) mixture using bare glassy carbon electrode (GCE) in Britton Robinson (BR) buffer (pH = 2.0). A multivariate calibration model was then applied to the exported preprocessed voltammetric data using partial least square (PLS) as a chemometric tool. Additionally, the model was cross-validated and the number of latent variables (LVs) were determined to produce a reliable model for simultaneous quantitation of the three drugs either in their synthetic mixtures or in their marketed pharmaceutical formulation with high accuracy and precision. Data preprocessing was used to tackle the problem of lacking bi-linearity which is commonly found in electrochemical data. The proposed chemometric model was able to provide fast and reliable technique for quantitative determination of antiparkinson drugs in their dosage forms. This was successfully achieved by utilizing sixteen mixtures as calibration set and nine mixtures as validation set. The percent recoveries for LD, CD and ENT were found to be 100.05% ± 1.28%, 100.04% ± 0.53% and 99.99% ± 1.25%, respectively. The obtained results of the proposed method were statistically compared to those of a previously reported High Performance Liquid Chromatography (HPLC) methodology. Finally, the presented analytical method strongly supports green analytical chemistry regarding the minimization of potentially dangerous chemicals and solvents, as well as reducing energy utilization and waste generation.