Cloud point extraction coupled with HPLC-UV for the determination of phthalate esters in environmental water samples

A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental water samples using high-performance liquid chromatography separation and ultraviolet detection （HPLC-UV）. The non-ionic surfactant Triton X-114 was chosen as extraction solvent. The parameters affecting extraction efficiency, such as concentrations of Triton X-114 and Na2SO4, equilibration temperature, equilibration time and centrifugation time were evaluated and optimized. Under the optimum conditions, the method can achieve preconcentration factors of 35, 88, 111 and detection of limits of 2.0, 3.8, 1.0 ng/ml for DEP, DEHP and DCP in 10-ml water sample, respectively. The proposed method was successfully applied to the determination of trace amount of phathalate esters in effluent water of the wastewater treatment plant and the lixivium of plastic fragments.

Determination of Ultratrace Amounts of Copper(Ⅱ) in Water Samples by Electrothermal Atomic Absorption Spectrometry After Cloud Point Extraction

A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyridylazo ） -2- naphthol was used as the chelating reagent and Triton X-114 as the mieellar-forming surfactant. CPE was conducted in a pH 8. 0 medium at 40 ℃ for 10 rain. After the separation of the phases by contrifugafion, the surfactant-rieh phase was diluted with 1 mL of a methanol solution of 0. 1 mol/L HNO3. Then 20μL of the diluted surfactant-rieh phase was injected into the graphite furnace for atomization in the absence of any matrix modifier. Various experimental conditions that affect the extraction and atomization processes were optimized. A detection limit of 5 ng/L was obtained after preconeentration. The linear dynamic range of the copper mass concentration was found to be 0-2.0 ng/mL, and the relative standard deviation was found to be less than 3. 1% for a sample containing 1.0 ng/mL Cu （ Ⅱ ）. This developed method was successfully applied to the determination of uhratraee amounts of Cu in drinking water, tap water, and seawater samples.

Determination of Trace Amounts of Nickel (Ⅱ) by Graphite Furnace Atomic Absorption Spectrometry Coupled with Cloud Point Extraction

A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.

Determination of Mecruy at Trace Level in Natural Water Samples by Hydride Generation Atomic Absorption Spectrophotometry after Cloud Point Extraction Preconcentration

A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentration of surfactant, and equilibration time on cloud point extraction were discussed. The enhancement factor of 20 and the detection limit of 0.039 μg/L were obtained for mercury with relative standard deviation of 4.8% （n = 11）.

Extraction of Sodium Copper Chlorophyll in Food Samples Based on Cloud Point Extraction with a Nonionic Surfactant

A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Several factors affecting the extraction efficiency of SCC and its subsequent determination, including the p H of the sample solution, salt and surfactant concentrations, and equilibration temperature and time, were studied and optimized. The extraction efficiency approached 99.4%.The calibration graph under the optimum conditions was linear in the concentration range of 3–220 mg/L with correlation coefficients> 0.9997(n = 8). The limit of detection for the analytes was 0.6 mg/L(S/N = 3). The proposed method is inexpensive, simple, and accurate for the extraction and determination of SCC in food samples.

Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.

Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,etc.Though considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so on.From the literature,it is observed that hard to deal with the temporal dimension in the action recognition process.Convolutional neural network(CNN)models could be used widely to solve this.With this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity recognition.The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose estimation.In the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human pose.Then,an optimal DCNN model is developed to classify the human activities label based on the extracted key points.For improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch count.The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

Cloud point extraction coupled with back extraction: a green methodology in analytical chemistry

Recently, cloud point extraction (CPE) coupled with back extraction (BE) has been suggestedas a promising alternative to liquid-liquid extraction. In CPE, non-ionic surfactants in aqueoussolutions form micelles and the solution becomes turbid when heated to the cloud pointtemperature. Microwave- or ultrasonic-assisted BE can be performed after CPE and beforeinjection of the sample for instrumental analysis by ultraviolet-visible spectroscopy, high-performance liquid chromatography, gas chromatography, gas chromatography-mass spectrometry, or liquid chromatography-mass spectrometry. This article reviews selected publishedscientific research on the application of CPE-BE to the determination of alkaloids, drugs andorganophosphorus compounds from several complex matrices. This method could bescaled-up for use in forensic science.