Comparing Different Means of Signal Treatment for Improving the Detection Power in HPLC-UV-HG-AFS

2012 ◽  
Vol 239-240 ◽  
pp. 1045-1051
Author(s):  
Jian Cui ◽  
Yan Wang ◽  
Xue Hong Zhao ◽  
Li Dai
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Gaussian Distribution ◽  
Analytical Methods ◽  
Arsenic Species ◽  
Detection Power ◽  
Arsanilic Acid ◽  
Organic Arsenic ◽  
Trace Concentrations ◽  
Signal Treatment

The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (organic arsenic species such as arsanilic acid, nitarsone and roxarsone) are to be determined in animal meat by HPLC-UV-HG-AFS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Fourier transform may be applied, but convolution with Gaussian distribution curves shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 2.4.

scholarly journals Adsorption of p-Arsanilic Acid on Iron (Hydr)oxides and Its Implications for Contamination in Soils

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 105
Author(s):  
Yifan Yang ◽  
Shiyong Tao ◽  
Zhichun Dong ◽  
Jing Xu ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Iron Oxides ◽  
Organic Matter Content ◽  
Feed Additives ◽  
Unit Surface Area ◽  
Potential Hazard ◽  
Order Kinetic ◽  
Positive Correlation ◽  
Arsanilic Acid ◽  
Organic Arsenic ◽  
Ph Conditions

Because of the diversification of industries in developing cities, the phenomenon of the simultaneous contamination of various kinds of pollutants is becoming common, and the environmental process of pollutants in multi-contaminated environmental mediums has attracted attention in recent years. In this study, p-arsanilic acid (ASA), a kind of organic arsenic feed additive that contains the arsenic group in a chemical structure, is used as a typical contaminant to investigate its adsorption on iron oxides and its implication for contaminated soils. The adsorption kinetics on all solids can be fitted to the pseudo-second-order kinetic model well. At the same mass dosage conditions, the adsorption amount per unit surface area on iron oxides follows the order α-FeOOH > γ-Fe2O3 > α-Fe2O3, which is significantly higher than that for actual soil, because of the lower content of iron oxides in actual soil. Lower pH conditions favor ASA adsorption, while higher pH conditions inhibit its adsorption as a result of the electrostatic repulsion and weakened hydrophobic interaction. The presence of phosphate also inhibits ASA adsorption because of the competitive effect. Correlations between the amount of ASA adsorption in actual soil and the Fe2O3 content, total phosphorus content, arsenic content, and organic matter content of actual soil are also investigated in this work, and a moderate positive correlation (R2 = 0.630), strong negative correlation (R2 = 0.734), insignificant positive correlation (R2 = 0.099), and no correlation (R2 = 0.006) are found, respectively. These findings would help evaluate the potential hazard of the usage of organic arsenic feed additives, as well as further the understanding of the geochemical processes of contaminants in complicated mediums.

Identification of Static Unbalance Wheel of Passenger Car Carried out on a Road

2014 ◽  
Vol 214 ◽  
pp. 48-57 ◽  
Author(s):  
Krzysztof Prażnowski ◽  
Sebastian Brol ◽  
Andrzej Augustynowicz
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Constant Velocity ◽  
Fourier Transforms ◽  
Static Condition ◽  
Rotational Frequency ◽  
Acceleration Sensor ◽  
Short Time Fourier Transform ◽  
Road Roughness ◽  
Short Time

This paper presents a method of identification of non-homogeneity or static unbalance of the structure of a car wheel based on a simple road test. In particular a method the detection of single wheel unbalance is proposed which applies an acceleration sensor fixed on windscreen. It measures accelerations cause by wheel unbalance among other parameters. The location of the sensor is convenient for handling an autonomous device used for diagnostic purposes. Unfortunately, its mounting point is located away from wheels. Moreover, the unbalance forces created by wheels spin are dumped by suspension elements as well as the chassis itself. It indicates that unbalance acceleration will be weak in comparison to other signals coming from engine vibrations, road roughness and environmental effects. Therefore, the static unbalance detection in the standard way is considered problematic and difficult. The goal of the undertaken research is to select appropriate transformations and procedures in order to determine wheel unbalance in these conditions. In this investigation regular and short time Fourier transform were used as well as wavelet transform. It was found that the use of Fourier transforms is appropriate for static condition (constant velocity) but the results proves that the wavelet transform is more suitable for diagnostic purposes because of its ability of producing clearer output even if car is in the state of acceleration or deceleration. Moreover it was proved that in the acceleration spectrum of acceleration measured on the windscreen a significant peak can be found when car runs with an unbalanced wheel. Moreover its frequency depends on wheel rotational frequency. For that reason the diagnostic of single wheel unbalance can be made by applying this method.

Wavelet Transform Selection Method for Biological Signal Treatment

2020 ◽  
pp. 23-34 ◽  
Author(s):  
Gonzalo Jiménez ◽  
Carlos Andrés Collazos Morales ◽  
Emiro De-la-Hoz-Franco ◽  
Paola Ariza-Colpas ◽  
Ramón Enrique Ramayo González ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Selection Method ◽  
Biological Signal ◽  
Signal Treatment

Wavelet transform to quantify heart rate variability and to assess its instantaneous changes

1999 ◽  
Vol 86 (3) ◽  
pp. 1081-1091 ◽  
Author(s):  
Vincent Pichot ◽  
Jean-Michel Gaspoz ◽  
Serge Molliex ◽  
Anestis Antoniadis ◽  
Thierry Busso ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Autonomous Nervous System ◽  
Dynamic Changes ◽  
Nonstationary Signals ◽  
Higher Doses

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.

scholarly journals Image analysis of the tree test using wavelet transform, singular value decomposition, and Fourier transform

2019 ◽  
Vol 90 (3) ◽  
pp. 284-293
Author(s):  
Keita Kawasugi ◽  
Kazuhisa Takemura ◽  
Yumi Iwamitsu ◽  
Hitomi Sugawara ◽  
Sakura Nishizawa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

The Study of Tire Pattern Noise by Using Wavelet Transform

2011 ◽  
Vol 105-107 ◽  
pp. 267-270 ◽  
Author(s):  
Sung Wook Hwang ◽  
Jin Hyuk Han ◽  
Ki Duck Sung ◽  
Sang Kwon Lee
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
The Other ◽  
Road Noise ◽  
Signal Process ◽  
The Road ◽  
Pattern Noise ◽  
On The Road ◽  
Non Stationary Signal ◽  
The Relationship

Tire noise is classified by pattern noise and road noise in a vehicle. Especially pattern noise has impulsive characteristics since it is generated by impacting of tire’s block on the road. Therefore, a special signal process is needed other than traditional Fourier Transform, because the characteristic of signal is varying with time. On the other hand, the pattern noise is a kind of non-stationary signal and is related to the impulsive train of pitch sequence of a block. In this paper, Wavelet Transform is applied to verify the impulse signal caused by impact of block and groove and to verify the relationship between the pattern noise and the train of pitch sequence.

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