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Records with Subject: Process Monitoring
Showing records 101 to 125 of 316. [First] Page: 1 2 3 4 5 6 7 8 9 Last
A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN
Hao Wu, Wangan Fu, Xin Ren, Hua Wang, Enmin Wang
February 27, 2023 (v1)
Keywords: Bayesian network, deep neural network, industrial safety, multimodality, process monitoring
The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely and reliable response decisions. Because practical industrial processes have multimodal operating conditions, the data distributions of process variables are different. The different data distributions may cause the fault detection model to be invalid. In addition, the fault diagnosis model cannot find the correct root cause variable of system failure by only identifying abnormal variables. There are correlations between the trend states of the process variables. If we do not consider these correlations, this may result in an incorrect fault root cause. Therefore, multimodal industrial process monitoring is a tough issue. In this paper, we propose a three-step framework for multimodal industrial process monitoring. The framework aims for multimodal industrial processes to detect the faulty status timely and then find the correct root variab... [more]
Deep-Learning Based Fault Events Analysis in Power Systems
Junho Hong, Yong-Hwa Kim, Hong Nhung-Nguyen, Jaerock Kwon, Hyojong Lee
February 27, 2023 (v1)
Keywords: convolutional neural networks, fault line location identification, power systems fault classification
The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault ty... [more]
Functional Logistic Regression for Motor Fault Classification Using Acoustic Data in Frequency Domain
Jakub Poręba, Jerzy Baranowski
February 27, 2023 (v1)
Keywords: acoustic signal, functional data analysis, functional logistic regression, motor diagnostics
Motor diagnostics is an important subject for consideration. Electric motors of different types are present in a multitude of object, from consumer goods through everyday use devices to specialized equipment. Diagnostic assessment of motors using acoustic signals is an interesting field, as microphones are present everywhere and are relatively easy sensors to process. In this paper, we analyze acoustic signals for the purpose of motor diagnostics using functional data analysis. We represent the spectrum (FFT) of the acoustic signals on a B-Spline basis and construct a classifier based on that representation. The results are promising, especially for binary classifiers, while multiclass (softmax regression) shows more sensitivity to dataset size. In particular, we show that while we are able to obtain almost perfect classification for binary cases, multiclass classifiers can struggle depending on the training/testing split. This is especially visible for determining the number of broken... [more]
Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning
Khalfan Al Kharusi, Abdelsalam El Haffar, Mostefa Mesbah
February 27, 2023 (v1)
Keywords: Bayesian optimization, fault classification, Fault Detection, inverter-based generators, Machine Learning, power system protection, Renewable and Sustainable Energy
Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time−frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the... [more]
Classification of Single Current Sensor Failures in Fault-Tolerant Induction Motor Drive Using Neural Network Approach
Maciej Skowron, Krystian Teler, Michal Adamczyk, Teresa Orlowska-Kowalska
February 27, 2023 (v1)
Keywords: current sensor failures, fault classification, Fault Detection, fault localization, fault-tolerant control, induction motor drive, neural network
In the modern induction motor (IM) drive system, the fault-tolerant control (FTC) solution is becoming more and more popular. This approach significantly increases the security of the system. To choose the best control strategy, fault detection (FD) and fault classification (FC) methods are required. Current sensors (CS) are one of the measuring devices that can be damaged, which in the case of the drive system with IM precludes the correct operation of vector control structures. Due to the need to ensure current feedback and the operation of flux estimators, it is necessary to immediately compensate for the detected damage and classify its type. In the case of the IM drives, there are individual suggestions regarding methods of classifying the type of CS damage during drive operation. This article proposes the use of the classical multilayer perceptron (MLP) neural network to implement the CS neural fault classifier. The online work of this classifier was coordinated with the active F... [more]
Monitoring of Thermal and Flow Processes in the Two-Phase Spray-Ejector Condenser for Thermal Power Plant Applications
Paweł Madejski, Piotr Michalak, Michał Karch, Tomasz Kuś, Krzysztof Banasiak
February 27, 2023 (v1)
Keywords: direct contact condenser, experimental test rig, mass flow measurement, spray-ejector condenser
The paper deals with the problem of accurate measuring techniques and experimental research methods for performance evaluation of direct contact jet-type flow condensers. The nominal conditions and range of temperature, pressure and flow rate in all characteristic points of novel test rig installation were calculated using the developed model. Next, the devices for measurement of temperature, pressure and flow rate in a novel test rig designed for testing the two-phase flow spray ejector condensers system (SEC) were studied. The SEC can find application in gas power cycles as the device dedicated to condensing steam in exhaust gases without decreasing or even increasing exhaust gas pressure. The paper presents the design assumptions of the test rig, its layout and results of simulations of characteristic points using developed test rig models. Based on the initial thermal and flow conditions, the main assumptions for thermal and flow process monitoring were formulated. Then, the discus... [more]
A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions
Chibuzo Nwabufo Okwuosa, Jang-wook Hur
February 24, 2023 (v1)
Keywords: fault diagnosis, feature engineering, Hilbert transform, Machine Learning, squirrel cage induction motor, support vector classifier
In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the... [more]
Empirical Wavelet Transform-Based Intelligent Protection Scheme for Microgrids
Syed Basit Ali Bukhari, Abdul Wadood, Tahir Khurshaid, Khawaja Khalid Mehmood, Sang Bong Rhee, Ki-Chai Kim
February 24, 2023 (v1)
Keywords: empirical wavelet transform, fault classification, Fault Detection, long short-term memory network, microgrid protection
Recently, the concept of the microgrid (MG) has been developed to assist the penetration of large numbers of distributed energy resources (DERs) into distribution networks. However, the integration of DERs in the form of MGs disturbs the operating codes of traditional distribution networks. Consequently, traditional protection strategies cannot be applied to MG against short-circuit faults. This paper presents a novel intelligent protection strategy (NIPS) for MGs based on empirical wavelet transform (EWT) and long short-term memory (LSTM) networks. In the proposed NIPS, firstly, the three-phase current signals measured by protective relays are decomposed into empirical modes (EMs). Then, various statistical features are extracted from the obtained EMs. Afterwards, the extracted features along with the three-phase current measurement are input to three different LSTM network to obtain exact fault type, phase, and location information. Finally, a trip signal based on the obtained fault... [more]
Generator Fault Classification Method Based on Multi-Source Information Fusion Naive Bayes Classification Algorithm
Yi Wang, Yuhao Huang, Kai Yang, Zhihan Chen, Cheng Luo
February 24, 2023 (v1)
Keywords: fault classification, finite element analysis, multi-source information fusion, Naive Bayes classification algorithm
The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve th... [more]
SR-GNN Based Fault Classification and Location in Power Distribution Network
Haojie Mo, Yonggang Peng, Wei Wei, Wei Xi, Tiantian Cai
February 24, 2023 (v1)
Keywords: distribution systems, fault classification, fault location, graph neural network, super-resolution
Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability an... [more]
A New Method of Fault Localization for 500 kV Transmission Lines Based on FRFT-SVD and Curve Fitting
Mohamed H. Saad, Mostafa M. Fouda, Abdelrahman Said
February 23, 2023 (v1)
Keywords: ATP-EMTP, curve fitting techniques, fault location, FRFT-SVD, long transmission line
The paper presents the Fractional Fourier Transform-Singular Value Decomposition (FRFT-SVD) method for the localization of various power system faults in a 200 km long, 500 kV Egyptian transmission line using sent end-line current signals. Transient simulations are carried out using Alternating Transient Program/Electromagnetic Transient Program (ATP-EMTP), and the outcomes are then examined in MATLAB to carry out a sensitivity analysis against measurement noises, sampling frequency, and fault characteristics. The proposed work employs current fault signals of five distinct kinds at nineteen intermediate points throughout the length of the line. The approach utilized to construct the localizer model is FRFT-SVD. It is much more effortless, precise, and effective. The FRFT-SVD is utilized in this technique to calculate 19 sets of indices of the greatest S value throughout the length of the line. The FRFT-SVD localizer model is also designed to be realistic, with power system noise corru... [more]
General Approach for Inline Electrode Wear Monitoring at Resistance Spot Welding
Christian Mathiszik, David Köberlin, Stefan Heilmann, Jörg Zschetzsche, Uwe Füssel
February 23, 2023 (v1)
Keywords: electrode tip-dressing, electrode wear, mushrooming, plateau forming, process monitoring, quality control, resistance spot welding, RSW, steel alloys
Electrodes for resistance spot welding inevitably wear out. In order to extend their service life, the tip-dressing process restores their original geometry. So far, however, the point in time for tip-dressing is mainly based on experience and not on process data. Therefore, this study aims to evaluate the in-situ or inline wear during the welding process without using additional sensors, and to base the timing for tip-dressing on continuous process monitoring, extending electrode life even further. Under laboratory conditions, electrode wear is analyzed by topographical measurements deepening the knowledge of the known main wear modes of resistance-spot-welding electrodes, mushrooming and plateau forming, and characterizing an electrode length delta over the number of spot welds. In general, electrode wear results in deformation of the electrode contact area, which influences process parameters and thereby weld quality. The conducted tests show correlation between this deformed contac... [more]
Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments
Federico Zuecco, Matteo Cicciotti, Pierantonio Facco, Fabrizio Bezzo, Massimiliano Barolo
February 23, 2023 (v1)
Keywords: batch processes, fault diagnosis, fault identification, Industry 4.0, principal component analysis, process monitoring, statistical process control, troubleshooting
Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the proces... [more]
Advanced Methods for Kiln-Shell Monitoring to Optimize the Waelz Process for Zinc Recycling
Markus Vogelbacher, Sina Keller, Wolfgang Zehm, Jörg Matthes
February 23, 2023 (v1)
Keywords: energy saving, kiln-shell, process monitoring, process supervision, temperature model, weather model, zinc recycling
The recycling of zinc in the Waelz process is an important part of the efficient use of resources in the steel processing cycle. The pyro-metallurgical processing of zinc-containing wastes takes place in a Waelz rotary kiln. Various measured variables are available to monitor the process. The temperature of the kiln-shell is analyzed by an infrared kiln-shell-scanner. In this paper, methods are presented which eliminate external weather-related disturbances on the temperature measured by the kiln-shell-scanner using a weather model and which extend the monitoring of the regularly necessary melting process to remove accretions. For this purpose, an adapted sigmoid estimation is introduced for the melting process, which provides new information about the current process status and a forecast of the further development of the melting process. As an assistance system for the plant operator, this enables an efficient execution of the melting process and reduces downtimes.
Special Issue “Advanced Process Monitoring for Industry 4.0”
Marco S. Reis, Furong Gao
February 23, 2023 (v1)
Industry 4 [...]
Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes
Luping Zhao, Xin Huang, Hao Yu
February 23, 2023 (v1)
Keywords: multi-mode model, multi-phase residual recursive model, process monitoring, quality prediction
In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance... [more]
Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases
Chih-Hung Jen, Chien-Chih Wang
February 23, 2023 (v1)
Keywords: empirical mode decomposition, intrinsic mode functions, linear discriminant analysis, statistical process control
Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of o... [more]
Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum
Xin Lu, Xiaoxu Liu, Bowen Li, Jie Zhong
February 23, 2023 (v1)
Keywords: data-driven, fault classification, multi-agent system, state prediction
When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism.
Interaction between the Standard and the Measurement Instrument during the Flow Velocity Sensor Calibration Process
Paweł Jamróz
February 23, 2023 (v1)
Keywords: blockage effect, calibration of the ventilation instruments, flow velocity sensors
The complex ventilation system development process is associated with the stages of modelling, design, execution, and testing. Each of these steps requires the use of measuring equipment that is capable of determining the basic parameters of the flow. In the process of calibrating instruments for measuring flow velocity, one of the limitations is the size of the calibrated devices positioned in the test section of the wind tunnel. This is related to the change in the flow condition within the vicinity of the calibrated anemometers, which is caused by the blockage effect. Instruments with significant dimensions in relation to the cross-sectional area of the wind tunnel test section may have an impact on the reference velocity as indicated by the standard. In such cases, the calibration results may be affected by additional systematic error. This article presents a study of this effect using a real case of a calibration laboratory and commonly used sensors. The influence of different typ... [more]
Online Calibration Method for Current Sensors Based on GPS
Shuang Zhao, Jun Liu, Yansong Li
February 23, 2023 (v1)
Keywords: current sensor, GPS timing, online calibration, synchronous acquisition
At present, most sensor calibration methods are off-line calibration, which not only makes them time-consuming and laborious, but also causes considerable economic losses. Therefore, in this study, an online calibration method of current sensors is proposed to address the abovementioned issues. The principle and framework of online calibration are introduced. One of the calibration indexes is angular difference. In order to accurately verify it, data acquisition must be precisely synchronized. Therefore, a precise synchronous acquisition system based on GPS timing is proposed. The influence of ionosphere on the accuracy of GPS signal is analyzed and a new method for measuring the inherent delay of GPS receiver is proposed. The synchronous acquisition performance of the system is verified by inter-channel synchronization experiment, and the results show that the synchronization of the system is accurate. Lastly, we apply our online calibration method to the current sensor; the experimen... [more]
Contrast Maximization-Based Feature Tracking for Visual Odometry with an Event Camera
Xiang Gao, Hanjun Xue, Xinghua Liu
February 23, 2023 (v1)
Keywords: contrast maximization, event camera, tracking template, visual odometry
As a new type of vision sensor, the dynamic and active-pixel vision sensor (DAVIS) outputs image intensity and asynchronous event streams in the same pixel array. We present a novel visual odometry algorithm based on the DAVIS in this paper. The Harris detector and the Canny detector are utilized to extract an initialized tracking template from the image sequence. The spatio-temporal window is selected by determining the life cycle of the asynchronous event streams. The alignment on timestamps is achieved by tracking the motion relationship between the template and events within the window. A contrast maximization algorithm is adopted for the estimation of the optical flow. The IMU data are used to calibrate the position of the templates during the update process that is exploited to estimate camera trajectories via the ICP algorithm. In the end, the proposed visual odometry algorithm is evaluated in several public object tracking scenarios and compared with several other algorithms. T... [more]
Nonstationary Process Monitoring Based on Alternating Conditional Expectation and Cointegration Analysis
Jingzhi Rao, Cheng Ji, Jiatao Wen, Jingde Wang, Wei Sun
February 23, 2023 (v1)
Keywords: actual industrial process, long-term equilibrium trend, nonlinear transformation
Traditional multivariate statistical methods, which are often used to monitor stationary processes, are not applicable to nonstationary processes. Cointegration analysis (CA) is considered an effective method to deal with nonstationary variables. If there is a cointegration relationship among the nonstationary series in the system, it indicates that a stable long-term dynamic equilibrium relationship exists among these variables. However, due to the complexity of modern industrial processes, there are nonlinear relations between variables, which are not considered by the traditional linear cointegration theory. Alternating conditional expectation (ACE) can perform nonlinear transformation on these variables to maximize the linear correlation of the transformed variables. It will be helpful to deal with the nonlinear relations by modeling with transformed variables. In this work, a new monitoring strategy based on ACE and CA is proposed. The data are first transformed by an ACE algorith... [more]
Research on Degradation State Recognition of Axial Piston Pump under Variable Rotating Speed
Rui Guo, Yingtang Liu, Zhiqian Zhao, Jingyi Zhao, Jianwei Wang, Wei Cai
February 23, 2023 (v1)
Keywords: ACMP, axial piston pump, degradation state recognition, SCT, variable rotating speed, XGBoost
Under the condition of variable rotating speed, it is difficult to extract the degradation characteristics of the axial piston pump, which also reduces the accuracy of degradation recognition. To address these problems, this paper proposes a degradation state recognition method for axial piston pumps by combining spline-kernelled chirplet transform (SCT), adaptive chirp mode pursuit (ACMP), and extreme gradient boosting (XGBoost). Firstly, SCT and ACMP are proposed to deal with the vibration signal instability and high noise of the axial piston pump under variable rotating speed. The instantaneous frequency (IF) of the axial piston pump can be extracted effectively by obtaining the accurate time-frequency distribution of signal components. Then, stable angular domain vibration signals are obtained by re-sampling, and multi-dimensional degradation characteristics are extracted from the angular domain and order spectrum. Finally, XGBoost is used to classify the selected characteristics t... [more]
On the Use of Surface Plasmon Resonance-Based Biosensors for Advanced Bioprocess Monitoring
Jimmy Gaudreault, Catherine Forest-Nault, Gregory De Crescenzo, Yves Durocher, Olivier Henry
February 23, 2023 (v1)
Keywords: bioprocess, biosensor, biotherapeutics production, monitoring, process analytical technology (PAT), quality by design (QbD), surface plasmon resonance (SPR), vaccines production
Biomanufacturers are being incited by regulatory agencies to transition from a quality by testing framework, where they extensively test their product after their production, to more of a quality by design or even quality by control framework. This requires powerful analytical tools and sensors enabling measurements of key process variables and/or product quality attributes during production, preferably in an online manner. As such, the demand for monitoring technologies is rapidly growing. In this context, we believe surface plasmon resonance (SPR)-based biosensors can play a role in enabling the development of improved bioprocess monitoring and control strategies. The SPR technique has been profusely used to probe the binding behavior of a solution species with a sensor surface-immobilized partner in an investigative context, but its ability to detect binding in real-time and without a label has been exploited for monitoring purposes and is promising for the near future. In this revi... [more]
Immunological Analytical Techniques for Cosmetics Quality Control and Process Monitoring
Martina Zangheri, Maria Maddalena Calabretta, Donato Calabria, Jessica Fiori, Massimo Guardigli, Elisa Michelini, Sonia Melandri, Assimo Maris, Mara Mirasoli, Luca Evangelisti
February 23, 2023 (v1)
Keywords: allergen, bacteria, cosmetics, ELISA, immunoassay, lateral flow immunoassay, toxins
Cosmetics analysis represents a rapidly expanding field of analytical chemistry as new cosmetic formulations are increasingly in demand on the market and the ingredients required for their production are constantly evolving. Each country applies strict legislation regarding substances in the final product that must be prohibited or regulated. To verify the compliance of cosmetics with current regulations, official analytical methods are available to reveal and quantitatively determine the analytes of interest. However, since ingredients, and the lists of regulated/prohibited substances, rapidly change, dedicated analytical methods must be developed ad hoc to fulfill the new requirements. Research focuses on finding innovative techniques that allow a rapid, inexpensive, and sensitive detection of the target analytes in cosmetics. Among the different methods proposed, immunological techniques are gaining interest, as they make it possible to carry out low-cost analyses on raw materials a... [more]
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