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"Quality Control"
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Thin-Layer Chromatography (TLC) in the Screening of Botanicals–Its Versatile Potential and Selected Applications
The aim of this paper is to present a comprehensive overview of the main aims and scopes in screening of botanicals, a task of which thin-layer chromatography (TLC) is, on an everyday basis, confronted with and engaged in. Stunning omnipresence of this modest analytical technique (both in its standard format (TLC) and the high-performance one (HPTLC), either hyphenated or not) for many analysts might at a first glance appear chaotic and random, with an auxiliary rather than leading role in research, and not capable of issuing meaningful final statements. Based on these reflections, our purpose is not to present a general review paper on TLC in screening of botanicals, but a blueprint rather (illustrated with a selection of practical examples), which highlights a sovereign and important role of TLC in accomplishing the following analytical tasks: (i) solving puzzles related to chemotaxonomy of plants, (ii) screening a wide spectrum of biological properties of plants, (iii) providing quality control of herbal medicines and alimentary and cosmetic products of biological origin, and (iv) tracing psychoactive plants under forensic surveillance.
Journal Article
Juran's quality essentials : for leaders
\"Dr. Juran's life work on quality--boiled down to a concise guide to creating a strong, successful, globally competitive enterprise Dr. Joseph Juran was a true visionary in the field of quality, and his words continue to inspire. One of his main mantras was \"Take care of the quality, and the rest will take care of itself.\" This work is devoted to just that--taking care of the quality. In it, the Juran Institute team presents the leadership values, beliefs, and actions of top companies, and identifies the strategies that have resulted in measurable success. Juran's Quality Essentials offers succinct, field-tested methods applicable to any industry, from service to manufacturing, and includes realistic timetables of implementation. This unique resource describes three universal quality management methods: 1. designing innovative products and services; 2. creating breakthroughs in current performance; and 3. assuring repeatable and compliant processes. Reveals how to align quality goals and methods to a company's strategic plan Provides a transformation model and roadmap Demonstrates how executive leadership is the key to a company's quality revolution--and how to make quality happen. Filled with insider tips for staying adaptable and using a benchmark to sustain performance Shows how to apply planning, control, and improvement to quality leadership for competitive advantage \"-- Provided by publisher.
Book
Power Quality in Power Systems and Electrical Machines
Power quality of power systems affects all connected electrical and electronic equipment. Power quality is a measure of deviations in voltage and frequency of the particular supply system. In recent years, there has been a considerable increase in nonlinear loads; in particular distributed loads, such as computers, TV monitors and lighting. These draw harmonic currents which, when distorted, have detrimental effects including interference, loss of reliability, increased operating costs, equipment overheating, motor failures, capacitor failure and inaccurate power metering. This subject is pertinent to engineers involved with electric power systems, electronic equipment, computers and manufacturing equipment. This book shows readers to understand the causes and effects of power quality problems such as non-sinusoidal wave shapes, voltage outages, losses due to poor power quality, and origins of single-time events such as voltage dips, voltage reductions and outages, along with techniques to mitigate these problems.
eBook
Advancing herbal medicine: enhancing product quality and safety through robust quality control practices
This manuscript provides an in-depth review of the significance of quality control in herbal medication products, focusing on its role in maintaining efficiency and safety. With a historical foundation in traditional medicine systems, herbal remedies have gained widespread popularity as natural alternatives to conventional treatments. However, the increasing demand for these products necessitates stringent quality control measures to ensure consistency and safety. This comprehensive review explores the importance of quality control methods in monitoring various aspects of herbal product development, manufacturing, and distribution. Emphasizing the need for standardized processes, the manuscript delves into the detection and prevention of contaminants, the authentication of herbal ingredients, and the adherence to regulatory standards. Additionally, it highlights the integration of traditional knowledge and modern scientific approaches in achieving optimal quality control outcomes. By emphasizing the role of quality control in herbal medicine, this manuscript contributes to promoting consumer trust, safeguarding public health, and fostering the responsible use of herbal medication products.
Journal Article
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements
The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
Journal Article
A fast and robust convolutional neural network-based defect detection model in product quality control
The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.
Journal Article