Quality Monitoring Techniques Research Articles

Survey on machine vision-based intelligent water quality monitoring techniques in water treatment plant: Fish activity behavior recognition-based schemes and applications

Abstract Water is a vital resource essential to the survival and development of all creatures. With the rapid growth of industry and agriculture, people face a severe threat of ecological destruction and environmental pollution while living earthly lives. Water pollution, in particular, harms people’s health the most. As a result, water supply security has become a top priority. As a critical point in water supply safety, monitoring water quality effectively and forecasting sudden water contamination on time has become a research hotspot worldwide. With the rapid development and wide applications of artificial intelligence and computer vision technologies, biological activity identification-based intelligent water quality monitoring methods have drawn widespread attention. They were taking fish activities as the water-quality indicator has gained extensive attention by introducing advanced computer vision and artificial intelligence technologies with low cost and ease of carrying. This article comprehensively reviews recent progress in the research and applications of machine vision-based intelligent water quality monitoring and early warning techniques based on fish activity behavior recognition. In detail, it addresses water quality-oriented fish detection and tracking, activity recognition, and abnormal behavior recognition-based intelligent water quality monitoring. It analyzes and compares the performance and their favorite application conditions. Finally, it summarizes and discusses the difficulties and hotspots of water quality monitoring based on the fish’s abnormal behavior recognition and their future development trends.

Comparative Analysis of Riverine Plastic Pollution Combining Citizen Science, Remote Sensing and Water Quality Monitoring Techniques

Comparative Analysis of Riverine Plastic Pollution Combining Citizen Science, Remote Sensing and Water Quality Monitoring Techniques

Organometallic modification of silica with europium endowing the fluorescence properties: the key technique for numerical quality monitoring

Organometallic modification of silica with europium endowing the fluorescence properties: the key technique for numerical quality monitoring

An Overview of Recent Analytical Techniques for Air Quality Monitoring and Assessment

Abstract: Access to clean air, a vital necessity for life, faces severe constraints globally due to industrialization and urbanization, leading to widespread air quality deterioration. To safeguard human health and the environment from detrimental effects, the essential components of proper monitoring, assessment, and management of air quality are paramount. Conventional air quality analytical techniques such as gas chromatography/ mass spectrometry, selected ion flow tube mass spectrometry, thermal desorption/ gas chromatography, and mass spectrometry are widely used for air quality analysis. These methods, however, are laborious, necessitate sample preparation, require expansive and hazardous reagents, and have a high cost of equipment and maintenance. As such, more rapid, sensitive, specific, cost-effective, portable, user-friendly, and environmentally friendly analytical tools are required for efficient air quality monitoring and control. Over the years, various techniques have emerged to address these challenges, including mobile sensors, microbial monitoring, the Internet of Things (IoT), biomonitoring, and bio- and nanosensors in both indoor and outdoor settings. This paper offers an overview of recent advancements in air quality monitoring and assessment methods. The review encompasses sample preparations for air pollutants, data analysis methodologies, and monitoring strategies. It also delves into the crucial role of microorganisms in air quality analysis. Additionally, the paper explores the applications of the Internet of Things (IoT) and biosensors in air quality monitoring and assessment, elucidating their roles in advancing these endeavors. The paper concludes by presenting insightful perspectives on the current state of air quality monitoring techniques and outlining future directions for research and development in this critical field.

The Use of Bromeliads as Biomonitors of Air Quality: A Global Review

Air pollution is a global problem in urban and rural areas, mainly related to vehicular traffic and industrial activities. Bromeliads have been widely used globally for active and passive air quality monitoring. However, a systematic review to facilitate the access and analysis of this information is yet to be made available. The objective of this work was to systematically review the use of bromeliads in biomonitoring of the air quality from articles published between 1990 and 2023 to analyze the progress and impact of the research related to the type of monitoring, species used, pollutants measured, and sampling protocols. The search was carried out in global (Scopus, ISI Web of Science, ScienceDirect, and MDPI) and regional (SciELO and Redalyc) databases with a total of 60 scientific articles, where the neotropical region with 31 articles and the Nearctic region with 11 articles were the most influential regions. In addition, more than 90% of the research has been published in high‐impact journals (quartile 1). Passive monitoring registered 25 articles compared to active monitoring with 23, with 23 species of the family Bromeliaceae, predominantly the genus Tillandsia. Tillandsia usneoides was the most common species used in active monitoring, while Tillandsia recurvata was used for passive monitoring. The measurement of heavy metals was the preferred technique (93% of the studies) for air quality monitoring, where Zn, Fe, Pb, Cr, and Mn and the nonessential elements K, Ca, and Na were the most measured. Results obtained by different research groups cannot be compared directly because different methodologies have been used, highlighting the importance of standardized techniques for future work. Thus, as a contribution in this direction, we propose a protocol to facilitate or standardize the selection of the proper methodology for developing air quality monitoring using bromeliads.

Dual-signal detection of tannic acid in red wines based on the peroxidase activity of carbon dots.

Herein, a novel type of phosphorus and iron-doped carbon dot (P,Fe-CD) with outstanding peroxidase activity and excellent fluorescence performance was hydrothermally synthesized to colorimetrically and fluorimetrically detect tannic acid (TA). In the presence of 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2, the P,Fe-CDs could oxidize colorless TMB to a blue oxidation product (oxTMB) resulting in an increased value of absorbance. Simultaneously, the fluorescence intensity of P,Fe-CDs at 430 nm could be quenched owing to the fluorescence resonance energy transfer (FRET) between P,Fe-CDs and the generated oxTMB. Meanwhile, after adding the TA to the system containing TMB, H2O2 and P,Fe-CDs, the value of absorbance could be decreased and the fluorescence could be recovered because of the reduction reaction between TA and oxTMB. Therefore, fluorescence intensity and value of absorbance could be applied to quantitatively detect TA with good linearities between the concentration of TA and the fluorescence intensity/value of absorbance (0.997 and 0.997 for the colorimetric signal and fluorimetric one, respectively) and low limits of detection (0.093 μmol L-1 and 0.053 μmol L-1 for the colorimetry and the fluorimetry, respectively), which was successfully applied to the detection of TA in red wines. Moreover, we applied a smartphone-assisted method to the point-of-care detection of TA with accurate results, providing a new technique for TA detection and food quality monitoring.

Harmonizing Environmental Protection Integration of Monitoring and purification Systems

Real-time water quality monitoring has advanced significantly with the integration of IoT-enabled pH and temperature sensors and the creation of a mobile application. This initiative intends to improve environmental management efforts by regularly measuring the temperature and pH of water bodies and giving stakeholders access to data that can be used to make decisions. Real-time pH and temperature data is collected by the monitoring system using Internet of Things (IoT) technology, and it is then sent to a web-based interface for analysis and visualization. The web interface is complemented with a mobile application that gives stakeholders access to temperature and pH data in real time, lets them get warnings, and lets them submit observations from their mobile devices.In order to further improve water quality monitoring and management techniques, future additions will involve the integration of advanced analytics, remote calibration and maintenance capabilities, and community monitoring networks. All things considered, this initiative advances environmentally responsible water management techniques and environmental care.

Quantifying seasonal to multi-decadal signals in coastal water quality using high- and low-frequency time series data

Abstract To inform water quality monitoring techniques and modeling at coastal research sites, this study investigated seasonality and trends in coastal lagoons on the eastern shore of Virginia, USA. Seasonality was quantified with harmonic analysis of low-frequency time-series, approximately 30 years of quarterly sampled data at thirteen mainland, lagoon, and ocean inlet sites, along with 4–6 years of high-frequency, 15-min resolution sonde data at two mainland sites. Temperature, dissolved oxygen, and apparent oxygen utilization (AOU) seasonality were dominated by annual harmonics, while salinity and chlorophyll-a exhibited mixed annual and semi-annual harmonics. Mainland sites had larger seasonal amplitudes and higher peak summer values for temperature, chlorophyll-a and AOU, likely from longer water residence times, shallower waters, and proximity to marshes and uplands. Based on the statistical subsampling of high-frequency data, one to several decades of low-frequency data (at quarterly sampling) were needed to quantify the climatological seasonal cycle within specified confidence intervals. Statistically significant decadal warming and increasing chlorophyll-a concentrations were found at a sub-set of mainland sites, with no distinct geographic patterns for other water quality trends. The analysis highlighted challenges in detecting long-term trends in coastal water quality at sites sampled at low frequency with large seasonal and interannual variability.

SR-FTIR microspectroscopy: Emerging 2D structural-chemical analytical technique for food quality and safety monitoring

Synchrotron Radiation Fourier Transform Infrared Microspectroscopy (SR-FTIR microspectroscopy) is a unique technique of 2D structural-chemical analysis that has been used successfully in diverse research fields. However, food scientists still seldom know this advanced analytical technique despite its huge potential in food research. Integration of SR-FTIR microspectroscopy with food science, requires comprehensive understanding of this emerging structural-chemical analytical technique. This article detailed the fundamental theory, working principle, mode of action, relevancy and constraints of application as well as capacity of integrating SR-FTIR microspectroscopy in food industries to assist in process optimization, quality control and safety confirmation. SR-FTIR microspectroscopy combines distinct working principle of spectroscopy and microscopy within IR spectral range, where high intensity light from synchrotron facility is used to achieve some advantages over other analytical approaches. This hyphenated technique has shown its capability to provide 2D structural-chemical information of food cereal grains, oil grains, processed food, foodborne pathogens, and so on, instigating this technique to be used for food processing, preservation, quality monitoring, and safety aspects. Despite the accessibility issue of SR-FTIR microspectroscopy, its application is flourishing and demands more exploration as an emerging food analytical technique.

Perspectives on Novel Technologies of Processing and Monitoring the Safety and Quality of Prepared Food Products.

With the changes of lifestyles and rapid growth of prepared food industry, prepared fried rice that meets the consumption patterns of contemporary young people has become popular in China. Although prepared fried rice is convenient and nutritious, it has the following concerns in the supply chain: (1) susceptible to contamination by microorganisms; (2) rich in starch and prone to stall; and (3) vegetables in the ingredients have the issues of water loss and discoloration, and meat substances are vulnerable to oxidation and deterioration. As different ingredients are used in prepared fried rice, their food processing and quality monitoring techniques are also different. This paper reviews the key factors that cause changes in the quality of prepared fried rice, and the advantages and limitations of technologies in the processing and monitoring processes. The processing technologies for prepared fried rice include irradiation, high-voltage electric field, microwave, radio frequency, and ohmic heating, while the quality monitoring technologies include Raman spectral imaging, near-infrared spectral imaging, and low-field nuclear magnetic resonance technology. These technologies will serve as the foundation for enhancing the quality and safety of prepared fried rice and are essential to the further development of prepared fried rice in the emerging market.

Predicting compressive strength of cement-stabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity

ABSTRACT The quality monitoring technique for Cement stabilised earth blocks (CSEBs) is so challenging that it is often neglected. This study has investigated the possibility of using machine learning to predict the compressive strength of CSEBs based on cement content, electrical resistivity and Ultrasonic pulse velocity (UPV) as a potential way to enhance quality control. The study considered three types of soil and different cement content in the preparation of CSEBs with 10 different cement-soil mixtures. Various machine learning models were proposed to predict the compressive strength of CSEBs. The models were evaluated using 180 experimental datasets, and the best model for predicting the compressive strength of CSEBs was selected. The ANN and BTR models performed better than the other machine learning models tested in this study for predicting the compressive strength of CSEBs. The results show that a combination of cement content, electrical resistivity and UPV can be used to assess the quality of CSEBs more accurately, which can contribute to the knowledge base and be applied in the real world. Materials scientists and engineers can use reliable predictive models to assess the strength properties of both new and old brick structures without damage or loss of use.

AIR QUALITY MONITORING SYSTEM DURING CORONA PANDEMIC

During the last two years, COVID-19 has affected the entire world and become a global health threat. It causes serious respiratory illness in animals as well as humans. Many factors influence the transmission of the virus including person-to-person contact and air pollution. Bad air quality adds to the problem of corona patients. In this work, the Internet of things (IoT) based air quality monitoring technique is explained for patients infected with Covid-19. The Proteus simulation tool is used with Arduino to detect five air quality factors which include carbon monoxide, Sulphur oxide, nitrogen dioxide, temperature, and humidity. Depending upon these factors air quality is divided into six categories through which it can be decided whether the environment is suitable for the patient or not. Results are displayed on a liquid crystal display and virtual terminal. The proposed solution provides real-time air quality monitoring at a lower cost. This tool indirectly offers multiple advantages including a lower risk of heart attack, removing allergies, minimizing breathing problems, helping in better sleep, humidity control, and increased happiness. It is a reliable system for both indoor and outdoor environments as it does not depend upon a single factor.

A post-correlation graphical way for continuous GNSS spoofing detection

Global navigation satellite system (GNSS) receivers are under the threat of spoofing attacks. The signal quality monitoring (SQM) technique is effective in monitoring the distorted auto-correlation function (ACF). However, it has limited detection effectiveness in detecting spoofing signals that are greatly overpowered and with significant code phase shifts. This paper introduces a new metric called ACF similarity to characterize the ACF distortion and power abnormality in the tracking loop based on image feature extraction techniques. The spoofing detection algorithm and the optimal threshold utilizing the generalized likelihood ratio test (GLRT) are further developed. The performance of the proposed detector has been verified by utilizing the Texas Spoofing Test Battery (TEXBAT) dataset and Monte Carlo simulation tests. Results show that with 1% false alarm rate, the proposed detector achieves a detection probability of 87% in the TEXBAT datasets and provides better detection sensitivity than the conventional SQM metrics.

Recent Research Trend of Resistance Spot Welding Quality Monitoring Technology in Korea

Resistance spot welding is a simple welding process with high welding speeds and one of the most economical and productive welding processes. It is applied in various industries such as automobile, aircraft, and home appliance; thus, the demand for monitoring welding quality is increasing. Owing to the nature of the resistance spot welding process, there are several factors that can be monitored, such as the dynamic resistance value, which can be determined using current and voltage measurements, the displacement of welding electrodes, and the indentation that occurs after welding. These process features can be monitored using cameras, LVDT sensors, and deep learning techniques and used for predicting weld quality. In this paper, we introduced the recent trends in resistance spot welding quality monitoring techniques in Korea using various sensors and techniques.

Weld depth dynamics measured with optical coherence tomography during remote laser beam oscillation welding of battery system

This paper represents nondestructive quality monitoring technique using optical coherence tomography (OCT). It addresses online monitoring of weld depth during laser beam oscillation welding and aims at the application in joining cells in large battery assemblies. The weld depth was continuously detected with OCT while the OCT beam position was adjusted highly dynamically in accordance with the scanning optics position. By displacing the OCT measurement beam according to the current machining direction, the correlation between the position of the laser beam in an oscillating circular pattern along the circular feed direction and the periodic fluctuations of the measured weld depth was explored. It was found that the deepest part of the keyhole is located at the trailing position of the laser beam. This effect can be attributed to the large heat input due to the overlapping circular movements. The results confirm once again that instant weld depth monitoring with OCT ensures superior weld quality.

DEVELOPMENT, OPTIMIZATION AND VALIDATION OF HPLC METHOD FOR ASSAY AND DISSOLUTION TESTING OF FUROSEMIDE TABLET FORMULATION UTILIZING DESIGN OF EXPERIMENT APPROACH

A robust HPLC method was developed and validated for the quantitative estimation of furosemide tablet and dissolution testing. The optimization of chromatographic conditions (% organic phase, flow rate and pH of aqueous phase) was done using the Box-Behnken experimental design. The creation of a reliable HPLC technique utilised the design of experiments methodology effectively. The furosemide peak was found to have a Rt value of 4.86 min under the optimal HPLC settings, which included a C18 (250 mm 4.6 mm, 5 m) column and a methanol: aqueous solution of formic acid buffer (0.2 % V/V, pH 2.5) as mobile phase.The optimized method was validated according to ICH Q2(R1) guideline. Using a fractional factorial design with six chromatographic components, the robustness of the approach was evaluated. The results showed that a multivariant approach was successfully used to improve a reliable HPLC technique for everyday quality monitoring of furosemide in tablet formulations

Ecological Environment Evaluation Based on Remote Sensing Ecological Index: A Case Study in East China over the Past 20 Years

East China is one of the most active regions in terms of economic and social development, and with the accelerated urbanization process, environmental problems are becoming increasingly prominent. The objective, quantitative, and timely evaluation of spatial and temporal changes in ecological quality is of great significance for environmental protection and decision making. The remote sensing ecological index (RSEI) is an objective, fast, and easy ecological quality monitoring and evaluation technique which has been widely used in the field of ecological research, but it often involves problems of cloud occlusion and stitching difficulties when used to conduct large-scale and long-term monitoring. In this paper, based on the Google Earth Engine (GEE) platform, an RSEI was constructed using MODIS data products to evaluate the spatial and temporal changes in ecological quality in East China over the past 20 years. The study shows the following: (1) The mean RSEI values in 2000, 2005, 2010, 2015, and 2020 were 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, with the mean values first decreasing and then showing a stable increasing trend. In Shanghai and Jiangsu, the mean RSEI values show a fluctuating characteristic of “falling and then rising”, and large respective decreases of 32.4% and 25.8% throughout the monitoring period. The RSEI values in Fujian Province showed a relatively stable upward trend during the study period (19% increase). (2) The RSEI spatially correlated clustering maps of the local indicators showed that the regions with a high degree of clustering are mainly located in Quzhou City, Zhejiang Province, Ningde City, Fujian Province, and northern Anhui Province (Bozhou and Huabei). With the promotion of ecological civilization and the enhancement of environmental protection awareness, the vegetation cover has significantly increased, which has led to the rise in RSEI values. The low values are mainly distributed in densely populated areas with more human activity, such as the central-eastern part of Jiangsu Province, central Anhui Province, Shanghai, and northern Zhejiang Province. With the development of cities, impervious surfaces occupy more and more ecological land, which eventually affects the regional RSEI values. (3) This research provides a promising method for the evaluation of spatial and temporal changes in ecological environment quality based on an RSEI and GEE. The image processing, based on GEE cloud computing, can help overcome the problems of missing remote sensing data, chromatic aberrations, and spatial and temporal inconsistency, which could greatly improve the efficiency of image processing and extend the application of the remote sensing ecological index to large-scale, long-term ecological monitoring. The research results can provide a reference for improving the applicability and accuracy of remote sensing ecological indices and provide a theoretical basis for ecological conservation and land management in the context of rapid urbanization.

Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks

Fruit infected by pests or diseases and fruit harvests with different levels of ripeness cause a lack of marketability, decrease in economic value, and increase in crop waste. In this study, we propose a robust and generalized deep convolutional neural network (CNN) model via fine-tuning the pre-trained models for detecting black spot disease and ripeness levels in orange fruit. A dataset containing 1896 confirmed orange images in the farm in four classes (unripe, half-ripe, ripe, and infected with black spot disease) was used. In order to prevent overfitting and increase the robustness and generalizability of the model, instead of using fundamental data augmentation techniques, a novel learning-to-augment strategy that creates new data using noisy and restored images was employed. Controllers using the Bayesian optimization algorithm were utilized to select the optimal noise parameters of Gaussian, speckle, Poisson, and salt-and-pepper noise to generate new noisy images. A convolutional autoencoder model was developed to produce newly restored images affected by optimized noise density. The dataset augmented by the best policies of the learning-to-augment strategy was used to fine-tune several pre-trained models (GoogleNet, ResNet18, ResNet50, ShuffleNet, MobileNetv2, and DenseNet201). The results showed that the learning-to-augment strategy for the fine-tuned ResNet50 achieved the best performance with 99.5% accuracy, and 100% F-measure by assigning images infected with black spot disease as the positive class. The proposed automatic disease and fruit quality monitoring technique can be also used for the detection of other diseases in agriculture and forestry.

Quality Assessment of Ecological Environment Based on Google Earth Engine: A Case Study of the Zhoushan Islands

With the development of society, the impact of human activities on the ecological environment is becoming increasingly intense, so the dynamic monitoring of the status of the ecological environment is of great significance to the management and protection of urban ecology. As an objective and rapid ecological quality monitoring and evaluation technique, the remote sensing based ecological index (RSEI) has been widely used in the field of ecological research. Free available Landsat series data has the character of a long time series and high spatial resolution provides the possibility to conduct large-scale and long-term monitoring of ecological environment quality. Compared with traditional methods, the Google Earth Engine (GEE) platform can save a lot of time and energy in the data acquisition and preprocessing steps. To monitor the quality of the ecological environment in Zhoushan from 2000 to 2020, the GEE platform was used for cloud computing to obtain the RSEI, which can reflect the quality of the ecological environment. The results show that (1) from 2000 to 2020, the average RSEI value in Zhoushan Islands decreased from 0.748 to 0.681, indicating that the overall ecological environment exhibited a degradation trend. (2) From 2000 to 2020, the change in the area of each ecological environment level indicates that the quality of the ecological environment in Zhoushan Islands exhibited a degradation trend. The proportion of the area with an excellent eco-environment grade decreased by 13.54%, and the proportion of the area with poor and fair eco-environment grades increased by 3.43%.

Investigating dielectric spectroscopy and soft sensing for nondestructive quality assessment of engineered tissues

Non-destructive, inline quality monitoring techniques that can overcome the limitations of traditional, offline assays are essential to support the scale-up production of tissue engineered medical products (TEMP). In this work, we investigate a new soft-sensing approach with non-destructive dielectric spectroscopy (DS) that synergistically utilizes inline sensor data and predictive analytics to estimate unmeasured TEMP quality profiles. First, the performance of DS during the assessment of gelatin methacrylate (GelMA) constructs containing human adipose-derived stem cells was investigated in comparison to a traditional biochemical assay. The effects of two critical biofabrication parameters (photocrosslinking duration and volume of growth media) on a key scalar metric (Δϵ) were determined over 11 days of in vitro culture, where the metric was associated with the permittivity response of cells to alternating electric fields during DS and corresponding cellular metabolic activity. To enable accurate quality prediction while minimizing direct data collection to reduce the risk of cytotoxicity from prolonged exposure to the DS sensor electrodes and electric fields, we then developed a bilinear basis mixed model (BBMM) as a soft sensor. With comprehensive consideration of different variation sources, this model was designed to estimate missing permittivity profiles of constructs based on the measured DS dataset and biofabrication parameters. Results of benchmarking showed that BBMM outperformed state-of-the-art vector-prediction methods from literature in two different missing data estimation mechanisms. The high-accuracy BBMM provides a novel DS-driven soft sensing system as an inline monitoring tool suitable for scaled-up or scaled-out TEMP production systems.