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Keywords = industrial standards

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11 pages, 1698 KiB  
Article
Quantifying Fermentable Sugars in Beer: Development and Validation of a Reliable HPLC-ELSD Method
by Pedro F. Lopes, Fábio B. Oliveira and Luis F. Guido
Appl. Sci. 2025, 15(12), 6412; https://doi.org/10.3390/app15126412 (registering DOI) - 6 Jun 2025
Abstract
A high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) method was developed and validated for analyzing fermentable and reducing sugars in brewing matrices. The method exhibited detection limits of 2.5–12.5 mg/L and quantification limits of 12.0–30.0 mg/L. Linearity was achieved for all [...] Read more.
A high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) method was developed and validated for analyzing fermentable and reducing sugars in brewing matrices. The method exhibited detection limits of 2.5–12.5 mg/L and quantification limits of 12.0–30.0 mg/L. Linearity was achieved for all sugars, fitted with a quadratic calibration model (R2 = 0.9998). Precision metrics revealed relative standard deviations (RSDs) below 2% for repeatability and below 6% for intermediate precision. Recovery rates between 86 and 119% confirmed robustness and minimal matrix interference. Application to brewing samples highlighted variability in sugar profiles, with sucrose concentrations in wort ranging from 3.5 to 22.0 g/L and maltose and maltotriose in finished beers between 0.80 and 1.50 g/L and 1.10–2.50 g/L, respectively. Batch variability analysis showed that brewing conditions had a greater impact on sugar concentrations than malt batch origin, with maltose variation reaching 34.6%. This HPLC-ELSD method provides a robust and reliable tool for sugar analysis in brewing, offering valuable insights into fermentation dynamics and batch consistency. Its application to industrial contexts underscores its potential for improving quality control and optimizing brewing processes. Full article
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22 pages, 3049 KiB  
Article
A Monographic Experimental Investigation into Flood Discharge Atomized Raindrop Size Distributions Under Low Ambient Pressure Conditions
by Dan Liu, Jijian Lian, Dongming Liu, Fang Liu, Bin Ma, Jizhong Shi, Linlin Yan, Yongsheng Zheng, Cundong Xu and Jinxin Zhang
Water 2025, 17(12), 1721; https://doi.org/10.3390/w17121721 - 6 Jun 2025
Abstract
The construction and operation of high dam projects at high altitudes have led to concerns about the effectiveness of flood discharge security predictions resulting from the greater flood discharge atomized rain caused by ambient pressure reduction. In this study, self-similar characteristics and variation [...] Read more.
The construction and operation of high dam projects at high altitudes have led to concerns about the effectiveness of flood discharge security predictions resulting from the greater flood discharge atomized rain caused by ambient pressure reduction. In this study, self-similar characteristics and variation in atomized raindrop size distributions are analyzed to understand the phenomenon of increased atomized rain intensity under low ambient pressure from a mesoscopic scale. The monographic experiments are characterized by a low ambient pressure range (0.66P0–1.02P0) and a high waterjet velocity range (13.89–15.74 m/s). When the ambient pressure decreases by 0.10P0 (P0 = 101.325 kPa) from the reference atmospheric pressure condition as the other conditions remain fixed, the total number concentration in a two-dimensional atomized raindrop spectrum (number/(54 cm2)) and the peak value of the individual three-dimensional number concentration (number/(m3·mm) increase, which can lead to the required industry standard protective level of atomized zones increasing by one level in some cases. In addition, the spectrum trend and typical particle size ranges of the atomized raindrop size distributions present self-similarity as the ambient pressure decreases. The above studies further confirm the effects of low-ambient pressure enhancement on flood discharge atomized rain intensity, which can provide a theoretical basis for the development of random splash simulation models characterized by low pressure for high-altitude hydropower stations. Full article
(This article belongs to the Topic Advances in Environmental Hydraulics)
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23 pages, 1261 KiB  
Article
Risk Management Practices in the Purchasing System of an Automotive Company
by Anabela Tereso, Cláudia Santos and João Faria
Systems 2025, 13(6), 444; https://doi.org/10.3390/systems13060444 - 6 Jun 2025
Abstract
This paper presents the results of a case study conducted in the purchasing department of Bosch Car Multimedia Portugal, aiming to analyze and improve risk management practices within its project environment. Projects in this department are characterized by high complexity and uncertainty, making [...] Read more.
This paper presents the results of a case study conducted in the purchasing department of Bosch Car Multimedia Portugal, aiming to analyze and improve risk management practices within its project environment. Projects in this department are characterized by high complexity and uncertainty, making effective risk management essential. The study adopts a multi-method qualitative approach, integrating document analysis, direct observation, semi-structured interviews, and questionnaires. A comprehensive literature review established the theoretical foundation and guided the identification of best practices in project risk management. The field research revealed significant gaps in the structuring, standardization, and cultural integration of risk management processes. A comparative analysis between theoretical models and current practices led to the development of a tailored risk management framework, including a practical good-practices manual and a workshop format designed to promote internal engagement and capacity-building. This work contributes both theoretically—by validating literature-based models in an industrial setting—and practically, by offering replicable tools for similar departments in the automotive sector. The findings highlight the necessity of fostering a proactive risk culture to ensure the sustained implementation and effectiveness of the proposed measures.  Full article
29 pages, 17942 KiB  
Review
Bibliometric Analysis of Coating Protection from 2015 to 2025
by Yin Hu, Tianyao Hong, Sheng Zhou, Yangrui Wang, Qihang Ye, Shiyu Sheng, Shifang Wang, Chuang He, Haijie He and Minjie Xu
Coatings 2025, 15(6), 686; https://doi.org/10.3390/coatings15060686 - 6 Jun 2025
Abstract
Composite protective coatings are critical for material durability but face challenges like fragmented knowledge and scalability issues. Existing research lacks the systematic integration of nanomaterial properties with macroscale performance and standardized evaluation protocols for hybrid systems. This study uses CiteSpace to analyze 18,363 [...] Read more.
Composite protective coatings are critical for material durability but face challenges like fragmented knowledge and scalability issues. Existing research lacks the systematic integration of nanomaterial properties with macroscale performance and standardized evaluation protocols for hybrid systems. This study uses CiteSpace to analyze 18,363 publications (2015–2025) from Web of Science, visualizing collaborative networks, keyword clusters, and citation bursts. China leads global research output (8508 publications), with the USA and India following, while materials science, chemistry, and physics dominate disciplines. Key themes include nanocomposite coatings (e.g., graphene oxide, MXene), corrosion resistance mechanisms, and sustainable technologies, with citation bursts highlighting nanocomposites and surface functionalization. The study reveals interdisciplinary synergies in 2D nanomaterial-polymer systems, thereby improving barrier properties and enabling stimuli-responsive inhibitor release, yet it identifies gaps in lifecycle sustainability and industrial scalability. By constructing a holistic knowledge framework, this work bridges theory and application, quantifying interdisciplinary linkages and pinpointing frontiers like smart, multifunctional coatings. This study integrates data-driven insights to facilitate cross-sector collaboration. It delivers a strategic framework to tackle global challenges in material durability, sustainability, and practical application. Full article
(This article belongs to the Special Issue Advances in Corrosion Behaviors and Protection of Coatings)
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17 pages, 18361 KiB  
Article
A Comprehensive Safety Assessment of Ralstonia eutropha H16 for Food Applications: Integrating Genomic, Phenotypic, and Toxicological Analyzes
by Xiaoyan You, Shuxia Song, Bing Li, Hui Wang, Le Zhang, Xiangyang Li, Junliang Chen, Zhiguang Zhu and Guoping Zhao
Microorganisms 2025, 13(6), 1323; https://doi.org/10.3390/microorganisms13061323 - 6 Jun 2025
Abstract
Ralstonia eutropha H16, a metabolically versatile bacterium, has gained prominence as a microbial platform for sustainable bioproduction. While its capabilities in synthesizing single-cell proteins and biodegradable materials are well documented, comprehensive strain-level safety evaluations remain insufficient for food-grade applications. This study systematically assessed [...] Read more.
Ralstonia eutropha H16, a metabolically versatile bacterium, has gained prominence as a microbial platform for sustainable bioproduction. While its capabilities in synthesizing single-cell proteins and biodegradable materials are well documented, comprehensive strain-level safety evaluations remain insufficient for food-grade applications. This study systematically assessed the safety of R. eutropha H16 through genomic, phenotypic, and toxicological analyzes. Genomic analyzes revealed the absence or minimal presence of virulence factors and antibiotic resistance genes, aligning with microbiological safety standards. Phenotypic investigations demonstrated a limited gastric fluid tolerance (pH 2.5, survival rate 25.70% after 3 h) and intestinal fluid persistence (pH 8, 44.67% viability after 3 h), coupled with an exceptional bile salt tolerance (0.2% w/v). Antioxidant assays confirmed the fermentation broth specifically scavenges DPPH free radicals (14.60 ± 1.24 μg Trolox/mL), whereas bacterial suspensions and cell-free supernatants exhibited a strong hydroxyl radical scavenging (>90 U/mL) and superoxide anion inhibition (>100 U/L). Acute toxicity testing indicated no mortality or histopathological abnormalities, with an LD50 value exceeding 1 × 10¹¹ CFU/kg. Subacute toxicity studies (28-day, 1 × 108–1 × 1010 CFU/kg) revealed no significant effects on growth, hematology, or organ function. Minor alterations in serum biochemistry might be attributed to physiological adaptation. Subacute exposure induced transient serum ALT fluctuations without hepatorenal dysfunction, while maintaining hematological parameters within physiological ranges. Collectively, these results substantiate the safety of R. eutropha H16 for food-related applications while underscoring the necessity of strain-specific risk assessments for industrial microbial platforms. Full article
(This article belongs to the Section Food Microbiology)
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16 pages, 11515 KiB  
Article
Real-Time Detection of Critical Moisture Levels in Fluidized Bed Drying Using Spectral Analysis
by Matheus Boeira Braga, Carlos Adriano Moreira da Silva, Kaciane Andreola, José Junior Butzge, Osvaldir Pereira Taranto and Carlos Alexandre Moreira da Silva
Powders 2025, 4(2), 16; https://doi.org/10.3390/powders4020016 - 6 Jun 2025
Abstract
The drying process of microcrystalline cellulose and adipic acid particles in a cylindrical fluidized bed was investigated using the Gaussian spectral technique to monitor fluid–dynamic regime transitions associated with surface moisture loss. Pressure fluctuation signals were recorded and analyzed to assess hydrodynamic behavior. [...] Read more.
The drying process of microcrystalline cellulose and adipic acid particles in a cylindrical fluidized bed was investigated using the Gaussian spectral technique to monitor fluid–dynamic regime transitions associated with surface moisture loss. Pressure fluctuation signals were recorded and analyzed to assess hydrodynamic behavior. Excess moisture significantly alters the bubbling characteristics of the bed, leading to instability in the fluidization regime. The results demonstrated that the Gaussian spectral technique effectively captured these hydrodynamic changes, particularly at the critical moisture content threshold, when compared with the drying rate curves of the materials. For microcrystalline cellulose and adipic acid particles, it is reasonable to conclude that a mean central frequency above 5.75–6.0 Hz and a standard deviation exceeding 3.7–3.8 Hz correspond to a bubbling regime, indicating that the critical drying point has been reached. This approach provides a non-intrusive and sensitive method for identifying transitions in the drying process, offering a valuable tool for real-time monitoring and control. The ability to track fluidization regime changes with high precision reinforces the potential of this technique for optimizing drying operations in the pharmaceutical, food, and chemical industries. Full article
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24 pages, 4055 KiB  
Article
Privacy-Preserving Interpretability: An Explainable Federated Learning Model for Predictive Maintenance in Sustainable Manufacturing and Industry 4.0
by Hamad Mohamed Hamdan Alzari Alshkeili, Saif Jasim Almheiri and Muhammad Adnan Khan
AI 2025, 6(6), 117; https://doi.org/10.3390/ai6060117 - 6 Jun 2025
Abstract
Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about [...] Read more.
Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about security and regulatory compliance; (2) a lack of interpretability, where opaque AI models provide limited transparency, making it difficult for operators to trust and act on failure predictions; and (3) adaptability issues, as many existing solutions struggle to maintain a consistent performance across diverse industrial environments. Addressing these challenges requires a privacy-preserving, interpretable, and adaptive Artificial Intelligence (AI) model that ensures secure, reliable, and transparent PdM while meeting industry standards and regulatory requirements. Methods: Explainable AI (XAI) plays a crucial role in enhancing transparency and trust in PdM models by providing interpretable insights into failure predictions. Meanwhile, Federated Learning (FL) ensures privacy-preserving, decentralized model training, allowing multiple industrial sites to collaborate without sharing sensitive operational data. This proposed research developed a sustainable privacy-preserving Explainable FL (XFL) model that integrates XAI techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) into an FL structure to improve PdM’s security and interpretability capabilities. Results: The proposed XFL model enables industrial operators to interpret, validate, and refine AI-driven maintenance strategies while ensuring data privacy, accuracy, and regulatory compliance. Conclusions: This model significantly improves failure prediction, reduces unplanned downtime, and strengthens trust in AI-driven decision-making. The simulation results confirm its high reliability, achieving 98.15% accuracy with a minimal 1.85% miss rate, demonstrating its effectiveness as a scalable, secure, and interpretable solution for PdM in Industry 4.0. Full article
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11 pages, 2324 KiB  
Proceeding Paper
Development of Autonomous Unmanned Aerial Vehicle for Environmental Protection Using YOLO V3
by Vijayaraja Loganathan, Dhanasekar Ravikumar, Maniyas Philominal Manibha, Rupa Kesavan, Gokul Raj Kusala Kumar and Sarath Sasikumar
Eng. Proc. 2025, 87(1), 72; https://doi.org/10.3390/engproc2025087072 - 6 Jun 2025
Abstract
Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a [...] Read more.
Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a single UAV model, it can be used for various purposes as per the user’s requirement. The UAVs are used for faster communication of identified information, entry through the critical atmospheres, and causing no harm to humans before entering a collapsed path. In relation to the above discussion, a UAV system is designed to classify and transmit information about the atmospheric conditions of the environment to a central controller. The UAV is equipped with advanced sensors that are capable of detecting air pollutants such as carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), ammonia (NH3), hydrogen sulfide (H2S), etc. These sensors present in the UAV model monitor the quality of air, time-to-time, as the UAV navigates through different areas and transmits real-time data regarding the air quality to a central unit; this data includes detailed information on the concentrations of different pollutants. The central unit analyzes the data that are captured by the sensor and checks whether the quality of air meets the atmospheric standards. If the sensed levels of pollutants exceed the thresholds, then the system present in the UAV triggers a warning alert; this alert is communicated to local authorities and the public to take necessary precautions. The developed UAV is furnished with cameras which are used to capture real-time images of the environment and it is processed using the YOLO V3 algorithm. Here, the YOLO V3 algorithm is defined to identify the context and source of pollution, such as identifying industrial activities, traffic congestion, or natural sources like wildfires. Full article
(This article belongs to the Proceedings of The 5th International Electronic Conference on Applied Sciences)
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19 pages, 8131 KiB  
Article
Life Cycle Carbon Footprint of Indonesian Refined Palm Oil and Its Embodied Emissions in Global Trade
by Hanlei Wang, Xia Li, Mingxing Sun, Yulei Xie and Hui Li
Land 2025, 14(6), 1223; https://doi.org/10.3390/land14061223 - 6 Jun 2025
Abstract
Indonesia plays a dominant role in the global refined palm oil (RPO) supply chain. Given the increasing global emphasis on carbon neutrality and sustainable trade, understanding the carbon footprint of Indonesian RPO and its embodied carbon emissions (ECE) in global trade is essential [...] Read more.
Indonesia plays a dominant role in the global refined palm oil (RPO) supply chain. Given the increasing global emphasis on carbon neutrality and sustainable trade, understanding the carbon footprint of Indonesian RPO and its embodied carbon emissions (ECE) in global trade is essential for identifying mitigation opportunities and aligning with international sustainability standards. This study integrates life cycle assessment and trade data to quantify the carbon footprint of RPO products and analyze the spatiotemporal patterns of trade-related ECE. Results show that producing 1 ton of RPO emits 2196.84 kg CO2e, with wastewater treatment (57.67%) and land use change (32.82%) as the main contributors. From 2010 to 2022, ECE induced by RPO exports rose from 35.79 Mt CO2e to 54.94 Mt CO2e (3.64% annual growth). Major ECE importers were India, China, and Pakistan, accounting for 20.36%, 14.29%, and 11.45% of Indonesia’s total trade-related ECE, respectively. Comprehensive sensitivity and uncertainty analyses conducted on key parameters confirmed the robustness of the above results. Based on these robust findings, integrated mitigation strategies targeting both production optimization and sustainable trade mechanisms are proposed to accelerate Indonesia’s RPO industry decarbonization. Full article
(This article belongs to the Section Land–Climate Interactions)
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20 pages, 2022 KiB  
Article
Prediction of Expected Fouling Time During Transmembrane Transition in Reverse Osmosis Systems
by Jozsef Lakner and Gabor Lakner
Membranes 2025, 15(6), 170; https://doi.org/10.3390/membranes15060170 - 6 Jun 2025
Abstract
Membrane filtration, including reverse osmosis filtration, is widely applied in water treatment worldwide, offering solutions to a broad range of separation challenges. However, due to the porous structure of membranes, they are prone to fouling, which reduces their efficiency and can eventually render [...] Read more.
Membrane filtration, including reverse osmosis filtration, is widely applied in water treatment worldwide, offering solutions to a broad range of separation challenges. However, due to the porous structure of membranes, they are prone to fouling, which reduces their efficiency and can eventually render the membranes incapable of functioning. In such cases, a systemic intervention becomes necessary, highlighting the importance of accurately predicting the expected fouling time. Various approaches for estimating fouling processes and times are well documented in the literature. However, a common limitation of these methods is that they typically assume constant and well-defined operating parameters over time. Under such stable conditions, the process can be described deterministically, and the fouling time can be predicted using straightforward extrapolation techniques. However, in industrial practice, process conditions often fluctuate due to multiple influencing factors, making fouling time a variable quantity. Therefore, it can be more appropriately treated as a random variable characterized by a mean value and standard deviation. Rather than predicting a precise fouling time, it is more relevant to define a probabilistic interval within which the fouling is expected to occur with a specified confidence level (e.g., 95%). The associated maintenance scheduling can then be optimized based on economic criteria. The probability-based model presented herein defines this interval based on operational measurements, thereby providing users with a time window during which maintenance should be planned. From this point forward, the exact timing of interventions becomes a matter of technical feasibility and economic optimization. Full article
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15 pages, 5492 KiB  
Review
Secure and Trusted Crowdsensing for Outdoor Air Quality Monitoring: State of the Art and Perspectives
by Claudio Marche, Emmanuele Massidda, Alessandro Sanna, Gianmarco Angius, Michele Nitti, Davide Maiorca and Stefano Lai
Sensors 2025, 25(12), 3573; https://doi.org/10.3390/s25123573 - 6 Jun 2025
Abstract
Air pollution is a major problem in the modern world; although it particularly impacts developing countries, which are experiencing fast and often uncontrolled industrialization, its effects constitute a global burden on the environment and health. At the same time, the costs of effective [...] Read more.
Air pollution is a major problem in the modern world; although it particularly impacts developing countries, which are experiencing fast and often uncontrolled industrialization, its effects constitute a global burden on the environment and health. At the same time, the costs of effective air quality monitoring programs are prohibitive for emerging economies, thus making any correction difficult to assess. Emerging technologies, such as distributed networks of sensors organized in the Internet of Things, are under the lens of scientific and industrial communities as a valuable, low-cost alternative to standard techniques. In this paper, we report a review of current approaches to distributed air quality monitoring. Specifically, we (1) emphasize the role of crowdsensing in leveraging sensor-enabled mobile devices for large-scale environmental data collection and (2) discuss criticalities, open challenges, and future perspectives in enforcing data security when such approaches are deployed in real application scenarios. Full article
(This article belongs to the Section Physical Sensors)
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24 pages, 3483 KiB  
Article
Exploring the Potential of Wi-Fi in Industrial Environments: A Comparative Performance Analysis of IEEE 802.11 Standards
by Luis M. Bartolín-Arnau, Federico Orozco-Santos, Víctor Sempere-Payá, Javier Silvestre-Blanes, Teresa Albero-Albero and David Llacer-Garcia
Telecom 2025, 6(2), 40; https://doi.org/10.3390/telecom6020040 - 5 Jun 2025
Abstract
The advent of Industry 4.0 brought about digitalisation and the integration of advanced technologies into industrial processes, with wireless networks emerging as a key enabler in the interconnection of smart devices, cyber–physical systems, and data analytics platforms. With the development of Industry 5.0 [...] Read more.
The advent of Industry 4.0 brought about digitalisation and the integration of advanced technologies into industrial processes, with wireless networks emerging as a key enabler in the interconnection of smart devices, cyber–physical systems, and data analytics platforms. With the development of Industry 5.0 and its emphasis on human–machine collaboration, Wi-Fi has positioned itself as a viable alternative for industrial wireless connectivity, supporting seamless communication between robots, automation systems, and human operators. However, its adoption in critical applications remains limited due to persistent concerns over latency, reliability, and interference in shared-spectrum environments. This study evaluates the practical performance of Wi-Fi standards from 802.11n (Wi-Fi 4) to 802.11be (Wi-Fi 7) across three representative environments: residential, laboratory, and industrial. Six configurations were tested under consistent conditions, covering various frequency bands, channel widths, and traffic types. Results prove that Wi-Fi 6/6E delivers the best overall performance, particularly in low-interference 6 GHz scenarios. Wi-Fi 5 performs well in medium-range settings but is more sensitive to congestion, while Wi-Fi 4 consistently underperforms. Early Wi-Fi 7 hardware does not yet surpass Wi-Fi 6/6E consistently, reflecting its ongoing development. Despite these variations, the progression observed across generations clearly demonstrates incremental gains in throughput stability and latency control. While these improvements already provide tangible benefits for many industrial communication scenarios, the most significant leap in industrial applicability is expected to come from the effective implementation of high-efficiency mechanisms. These include OFDMA, TWT, scheduled uplink access, and enhanced QoS features. These capabilities, already embedded in the Wi-Fi 6 and 7 standards, represent the necessary foundation to move beyond conventional best-effort connectivity and toward supporting critical, latency-sensitive industrial applications. Full article
20 pages, 4598 KiB  
Article
Feature Decoupling-Guided Annotation Framework for Surface Defects on Steel Strips
by Weiqi Yuan and Wentao Liu
Electronics 2025, 14(11), 2304; https://doi.org/10.3390/electronics14112304 - 5 Jun 2025
Abstract
Surface defect detection on steel strips is a critical step in quality control for industrial products. While existing research has made some progress in optimizing annotation strategies and improving efficiency, issues such as feature aliasing during the annotation process, the insufficient utilization of [...] Read more.
Surface defect detection on steel strips is a critical step in quality control for industrial products. While existing research has made some progress in optimizing annotation strategies and improving efficiency, issues such as feature aliasing during the annotation process, the insufficient utilization of boundary information, and the inaccurate representation of complex defect patterns remain inadequately addressed. To tackle these challenges, this paper proposes an annotation optimization framework from the perspective of feature analysis. The framework decomposes defect features into geometric features and grayscale distribution features and, based on feature decoupling theory, classifies defects into three typical patterns: block, linear, and textured defects. For each pattern, the minimum annotation units that preserved essential features were designed, enabling the standardized representation of complex defects and precise boundary localization. Experiments on the NEU-DET dataset showed that this annotation framework improves the average mAP of six mainstream detection models by 4.9 percentage points, validating its effectiveness in enhancing the detection performance. Additionally, this paper introduces an Efficiency–Cost Ratio (ECR) evaluation metric to quantify the relationship between the annotation cost and performance improvement. The study found that block and linear defect detection achieved optimal performance with only 50% annotation effort. This research not only improved the performance of defect detection models but also quantified the annotation resource utilization efficiency, providing robust theoretical support and practical guidance for efficient defect detection in complex industrial scenarios. Full article
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38 pages, 1026 KiB  
Review
Smart Fermentation Technologies: Microbial Process Control in Traditional Fermented Foods
by Chong Shin Yee, Nur Asyiqin Zahia-Azizan, Muhamad Hafiz Abd Rahim, Nurul Aqilah Mohd Zaini, Raja Balqis Raja-Razali, Muhammad Ameer Ushidee-Radzi, Zul Ilham and Wan Abd Al Qadr Imad Wan-Mohtar
Fermentation 2025, 11(6), 323; https://doi.org/10.3390/fermentation11060323 - 5 Jun 2025
Abstract
Traditional fermented foods are appreciated worldwide for their cultural significance and health-promoting properties. However, traditional fermentation production suffers from many obstacles such as microbial variability, varying quality, and lack of scalability. The implementation of smart fermentation technologies, including biosensors, the Internet of Things [...] Read more.
Traditional fermented foods are appreciated worldwide for their cultural significance and health-promoting properties. However, traditional fermentation production suffers from many obstacles such as microbial variability, varying quality, and lack of scalability. The implementation of smart fermentation technologies, including biosensors, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), hold the key to the optimization of microbial process control, enhance product consistency, and improve production efficiency. This review summarizes modern developments in real-time microbial monitoring, IoT, AI, and ML tailored to traditional fermented foods. Despite significant technical advancements, challenges related to high costs, the absence of standardized frameworks, and access restrictions for small producers remain substantial limitations. This review proposed a future direction prioritizing modular, scalable solutions, open-source innovation, and environmental sustainability. In alignment with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), smart fermentation technologies advance sustainable industry through innovation and serve as a critical bridge between traditional craftsmanship and Industry 4.0, fostering inclusive development while preserving microbial biodiversity and cultural heritage. Full article
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17 pages, 891 KiB  
Article
Volatile Profiling of Tongcheng Xiaohua Tea from Different Geographical Origins: A Multimethod Investigation Using Sensory Analysis, E-Nose, HS-SPME-GC-MS, and Chemometrics
by Ge Jin, Chenyue Bi, Anqi Ji, Jieyi Hu, Yuanrong Zhang, Lumin Yang, Sunhao Wu, Zhaoyang Shen, Zhou Zhou, Xiao Li, Huaguang Qin, Dan Mu, Ruyan Hou and Yan Wu
Foods 2025, 14(11), 1996; https://doi.org/10.3390/foods14111996 - 5 Jun 2025
Abstract
The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with [...] Read more.
The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and β-ionone driving aroma profiles. The partial least squares–discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which α-pinene and β-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry. Full article
(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)
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