Quality Control Monitoring Research Articles

Artificial intelligence-driven tool for spectral analysis: identifying pesticide contamination in bees from reflectance profiling

Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides—dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)—as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98% accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27seconds. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Cu2+-mediated telomeric dimeric G-quadruplex DNAzyme for highly sensitive colorimetric detection of deferasirox

Deferasirox (DEF) is an important iron chelator for treatment of iron overload–related diseases. Monitoring DEF concentration in human serum will provide some valuable information for clinical diagnosis and therapy of such diseases. In this study, we developed a peroxidase-mimicking colorimetric sensor for the detection of DEF by simple assembly of a telomeric dimeric G-quadruplex DNAzyme with Cu2+. The DNAzyme–catalyzed oxidation of 3,3′,5,5′-tetramethylbenzidine in the presence of H2O2 can generate a quantitative colorimetric signal, and the color change can be discerned by the naked eye. Compared with the reaction rate of the monomeric G-quadruplex–Cu2+ DNAzyme, the reaction rate of the dimeric G-quadruplex–Cu2+ (G2–Cu2+) DNAzyme is significantly accelerated, and the reaction rate gradually increases and then reaches a plateau with increasing number of TTA spacers. Herein, the G2–Cu2+ DNAzyme is chosen for the highly sensitive detection of DEF based on the DEF–Cu2+ complex–induced inhibition of its peroxidase-mimicking activities. The limit of detection (LOD) of DEF is achieved as low as 0.03 μM, and the linear range is from 0.05 to 1.2 μM. The proposed strategy exhibits excellent selectivity in the presence of potential interferents, such as metal ions and small molecules. Importantly, the G2–Cu2+ DNAzyme is further expanded to detect DEF in dispersible tablets and human serum samples. Overall, this G2–Cu2+ DNAzyme provides a simple, low-cost, and rapid platform for DEF detection. This novel strategy is the first example of DEF analysis by utilizing signal amplification technology based on the G-quadruplex DNAzyme and holds great potential for DEF quality control and therapeutic drug monitoring.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.

Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning

Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning

A unique and convenient electrochemical biosensor for sodium based on a Na+-pumping rhodopsin

Sodium ion (Na+) detection plays a crucial role in various fields, including clinical diagnostics, environmental monitoring, and food quality control. However, existing techniques often lack sensitivity, selectivity, or practicality. This study presents a novel protein-based electrochemical biosensor for selective and quantitative Na+ detection, utilizing the microbial rhodopsin Nonlabens dokdonensis rhodopsin 2 (NdR2) reconstituted into liposome nanoparticles and immobilized on indium tin oxide (ITO) electrodes. Upon light illumination, the biosensor generates photocurrents proportional to the Na+ concentration in the sample. Extensive characterization unveiled the biosensor’s remarkable selectivity towards Na+ over other cationic species, including K+, Ca2+, Mg2+, and Cs+. Quantitative analysis revealed two linear response ranges: 2–50 mM and 50–200 mM, with a detection limit of 75 μM. The biosensor demonstrated exceptional thermal stability, withstanding 50 °C for 90 min with minimal signal deterioration. Long-term storage stability tests further highlighted its robustness, with consistent Na+ response for up to 17 days. Furthermore, the biosensor successfully detected Na+ in fetal bovine serum, although with a distinct photocurrent profile potentially influenced by interfering components. This pioneering work introduces a highly selective, sensitive, and stable protein-based biosensor for Na+ detection, offering promising implications for diverse applications in clinical diagnostics, environmental monitoring, and quality control, while paving the way for further advancements in biosensing technologies.

Field-Applicable Loop-Mediated Isothermal Amplification for the Detection of Seven Common Human Papillomavirus Subtypes.

Persistent HPV infection is a major risk factor for the subsequent development of cervical cancer. LAMP is simple and suitable for field detection in the resource-limited settings. In this study, hydroxy naphthol blue (HNB)-based visual LAMP and evagreen-based fluorescent LAMP coupled with a microfluidic chip (LAMP-chip) were established for the field detection of seven subtypes of HPV. The analytical sensitivity was 19-233 copies/reaction. The overall clinical sensitivity was 97.35% for visual LAMP and 98.23% for LAMP-chip. Both LAMP assays exhibited 100% specificity and were completed in less than 50 min. Additionally, both assays did not require complicated nucleic acid extraction and purification steps. A complete quality control monitoring system (including internal control, positive quality control and negative control) in the LAMP assays further ensured the credibility of the results. Our findings demonstrated that the proposed LAMP assays have the potential to be applied in the testing of common HPV DNA in field investigations (visual LAMP) or within communities and primary health centers (LAMP-chip).

Room-temperature self-calibrating sensor based on CsPbBr3/SnO2 for detecting low-concentration sulfur dioxide

Sulfur dioxide (SO2) is a colorless toxic gas, and accurate monitoring of its concentration is important for safety. However, accuracy is often interfered by humidity. To address the issues, in this work, a self-calibrating SO2 sensor based on CsPbBr3/SnO2 was prepared, the sensor exhibited an excellent response to parts per billion (ppb) levels of SO2 at room temperature, which is rarely achieved by other SO2 sensors. The response sensitivity of the sensor to 600 ppb SO2 reached up to 0.53 with response/recovery times of 17/285 s. In addition, the CsPbBr3/SnO2-based SO2 gas sensor showed a low detection limit, good reproducibility, and high selectivity. Moreover, theoretical calculations show that the gas recognition mechanism is attributed to the significant changes in the band structure and density of states of the heterojunction materials CsPbBr3/SnO2 after gas adsorption, thereby altering the charge carrier transport behavior and electrical properties. A self-calibrating algorithm MobileNetV2 was employed to eliminate humidity interference and effectively identify low-concentration SO2 in humid environments, and the recognition accuracy rate is 0.85. This work demonstrates the production of a high-performance SO2 gas sensor, and provides an effective method to eliminate the interference of humidity, making it significant in environmental monitoring and air quality control.

Nanoclay composites in electrochemical sensors

Nanoclays are layered structures in the nanoscale range with widespread application due to their unique properties such as swelling, cation exchange capacity, and ease of functionalisation using metals, metal oxides, and organic compounds such as carbon paste, polymers, and other biomolecules that form nanoclay composites. Nanoclay functionalisation with silver (Ag), zinc oxide (ZnO), and bimetallic silver–gold (Ag–Au) using hydrophilic and hydrophobic clays is here evaluated and discussed. The composites’ synthesis and morphological, crystallinity, and electroactive properties in comparison with pure nanoclay are also assessed. The layered structure and crystallinity of all these nanoclay composites were slightly changed. The clumped layered structures on the surface of the nanocomposites had dispersed white spots that indicated possible surface modification. The nano-films of the composites’ electroactivity were comparatively high, as seen from the increase in current in the cyclic voltammetry characterisation voltammograms and the differential pulse voltammograms of the pharmaceutical detection. Efavirenz, nevirapine, and zidovudine detection was improved by modification of the nanocomposite with human serum albumin (HSA), as shown by the higher current, thus indicating improved conductivity of the composites compared to the pure nanoclays. Applying HAS-modified nanocomposites in the analysis of efavirenz, nevirapine, and zidovudine on a glassy carbon electrode (GCE) showed good linearity and acceptable detection limits comparable to those of previous studies. Therefore, it has potential for application in pharmaceutical quality control and environmental monitoring.

Graphene oxide functionalized with silatrane as probe for electrochemical recognition of acetaminophen in pharmaceuticals tablets

Acetaminophen, commonly known as paracetamol, is a widely used analgesic and antipyretic drug that is frequently found in over-the-counter and prescription medications. Due to its extensive use, precise monitoring of acetaminophen levels is crucial for ensuring patient safety, especially to prevent accidental overdoses which can lead to severe liver damage. In this study, a novel electrochemical sensor based on graphene oxide (GO) functionalized with silatrane was developed for the detection of acetaminophen. The hybrid material was synthesized and electrodeposited on a glassy carbon electrode, providing a highly selective, sensitive, and reproducible platform for acetaminophen detection. The sensor exhibited a linear detection range from 2.5 to 100 μM with a limit of detection of 84.8 nM. The effectiveness of this GO-based sensing platform was demonstrated through the successful quantification of acetaminophen in pharmaceutical tablets such as Dolo 650, Paracip, and Crocin. The results highlight the potential application of this sensor in quality control and safety monitoring of pharmaceutical formulations, ensuring accurate determination of acetaminophen content.

Sorption-spectrophotometric determination of bismuth ion (III) using immobilised xylenol orange on modified polyacrylonitrile

ABSTRACT This study presents a new sorption-spectrophotometric method for determining bismuth (III) ions using a new adsorbent for PPM-1 fibre immobilised with xylenol orange. The complex exhibited a maximum reflectance at 550 nm and absorption at 530 nm, indicating stable formation. The method demonstrated high sensitivity with a detection limit of 0.1 µg/mL and high selectivity, minimising interference from other metal ions. Optimal pH for complex formation was 1.37, with faster response and higher sensitivity in the immobilised state (detection limit 0.12 µg/mL, formation time 10 minutes). The complex adhered to the Bouguer-Lambert-Beer law within 3–42 µg/25 mL. Interferences from Cu2+, Ag+, Pd2+, and Fe3+ were reduced when the reagent was immobilised. Conductometric analysis confirmed the stable formation of the complex. Validation using model mixtures and pharmaceutical preparations showed accurate bismuth determination with an error margin of 0.005. For example, a mixture containing 25 µg of bismuth with zinc and cadmium yielded 25.20 µg, and De-nol had a found content of 49.50 µg for 50.00 µg added. IR spectroscopy confirmed complex formation with significant shifts in absorption bands. SEM and EDS analysis validated the reagent’s effective immobilisation and interaction with bismuth ions. The method provides a rapid, accurate, and environmentally friendly approach for bismuth analysis, demonstrating high reproducibility, sensitivity, and selectivity, suitable for environmental monitoring and pharmaceutical quality control.

Multi-Residue Analysis of Thyreostats in Animal Muscle Tissues by Hydrophilic Interaction Liquid Chromatography Tandem Mass Spectrometry: A Thorough Chromatographic Study

Τhyreostats (TSs) are veterinary drugs used in livestock farming for fattening. Their administration is banned in the European Union since 1981, and their monitoring for food quality and safety control requires sensitive and confirmatory methods. The present study describes the development and validation of a hydrophilic interaction liquid chromatography tandem mass spectrometry (HILIC-MS/MS) method for the simultaneous determination of 2-thiouracil (TU), 6-methyl-2-thiouracil (MTU), 6-propyl-2-thiouracil (PTU), 6-phenyl-2-thiouracil (PhTU), tapazole (TAP), and 2-mercaptobenzimidazole (MBI) in bovine muscle tissues. Investigation of the retention mechanism of the six analytes on the selected amide-based stationary phase showed that hydrophilic partition was the dominant interaction. The sample preparation included extraction with ACN/H2O (80/20), followed by dispersive solid-phase extraction (d-SPE) with C18 sorbent and hexane partitioning. The method was validated according to European guidelines using internal standards, including isotopically labelled ones. The method’s LODs ranged between 2.8 ng g−1 (6-phenyl-2-thiouracil) and 4.1 ng g−1 (2-thiouracil). Application of the proposed method to 48 bovine tissue samples showed non-detectable results.

Signal-enhanced electrochemical sensor employing MWCNTs/CMK-3/AuNPs and Au@Pd core-shell structure for sensitive determinationof AFB1 in complex matrix.

A sandwich electrochemical sensor was fabricated based on multi-walled carbon nanotubes/ordered mesoporous carbon/AuNP (MWCNTs/CMK-3/AuNP) nanocomposites and porous core-shell nanoparticles Au@PdNPs to achieve rapid and sensitive detection of AFB1 in complex matrices. MWCNTs/CMK-3/AuNP nanocomposite, which was prepared by self-assembly method, served as a substrate material to increase the aptamer loading and improve the conductivity and electrocatalytic activity of the electrode for the first signal amplification. Then, Au@PdNPs, which were synthesized by one-pot aqueous phase method, were applied as nanocarriers loaded with plenty of capture probe antibody (Ab) and signal molecule toluidine blue (Tb) to form the Au@PdNPs-Ab-Tb bioconjugates for secondary signal amplification. The sensing system could still significantly improve the signal output intensity even in the presence of ultra-low concentration target compounddue to the dual signal amplification of MWCNTs/CMK-3/AuNP nanocomposites and Au@PdNPs-Ab-Tb. The method exhibited high selectivity, low detection limit (9.13fg/mL), and strong stability to differentiate AFB1 from other mycotoxins. Furthermore, the sensor has been successfully applied to the quantitative determination of AFB1 in corn, malt, and six herbs, which has potential applications in food safety, quality control, and environmental monitoring.

A review of organometallic compounds as versatile sensors in environmental, medical, and industrial applications

Abstract Sensing technology is gaining attention and continuously advancing, making it a recommended element of individualized healthcare management. This is due to the powers exhibited by organometallic compounds, which are further enhanced by the field of bioengineering. Organometallic compounds have a wide range of biological activity and find uses in industrial and material science fields. Their unique ability to specifically target and overcome constraints faced by traditional counterparts makes them potential contenders for sensor technology. These compounds are highly sensitive to changes in their environment, allowing them to be utilized as sensors for detecting various chemicals or conditions. Additionally, the versatility of organometallic compounds enables their integration into different sensor platforms, making them suitable for environmental monitoring, medical diagnostics, and industrial quality control. This article provides a comprehensive summary of recent advancements in the design and synthesis of organometallic compounds, with a specific emphasis on their potential use as sensors. It also discusses the changes made to the structure, the processes used for functionalization, the incorporation of microfluidics, and the resulting impact on the materials’ sensing capabilities. These biologically derived methods align with sustainability goals and enhance the affordability, applicability, and effectiveness of sensing.

(Invited) Triboelectric Nanogenerators for Self-Powered Sensing and Human-Machine-Interfaces

Wearable and portable electronics are garnering substantial interest for applications ranging from personalized healthcare monitoring to implantable devices and prosthetics. The demand for long-term operation poses significant challenges to their power supply. To address this, the development of novel self-powered systems holds considerable promise. In this talk, I will discuss our recent advancements in crafting triboelectric nanogenerators (TENGs) for self-powered sensing and human-machine interfaces (HMIs). I will first introduce our multifunctional tactile sensors inspired by human skin. These sensors combine a triboelectric nanogenerator layer with a piezoresistive sensing layer, emulating the ability of human skin to detect a variety of static and dynamic stimuli. This sensor can successfully recognize voice patterns, monitor physiological signals, and track human motion in real-time. Then, I will unveil our self-powered smart packaging system, which is driven by a novel desiccant-based triboelectric nanogenerator (D-TENG). This system offers a viable method for continuous monitoring of product conditions and quality control throughout the supply chain, thus minimizing food waste and maintaining a comprehensive log of logistics, especially critical in vaccine transport. Lastly, I will present our novel breath-driven triboelectric sensor in respiratory monitoring. This sensor can precisely detect breath variations and convert different breathing patterns into electrical signals, all without impeding normal breathing. It enables severely disabled individuals to control household appliances through a breath-driven HMI and provides an intelligent monitoring system for emergency situations. This innovative work paves the way for future development in self-powered sensors and advanced HMIs, enhancing assistive technology for individuals with disabilities.

Comprehensive Metatranscriptomic Analysis of Plant Viruses in Imported Frozen Cherries and Blueberries.

The possibility of new viruses emerging in various regions worldwide has increased due to a combination of factors, including climate change and the expansion of international trading. Plant viruses spread through various transmission routes, encompassing well-known avenues such as pollen, seeds, and insects. However, research on potential transmission routes beyond these known mechanisms has remained limited. To address this gap, this study employed metatranscriptomic analysis to ascertain the presence of plant viruses in imported frozen fruits, specifically cherries and blueberries. This analysis aimed to identify pathways through which plant viruses may be introduced into countries. Virome analysis revealed the presence of six species of plant viruses in frozen cherries and blueberries: cherry virus A (CVA), prunus necrotic ringspot virus (PNRSV), prune dwarf virus (PDV), prunus virus F (PrVF), blueberry shock virus (BlShV), and blueberry latent virus (BlLV). Identifying these potential transmission routes is crucial for effectively managing and preventing the spread of plant viruses and crop protection. This study highlights the importance of robust quality control measures and monitoring systems for frozen fruits, emphasizing the need for proactive measures to mitigate the risk associated with the potential spread of plant viruses.

Design, Characterization and Biopharmaceutical Applications of Novel Green Boron-Carbon Quantum Dots for Quantification of Apalutamide; Greenness Evaluations.

The diagnosis of prostate cancer has been evolving in the current decade, with expected mortality rates of 499,000 death by the year 2030. Apalutamide (APL) has been approved in 2018 as the first drug for the controlling of prostate cancer. APL significant success warrantied its high global sales, which are expected to surpass 58% of segment market sales (together with another drug; enzalutamide). Therefore, new, fast and environmentally friendly analytical methods are required for its determination for the quality control and biological monitoring purposes. The proposed research designs and evaluates the first fluorimetric approach based on novel porous green boron-doped carbon quantum dots (B@CDs) for the determination of APL in biopharmaceutical matrices. The synthetic approach has high quantum yield (31.15%). B@CDs were characterized using several tools, including transmission electron microscopy (TEM), dynamic light scattering (DLS), FTIR and Energy dispersive X-ray spectroscopy (EDX) which proved their improved surface properties with an average nano-diameter of 3.0nm. The interaction between B@CDs and APL led to enhancement their fluorescence at 441nm (excitation at 372nm). The approach was validated for the determination of APL within concentration range of 15.0-700.0 ng mL- 1 with quantification limit LOQ 4.37 ng mL- 1 and detection limit LOD 1.44 ng mL- 1. The approach was successfully applied for the determination of APL in human plasma and pharmaceutical monitoring of its marketed tablet form. Then, the approach was assessed for its environmental impact using different metrics and proved its ecological greenness.

Effect of quality control program on surgical management in advanced ovarian cancer.

We investigated the effect of our quality control (QC) program on the management strategy, completeness of the surgery, and clinical outcomes in advanced ovarian cancer. A retrospective review of medical records from January 2005 to December 2019 identified 129 patients with advanced ovarian cancer. Cases were categorized into group 1 (2005-2013) and group 2 (2014-2019) before and after implementation of the QC program. Comparisons included clinicopathological variables, operative details, recurrence and survival outcomes. In Group 2 (n=44), after QC program implementation, primary debulking surgery (PDS) decreased (87.1% vs. 63.6%) and interval debulking surgery (IDS) increased (12.9% vs. 36.4%), indicating a shift in surgical strategy. Optimal resection rates improved significantly for PDS in group 2 (50.0% to 75.0%, p=0.007) and remained high for IDS in both groups (81.8% vs. 81.3%, p>0.999). Post-QC, advanced debulking procedures and co-operation with other departments increased in the IDS (p<0.05). Intra/post-operative complication rates were statistically comparable (p>0.05), whereas postoperative hospital stay was significantly shorter in group 2 (17 days vs. 22 days, p=0.001). Median recurrence-free survival increased after QC, although not statistically significant (19.18 months vs. 25.38 months, p=0.855). With QC program, treatment strategies and clinical outcomes were significantly improved in advanced ovarian cancer. Systematic QC monitoring program should be considered as routine surveillance for better surgical outcomes.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.