Smartphone-based lateral flow imaging system for detection of food-borne bacteria E.coli O157:H7

Smartphone based lateral flow immunoassay quantifications

Lateral Flow Immunoassay (LFI) is a disposable tool designed to detect target substances using minimal resources. For qualitative analysis, LFI does not require a device (i.e., reader) to interpret test results. However, various studies have been conducted to implement quantitative analysis using LFI systems, incorporating LFI along with electrical/electronic readers, to overcome the limitations associated with qualitative LFI analysis. The reader used for the quantitative analysis of LFI should ensure mobility for easy on-site diagnostics and inspections, be user-friendly in operation, and have a fast processing speed until the results are obtained. Due to these requirements, smartphones are increasingly utilized as readers in quantitative analysis of LFI. Among the various components constituting a smartphone, high-performance cameras can serve as sensors converting visual signals into electrical signals. With powerful processing units, large storage capacity, and network capabilities for transmitting analysis results, smartphones are also utilized as interfaces for quantitative analysis. Absolutely, the widespread global use of smartphones is a key advantage, leading to their utilization as diagnostic devices for acquiring, analyzing, storing, and transmitting assay test results. This paper summarizes research cases where smartphones are utilized as readers for quantitative LFI systems used in confirming contamination in food or the environment, detecting drugs, and diagnosing diseases in humans or animals. The systems are classified based on the types of label particles used in the assay, and efforts to improve the quantitative analysis performance for each are examined. Cases where smartphones were used as LFI readers for the diagnosis of the 2019 Coronavirus Disease (COVID-19), which has recently caused significant global damage, have also been investigated.

Microbiological and chemical hazards in cultured meat and methods for their detection

Cultured meat, which involves growing meat in a laboratory rather than breeding animals, offers potential benefits in terms of sustainability, health, and animal welfare compared to conventional meat production. However, the cultured meat production process involves several stages, each with potential hazards requiring careful monitoring and control. Microbial contamination risks exist in the initial cell collection from source animals and the surrounding environment. During cell proliferation, hazards may include chemical residues from media components such as antibiotics and growth factors, as well as microbial issues from improper bioreactor sterilization. In the differentiation stage where cells become muscle tissue, potential hazards include residues from scaffolding materials, microcarriers, and media components. Final maturation and harvesting stages risk environmental contamination from nonsterile conditions, equipment, or worker handling if proper aseptic conditions are not maintained. This review examines the key microbiological and chemical hazards that must be monitored and controlled during the manufacturing process for cultured meats. It describes some conventional and emerging novel techniques that could be applied for the detection of microbial and chemical hazards in cultured meat. The review also outlines the current evolving regulatory landscape around cultured meat and explains how thorough detection and characterization of microbiological and chemical hazards through advanced analytical techniques can provide crucial data to help develop robust, evidence-based food safety regulations specifically tailored for the cultured meat industry. Implementing new digital food safety methods is recommended for further research on the sensitive and effective detection of microbiological and chemical hazards in cultured meat.

Modernization of digital food safety control

Foodborne illness remains a pressing global issue due to the complexities of modern food supply chains and the vast array of potential contaminants that can arise at every stage of food processing from farm to fork. Traditional food safety control systems are increasingly challenged to identify these intricate hazards. The U.S. Food and Drug Administration's (FDA) New Era of Smarter Food Safety represents a revolutionary shift in food safety methodology by leveraging cutting-edge digital technologies. Digital food safety control systems employ modern solutions to monitor food quality by efficiently detecting in real time a wide range of contaminants across diverse food matrices within a short timeframe. These systems also utilize digital tools for data analysis, providing highly predictive assessments of food safety risks. In addition, digital food safety systems can deliver a secure and reliable food supply chain with comprehensive traceability, safeguarding public health through innovative technological approaches. By utilizing new digital food safety methods, food safety authorities and businesses can establish an efficient regulatory framework that genuinely ensures food safety. These cutting-edge approaches, when applied throughout the food chain, enable the delivery of safe, contaminant-free food products to consumers.

New technologies and reagents in lateral flow assay (LFA) designs for enhancing accuracy and sensitivity

Lateral flow assays (LFAs) are a popular method for quick and affordable diagnostic testing because they are easy to use, portable, and user-friendly. However, LFA design has always faced challenges regarding sensitivity, accuracy, and complexity of the operation. By integrating new technologies and reagents, the sensitivity and accuracy of LFAs can be improved while minimizing the complexity and potential for false positives. Surface enhanced Raman spectroscopy (SERS), photoacoustic techniques, fluorescence resonance energy transfer (FRET), and the integration of smartphones and thermal readers can improve LFA accuracy and sensitivity. To ensure reliable and accurate results, careful assay design and validation, appropriate controls, and optimization of assay conditions are necessary. Continued innovation in LFA technology is crucial to improving the reliability and accuracy of rapid diagnostic testing and expanding its applications to various areas, such as food testing, water quality monitoring, and environmental testing.

Lateral Flow Assay: A Summary of Recent Progress for Improving Assay Performance

Lateral flow tests are one of the most important types of paper-based point-of-care (POCT) diagnostic tools. It shows great potential as an implement for improving the rapid screening and management of infections in global pandemics or other potential health disorders by using minimally expert staff in locations where no sophisticated laboratory services are accessible. They can detect different types of biomarkers in various biological samples and provide the results in a little time at a low price. An important challenge regarding conventional LFAs is increasing their sensitivity and specificity. There are two main approaches to increase sensitivity and specificity, including assay improvement and target enrichment. Assay improvement comprises the assay optimization and signal amplification techniques. In this study, a summarize of various sensitivity and specificity enhancement strategies with an objective evaluation are presented, such as detection element immobilization, capillary flow rate adjusting, label evolution, sample extraction and enrichment, etc. and also the key findings in improving the LFA performance and solving their limitations are discussed along with numerous examples.

Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning

The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress toward practical applications. Recently, artificial intelligence (AI) has demonstrated powerful representational learning and generalization capabilities in computer vision. We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for the quantitative detection of commercial internet of things (IoT) devices. This universal UCNPs quantitative detection strategy combines high accuracy, sensitivity, and applicability in the field detection environment. By using transfer learning to train AI models in a small self-built database, we not only significantly improved the accuracy and robustness of quantitative detection, but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment. Then, the trained AI model was deployed in IoT devices, whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results. We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset. The AI quickly provided ultra-high accuracy prediction results (some models could reach 100% accuracy) even when strong noise was added. Simultaneously, the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors. We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics (IVD) industry.

Bioaffinity Nanoprobes for Foodborne Pathogen Sensing

Bioaffinity nanoprobes are a type of biosensor that utilize the specific binding properties of biological molecules, such as antibodies, enzymes, and nucleic acids, for the detection of foodborne pathogens. These probes serve as nanosensors and can provide highly specific and sensitive detection of pathogens in food samples, making them an attractive option for food safety testing. The advantages of bioaffinity nanoprobes include their ability to detect low levels of pathogens, rapid analysis time, and cost-effectiveness. However, limitations include the need for specialized equipment and the potential for cross-reactivity with other biological molecules. Current research efforts focus on optimizing the performance of bioaffinity probes and expanding their application in the food industry. This article discusses relevant analytical methods, such as surface plasmon resonance (SPR) analysis, Fluorescence Resonance Energy Transfer (FRET) measurements, circular dichroism, and flow cytometry, that are used to evaluate the efficacy of bioaffinity nanoprobes. Additionally, it discusses advances in the development and application of biosensors in monitoring foodborne pathogens.

Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows

Despite their simplicity, lateral flow immunoassays (LFIAs) remain a crucial weapon in the diagnostic arsenal, particularly at the point-of-need. However, methods for analysing LFIAs still rely heavily on sub-optimal human readout and rudimentary end-point analysis. This negatively impacts both testing accuracy and testing times, ultimately lowering diagnostic throughput. Herein, we present an automated computational imaging method for processing and analysing multiple LFIAs in real-time and in parallel. This method relies on the automated detection of signal intensity at the test line, control line, and background, and employs statistical comparison of these values to predictively categorise tests as "positive", "negative", or "failed". We show that such a computational methodology can be transferred to a smartphone and detail how real-time analysis of LFIAs can be leveraged to decrease the time-to-result and increase testing throughput. We compare our method to naked-eye readout and demonstrate a shorter time-to-result across a range of target antigen concentrations and fewer false negatives compared to human subjects at low antigen concentrations.

Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue

Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy.

Advances, applications, and limitations of portable and rapid detection technologies for routinely encountered foodborne pathogens

Traditional foodborne pathogen detection methods are highly dependent on pre-treatment of samples and selective microbiological plating to reliably screen target microorganisms. Inherent limitations of conventional methods include longer turnaround time and high costs, use of bulky equipment, and the need for trained staff in centralized laboratory settings. Researchers have developed stable, reliable, sensitive, and selective, rapid foodborne pathogens detection assays to work around these limitations. Recent advances in rapid diagnostic technologies have shifted to on-site testing, which offers flexibility and ease-of-use, a significant improvement from traditional methods' rigid and cumbersome steps. This comprehensive review aims to thoroughly discuss the recent advances, applications, and limitations of portable and rapid biosensors for routinely encountered foodborne pathogens. It discusses the major differences between biosensing systems based on the molecular interactions of target analytes and biorecognition agents. Though detection limits and costs still need further improvement, reviewed technologies have high potential to assist the food industry in the on-site detection of biological hazards such as foodborne pathogens and toxins to maintain safe and healthy foods. Finally, this review offers targeted recommendations for future development and commercialization of diagnostic technologies specifically for emerging and re-emerging foodborne pathogens.

Toward Next Generation Lateral Flow Assays: Integration of Nanomaterials

Lateral flow assays (LFAs) are currently the most used point-of-care sensors for both diagnostic (e.g., pregnancy test, COVID-19 monitoring) and environmental (e.g., pesticides and bacterial monitoring) applications. Although the core of LFA technology was developed several decades ago, in recent years the integration of novel nanomaterials as signal transducers or receptor immobilization platforms has brought improved analytical capabilities. In this Review, we present how nanomaterial-based LFAs can address the inherent challenges of point-of-care (PoC) diagnostics such as sensitivity enhancement, lowering of detection limits, multiplexing, and quantification of analytes in complex samples. Specifically, we highlight the strategies that can synergistically solve the limitations of current LFAs and that have proven commercial feasibility. Finally, we discuss the barriers toward commercialization and the next generation of LFAs.

When smartphone enters food safety: A review in on-site analysis for foodborne pathogens using smartphone-assisted biosensors

Pathogens are one of the supreme threats for the public health around the world in food supply chain. The on-site monitoring is an emerging trend for screening pathogens during the food processing and preserving. Traditional analytical tools have been unable to satisfy the current demands. Smartphones have enormous potentials for achieving on-site detection of foodborne pathogens, with intrinsic advantages such as small size, high accessibility, fast processing speed, and powerful imaging capacity. This review aims to synthesize the current advances in smartphone-assisted biosensors (SABs) for sensing foodborne pathogens, and briefly put forward the problem that consist in the research. We present the role of nanotechnology and recognition modes targeting foodborne pathogens in SABs, and discuss the signal conversion platforms coupling with smartphone. The challenges and perspectives in SABs are also proposed. The smartphone analytics area is moving forward, and it much be subject to careful quality standards and validation.

Colloidal silver-based lateral flow immunoassay for detection of profenofos pesticide residue in vegetables

A colloidal silver nanoparticle (AgNP)-based lateral flow immunoassay (LFIA) was evaluated in terms of the rapid detection of profenofos (PEO) pesticide residue in vegetables. Colloidal AgNPs, of a diameter of approximately 20 nm, were surface-modified with trisodium citrate dehydrate (TSC) in order to improve their stability and dispersion. An anti-profenofos polyclonal antibody (pAb) was successfully immobilized on the surface of the AgNPs by ionic interaction and characterized using UV-vis, SEM, TEM, FTIR and XPS analyses. Surface modification of Ag-pAb conjugates of varying pH, pAb content and cross-reactivity was employed to design and prepare labels for use in an LFIA to examine whether these factors affect the performance of the assay. The visible detection limit and optical detection limit of the PEO test strip were 0.20 and 0.01 ppm, respectively, in PEO standard solution. This assay showed no cross-reaction with omethoate, methamidophos or pyraclofos. Finally, the PEO test strip was effectively applied for the detection of PEO in liquid vegetables A and B, with optical detection limits of 0.09 and 0.075 ppm, respectively.

Gold Nanoparticle-Mediated Lateral Flow Assays for Detection of Host Antibodies and COVID-19 Proteins

Coronaviruses, that are now well-known to the public, include a family of viruses that can cause severe acute respiratory syndrome (SARS) and other respiratory diseases, such as Middle East respiratory syndrome (MERS). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the seventh member of this coronavirus family, was detected in 2019 and can cause a number of respiratory symptoms, from dry cough and fever to fatal viral pneumonia. Various diagnostic assays ranging from real-time polymerase chain reaction (RT-PCR) to point-of-care medical diagnostic systems have been developed for detection of viral components or antibodies targeting the virus. Point-of-care assays allow rapid diagnostic assessment of infectious patients. Such assays are ideally simple, low-cost, portable tests with the possibility for on-site field detection that do not require skilled staff, sophisticated equipment, or sample pretreatment, as compared to RT-PCR. Since early 2021 when new SARS-CoV-2 variants of concern increased, rapid tests became more crucial in the disease management cycle. Among rapid tests, gold nanoparticle (GNP)-based lateral flow assays (LFAs) have high capacity for performing at the bedside, paving the way to easy access to diagnosis results. In this review, GNP-based LFAs used for either COVID-19 proteins or human response antibodies are summarized and recommendations for their improvement have been suggested.

Bioinspired fabrication of bifunctional antibody-enzyme co-assembled nanocomposites for chemiluminescence immunoassays of E. coli O157:H7

Immunoassays based on enzyme-labeled antibodies have been widely used in the food safety field. However, the production process of enzyme-labeled antibodies is complicated and the low storage stability limits their application. Herein, antibody-horseradish peroxidase (HRP) co-assembled nanocomposites (AHC NCs) with outstanding advantages such as enhanced stability, lower cost, and substrate affinity were successfully prepared via a one-pot green method. Then the AHC NCs were employed as an alternative to traditional enzyme-labeled antibodies to develop a chemiluminescence enzyme immunoassay (CLEIA) toward Escherichia coli (E. coli) O157:H7. Under optimal conditions, E. coli O157:H7 can be detected in a linear range from 1 × 10³ CFU mL^-1 to 5 × 10⁶ CFU mL^-1, while the limit of detection (LOD) is as low as 2.2 × 10² CFU mL^-1 (3σ). A series of repeatability studies showed reproducible results with a coefficient of variation of less than 7%. In addition, the proposed CLEIA was successfully applied to the analysis of spiked samples (tap water) and gave quantitative recoveries from 93.72% to 100.72%. This work demonstrates that the developed CLEIA can be applied as a universal platform for specific detection of diversified analytes.

Amplified parallel antigen rapid test for point-of-care salivary detection of SARS-CoV-2 with improved sensitivity

In the ongoing COVID-19 pandemic, simple, rapid, point-of-care tests not requiring trained personnel for primary care testing are essential. Saliva-based antigen rapid tests (ARTs) can fulfil this need, but these tests require overnight-fasted samples; without which independent studies have demonstrated sensitivities of only 11.7 to 23.1%. Herein, we report an Amplified Parallel ART (AP-ART) with sensitivity above 90%, even with non-fasted samples. The virus was captured multimodally, using both anti-spike protein antibodies and Angiotensin Converting Enzyme 2 (ACE2) protein. It also featured two parallel flow channels. The first contained spike protein binding gold nanoparticles which produced a visible red line upon encountering the virus. The second contained signal amplifying nanoparticles that complex with the former and amplify the signal without any linker. Compared to existing dual gold amplification techniques, a limit of detection of one order of magnitude lower was achieved (0.0064 ng·mL^-1). AP-ART performance in detecting SARS-CoV-2 in saliva of COVID-19 patients was investigated using a case-control study (139 participants enrolled and 162 saliva samples tested). Unlike commercially available ARTs, the sensitivity of AP-ART was maintained even when non-fasting saliva was used. Compared to the gold standard reverse transcription-polymerase chain reaction testing on nasopharyngeal samples, non-fasting saliva tested on AP-ART showed a sensitivity of 97.0% (95% CI: 84.7-99.8); without amplification, the sensitivity was 72.7% (95% CI: 83.7-94.8). Thus, AP-ART has the potential to be developed for point-of-care testing, which may be particularly important in resource-limited settings, and for early diagnosis to initiate newly approved therapies to reduce COVID-19 severity.

An Overview for the Nanoparticles-Based Quantitative Lateral Flow Assay

The development of the lateral flow assay (LFA) has received much attention in both academia and industry because of their broad applications to food safety, environmental monitoring, clinical diagnosis, and so forth. The user friendliness, low cost, and easy operation are the most attractive advantages of the LFA. In recent years, quantitative detection has become another focus of LFA development. Here, the most recent studies of quantitative LFAs are reviewed. First, the principles and corresponding formats of quantitative LFAs are introduced. In the biomaterial and nanomaterial sections, the detection, capture, and signal amplification biomolecules and the optical, fluorescent, luminescent, and magnetic labels used in LFAs are described. The invention of dedicated strip readers has drawn further interest in exploiting the better performance of LFAs. Therefore, next, the development of dedicated reader devices is described and the usefulness and specifications of these devices for LFAs are discussed. Finally, the applications of LFAs in the detection of metal ions, biotoxins, pathogenic microorganisms, veterinary drugs, and pesticides in the fields of food safety and environmental health and the detection of nucleic acids, biomarkers, and viruses in clinical analyses are summarized.

On-Site Detection of Carcinoembryonic Antigen in Human Serum

Real-time connectivity and employment of sustainable materials empowers point-of-care diagnostics with the capability to send clinically relevant data to health care providers even in low-resource settings. In this study, we developed an advantageous kit for the on-site detection of carcinoembryonic antigen (CEA) in human serum. CEA sensing was performed using cellulose-based lateral flow strips, and colorimetric signals were read, processed, and measured using a smartphone-based system. The corresponding immunoreaction was reported by polydopamine-modified gold nanoparticles in order to boost the signal intensity and improve the surface blocking and signal-to-noise relationship, thereby enhancing detection sensitivity when compared with bare gold nanoparticles (up to 20-fold in terms of visual limit of detection). Such lateral flow strips showed a linear range from 0.05 to 50 ng/mL, with a visual limit of detection of 0.05 ng/mL and an assay time of 15 min. Twenty-six clinical samples were also tested using the proposed kit and compared with the gold standard of immunoassays (enzyme linked immunosorbent assay), demonstrating an excellent correlation (R = 0.99). This approach can potentially be utilized for the monitoring of cancer treatment, particularly at locations far from centralized laboratory facilities.

Ten Years of Lateral Flow Immunoassay Technique Applications: Trends, Challenges and Future Perspectives

The Lateral Flow Immunoassay (LFIA) is by far one of the most successful analytical platforms to perform the on-site detection of target substances. LFIA can be considered as a sort of lab-in-a-hand and, together with other point-of-need tests, has represented a paradigm shift from sample-to-lab to lab-to-sample aiming to improve decision making and turnaround time. The features of LFIAs made them a very attractive tool in clinical diagnostic where they can improve patient care by enabling more prompt diagnosis and treatment decisions. The rapidity, simplicity, relative cost-effectiveness, and the possibility to be used by nonskilled personnel contributed to the wide acceptance of LFIAs. As a consequence, from the detection of molecules, organisms, and (bio)markers for clinical purposes, the LFIA application has been rapidly extended to other fields, including food and feed safety, veterinary medicine, environmental control, and many others. This review aims to provide readers with a 10-years overview of applications, outlining the trends for the main application fields and the relative compounded annual growth rates. Moreover, future perspectives and challenges are discussed.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp

Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 10⁴ CFU/mL commercially available lateral-flow assays.

Design and Validation of a Portable Machine Learning-Based Electronic Nose

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

A novel smartphone-based colorimetric aptasensor for on-site detection of Escherichia coli O157:H7 in milk

Effective testing tools for Escherichia coli O157:H7 can prevent outbreaks of foodborne illness. In this paper, a smartphone-based colorimetric aptasensor was developed using functionalized gold nanoparticles (GNP) and multi-walled carbon nanotubes (MWCNT) for monitoring E. coli O157:H7 in milk. The maximum absorption peak of GNP bonded with aptamer (Apt) generated evident transformation from 518 to 524 nm. The excess GNP-Apt was removed by functionalized MWCNT magnetized with carbonyl iron powder (CIP) and hybridized with a DNA probe, whereas the GNP-Apt immobilized on E. coli O157:H7 remained in the system. In the presence of a high-salt solution, the GNP-Apt that captured E. coli O157:H7 remained red, but the free GNP-Apt aggregated and appeared blue. The chromogenic results were analyzed by a smartphone-based colorimetric device that was fabricated using acrylic plates, a light-emitting diode, and a mobile power pack. To our knowledge, this was the first attempt to use a smartphone-based colorimetric aptasensor employing the capture of GNP-Apt coupled with separation of MWCNT@CIP probe to detect E. coli O157:H7. The aptasensor exhibited good reproducibility and no cross-reaction for other bacteria. A concentration of 8.43 × 10³ cfu/mL of E. coli O157:H7 could be tested in pure culture, and 5.24 × 10² cfu/mL of E. coli O157:H7 could be detected in artificially contaminated milk after 1 h of incubation. Therefore, the smartphone-based colorimetric aptasensor was an efficient tool for the detection of E. coli O157:H7 in milk.

Review: Trends in point-of-care diagnosis for Escherichia coli O157:H7 in food and water

Escherichia coli O157:H7, a Shiga-producing E. coli is a major pathogenic E. coli strain which since the early 1980s has become a crucial food and water-borne pathogen. Several management strategies can be applied to control the spread of infection; however early diagnosis represents the optimum preventive strategy to minimize the infection. Therefore, it is crucial to detect this pathogen in a fast and efficient manner in order to reduce the morbidity and mortality. Currently used gold standard tests rely on culture and pre-enrichment of E. coli O157:H7 from the contaminated source; they are time consuming and laborious. Molecular methods such as polymerase chain reaction are sensitive; however, they require expensive instrumentation. Therefore, there is a requirement for Accurate, Sensitive, Specific, User friendly, Rapid, Equipment free and Deliverable (ASSURED) detection methods for use in the laboratory and in the field. Emerging technologies such as isothermal amplification methods, biosensors, surface enhanced Raman Spectroscopy, paper-based diagnostics and smartphone-based digital methods are recognized as new approaches in the field of E. coli O157:H7 diagnostics and are discussed in this review. Mobile PCR and CRISPR-Cas diagnostic platforms have been identified as new tools in E. coli O157:H7 POC diagnostics with the potential for implementation by industry. This review describes advances and progress in the field of E. coli O157:H7 diagnosis in the context of food and water industry. The focus is on emerging high throughput point-of-care (POC) E. coli O157:H7 diagnostics and the requirement for the transformation to service routine diagnostics in the food and water industry.

Point-of-Care Diagnoses and Assays Based on Lateral Flow Test

Analytical devices for point-of-care diagnoses are highly desired and would improve quality of life when first diagnoses are made early and pathologies are recognized soon. Lateral flow tests (LFTs) are such tools that can be easily performed without specific equipment, skills, or experiences. This review is focused on the use of LFT in point-of-care diagnoses. The principle of the assay is explained, and new materials like nanoparticles for labeling, new recognition molecules for interaction with an analyte, and new additional instrumentation like signal scaling by a smartphone camera are described and discussed. Advantages of the LFT devices as well as their limitations are described and discussed here considering actual papers that are properly cited.

Flow-based System: A Highly Efficient Tool Speeds Up Data Production and Improves Analytical Performance

In this review, we cite references from the period between 2015 and 2020 related to the use of a flow-based system as a tool to obtain a modern analytical system for speeding up data production and improving performance. Based on a great deal of concepts for automatic systems, there are several research groups introduced in the development of flow-based systems to increase sample throughput while retaining the reproducibility and repeatability as well as to propose new platforms of flow-based systems, such as microfluidic chip and paper-based devices. Additionally, to apply a developed system for on-site analysis is one of the key features for development. We believe that this review will be very interested and useful for readers because of its impact on developing novel analytical systems. The content of the review is categorized following their applications including quality control and food safety, clinical diagnostics, environmental monitoring and miscellaneous.

Highly Sensitive Optical Detection of Escherichia coli Using Terbium-Based Metal-Organic Framework

Metal-organic frameworks (MOFs) are envisaged as highly useful for the development of biosensors. Herein, for the first time, we report the optical detection of Escherichia coli using a water-dispersible terbium MOF (Tb-BTC; BTC, 1,3,5-benzenetricarboxylic acid). The successful synthesis of Tb-BTC is verified using spectroscopic and morphological techniques like UV-vis, fluorescence and FTIR spectroscopy, X-ray diffraction analysis, and electron microscopy. Tb-BTC has been bio-interfaced with anti-E. coli antibodies and then investigated as a biosensor for E. coli. The biosensor displays detection ability in an analyte concentration range of 1.3 × 10² to 1.3 × 10⁸ cfu/mL with a detection limit of 3 cfu/mL, having a response time of 5 min and a total analysis time of about 20-25 min. The results are also found to be reproducible and specific in the presence of some other interfering bacterial species. As demonstrated, the present sensor provides highly sensitive and specific detection of E. coli in fruit juice sample. To the best of our knowledge, this is the first report to showcase the potential of the MOF-based fluorescent biosensor for the detection of E. coli.

Point-of-Need Diagnostics for Foodborne Pathogen Screening

Foodborne illness is a major public health issue that results in millions of global infections annually. The burden of such illness sits mostly with developing countries, as access to advanced laboratory equipment and skilled lab technicians, as well as consistent power sources, is limited and expensive. Current gold standards in foodborne pathogen screening involve labor-intensive sample enrichment steps, pathogen isolation and purification, and costly readout machinery. Overall, time to detection can take multiple days, excluding the time it takes to ship samples to off-site laboratories. Efforts have been made to simplify the workflow of such tests by integrating multiple steps of foodborne pathogen screening procedures into a singular device, as well as implementing more point-of-need readout methods. In this review, we explore recent advancements in developing point-of-need devices for foodborne pathogen screening. We discuss the detection of surface markers, nucleic acids, and metabolic products using both paper-based and microfluidic devices, focusing primarily on developments that have been made between 2015 and mid-2020.