Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food

Smart windows based on ultraviolet-B persistent luminescence phosphors for bacterial inhibition and food preservation

Herein, ultraviolet B (UVB) persistent luminescence phosphors containing SrAl12O19: Ce3+, Sc3+ nanoparticles were reported. Thermoluminescence (TL) spectrum analysis reveals that the shallow trap induced by Sc3+ co-doping plays an important role in photoluminescence persistent luminescence (PersL) development, while the deep trap dominates the generation of optical stimulated luminescence (OSL). Owing the appearance of deep trap, the OSL is observed under light (700 nm - 900 nm) excitation. UVB luminescence exerts good bactericidal effects on pathogenic bacteria involved in the process of food spoilage. Thus, the smart window with SrAl12O19: Ce3+, Sc3+/PDMS produces UVB PersL to efficiently inactivate Escherichia coli and Staphylococcus aureus. In addition, the presence of the smart window delays the critical point of pork decay, and greatly reduces the time of pork spoilage. It maximizes the convenience of eradicating bacteria and preserving food, thus offering a fresh perspective on the use of UV light for food sterilization and preservation.

Food-borne bacteria analysis using a diatomite bioinspired SERS platform

Rapid and sensitive detection of food-borne bacteria has remained challenging over the past few decades. We propose a surface-enhanced Raman scattering sensing strategy based on a novel bioinspired surface-enhanced Raman scattering substrate, which can directly detect dye molecular residues and food-borne pathogen microorganisms in the environment. The surface-enhanced Raman scattering platform consists of a natural diatomite microporous array decorated with a metal-phenolic network that enables the in situ reduction of gold nanoparticles. The as-prepared nanocomposites display excellent surface-enhanced Raman scattering activity with the lowest limit of detection and the maximum Raman enhancement factor of dye molecules up to 10-11 M and 1.18 × 107, respectively. For food-borne bacterial detection, a diatomite microporous array decorated with a metal polyphenol network and gold nanoparticle-based surface-enhanced Raman scattering analysis is capable of distinguishing the biochemical fingerprint information of Staphylococcus aureus and Escherichia coli, indicating the great potential for strain identification.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Microfluidic paper analytic device (μPAD) technology for food safety applications

Foodborne pathogens, food adulterants, allergens, and toxic chemicals in food can cause major health hazards to humans and animals. Stringent quality control measures at all stages of food processing are required to ensure food safety. There is, therefore, a global need for affordable, reliable, and rapid tests that can be conducted at different process steps and processing sites, spanning the range from the sourcing of food to the end-product acquired by the consumer. Current laboratory-based food quality control tests are well established, but many are not suitable for rapid on-site investigations and are costly. Microfluidic paper analytical devices (μPADs) are a fast-growing field in medical diagnostics that can fill these gaps. In this review, we describe the latest developments in the applications of microfluidic paper analytic device (μPAD) technology in the food safety sector. State-of-the-art μPAD designs and fabrication methods, microfluidic assay principles, and various types of μPAD devices with food-specific applications are discussed. We have identified the prominent research and development trends and future directions for maximizing the value of microfluidic technology in the food sector and have highlighted key areas for improvement. We conclude that the μPAD technology is promising in food safety applications by using novel materials and improved methods to enhance the sensitivity and specificity of the assays, with low cost.

Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (μPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and μPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Dipicolylamine-Zn Induced Targeting and Photo-Eliminating of Pseudomonas aeruginosa and Drug-Resistance Gram-Positive Bacteria

The emergence of drug-resistant bacteria, particularly resistant strains of Gram-negative bacteria, such as Pseudomonas aeruginosa, poses a significant threat to public health. Although antibacterial photodynamic therapy (APDT) is a promising strategy for combating drug-resistant bacteria, actively targeted photosensitizers (PSs) remain unknown. In this study, a PS based on dipicolylamine (DPA), known as WZK-DPA-Zn, is designed for the selective identification of P. aeruginosa and drug-resistant Gram-positive bacteria. WZK-DPA-Zn exploits the synergistic effects of DPA-Zn2+ coordination and cellular uptake, which could effectively anchor P. aeruginosa within a brief period (10 min) without interference from other Gram-negative bacteria. Simultaneously, the cationic nature of WZK-DPA-Zn enhances its interaction with Gram-positive bacteria via electrostatic forces. Compared to traditional clinical antibiotics, WZK-DPA-Zn shows exceptional antibacterial activity without inducing drug resistance. This effectiveness is achieved using the APDT strategy when irradiated with white light or sunlight. The combination of WZK-DPA-Zn with Pluronic-based thermosensitive hydrogel dressings (WZK-DPA-Zn@Gel) effectively eliminates mixed bacterial infections and accelerates wound healing, thereby achieving a synergistic effect where "1+1>2." In summary, this study proposes a precise strategy employing DPA-Zn as the targeting moiety of a PS, facilitating the rapid elimination of P. aeruginosa and drug-resistant Gram-positive bacteria using APDT.

Progress in methods for the detection of viable Escherichia coli

Escherichia coli (E. coli) is a prevalent enteric bacterium and a necessary organism to monitor for food safety and environmental purposes. Developing efficient and specific methods is critical for detecting and monitoring viable E. coli due to its high prevalence. Conventional culture methods are often laborious and time-consuming, and they offer limited capability in detecting potentially harmful viable but non-culturable E. coli in the tested sample, which highlights the need for improved approaches. Hence, there is a growing demand for accurate and sensitive methods to determine the presence of viable E. coli. This paper scrutinizes various methods for detecting viable E. coli, including culture-based methods, molecular methods that target DNAs and RNAs, bacteriophage-based methods, biosensors, and other emerging technologies. The review serves as a guide for researchers seeking additional methodological options and aiding in the development of rapid and precise assays. Moving forward, it is anticipated that methods for detecting E. coli will become more stable and robust, ultimately contributing significantly to the improvement of food safety and public health.

Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection

Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.

Material Breakthroughs in Smart Food Monitoring: Intelligent Packaging and On-Site Testing Technologies for Spoilage and Contamination Detection

Despite extensive commercial and regulatory interventions, food spoilage and contamination continue to impose massive ramifications on human health and the global economy. Recognizing that such issues will be significantly eliminated by the accurate and timely monitoring of food quality markers, smart food sensors have garnered significant interest as platforms for both real-time, in-package food monitoring and on-site commercial testing. In both cases, the sensitivity, stability, and efficiency of the developed sensors are largely informed by underlying material design, driving focus toward the creation of advanced materials optimized for such applications. Herein, a comprehensive review of emerging intelligent materials and sensors developed in this space is provided, through the lens of three key food quality markers - biogenic amines, pH, and pathogenic microbes. Each sensing platform is presented with targeted consideration toward the contributions of the underlying metallic or polymeric substrate to the sensing mechanism and detection performance. Further, the real-world applicability of presented works is considered with respect to their capabilities, regulatory adherence, and commercial potential. Finally, a situational assessment of the current state of intelligent food monitoring technologies is provided, discussing material-centric strategies to address their existing limitations, regulatory concerns, and commercial considerations.

G-Quadruplex/Hemin-Mediated Polarity-Switchable and Photocurrent-Amplified System for Escherichia coli O157:H7 Detection

The contamination of food by pathogens is a serious problem in global food safety, and current methods of detection are costly, time-consuming, and cumbersome. Therefore, it is necessary to develop rapid, portable, and sensitive assays for foodborne pathogens. In addition, assays for foodborne pathogens must be resistant to interference resulting from the complex food matrix to prevent false positives and negatives. In this study, hemin and reduced graphene oxide-MoS2 sheets (GMS) were used to design a near-infrared (NIR)-responsive photoelectrochemical (PEC) aptasensor with target-induced photocurrent polarity switching based on a hairpin aptamer (Hp) with a G-quadruplex motif. A ready-to-use analytical device was developed by immobilizing GMS on the surface of a commercial screen-printed electrode, followed by the attachment of the aptamer. In the presence of Escherichia coli O157:H7, the binding sites of Hp with the G-quadruplex motif were opened and exposed to hemin, leading to the formation of a G-quadruplex/hemin DNAzyme. Crucially, after binding to hemin, the charge transfer pathway of GMS changes, resulting in a switch of the photocurrent polarity. Further, G-quadruplex/hemin DNAzyme enhanced the cathodic photocurrent, and the proposed sensor exhibited a wide linear range ((25.0-1.0) × 107 CFU/mL), a low limit of detection (2.0 CFU/mL), and good anti-interference performance. These findings expand the applications of NIR-responsive PEC materials and provide versatile PEC methods for detecting biological analytes, especially for food safety testing.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

A Novel Dual Bacteria-Imprinted Polymer Sensor for Highly Selective and Rapid Detection of Pathogenic Bacteria

The rapid, sensitive, and selective detection of pathogenic bacteria is of utmost importance in ensuring food safety and preventing the spread of infectious diseases. Here, we present a novel, reusable, and cost-effective impedimetric sensor based on a dual bacteria-imprinted polymer (DBIP) for the specific detection of Escherichia coli O157:H7 and Staphylococcus aureus. The DBIP sensor stands out with its remarkably short fabrication time of just 20 min, achieved through the efficient electro-polymerization of o-phenylenediamine monomer in the presence of dual bacterial templates, followed by in-situ template removal. The key structural feature of the DBIP sensor lies in the cavity-free imprinting sites, indicative of a thin layer of bacterial surface imprinting. This facilitates rapid rebinding of the target bacteria within a mere 15 min, while the sensing interface regenerates in just 10 min, enhancing the sensor's overall efficiency. A notable advantage of the DBIP sensor is its exceptional selectivity, capable of distinguishing the target bacteria from closely related bacterial strains, including different serotypes. Moreover, the sensor exhibits high sensitivity, showcasing a low detection limit of approximately 9 CFU mL-1. The sensor's reusability further enhances its cost-effectiveness, reducing the need for frequent sensor replacements. The practicality of the DBIP sensor was demonstrated in the analysis of real apple juice samples, yielding good recoveries. The integration of quick fabrication, high selectivity, rapid response, sensitivity, and reusability makes the DBIP sensor a promising solution for monitoring pathogenic bacteria, playing a crucial role in ensuring food safety and safeguarding public health.

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to μg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

CRISPR-based biosensors for pathogenic biosafety

Pathogenic biosafety is a worldwide concern. Tools for analyzing pathogenic biosafety, that are precise, rapid and field-deployable, are highly demanded. Recently developed biotechnological tools, especially those utilizing CRISPR/Cas systems which can couple with nanotechnologies, have enormous potential to achieve point-of-care (POC) testing for pathogen infection. In this review, we first introduce the working principle of class II CRISPR/Cas system for detecting nucleic acid and non-nucleic acid biomarkers, and highlight the molecular assays that leverage CRISPR technologies for POC detection. We summarize the application of CRISPR tools in detecting pathogens, including pathogenic bacteria, viruses, fungi and parasites and their variants, and highlight the profiling of pathogens' genotypes or phenotypes, such as the viability, and drug-resistance. In addition, we discuss the challenges and opportunities of CRISPR-based biosensors in pathogenic biosafety analysis.

Design of Biodegradable, Climate-Specific Packaging Materials That Sense Food Spoilage and Extend Shelf Life

The AgriFood systems in tropical climates are under strain due to a rapid increase in human population and extreme environmental conditions that limit the efficacy of packaging technologies to extend food shelf life and guarantee food safety. To address these challenges, we rationally designed biodegradable packaging materials that sense spoilage and prevent molding. We nanofabricated the interface of 2D covalent organic frameworks (COFs) to reinforce silk fibroin (SF) and obtain biodegradable membranes with augmented mechanical properties and that displayed an immediate colorimetric response (within 1 s) to food spoilage, using packaged poultry as an example. Loading COF with antimicrobial hexanal also mitigated biotic spoilage in high-temperature and -humidity conditions, resulting in a four-order of magnitude decrease in the total amount of mold growth in soybeans packaged in silk-COF, when compared to cling film (i.e., polyethylene). Together, the integration of sensing, structural reinforcement, and antimicrobial agent delivery within a biodegradable nanocomposite framework defines climate-specific packaging materials that can decrease food waste and enhance food safety.

Rapid Identification and Monitoring of Multiple Bacterial Infections Using Printed Nanoarrays

Fast and accurate detection of microbial cells in clinical samples is highly valuable but remains a challenge. Here, a simple, culture-free diagnostic system is developed for direct detection of pathogenic bacteria in water, urine, and serum samples using an optical colorimetric biosensor. It consists of printed nanoarrays chemically conjugated with specific antibodies that exhibits distinct color changes after capturing target pathogens. By utilizing the internal capillarity inside an evaporating droplet, target preconcentration is achieved within a few minutes to enable rapid identification and more efficient detection of bacterial pathogens. More importantly, the scattering signals of bacteria are significantly amplified by the nanoarrays due to strong near-field localization, which supports a visualizable analysis of the growth, reproduction, and cell activity of bacteria at the single-cell level. Finally, in addition to high selectivity, this nanoarray-based biosensor is also capable of accurate quantification and continuous monitoring of bacterial load on food over a broad linear range, with a detection limit of 10 CFU mL^-1 . This work provides an accessible and user-friendly tool for point-of-care testing of pathogens in many clinical and environmental applications, and possibly enables a breakthrough in early prevention and treatment.

Visual-Olfactory Synergistic Perception Based on Dual-Focus Imaging and a Bionic Learning Architecture

The synergistic interaction of vision and olfaction is critical for both natural and artificial intelligence systems to recognize and adapt to complex environments. However, current bioinspired systems with visual and olfactory sensations are mostly assembled with separate and heterogeneous sensors, inevitably leading to bulky systems and incompatible datasets. Here, we demonstrate on-chip integration of visual and olfactory sensations through a dual-focus imaging approach. By combining lens-based visual imaging and lensless colorimetric imaging, a target object and its odor fingerprint can be captured with a single complementary metal-oxide-semiconductor imager, and the obtained multimodal images are analyzed with a bionic learning architecture for information fusion and perception. To demonstrate the capability of this system, we adapted it to food detection and achieved 100% accuracy in identifying meat freshness and category with a 10 s sampling time. In addition to the highly integrated sensor design, our approach exhibits superior accuracy and efficiency in object recognition, providing a promising approach for robotic sensing and perception.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to μg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

Paper-based sensors for bacteria detection

The detection of pathogenic bacteria is essential to prevent and treat infections and to provide food security. Current gold-standard detection techniques, such as culture-based assays and polymerase chain reaction, are time-consuming and require centralized laboratories. Therefore, efforts have focused on developing point-of-care devices that are fast, cheap, portable and do not require specialized training. Paper-based analytical devices meet these criteria and are particularly suitable to deployment in low-resource settings. In this Review, we highlight paper-based analytical devices with substantial point-of-care applicability for bacteria detection and discuss challenges and opportunities for future development.

Ultrasensitive multichannel immunochromatographic assay for rapid detection of foodborne bacteria based on two-dimensional film-like SERS labels

Rapid and sensitive detection of multiple foodborne bacteria without DNA amplification is still challenging. Here, we proposed an immunochromatographic assay (ICA) with multiplex analysis ability and high sensitivity for direct detection of bacteria in real food samples, based on an improved surface-enhanced Raman scattering (SERS) sensing strategy. Multifunctional Au shell-coated graphene oxide nanosheets (GO@Au) were fabricated and for the first time introduced into the ICA system as a two-dimensional film-like SERS label, which possessed huge surface area, excellent stability, and superior SERS activity. Different from the conventional spherical nanotags, the antibody-conjugated GO@Au nanosheet effectively and rapidly adhered to bacterial cells, improved the dispersibility of bacteria-nanolabel complexes on the ICA strips, and provided numerous stable hotspots for SERS signal enhancement. The combination of GO@Au labels and the ICA system achieved the multiplex and ultrasensitive determination of three major foodborne pathogens, namely, Staphylococcus aureus, Escherichia coli O157:H7, and Salmonella typhimurium, in a single test, with low detection limits (8, 10, and 10 cells/mL) and short detection time (20 min). The proposed biosensor demonstrated high stability and good accuracy in various food samples and is thus a promising tool for the rapid identification of bacteria.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

An Integrated Food Freshness Sensor Array System Augmented by a Metal-Organic Framework Mixed-Matrix Membrane and Deep Learning

The static labels presently prevalent on the food market are confronted with challenges due to the assumption that a food product only undergoes a limited range of predefined conditions, which cause elevated safety risks or waste of perishable food products. Hence, integrated systems for measuring food freshness in real time have been developed for improving the reliability, safety, and sustainability of the food supply. However, these systems are limited by poor sensitivity and accuracy. Here, a metal-organic framework mixed-matrix membrane and deep learning technology were combined to tackle these challenges. UiO-66-OH and polyvinyl alcohol were impregnated with six chromogenic indicators to prepare sensor array composites. The sensors underwent color changes after being exposed to ammonia at different pH values. The limit of detection of 80 ppm for trimethylamine was obtained, which was practically acceptable in the food industry. Four state-of-the-art deep convolutional neural networks were applied to recognize the color change, endowing it with high-accuracy freshness estimation. The simulation test for chicken freshness estimation achieved accuracy up to 98.95% by the WISeR-50 algorithm. Moreover, 3D printing was applied to create a mold for possible scale-up production, and a portable food freshness detector platform was conceptually built. This approach has the potential to advance integrated and real-time food freshness estimation.

LISzyme Biosensors: DNAzymes Embedded in an Anti-biofouling Platform for Hands-free Real-Time Detection of Bacterial Contamination in Milk

Nonspecific binding is a significant challenge associated with biosensors in complex food textures. To overcome this, we have developed LISzymes, which are DNAzymes incorporated in lubricant-infused surfaces (LISs). Using milk as a complex background matrix, we show that LISzyme biosensors are significantly more effective in preventing nonspecific binding compared to other commonly used "blocking" methods. The use of lubricant infusion to treat sensing surfaces results in a 4-fold increase in the signal-to-noise ratio obtained with the DNAzyme with respect to untreated surfaces, when detecting the presence of specific bacteria in milk. This is a striking improvement upon previous DNAzyme sensors. We also show that the use of LISs does not affect the DNAzyme's ability to effectively and specifically detect its target─a protein specifically produced by Escherichia coli (E. coli), in a complex sample matrix such as milk. LISzymes drastically improve DNAzyme performance, resulting in target detection associated with E. coli at concentrations as low as 250 CFU/mL in milk in less than an hour, which is currently not possible using other optical platforms. LISzymes are promising tools for the real-time monitoring of food contamination and may prove valuable within many other biosensing applications.

Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L₂ regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.