Plant Biomarker Recognition by Molecular Imprinting Based Localized Surface Plasmon Resonance Sensor Array: Performance Improvement by Enhanced Hotspot of Au Nanostructure

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

Size-Based Norfentanyl Detection with SWCNT@UiO-MOF Composites

Single-walled carbon nanotube (SWCNT)@metal-organic framework (MOF) field-effect transistor (FET) sensors generate a signal through analytes restricting ion diffusion around the SWCNT surface. Four composites made up of SWCNTs and UiO-66, UiO-66-NH2, UiO-67, and UiO-67-CH3 were synthesized to explore the detection of norfentanyl (NF) using SWCNT@MOF FET sensors with different pore sizes. Liquid-gated FET devices of SWCNT@UiO-67 showed the highest sensing response toward NF, whereas SWCNT@UiO-66 and SWCNT@UiO-66-NH2 devices showed no sensitivity improvement compared to bare SWCNT. Comparing SWCNT@UiO-67 and SWCNT@UiO-67-CH3 indicated that the sensing response is modulated by not only the size-matching between NF and MOF channel but also NF diffusion within the MOF channel. Additionally, other drug metabolites, including norhydrocodone (NH), benzoylecgonine (BZ), and normorphine (NM) were tested with the SWCNT@UiO-67 sensor. The sensor was not responding toward NH and or BZ but a similar sensing result toward NM because NM has a similar size to NF. The SWCNT@MOF FET sensor can avoid interference from bigger molecules but sensor arrays with different pore sizes and chemistries are needed to improve the specificity.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis

In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures.

Perceptual Soft End-Effectors for Future Unmanned Agriculture

As consumers demand ever-higher quality standards for agricultural products, the inspection of such goods has become an integral component of the agricultural production process. Unfortunately, traditional testing methods necessitate the deployment of numerous bulky machines and cannot accurately determine the quality of produce prior to harvest. In recent years, with the advancement of soft robot technology, stretchable electronic technology, and material science, integrating flexible plant wearable sensors on soft end-effectors has been considered an attractive solution to these problems. This paper critically reviews soft end-effectors, selecting the appropriate drive mode according to the challenges and application scenarios in agriculture: electrically driven, fluid power, and smart material actuators. In addition, a presentation of various sensors installed on soft end-effectors specifically designed for agricultural applications is provided. These sensors include strain, temperature, humidity, and chemical sensors. Lastly, an in-depth analysis is conducted on the significance of implementing soft end-effectors in agriculture as well as the potential opportunities and challenges that will arise in the future.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

Optical Sensing Technologies to Elucidate the Interplay between Plant and Microbes

Plant-microbe interactions are critical for ecosystem functioning and driving rhizosphere processes. To fully understand the communication pathways between plants and rhizosphere microbes, it is crucial to measure the numerous processes that occur in the plant and the rhizosphere. The present review first provides an overview of how plants interact with their surrounding microbial communities, and in turn, are affected by them. Next, different optical biosensing technologies that elucidate the plant-microbe interactions and provide pathogenic detection are summarized. Currently, most of the biosensors used for detecting plant parameters or microbial communities in soil are centered around genetically encoded optical and electrochemical biosensors that are often not suitable for field applications. Such sensors require substantial effort and cost to develop and have their limitations. With a particular focus on the detection of root exudates and phytohormones under biotic and abiotic stress conditions, novel low-cost and in-situ biosensors must become available to plant scientists.

Dual-Mode Optical Sensor Array for Detecting and Identifying Perillaldehyde in Solution Phase and Plant Leaf with Smartphone

Promising techniques for detecting and quantifying active components in the plants and foods have received global concern in smart agriculture. Dual-mode optical assays are becoming more attractive and popular thanks to robust and reliable analysis parameters. We herein unveil a novel turn-on and dual-mode sensor array comprising three kinds of reactive indicators including ring-closed rhodamine-hydrazine, squaraine-hydrazine, and 2,4-dinitrophenylhydrazine for evaluating perillaldehyde. Significant colorimetric and fluorescent changes were triggered through reacting primary amine/hydrazine with the active aldehyde group in perillaldehyde, thus turning on the chromogenic responses of all the indicators. Optimal colorimetric sensing showed good responses to perillaldehyde ranged up to 100 mM in ethanol. Dramatic fluorescence enhancement was also exhibited, illustrating good selectivity as well as high sensitivity (detection limit ∼20.0 μM). Inspired by rapid chemical reactions and distinct optical changes, distinct sensor array strips loaded with the optimal solid-state reactive indicators were developed for evaluating the perillaldehyde content in the perilla frutescence leaves. Smartphone-enabled readout system and digital data processing were further performed for chemometric analysis. A good correlation was obtained and the semiquantitative evaluation of the perillaldehyde content could be achieved within 15 min, possessing the significant features of naked-eye recognition, easy operation, and disposability. To the best of our knowledge, present work demonstrated the use of chromogenic sensing strips to evaluate the active perillaldehyde content in solution and vapor phases for the first time. Taken together, these characteristics also indicate that the present turn-on sensor array has great potential applications in the precise detection and evaluation of perillaldehyde in the forthcoming smart agriculture.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Template Imprinting Versus Porogen Imprinting of Small Molecules: A Review of Molecularly Imprinted Polymers in Gas Sensing

The selective sensing of gaseous target molecules is a challenge to analytical chemistry. Selectivity may be achieved in liquids by several different methods, but many of these are not suitable for gas-phase analysis. In this review, we will focus on molecular imprinting and its application in selective binding of volatile organic compounds and atmospheric pollutants in the gas phase. The vast majority of indexed publications describing molecularly imprinted polymers for gas sensors and vapour monitors have been analysed and categorised. Specific attention was then given to sensitivity, selectivity, and the challenges of imprinting these small volatile compounds. A distinction was made between porogen (solvent) imprinting and template imprinting for the discussion of different synthetic techniques, and the suitability of each to different applications. We conclude that porogen imprinting, synthesis in an excess of template, has great potential in gas capture technology and possibly in tandem with more typical template imprinting, but that the latter generally remains preferable for selective and sensitive detection of gaseous molecules. More generally, it is concluded that gas-phase applications of MIPs are an established science, capable of great selectivity and parts-per-trillion sensitivity. Improvements in the fields are likely to emerge by deviating from standards developed for MIP in liquids, but original methodologies generating exceptional results are already present in the literature.

Rigid Bay-Conjugated Perylene Bisimide Rotors: Solvent-Induced Excited-State Symmetry Breaking and Resonance-Enhanced Two-Photon Absorption

Intramolecular charge transfer and excited-state symmetry breaking have a significant effect on the nonlinear optical properties of multipolar chromophores. Rigid and nonplanar perylene bisimide derivatives (PBIs) functionalized at bay positions were comparatively and comprehensively investigated. In apolar solvents, two quadrupolar molecular rotors showed an obvious decrease of the A_0-0/A_0-1 ratios, suggesting strong exciton coupling with the adjacent PBI units initiated by the π-π stacking. The vanishment of the preferable dimer emission in polar solvents supported the plausible phenomena of excited-state symmetry breaking, thanks to the facile rotation around the rigid linkers. Comparative femtosecond transition absorption studies confirmed their notable differences in relaxation dynamics and the generation of radical anions (PBI^•-) and cations (PBI^•+). The maxima two-photon absorption (2PA) wavelengths obtained for the molecular rotors were slightly red-shifted to 670 nm with intrinsic resonance-enhanced characteristics, reflecting the synergistic effect of functional positions and molecular architectures. Meanwhile, the obvious increase of significant 2PA cross-section values in polar solvents illustrated the stabilization of the symmetry-broken dipolar states. Further femtosecond Z-scan also manifested the contribution of excited-state dynamics on the nonlinear optical properties of multipolar chromophores.

Fully Inkjet-Printed Chemiresistive Sensor Array Based on Molecularly Imprinted Sol-Gel Active Materials

The fabrication of chemiresistive sensors by inkjet printing is recognized as a breakthrough in gas-sensing applications. One challenge of this technology, however, is how to enhance the cross-selectivity of the sensor array. Herein, we present a ketjen black (KB) ink and molecularly imprinted sol-gel (MISG) inks to support the fabrication of a fully inkjet-printed chemiresistive sensor array, enabling the highly accurate recognition of volatile organic acids (VOAs) on the molecular level. The MISG/KB sensor array was prepared on a glossy photographic paper with a three-layer structure: a circuit layer by a commercial silver ink, a conductive layer by a KB ink, and an active selective layer by MISG inks imprinted by different templates. Hexanoic acid (HA), heptanoic acid, and octanoic acid were used as templates to prepare the MISGs and as targets to evaluate the detection and discrimination performance of the sensor array. Three resultant MISG/KB sensors exhibited high sensitivity and selectivity to VOA vapors. The limit of detection and imprinting factor were 0.018 ppm and 7.82, respectively, for HA-MISG/KB sensors to the corresponding target. With linear discriminant analysis of the gas responses, the MISG/KB sensor array can realize high discrimination to VOAs in single and binary mixtures. Furthermore, the proposed sensor array showed strong sensor robustness with excellent consistency, durability, bending, and humidity resistance. This work developed a fully inkjet-printed chemiresistive sensor array, enabling the realization of high cross-selectivity detection, achieving low-cost, scalable, and highly reproducible sensor fabrication, moving it closer to reliable, commercial, and wearable multi-analyte human body odor analysis potential.

Potential of metal and metal oxide nanoparticles in plant disease diagnostics and management: Recent advances and challenges

Plant diseases caused by phytopathogens are a severe threat to global food production. Management of plant diseases mostly rely on the application of pesticides which have several adverse effects on the ecosystem. Innovative and high-performance diagnostic tools are useful for the early detection of phytopathogens. Emerging role of metal and metal oxides nanoparticles (NPs) in plant disease diagnostics to combat crop diseases has been described. These NPs constitute new weapons against plant pathogens and facilitate the early diagnosis/management of crop diseases specifically in resource-poor conditions. The interactions between NPs, phytopathogens and plants showed great diversity and multiplicity which reduces chances of the development of resistant pathogen strains. The present article discusses the available literature as well as challenges and research gaps that are essential in the successful utilization of metal and metal oxide NPs for precise and timely detection and management of plant diseases.

Molecularly imprinted sol-gel/Au@Ag core-shell nano-urchin localized surface plasmon resonance sensor designed in reflection mode for detection of organic acid vapors

A molecularly imprinted sol-gel (MISG)/Au@Ag core-shell NU sensor is proposed for organic vapor detection in an optical fiber-based reflection mode. The compact structure design of the system in the reflection model is promising for practical use as a portable and rapid responsivity sensing probe. Volatile organic acids (OAs) are analogs to biogenetic volatile organic vapors related to specific human diseases. Here, Au@Ag core-shell nano-urchins exhibiting branched tips were synthesized and deposited on indium tin oxide (ITO) glass in small dimer and trimmer clusters to generate an enhanced electric field. A MISG solution was then spin-coated on the substrate to fabricate MISG-LSPR sensors, and three types of MISGs were developed for the detection of hexanoic acid, heptanoic acid and octanoic acid. The normalized spectral response indicated selectivity of the MISG-LSPR sensors for the corresponding template OAs. With Native Bayes and linear discriminant analysis of the sensor responses, where the latter were detected by the proposed system, single- and mixed-OA vapors could be classified into separate clusters. This signified that the proposed MISG-LSPR sensor can be applied toward pattern recognition of single vapors or multiple vapor mixtures.

Agricultural nanodiagnostics for plant diseases: recent advances and challenges

Crop diseases caused by pathogenic microorganisms pose severe threats to the global food supply. Effective diagnostic tools for timely determination of plant diseases become essential to the assurance of agricultural sustainability and global food security. Nucleic acid- and antibody-based molecular assays are gold-standard methodologies for the diagnosis of plant diseases, but the analyzing procedures are complex and laborious. The prominent physical or chemical properties of nanomaterials have enabled their use as innovative and high-performance diagnostic tools for numerous plant pathogens and other important disease biomarkers. Engineered nanomaterials have been incorporated into traditional laboratory molecular assays or sequencing technologies that offer notable enhancement in sensitivity and selectivity. Meanwhile, nanostructure-supported noninvasive detection tools combined with portable imaging devices (e.g., smartphones) have paved the way for fast and on-site diagnosis of plant diseases and long-term monitoring of plant health conditions, especially in resource-poor settings.

A Novel Immunosensing Method Based on the Capture and Enzymatic Release of Sandwich-Type Covalently Conjugated Thionine-Gold Nanoparticles as a New Fluorescence Label Used for Ultrasensitive Detection of Hepatitis B Virus Surface Antigen

A novel ultrasensitive and simple amplified immunosensing strategy is designed based on a surface-enhanced fluorescence (SEF) nanohybrid made from covalently conjugated thionine-gold nanoparticles (GNP-Th), as a novel amplified fluorescence label, and magnetic nanoparticles (MNPs), as a biological carrier, used for hepatitis B virus surface antigen (HBsAg) detection. This immunosensing strategy operates on the basis of the capture and then release of the amplified fluorescence label. Capturing of the antiHBs-antibody (Ab)-modified GNP-thionine hybrid (GNP-Th-Ab) is carried out through the formation of a two-dimensional (sandwich) probe between this amplified label and antiHBs-antibody-modified magnetic nanoparticles (MNP-Ab), in the presence of a target antigen and using an external magnetic force. Afterward, releasing of the captured fluorescence label is performed using a protease enzyme (pepsin) by a digestion mechanism of grafted antibodies on the GNP-thionine hybrid. As a result of antibody digestion, the amplified fluorescent hybrids (labels) are released into the solution. To understand the mechanism of enhanced fluorescence, the nature of the interaction between thionine and gold nanoparticles is studied using the B3LYP density functional method. In such a methodology, several new mechanisms and structures are used simultaneously, including a SEF-based metal nanoparticle-organic dye hybrid, dual signal amplification in a two-dimensional probe between the GNP-thionine hybrid and MNPs, and a novel releasing method using protease enzymes. These factors improve the sensitivity and speed, along with the simplicity of the procedure. Under optimal conditions, the fluorescence signal increases with the increment of HBs antigen concentration in the linear dynamic range of 4.6 × 10-9 to 0.012 ng/mL with a detection limit (LOD) of 4.6 × 10-9 ng/mL. The proposed immunosensor has great potential in developing ultrasensitive and rapid diagnostic platforms.

Tetrahydrocannabinol Detection Using Semiconductor-Enriched Single-Walled Carbon Nanotube Chemiresistors

Semiconductor-enriched single-walled carbon nanotubes (s-SWCNTs) have potential for application as a chemiresistor for the detection of breath compounds, including tetrahydrocannabinol (THC), the main psychoactive compound found in the marijuana plant. Herein we show that chemiresistor devices fabricated from s-SWCNT ink using dielectrophoresis can be incorporated into a hand-held breathalyzer with sensitivity toward THC generated from a bubbler containing analytical standard in ethanol and a heated sample evaporator that releases compounds from steel wool. The steel wool was used to capture THC from exhaled marijuana smoke. The generation of the THC from the bubbler and heated breath sample chamber was confirmed using ultraviolet-visible absorption spectroscopy and mass spectrometry, respectively. Enhanced selectivity toward THC over more volatile breath components such as CO₂, water, ethanol, methanol, and acetone was achieved by delaying the sensor reading to allow for the desorption of these compounds from the chemiresistor surface. Additionally, machine learning algorithms were utilized to improve the selective detection of THC with better accuracy at increasing quantities of THC delivered to the chemiresistor.

Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles

Plant pathogen detection conventionally relies on molecular technology that is complicated, time-consuming and constrained to centralized laboratories. We developed a cost-effective smartphone-based volatile organic compound (VOC) fingerprinting platform that allows non-invasive diagnosis of late blight caused by Phytophthora infestans by monitoring characteristic leaf volatile emissions in the field. This handheld device integrates a disposable colourimetric sensor array consisting of plasmonic nanocolorants and chemo-responsive organic dyes to detect key plant volatiles at the ppm level within 1 min of reaction. We demonstrate the multiplexed detection and classification of ten individual plant volatiles with this field-portable VOC-sensing platform, which allows for early detection of tomato late blight 2 d after inoculation, and differentiation from other pathogens of tomato that lead to similar symptoms on tomato foliage. Furthermore, we demonstrate a detection accuracy of ≥95% in diagnosis of P. infestans in both laboratory-inoculated and field-collected tomato leaves in blind pilot tests. Finally, the sensor platform has been beta-tested for detection of P. infestans in symptomless tomato plants in the greenhouse setting.

Detecting Fingerprints of Waterborne Bacteria on a Sensor

Human fecal contamination is a crucial threat that results in difficulties in access to clean water. Enterococcus faecalis is a bacteria which is utilized as an indicator in polluted water. Nevertheless, existing strategies face several challenges, including low affinity and the need for labelling, which limit their access to large scale applications. Herein, a label-free fingerprint of the surface proteins of waterborne bacteria on a sensor was demonstrated for real-time bacteria detection from aqueous and water samples. The kinetic performance of the sensor was evaluated and shown to have a range of detection that spanned five orders of magnitude, having a low detection limit (3.4 × 104 cfu/mL) and a high correlation coefficient (R2 = 0.9957). The sensor also designated a high selectivity while other competitor bacteria were employed. The capability for multiple usage and long shelf-life are superior to other modalities. This is an impressive surface modification method that uses the target itself as a recognition element, ensuring a broad range of variability to replicate others with different structure, size and physical and chemical properties.

Molecularly Imprinted Polymer-Based Hybrid Materials for the Development of Optical Sensors

We report on the development of new optical sensors using molecularly imprinted polymers (MIPs) combined with different materials and explore the novel strategies followed in order to overcome some of the limitations found during the last decade in terms of performance. This review pretends to offer a general overview, mainly focused on the last 3 years, on how the new fabrication procedures enable the synthesis of hybrid materials enhancing not only the recognition ability of the polymer but the optical signal. Introduction describes MIPs as biomimetic recognition elements, their properties and applications, emphasizing on each step of the fabrication/recognition procedure. The state of the art is presented and the change in the publication trend between electrochemical and optical sensor devices is thoroughly discussed according to the new fabrication and micro/nano-structuring techniques paving the way for a new generation of MIP-based optical sensors. We want to offer the reader a different perspective based on the materials science in contrast to other overviews. Different substrates for anchoring MIPs are considered and distributed in different sections according to the dimensionality and the nature of the composite, highlighting the synergetic effect obtained as a result of merging both materials to achieve the final goal.

Ultrasensitive Sniff-Cam for Biofluorometric-Imaging of Breath Ethanol Caused by Metabolism of Intestinal Flora

We developed a gas-imaging system (sniff-cam) for gaseous ethanol (EtOH) with improved sensitivity. The sniff-cam was applied to measure the extremely low concentration distribution of breath EtOH without the consumption of alcohol, which is related to the activity of the oral or gut bacterial flora. A ring-type ultraviolet-light-emitting diode was mounted around a camera lens as an excitation light source, which enabled simultaneous excitation and imaging of the fluorescence. In the EtOH sniff-cam, a nicotinamide adenine dinucleotide (NAD)-dependent alcohol dehydrogenase (ADH) was used to catalyze the redox reaction between EtOH and the oxidized form of NAD (NAD⁺). Upon application of gaseous EtOH to the ADH-immobilized mesh that was soaked in an NAD⁺ solution and placed in front of the camera, NADH was produced through an ADH-mediated reaction. NADH expresses fluorescence at an emission wavelength of 490 nm and excitation wavelength of 340 nm. Thus, the concentration distribution of EtOH was visualized by measuring the distribution of the fluorescence light intensity from NADH on the ADH-immobilized mesh surface. First, a comparison of image analysis methods based on the red-green-blue color (RGB) images and the optimization of the buffer pH and NAD⁺ solution concentration was performed. The new sniff-cam showed a 25-fold greater sensitivity and broader dynamic range (20.6-300000 ppb) in comparison to those of the previously fabricated sniff-cam. Finally, we measured the concentration distribution of breath EtOH without alcohol consumption using the improved sniff-cam and obtained a value of 116.2 ± 35.7 ppb (n = 10).