Colorimetric Sensor Arrays for Volatile Organic Compounds

Paper-based optical sensor arrays for simultaneous detection of multi-targets in aqueous media: A review

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media.

Smartphone-based detection and discrimination of amine vapors by a single dye-adsorbed material

Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,β,α',β'-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

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.

Comprehensive Qualitative and Quantitative Colorimetric Sensing of Volatile Organic Compounds Using Monolayered Metal-Organic Framework Films

Cross-responsive chemical sensors are in high demand owing to their ability to distinguish a broad range of analytes. In this study, a vapochromic sensor array based on metal-organic frameworks (MOFs), which exhibits distinct patterns when exposed to volatile organic compounds (VOCs) and humidity, is developed. Conventional sensor arrays consist of various receptors that produce different responses. The vapochromic MOF-based sensor comprises dicopper paddlewheel clusters and dimethylamine azobenzene as binary colorimetric sensing moieties. Upon exposure to VOCs, the constructed sensor encompasses a broad spectrum of colors, ranging from green to red. Furthermore, the color of the MOF is influenced by the solvent used during the pretreatment. Consequently, monolayered MOF thin films can be adapted to multicomponent array systems by immersing the MOF in different solvents. This system provides both qualitative and quantitative sensing, generating unique color patterns corresponding to specific VOC types. Notably, the sensor successfully discriminates each of 14 common VOCs and water and accurately categorizes unknown samples. Moreover, the system undergoes reversible color changes in response to humidity, obviating the need for high-temperature regeneration steps. This novel approach offers insights into the versatile applications of MOFs by creating a colorimetric sensor array capable of detecting various analytes.

Positively Charged Organosilanes Covalently Linked to the Silica Network as Modulating Tools for the Salinity Correction of pH Values Obtained with Colorimetric Sensor Arrays (CSAs)

Seven increasing levels of water salinity from 0.029 to 0.600 M (as NaCl) were used to investigate the dependence of pH measurement, performed using colorimetric sensor arrays (CSAs), on ionic strength. The CSAs were arrays of sensing spots prepared in the form of sol-gel-embedding Bromothymol Blue (BB) and Bromocresol Green (BCG) in a porous nitrocellulose support. The support was impregnated over the entire thickness (≈100 µm), allowing for the signal (Hue) acquisition on the opposite side to the contact with the sample solution. Three CSAs were prepared, M1, M2, and M3. M1 contained a free cationic surfactant, hexadecyltrimethylammonium p-toluenesulfonate (CTApTs), for modulating the pKa of the indicators. In M2, the surfactant dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride (DTSACl) was covalently bonded to the sol-gel. M3 was prepared like M2 but using a larger amount of ethanol as the solvent for the synthesis. The modulation of the CTApTs or the DTSACl concentration enabled the tuning of the pKa. In general, the pKa modulation ability decreased with the increase in salinity. The presence of a surfactant covalently linked to the backbone partially reduced the competitiveness of the anionic species, improving the results. Nevertheless, the salt effect was still present, and a correction algorithm was required. Between pH 5.00 and 12.00, this correction could be made automatically by using spots taken as references to produce sensors independent of salinity. As the salt effect is virtually absent above 0.160 M, M2 and M3 can be used for future applications in seawater.

Catechol oxidase nanozyme based colorimetric sensors array for highly selective distinction among multiple catecholamines

In order to effectively monitor multiple catecholamine (CA) neurotransmitters with extreme similar structures, a rapid, sensitive and selective detection strategy has become an urgent problem to be solved. In this paper, a novel colorimetric sensors array based on CuNCs protected by various ligands such as tannic acid, ascorbic acid and polymethylacrylic acid (CuNCs@TA, CuNCs@AA and CuNCs@PMAA) was constructed. All of these CuNCs could mimic catechol oxidase to selective catalyze catechol-type analogues (such as CAs) to corresponding quinones along with color changes. Furthermore, experiments and theory calculations demonstrated that Cr6+-modification on the surface of CuNCs facilitated the steady-state kinetics of enzymatic activity. Based on these CuNCs as sensing probes, this sensors array can quickly detect different CAs (such as epinephrine (EP), including dopamine (DA), norepinephrine (NE) and l-dopa) with similar structures. When those analogues were added to the CuNC-based colorimetric array sensors, different absorbance changes were produced at 485 nm. Linear discriminant analysis (LDA) showed that the tri-probe colorimetric array sensors could recognize and distinguish these analogues, and corresponding binary and ternary mixtures could be well categorized. The value of Factor 1 of an array with varied CA concentrations had a good linear correlation, and the detection limit (LOD) was as low as 10-8∼10-9 mol/L. Four CA analogues in real samples were identified by CuNCs-based colorimetric array sensors. This work provides a fast and convenient experimental basis for monitoring the complex structure CAs neurotransmitters.

Portable Sensor Array for On-Site Detection and Discrimination of Pesticides and Herbicides Using Multivariate Analysis

Modern agricultural practice relies heavily on pesticides and herbicides to increase crop productivity, and consequently, their residues have a negative impact on the environment and public health. Thus, keeping these issues in account, herein we developed an azodye-based chromogenic sensor array for the detection and discrimination of pesticides and herbicides in food and soil samples, utilizing machine learning approaches such as hierarchical clustering analysis, principal component analysis, linear discriminant analysis (LDA), and partial least square regression (PLSR). The azodye-based sensor array was developed in combination with various metal ions owing to their different photophysical properties, which led to distinct patterns toward various pesticides and herbicides. The obtained distinct patterns were recognized and processed through automated multivariate analysis, which enables the selective and sensitive identification and discrimination of various target analytes. Further, the qualitative and quantitative determination of target analytes were performed using LDA and PLSR; the results obtained show a linear correlation with varied concentrations of target analytes with R2 values from 0.89 to 0.96, the limit of detection from 5.3 to 11.8 ppm with a linear working range from 1 to 30 μM toward analytes under investigation. Further, the developed sensor array was successfully utilized for the discrimination of a binary mixture of pesticide (chlorpyrifos) and herbicide (glyphosate).

Four levels of in-sensor computing in bionic olfaction: from discrete components to multi-modal integrations

Sensing and computing are two important ways in which humans attempt to perceive and understand the analog world through digital devices. Analog-to-digital converters (ADCs) discretize analog signals while the data bus transmits digital data between the components of a computer. With the increase in sensor nodes and the application of deep neural networks, the energy and time consumption limit the increment of data throughput. In-sensor computing is a computing paradigm that integrates sensing, storage, and processing in one device without ADCs and data transfer. According to the integration degree, herein, we summarize four levels of in-sensor computing in the field of artificial olfactory. In the first level, we show that different functions are conducted by using discrete components. Next, the data conversion and transfer are exempt within the in-memory computing architecture with necessary data encoding. Subsequently, in-sensor computing is integrated into a single device. Finally, multi-modal in-sensor computing is proposed to improve the quality and reliability of the classification results. At the end of this minireview, we provide an outlook on the use of metal nanoparticle devices to achieve such in-sensor computing for bionic olfaction.

Sensitive and simple multi-ion detection using organic nanocrystal enrichment in paper analytical devices

Microfluidic paper analytical devices (μPADs) are among the most promising platforms for heavy metal ion analysis. On the other hand, achieving simple and highly sensitive analysis of μPADs is challenging. In this study, we developed a simple enrichment method for sensitive multi-ion detection utilizing water-insoluble organic nanocrystals accumulated on μPAD. By combining the enrichment method with multivariate data analysis, three metal ion concentrations in the ion mixtures were simultaneously quantified with high sensitivity owing to the sensitive responses of the organic nanocrystals. In this work, we successfully quantified Zn²⁺, Cu²⁺, and Ni²⁺ at 20 ng L^-1 in the mixed ion solution using only two dye indicators with a larger sensitivity improvement than those reported in previous studies. Interference studies revealed possibilities for a practical application in real sample analysis. This developed approach also can be used for other analytes.

Molecular modeling and simulation of transition metal-doped molybdenum disulfide biomarkers in exhaled gases for early detection of lung cancer

BACKGROUND

The presence of volatile organic compounds (VOCs) in the exhaled breath of lung cancer patients is the only available source for detecting the disease at its initial stage. Exhaled breath analysis depends purely on the performance of the biosensors. The interaction between VOCs and pristine MoS₂ is repulsive in nature. Therefore, modifying MoS₂ via surficial adsorption of the transition metal nickel is of prime importance. The surficial interaction of six VOCs with Ni-doped MoS₂ led to substantial variations in the structural and optoelectronic properties compared to those of the pristine monolayer. The remarkable improvement in the conductivity, thermostability, good sensing response, and recovery time of the sensor exposed to six VOCs revealed that a Ni-doped MoS₂ exhibits impressive properties for the detection of exhaled gases. Different temperatures have a significant impact on the recovery time. Humidity has no effect on the detection of exhaled gases upon exposure to VOCs. The obtained results may encourage the use of exhaled breath sensors by experimentalists and oncologists to enable potential advancements in lung cancer detection.

METHODS

The surface adsorption of transition metal and its interaction with volatile organic compounds on a MoS₂ surface was studied by using Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA). The pseudopotentials used in the SIESTA calculations are norm-conserving in their fully nonlocal forms. The atomic orbitals with finite support were used as a basis set, allowing unlimited multiple-zeta and angular momenta, polarization, and off-site orbitals. These basis sets are the key for calculating the Hamiltonian and overlap matrices in O(N) operations. The present hybrid density functional theory (DFT) is a combination of PW92 and RPBE methods. Additionally, the DFT+U approach was employed to accurately ascertain the coulombic repulsion in the transition elements.

Diffusion-modulated colorimetric sensor for continuous gas detection

Due to their high sensitivity and selectivity, low cost, and good compatibility for sensor array integration, colorimetric gas sensors are widely used in hazardous gas detection, food freshness assessment, and gaseous biomarker identification. However, colorimetric gas sensors are usually designed for one-time discrete measurement because the sensing materials are entirely exposed to analytes during the sensing process. The fast consumption of sensing materials limits colorimetric sensors' applications in continuous analytes monitoring, increases the operation complexity and brings challenges for calibration. In this work, we reported a novel sensor design to prolong the lifetime of colorimetric gas sensors by engineering the gas diffusion process to preserve the sensing materials. We compared two geometries for gas diffusion control in a sensing matrix through simulation and experiment on an ammonia sensing platform. We found that the 2-dimensional gas diffusion geometry enabled a better sensor performance, including more stable and higher sensitivity and a more linear response to ammonia concentration compared to 1-dimensional gas diffusion geometry. We also demonstrated the usability of this diffusion-modulated colorimetric sensor for continuous environmental ammonia monitoring.

Anthocyanin-rich black carrot (Daucus carota ssp. sativus var. atrorubens Alef.) and red cabbage (Brassica oleracea) extracts incorporated biosensor for colorimetric detection of Helicobacter pylori with color image processing

In this work, we developed novel colorimetric biosensors consisting of anthocyanin-rich either black carrot (Daucus carota ssp. sativus var. atrorubens Alef.) or red cabbage (Brassica oleracea) extracts for rapid, sensitive, and economic detection of Helicobacter pylori (H. pylori). We comparatively prepared two test solutions as biosensors including anthocyanin-rich black carrot extract (Anth@BCE) and red cabbage extract (Anth@RCE), both of which fixed to pH 2.5 and investigated their colorimetric responses based on electronic structure and electron density of anthocyanins. We successfully used anthocyanin-rich BCE and RCE as natural pH indicators in detection of H. pylori and introduced their advantages like non-toxicity, easy accessibility, and high stability compared to synthetic indicators. The BCE and RCE tests gave the best color change in the presence of 103 CFU/mL (at 60 min) and 104 CFU/mL (at 75 min) H. pylori suspensions prepared in an artificial gastric fluid. The limit of detection was down to 10 CFU/mL for RCE and BCE tests by increasing incubation time (≥ 5 h). We further made an additional study that color differences in the colorimetric responses observed by naked eyes were supported by digital image processing with RGB (Red Green Blue) and Delta-E (ΔE) analysis. It is confirmed that results evaluated by naked eyes and digital image processing are well consistent with each other. These findings proposed that these colorimetric tests can be implemented to pH dependent detection of various microorganisms and can be effectively transferred from laboratory work to clinics in the near future.

Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor

Colorimetric sensing technology for the detection of explosives, drugs, and their precursor chemicals is an important and effective approach. In this work, we use various machine learning models to detect these substances from colorimetric sensing experiments conducted in controlled environments. The detection experiments based on the response of a colorimetric chip containing 26 chemo-responsive dyes indicate that homemade explosives (HMEs) such as hexamethylene triperoxide diamine (HMTD), triacetone triperoxide (TATP), and methyl ethyl ketone peroxide (MEKP) used in improvised explosives devices are detected with true positive rate (TPR) of 70-75%, 73-90% and 60-82% respectively. Time series classifiers such as Convolutional Neural Networks (CNN) are explored, and the results indicate that improvements can be achieved with the use of kinetics of the chemical responses. The use of CNNs is limited, however, to scenarios where a large number of measurements, typically in the range of a few hundred, of each analyte are available. Feature selection of important dyes using the Group Lasso (GPLASSO) algorithm indicated that certain dyes are more important in discrimination of an analyte from ambient air. This information could be used for optimizing the colorimetric sensor and extend the detection to more analytes.

Preparation of Colorimetric Sensor Array System to Evaluate the Effects of Alginate Edible Coating on Boiled-Dried Anchovy

The colorimetric sensor array (CSA) is a simple, rapid, and cost-effective system widely used in food science to assess food quality by identifying undesirable volatile organic compounds. As a prospective alternative to conventional techniques such as total volatile basic nitrogen, peroxide value, and thiobarbituric acid reactive substance analysis, the CSA system has garnered significant attention. This study evaluated the quality of edible-coated food products using both conventional and CSA methods in order to demonstrate that the CSA approach is a feasible alternative to conventional methods. Boiled-dried anchovies (BDA) were selected as the model food product, and the sample's quality was assessed as a function of storage temperature and incubation period using conventional techniques and the CSA system. The surface of BDA was coated with an edible alginate film to form the surface-modified food product. The conventional methods revealed that an increase in storage temperature and incubation time accelerated the lipid oxidation process, with the uncoated BDA undergoing lipid oxidation at a faster rate than the coated BDA. Utilizing multivariate statistical analysis, the CSA approach essentially yielded the same results. In addition, the partial least square regression technique revealed a strong correlation between the CSA system and conventional methods, indicating that the CSA system may be a feasible alternative to existing methods for evaluating the quality of food products with surface modifications.

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.

Humidity-Independent Artificial Olfactory Array Enabled by Hydrophobic Core-Shell Dye/MOFs@COFs Composites for Plant Disease Diagnosis

As a class of important artificial olfactory system, the colorimetric sensor array possesses great potential for commercialization due to its cost-effectiveness and portability. However, when applied to practical applications, the humidity interference from ambient environment and dissatisfactory sensitivity for trace target VOCs are largely unsolved problems. To overcome the problems, we developed a series of dye/MOFs@COFs gas-sensing materials with core-shell structure using a hydrophobization strategy by encapsulation of dye/metal-organic frameworks (MOFs) into hydrophobic covalent organic frameworks (COFs). Benefiting from the hydrophobic property of the COF shell, the dye/MOFs@COFs composites were endowed with excellent humidity-resistance even under 100% relative humidity (RH). Moreover, due to the uniform distribution of dyes on the porous MOFs, the dye/MOFs@COFs sensors also exhibited improved sensitivity at the sub-ppm level, compared with conventional dye sensors. On basis of the excellent humidity-resistance and improved sensitivity, an artificial olfactory array based on dye/MOFs@COFs composites was proven to be a successful practical application in early and accurate detection of wheat scab (1 day after inoculation) by monitoring its released VOC markers. The synthetic strategy for core-shell dye/MOFs@COFs is applicable to a wide range of colorimetric sensor arrays, endowing them with excellent humidity-resistance and sensitivity for the feasibility of practical applications.

Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14–88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.

Volatile Organic Compound Sensing Array and Optoelectronic Filter System using Ion-Pairing Dyes with a Wide Visible Spectrum

An ideal dye-based sensing array has essential design requirements, including facile preparation methodology, tolerance to water vapor, a broad range of color-responsive changes, and a simple readout system. Here, a brief synthetic route is developed for ion-pairing dyes exhibiting unusual chromatic changes across the entire visible spectrum. It requires only mixing and precipitation under mild conditions. The dyes are applied to a sensing array containing 12 sensing elements with different initial states. Owing to the numerous color variations of the dyes, the color map generated by the array is highly simple yet sufficiently accurate to distinguish among the different functional groups (such as amines, aldehydes, and carboxylic acids) as well as carbon chain lengths. Principle component analysis (PCA) demonstrates that volatile organic compounds (VOCs) can be well classified according to the color changes of the sensing array. The ion-pairing dyes are embedded into 3D stacked nanofibers via electrospinning, and function as effective harmful-gas (e.g., formaldehyde) sensors with sub-ppm theoretical detection limits (0.15 ppm). Finally, the 3D stacked nanofibers can be employed in an optoelectronic filter system that automatically checks for formaldehyde in the surroundings and also confirms the effective removal of the detected formaldehyde by the gas filter cartridge.

A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems

Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy

Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing.

Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.

A review on colorimetric indicators for monitoring product freshness in intelligent food packaging: Indicator dyes, preparation methods, and applications

Intelligent food packaging system exhibits enhanced communication function by providing dynamic product information to various stakeholders (e.g., consumers, retailers, distributors) in the supply chain. One example of intelligent packaging involves the use of colorimetric indicators, which when subjected to external stimuli (e.g., moisture, gas/vapor, electromagnetic radiation, temperature), display discernable color changes that can be correlated with real-time changes in product quality. This type of interactive packaging system allows continuous monitoring of product freshness during transportation, distribution, storage, and marketing phases. This review summarizes the colorimetric indicator technologies for intelligent packaging systems, emphasizing on the types of indicator dyes, preparation methods, applications in different food products, and future considerations. Both food and nonfood indicator materials integrated into various carriers (e.g., paper-based substrates, polymer films, electrospun fibers, and nanoparticles) with material properties optimized for specific applications are discussed, targeting perishable products, such as fresh meat and fishery products. Colorimetric indicators can supplement the traditional "Best Before" date label by providing real-time product quality information to the consumers and retailers, thereby not only ensuring product safety, but also promising in reducing food waste. Successful scale-up of these intelligent packaging technologies to the industrial level must consider issues related to regulatory approval, consumer acceptance, cost-effectiveness, and product compatibility.

Fabrication of a "progress bar" colorimetric strip sensor array by dye-mixing method as a potential food freshness indicator

Colorimetric sensing is a low-cost, intuitive method for monitoring the freshness of food. We prepared a colorimetric strip sensor array by mixing different amounts of bromophenol blue (BPB) and bromocresol green (BCG). As results of NH₃ simulation, the array strip turned from yellow to blue, and the number of blue spots increased with the increasing NH₃, like a progress bar. Although the actual color is quite different, the color-changing trend was consistent with the simulated model calculated by a computer. The progress bar results remained stable under three lighting conditions. Furthermore, in the Cod preservation experiment, the color-changing progress of the strip sensor array is consistent with the simulation and can indicate Cod freshness while providing more distinguish levels. Therefore, a "progress bar" indicator built by this strategy possess the potential of realizing nondestructive, more accurate, and commercially available food quality monitoring through the naked eye and smart equipment recognition.

A feasibility study for rapid evaluation of emulsion oxidation using synchronous fluorescence spectroscopy coupled with chemometrics

A rapid method based on three-dimensional synchronous fluorescence spectroscopy was developed for emulsion oxidation evaluation. This method was selected because of its high sensitivity to dissolved organic matter typically occurring in the lipid oxidation. Spectral signal and chemical reference measurements were recorded for each emulsion sample as input and output data for the model construction. Characteristic values were extracted from the spectral data by the application of parallel factor (PARAFAC) analysis. Partial least squares regression (PLSR) was then used to construct a regression model for the rapid determination of emulsion oxidation. The correlation coefficient of the calibration and prediction sets were used as the performance parameters for the PLSR models as follows: R = 0.929, 0.973 for emulsion samples stored at 25℃; R = 0.897, 0.903 for emulsion samples stored at 70℃. The overall results demonstrated that the fluorescence spectroscopy, coupled with PARAFAC and PLSR algorithms, could be successfully used as a rapid method for the emulsion oxidation evaluation.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

Evaluation of adulteration in distillate samples of Rosa damascena Mill using colorimetric sensor arrays, chemometric tools and dispersive liquid-liquid microextraction-GC-MS

INTRODUCTION

Rosa damascena Mill distillate and its essential oil are widely used in cosmetics, perfumes and food industries. Therefore, the methods of detection for its authentication is an important issue.

OBJECTIVES

We suggest colorimetric sensor array and chemometric methods to discriminate natural Rosa distillate from synthetic adulterates.

MATERIAL AND METHODS

The colour responses of 20 indicators spotted on polyvinylidene fluoride (PVDF) substrate were monitored with a flatbed scanner; then their digital representation was analysed with principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA).

RESULTS

Accurate discrimination of the diluted- and synthetic-mixture samples from the original ones was achieved by PLS-DA and SIMCA models with error rate of 0.01 and 0, specificity of 0.98 and 1, sensitivity of 1 and 1, and accuracy of 0.98 and 0.96, respectively. Discrimination of the synthetic adulterate from the original samples was achieved with error rate of 0.03 and 0.03, specificity of 0.94 and 0.93, sensitivity of 1 and 1, and accuracy of 0.93 and 0.71 with PLS-DA and SIMCA models, respectively. Moreover, the chemical constituents of the samples were analysed using dispersive liquid-liquid microextraction and gas chromatography-mass spectrometry (GC-MS). The main constituents of the distillate were geraniol, citronellol, and phenylethyl alcohol in different percentages, in both original and synthetic adulterate samples.

CONCLUSION

These results point out the successful combination of colorimetric sensor array and PLS-DA and SIMCA as a fast, sensitive and inexpensive screening tool for discrimination of original samples of R. damascena Mill distillate from those prepared from synthetic Rosa essential oils.

Visualising household air pollution: Colorimetric sensor arrays for monitoring volatile organic compounds indoors

Indoor air quality monitoring as it relates to the domestic setting is an integral part of human exposure monitoring and health risk assessment. Hence there is a great need for easy to use, fast and economical indoor air quality sensors to monitor the volatile organic compound composition of the air which is known to be significantly perturbed by the various source emissions from activities in the home. To meet this need, paper-based colorimetric sensor arrays were deployed as volatile organic compound detectors in a field study aiming to understand which activities elicit responses from these sensor arrays in household settings. The sensor array itself is composed of pH indicators and aniline dyes that enable molecular recognition of carboxylic acids, amines and carbonyl-containing compounds. The sensor arrays were initially deployed in different rooms in a single household having different occupant activity types and levels. Sensor responses were shown to differ for different room settings on the basis of occupancy levels and the nature of the room emission sources. Sensor responses relating to specific activities such as cooking, cleaning, office work, etc were noted in the temporal response. Subsequently, the colorimetric sensor arrays were deployed in a broader study across 9 different households and, using multivariate analysis, the sensor responses were shown to correlate strongly with household occupant activity and year of house build. Overall, this study demonstrates the significant potential for this type of simple approach to indoor air pollution monitoring in residential environments.

Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient's exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

Discrimination of rice varieties using smartphone-based colorimetric sensor arrays and gas chromatography techniques

A smartphone-based colorimetric sensor array system was established for discrimination of rice varieties having different geographical origins. Purposely, aroma profiling of nine rice varieties was performed using solid-phase microextraction gas chromatography-mass spectrometry. Alcohols, aldehydes, alkanes, ketones, heterocyclic compounds, and organic acids represent the abundant compounds. Colorimetric sensor array system produced a characteristic color difference map upon its exposure to volatile compounds of rice. Discrimination of rice varieties was subsequently achieved using principal component analysis, hierarchical clustering analysis, and k-nearest neighbors. Rice varieties from same geographical source were clustered together in the scatter plot of principal component analysis and hierarchical clustering analysis dendrogram. The k-nearest neighbors algorithm delivered optimal results with discrimination rate of 100% for both calibration and prediction sets using sensor array system. The smartphone-based colorimetric sensor array system and gas chromatography technique were able to effectively differentiate rice varieties with the advantage of being simple, rapid, and low-cost.

Development of pattern recognition based on nanosheet-DNA probes and an extendable DNA library

Pattern recognition, also called "array sensing," is a recognition strategy with a wide and expandable analysis range, based on high-throughput analysis data. In this work, we constructed a sensor array for the identification of targets including bacterial pathogens and proteins by using FAM-labeled DNA probes and 2D nanosheet materials. We designed an ordered and extendible DNA library for the collection of recognition probes. Unlike traditional DNA probes with random and massive sequences, our DNA library was constructed following a 5-digit binary number (00000-11111, 0 = CCC, and 1 = TTT), and especially, 8 special symmetry sequences were chosen from the library. Two different nanosheet materials were used as the quencher. When targets were added, the interaction between DNA and the nanosheets was competitively affected, and as a result, the fluorescence signal changed accordingly. Finally, by using our fluorescent sensor array, 17 bacteria and 8 proteins were precisely recognized. We believe that our work has provided a simple and valuable strategy for the improvement of the recognition range and discrimination precision for the development of pattern recognition.

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.

Investigation of colorimetric biosensor array based on programable surface chemistry of M13 bacteriophage towards artificial nose for volatile organic compound detection: From basic properties of the biosensor to practical application

Various threats such as explosives, drugs, environmental hormones, and spoiled food manifest themselves with the presence of volatile organic compounds (VOCs) in our environment. In order to recognize and respond to these threats early, the demand for highly sensitive and selective electronic noses is increasing. The M13 bacteriophage-based optoelectronic nose is an excellent candidate to meet all these requirements. However, the phage-based electronic nose is still in its infancy, and strategies that include a systematic approach and development are still essential. Here, we have integrated theoretical and experimental approaches to analyze the correlation between the surface chemistry of genetically engineered phage and the phage-based optoelectronic nose properties. The reactivity of the genetically engineered phage color film to some VOCs were quantitatively analyzed, and the correlation with the binding affinity value calculated by Density-functional theory (DFT) was compared. This demonstrates that phage color films have controllable reactivity through a genetic engineering. We have selected phages that are advantageous in distinguishing each VOCs in this work through hierarchical cluster analysis (HCA). The reason for this difference was verified through the optimized geometry calculated by DFT. Through this, it was confirmed that the tryptophan-based and the Histidine-based of genetically engineered phage film are important in distinguishing the VOCs (Y-hexanolactone, 2-isopropyl-4-methylthiazole, ethanol, acetone, ethyl acetate, and acetaldehyde) used in this work to evaluate the peach freshness quality. This was applied to the design of a field-applied phage-based optoelectronic nose and verified by measuring the freshness of the actual fruit.

Investigation of the relationship between skin-emitted volatile fatty acids and skin surface acidity in healthy participants - a pilot study

Volatile organic compounds (VOCs) emitted from human skin are of great interest in general in research fields including disease diagnostics and comprise various compound classes including acids, alcohols, ketones and aldehydes. The objective of this research is to investigate the volatile fatty acid (VFA) emission as recovered from healthy participant skin VOC samples and to characterise its association with skin surface acidity. VOC sampling was performed via headspace-solid phase microextraction (HS-SPME) with analysis via gas chromatography-mass spectrometry (GC-MS). Several VFAs were recovered from participants, grouped based on gender and site (female forehead, female forearm, male forearm). Saturated VFAs (C9, C12, C14, C15, C16) and the unsaturated VFA C16:1 (recovered only from the female forehead) were considered for this study. VFA compositions and abundances are discussed in the context of body site and corresponding gland type and distribution, and their quantitative association with skin acidity investigated. Normalised chromatographic peak areas of the recovered VFAs were found to linearly correlate with hydrogen ion concentration measured at each of the different sites considered and is the first report to our knowledge to demonstrate such an association. Our observations are explained in terms of the free fatty acid (FFA) content at the skin surface which is well-established as being a major contributor to skin surface acidity. Furthermore, it is interesting to consider that these VFA emissions from skin, governed by equilibrium vapour pressures exhibited at the skin surface, will be dependent on skin pH. It is proposed that these pH-modulated equilibrium vapour pressures of the acids could be resulting in an enhanced VFA emission sensitivity with respect to skin surface pH. To translate our observations made here for future wearable biodiagnostic applications, the measurement of skin surface pH based on the volatile emission was demonstrated using a pH indicator dye in the form of a planar colorimetric sensor, which was incorporated into a wearable platform and worn above the palm surface. As acidic skin surface pH is required for optimal skin barrier function and cutaneous antimicrobial defence, it is envisaged that these colorimetric volatile acid sensors could be deployed in robust wearable formats for monitoring health and disease applications in the future.

Colorimetric Sensing of Volatile Organic Compounds Produced from Heated Cooking Oils

Measurement of cooking-associated air pollution indoors is an integral part of exposure monitoring and human health risk assessment. There is a need for easy to use, fast, and economical detection systems to quantify the various emissions from different sources in the home. Addressing this challenge, a colorimetric sensor array (CSA) is reported as a new method to characterize volatile organic compounds produced from cooking, a major contributor to indoor air pollution. The sensor array is composed of pH indicators and aniline dyes from classical spot tests, which enabled molecular recognition of a variety of aldehydes, ketones, and carboxylic acids as demonstrated by hierarchical clustering and principal component analyses. To demonstrate the concept, these CSAs were employed for differentiation of emissions from heated cooking oils (sunflower, rapeseed, olive, and groundnut oils). Sensor results were validated by gas chromatography-mass spectrometry analysis, highlighting the potential of the sensor array for evaluating cooking emissions as a source of indoor air pollution.

Simultaneous detection and identification of thiometon, phosalone, and prothioconazole pesticides using a nanoplasmonic sensor array

In this work, a colorimetric sensor array has been designed for the identification and discrimination of thiometon (TM) and phosalone (PS) as organophosphate pesticides and prothioconazole (PC) as a triazole pesticide. For this purpose, two different plasmonic nanoparticles including unmodified gold nanoparticles (AuNPs) and unmodified silver nanoparticles (AgNPs) were used as sensing elements. The principle of the proposed strategy relied on the aggregation AuNPs and AgNPs through the cross-reactive interaction between the target pesticides and plasmonic nanoparticles. Therefore, these aggregation-induced UV-Vis spectra changes were utilized to discriminate the target pesticides with the help of linear discriminant analysis (LDA). Besides, we have employed the bar plots and the heat maps as visual non-statistical methods to differentiate the pesticides in a wide range of concentrations (i.e., 20-5000 ng mL^-1). Multivariate calibration plots from partial least squares (PLS)- regression indicated that the responses linearly depend on the pesticide concentrations in the range of 100-1000 ng mL^-1 with the limit of detections (LOD) of 66.8, 68.3, and 41.4 ng mL^-1, for TM, PS, and PC, respectively. Finally, the potential applicability of the proposed sensor array has been evaluated for the detection and identification of the pesticides in the mixtures, water samples, and cucumber fruit.

Mitigation of Humidity Interference in Colorimetric Sensing of Gases

Colorimetric sensing technologies have been widely used for both quantitative detection of specific analyte and recognition of a large set of analytes in gas phase, ranging from environmental chemicals to biomarkers in breath. However, the accuracy and reliability of the colorimetric gas sensors are threatened by the humidity interference in different application scenarios. Though substantial progress has been made toward new colorimetric sensors development, unless the humidity interference is well addressed, the colorimetric sensors cannot be deployed for real-world applications. Although there are comprehensive and insightful review articles about the colorimetric gas sensors, they have focused more on the progress in new sensing materials, new sensing systems, and new applications. There is a need for reviewing the works that have been done to solve the humidity issue, a challenge that the colorimetric gas sensors commonly face. In this review paper, we analyzed the mechanisms of the humidity interference and discussed the approaches that have been reported to mitigate the humidity interference in colorimetric sensing of environmental gases and breath biomarkers. Finally, the future perspectives of colorimetric sensing technologies are also discussed.

The Optoelectronic Nose

How does one tell the difference between one molecule or mixture of molecules from another? Chemical sensing seeks to probe physical or chemical properties of molecular or ionic species (i.e., analytes) and transform that information into a useful and distinguishable output. The olfactory system of animals is the prototype of chemical sensing. Even for human beings (who are generally more visual than olfactory creatures), the sense of smell is one of our most basic capabilities, and we can discriminate among many thousands, and possibly even billions, of different odors. The chemical specificity of the olfactory system does not come from specific receptors for specific analytes (i.e., the traditional lock-and-key model of enzyme-substrate interactions), but rather olfaction uses pattern recognition of the combined responses of several hundred olfactory receptors.In analogy to olfaction, colorimetric sensor arrays provide high dimensional data from the color changes of chemically responsive colorants as they are exposed to analytes. These colorants include pH responsive dyes, Lewis acid/base indicators, redox dyes, vapochromics, and surface-modified silver nanoparticles. The color difference maps so created provide chemical sensing with high sensitivity (often down to ppb levels), impressive discrimination among very similar analytes, and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, both in the gas and liquid phases. Such colorimetric arrays probe a wide range of the chemical reactivity of analytes, rather than the limited dimensionality of physical properties (e.g., mass) or physisorption (e.g., traditional electronic noses). Our sensor arrays are disposable and simple to produce by either inkjet or robotic dip-pen printing onto the surface of porous polymer membranes or even paper.Design of both sensor arrays and optical readers for their analysis has advanced to a fully self-contained pocket-sized instrument, the optoelectronic nose. Quantitative analysis requires appropriate chemometric methods for pattern recognition of data with inherently high dimensionality, e.g., hierarchical cluster analysis and support vector machines. A wide range of applications for the colorimetric sensor arrays has been developed, including personal dosimetry of toxic industrial chemicals, detection of explosives or fire accelerants, monitoring pollutants for artwork and cultural heritage preservation, quality control of foods and beverages, rapid identification of bacteria and fungi, and detection of disease biomarkers in breath or urine. The development of portable, high-accuracy instrumentation using standard imaging devices with the capability of onboard, real-time analysis has had substantial progress and increasingly meets the expectations for real-world use.

Visual detection of different metal ions based on the tug of war between triangular Au nanoparticles and metal ions against mercaptans

Herein, a new colorimetric sensor array was developed for the first time, which can rapidly recognize 9 types of metal ions (e.g., Ni2+, Zn2+, Cd2+, Cu2+, Cr3+, Fe2+, Se2+, Mn2+, and Mg2+). The colorimetric characteristics of the sensor array were closely related to the oxidation etching of the triangular gold nanoplates (AuNPLs) by hydrogen peroxide (H2O2), catalyzed by horseradish peroxidase (HRP). In the design, two types of thiols (glutathione (GSH) and cysteine (Cys)) as recognition elements were employed to construct the sensor units (AuNPLs/GSH and AuNPLs/Cys) and adjust the etching degrees of AuNPLs in the presence of various metal ions. The differential binding affinities between metal ions and thiols will lead to different degrees of oxidation etching of AuNPLs with hydrogen peroxide, exhibiting characteristic colors, which can be visually distinguished by the naked eye. Thus, the colorimetric sensor array provides a new way for the discrimination of various metal ions, thereby simplifying the water quality analysis.

User-friendly lab-on-paper optical sensor for the rapid detection of bacterial spoilage in packaged meat products

A lab-on-paper colorimetric sensor for detection and quantification of bacterial meat spoilage is reported.

Hydrogel gratings with patterned analyte responsive dyes for spectroscopic sensing

This is an unprecedented report of hydrogel gratings with an analyte responsive dye immobilised in alternating strips where the patterned dye is its own dispersive element to perform spectroscopy. At each wavelength, the diffraction efficiency of hydrogel gratings is a function of dye absorbance, which in turn is dependent on the concentration of analytes in samples. Thus, changes in intensity of diffracted light of hydrogel gratings were measured for sensing of analytes. Equally, the ratio of diffracted intensities at two wavelengths was used for quantification of analytes to reduce errors caused by variations in intensity of light sources and photobleaching of dyes. 15.27 μm pitch gratings were fabricated by exposing 175 μm thick films of photofunctionalisable poly(acrylamide) hydrogel in a laser interferometric lithography setup, generating an array of alternating lines with and without free functional groups. The freed functional groups were reacted with pH sensitive fluorescein isothiocyanate to create gratings for measurement of pH. The ratio of intensity of diffracted light of hydrogel gratings at 430 and 475 nm was shown to be linear over 4 pH units, which compares favourably with ∼2 pH units for conventional absorption spectroscopy. This increased dynamic range was a result of cancellation of the opposite non-linearities in the pH response of the analyte responsive dye and the diffraction efficiency as a function of dye absorbance.

This is an unprecedented report of hydrogel gratings with an analyte responsive dye immobilised in alternating strips where the patterned dye is its own dispersive element to perform spectroscopic sensing.