Optical sensor arrays for chemical sensing: the optoelectronic nose

A novel immunoassay technique using principal component analysis for enhanced detection of emerging viral variants

Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown disease variants has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown variant that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

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.

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

A substituent-modified new salicylaldehyde-diphenyl-azine based AIEgen: A promising skeleton for copper ion sensor

In this study, we have reported a novel 4-bromo-salicylaldehyde-diphenyl-azine (B-1), a new member of salicylaldehyde-diphenyl-azine (SDPA) family known for its excellent sensing properties. In contrast to the previously reported AIEgens, we found that the bromo-substitution at the 4th position of the salicylaldehyde moiety blue-shifted the emission by 10 and 15 nm as compared to the unsubstituted (Tong et.al 2017) and Bromo at the 5th position (Jain et.al 2023) respectively. Moreover, B-1 crystallizes instantly as the cooling process starts, which was not observed in the previously reported scaffolds. The sensing investigation again demonstrated the precise and ultrasensitive behavior of B-1 for copper ions. B-1 has a very low LOD value i.e. 29.2 x 10-8 M with a high association constant and binds with copper ion in 2:1 mode. This time we also analyzed the practical applicability in the solid phase using cotton swabs and performed the real-time estimation of copper ions in water and biological samples like urine and blood serum. The excellent percentage recovery and the RSD value suggest the precision of the experiments. Further, we also perform the sensing in living cancer HeLa cells. Altogether, we found that the SDPA skeleton is precise and ultrasensitive for copper ions and versatile which can be used variously to detect copper ions in the real world. This research will surely help in developing new specific skeleton-based AIEgens with desirable emission properties and precise applications in the future.

Recent advances in photoluminescent fluorescent probe technology for food flavor compounds analysis

The real-time, precise qualitative and quantitative sensing of food flavor compounds is crucial for ensuring food safety, quality, and consumer acceptance. As indicators for food flavor labeling, it is vital to delve deep into the specific ingredient and content of food flavor compounds to assess the food flavor quality, but still facing huge challenges. Photoluminescent fluorescent probe technology, with fast detection and high sensitivity, has shown immense potentials in detecting food flavor compounds. In this review, the classification and optical sensing mechanism of photoluminescent fluorescent probe technology are described in detail. Besides, challenges in applying photoluminescent fluorescent probe technology to analyze food flavor compounds are outlined to indicate future research directions. We hope this review can provide an insight for the applications of photoluminescent fluorescent probe technology in the evaluation of food flavor quality in future.

Photonic Solutions for Challenges in Sensing

Sensing technologies support timely and critical decisions to save precious resources in healthcare, veterinary care, food safety, and environmental protection. However, the design of sensors demands strict technical characteristics for real-world applications. In this Viewpoint, we discuss the main challenges to tackle in the sensing field and how photonics represents a valuable tool in this sphere.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Modeling and optimization of the ratio of fluorophores: a step towards enhancing the sensitivity of ratiometric probes

In the ratiometric fluorescent (RF) strategy, the selection of fluorophores and their respective ratios helps to create visual quantitative detection of target analytes. This study presents a framework for optimizing ratiometric probes, employing both two-component and three-component RF designs. For this purpose, in a two-component ratiometric nanoprobe designed for detecting methyl parathion (MP), an organophosphate pesticide, yellow-emissive thioglycolic acid-capped CdTe quantum dots (Y-QDs) (analyte-responsive), and blue-emissive carbon dots (CDs) (internal reference) were utilized. Mathematical polynomial equations modeled the emission profiles of CDs and Y-QDs in the absence of MP, as well as the emission colors of Y-QDs in the presence of MP separately. In other two-/three-component examples, the detection of dopamine hydrochloride (DA) was investigated using an RF design based on blue-emissive carbon dots (B-CDs) (internal reference) and N-acetyl L-cysteine functionalized CdTe quantum dots with red/green emission colors (R-QDs/G-QDs) (analyte-responsive). The colors of binary/ternary mixtures in the absence and presence of MP/DA were predicted using fitted equations and additive color theory. Finally, the Euclidean distance method in the normalized CIE XYZ color space calculated the distance between predicted colors, with the maximum distance defining the real-optimal concentration of fluorophores. This strategy offers a more efficient and precise method for determining optimal probe concentrations compared to a trial-and-error approach. The model's effectiveness was confirmed through experimental validation, affirming its efficacy.

A novel colorimetric and fluorometric dual-signal identification of organics and Baijiu based on nanozymes with peroxidase-like activity

Nanozymes were nanomaterials with enzymatic properties. They had diverse functions, adjustable catalytic activity, high stability, and easy large-scale production, attracting interest in biosensing. However, nanozymes were scarcely applied in Baijiu identification. Herein, a colorimetric and fluorometric dual-signal determination mediated by a nanozyme-H2O2-TMB system was developed for the first time to identify organics and Baijiu. Since the diverse peroxidase-like activity of nanozymes, resulted in different degrees of oxidized TMB. Based on this, 21 organics were identified qualitatively and quantitatively by colorimetric method with a rapid response (<12 min), broad linearity (0.0005-35 mM), and low detection limits (a minimum of 30 nM for glutaric acids). Furthermore, the fluorometric method exhibited excellent potential for accurate determination of organics, with detection ranges of 2-200 µmol/L (LOD: 0.22 µmol/L) for l-ascorbic acid and 2-300 µmol/L (LOD: 0.59 µmol/L) for guaiacol. Finally, the sensor was successfully applied to identify fake Baijiu and Baijiu from 16 different brands.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH-, CO32-, PO43-, NH3, ClO-, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001-1.0 mol L-1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864-0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

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

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

Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease

Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.

Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.

Ratiometric Imaging of Catecholamine Neurotransmitters with Nanosensors

Neurotransmitters are important signaling molecules in the brain and are relevant in many diseases. Measuring them with high spatial and temporal resolutions in biological systems is challenging. Here, we develop a ratiometric fluorescent sensor/probe for catecholamine neurotransmitters on the basis of near-infrared (NIR) semiconducting single wall carbon nanotubes (SWCNTs). Phenylboronic acid (PBA)-based quantum defects are incorporated into them to interact selectively with catechol moieties. These PBA-SWCNTs are further modified with poly(ethylene glycol) phospholipids (PEG-PL) for biocompatibility. Catecholamines, including dopamine, do not affect the intrinsic E11 fluorescence (990 nm) of these (PEG-PL-PBA-SWCNT) sensors. In contrast, the defect-related E11* emission (1130 nm) decreases by up to 35%. Furthermore, this dual functionalization allows tuning selectivity by changing the charge of the PEG polymer. These sensors are not taken up by cells, which is beneficial for extracellular imaging, and they are functional in brain slices. In summary, we use dual functionalization of SWCNTs to create a ratiometric biosensor for dopamine.

Gas-responsive two-dimensional metal-organic framework composites for trace visualization of volatile organic compounds

Highly sensitive and specific identification of complex volatile organic compound mixtures has always been a huge challenge in the field of gas detection. To address this issue, the gas-responsive two-dimensional metal-organic framework (MOF) composites have been designed for fabricating a colorimetric sensor arrays for extremely sensitive detection of volatile organic compounds (VOCs). The physically exfoliated MOF nanosheets Zn2(bim)4 with large surface area and abundant unsaturated active sites were used for loading various dyes to form dye/Zn2(bim)4 composites. Due to the protective effect on dye activity and preconcentration for VOCs, the dye/Zn2(bim)4 composites-based colorimetric sensor arrays showed significantly enhanced sensitivity compared with the corresponding dyes for the detection of various VOCs. The mechanical flexibility of the dye/MOF nanosheets endowed the excellent film-forming properties on various substrates for fabricating the colorimetric sensor arrays. Besides owing to the hydrophobic property and the protection of the Zn2(bim)4 nanosheets, the dye/Zn2(bim)4 sensor arrays exhibited excellent anti-interference including humidity and temperature influence. On the basis of the fantastic properties of dye/Zn2(bim)4 composites for VOCs detection, the dye/Zn2(bim)4 sensor arrays were applied for the early perception of the plant disease late blight via ultra-sensitive and highly specific sensing the VOCs released from the infected plants.

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.

Smartphone-assisted quantitative colorimetric identification of thrombin based on peroxidase mimetic features of fibrinogen-gold nanozymes

Fibrinogen-modified gold nanoparticles (Fib-AuNPs) and 3,3',5,5'-tetramethylbenzidine (TMB) substrate besides hydrogen peroxide (H2O2) were applied for assessment of the biomarker thrombin. Fib-AuNPs have catalytic active sites for the oxidation of TMB besides H2O2 and cause the color change of the substrate. Moreover, they can lead to the enhancement in the absorption wavelengths of 650 and 370 nm. By the addition of thrombin to Fib-AuNPs, fibrinogen turns into fibrin, and AuNPs are surrounded by fibrin. Therefore, their active catalytic sites for the oxidation of TMB besides H2O2 are covered by fibrin and cannot cause color change and absorption increase as before. The relationship between the average variations of the color intensity and changes in the absorption wavelengths at 650 and 350 nm with different concentrations of bovine thrombin added to Fib-AuNPs was studied. In such manner, three sensitive colorimetric approaches have been developed for the identification of bovine thrombin with the linear range of 20-120 pM and the limit of detection (LOD) of 17.54 pM for the average color intensity (G + B), the linear range of 20-120 pM and the LOD of 13.41 pM for the absorption peak at 650 nm, and the linear range of 40-140 pM with the LOD of 18.85 pM for absorption peak at 370 nm. The practical application of this biosensing platform was indicated through the successful determination of bovine thrombin in bovine serum. The satisfactory RSD ( < 10%) and recovery values (99.11-107.61%) confirmed the feasibility of the fabricated sensor.

A novel SERS sensor array based on AuNRs and AuNSs inverse-etching for the discrimination of five antioxidants

Antioxidants play an important role in life health and food safety. Herein, an inverse-etching platform based on gold nanorods (AuNRs) and gold nanostars (AuNSs) for high-throughput discrimination of antioxidants was constructed. Under the action of hydrogen peroxide (H2O2) and horseradish peroxidase (HRP), 3,3',5,5'-tetramethylbenzidine (TMB) would be oxidized to TMB+ or TMB2+. HRP reacts with H2O2 to release oxygen free radicals, which then react with TMB. Au nanomaterials can react with TMB2+, at the same time, Au was oxidized into Au (I), leading to the etching of the shape. Antioxidants, with good reduction ability, would prevent the further oxidation of TMB+ to TMB2+. So the presence of antioxidants will prevent further oxidation while avoiding the etching of Au in the catalytic oxidation process, thereby achieved inverse etching. Distinctive surface enhanced Raman scattering (SERS) fingerprint of five antioxidants were obtained based on the differential ability to scavenge free radicals. Five antioxidants, including ascorbic acid (AA), melatonin (Mel), glutathione (GSH), tea polyphenols (TPP), and uric acid (UA) were successfully distinguished by using linear discriminant analysis (LDA), heat map analysis and hierarchical cluster analysis (HCA). The study exhibits an effective inverse-etching based SERS sensor array for the response of antioxidants, which has great reference value in the field of human disease and food detection.

Direct evaluation of polarity of the ligand binding pocket in retinoid X receptor using a fluorescent solvatochromic agonist

High selectivity of small-molecule drug candidates for their target molecule is important to minimize potential side effects. One factor that contributes to the selectivity is the internal polarity of the ligand-binding pocket (LBP) in the target molecule, but this is difficult to measure. Here, we first confirmed that the retinoid X receptor (RXR) agonist 6-(ethyl(1-isobutyl-2-oxo-4-(trifluoromethyl)-1,2-dihydroquinolin-7-yl)amino)nicotinic acid (NEt-iFQ, 1) exhibits fluorescence solvatochromism, i.e., its Stokes shift depends on the polarity of the solvent, and then we utilized this property to directly measure the internal polarity of the RXRα-LBP. The Stokes shift of 1 when bound to the RXRα-LBP corresponded to that of 1 in chloroform solution. This finding is expected to be helpful for designing RXR-selective ligands. A similar approach should be appliable to evaluate the internal polarity of the LBPs of other receptors.

Thermal control of organic semiconductors for trace detection of explosives

Organic semiconductors can be applied as ultra-sensitive fluorescent sensors for detecting trace vapours of explosives. The detection of explosives is manifest by the fluorescence quenching of the sensors. However, for many organic fluorescent sensors, the fluorescence quenching is irreversible and imposes a limitation in terms of reusability. Here we present a study of the thermal control of thin-film fluorescent sensors made from the commercial fluorescent polymer Super Yellow (SY). Thermal control of the sensor's temperature results in the desorption of the absorbed analytes, nitroaromatic explosives (2,4-DNT and DNB), and a taggant molecule (DMDNB). The amount of photoluminescence (PL) quenching and the desorption temperature of analytes provides a route to discriminate between the analytes, and additonally make the SY sensors reusable.

Recent advances in whiskey analysis for authentication, discrimination, and quality control

In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.

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.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Room-Temperature Monitoring of CH4 and CO2 Using a Metal-Organic Framework-Based QCM Sensor Showing Inherent Analyte Discrimination

The detection of methane and carbon dioxide is of growing importance due to their negative impact on global warming. This is true for both environmental monitoring and leak detection in industrial processes. Although solid-state sensors are technologically mature, they have limitations that prohibit their use in certain situations, e.g., explosive atmospheres. Thus, there is a need to develop new types of sensor materials. Herein, we demonstrate a simple, low-cost, metal-organic framework (MOF)-based gas leak detection sensor. The system is based on gravimetric sensing by using a quartz crystal microbalance. The quartz crystal is functionalized by layer-by-layer growth of a thin metal-organic framework film. This film shows selective uptake of methane or carbon dioxide under atmospheric conditions. The hardware has low cost, simple operation, and theoretically high sensitivity. Overall, the sensor is characterized by simplicity and high robustness. Furthermore, by exploiting the different adsorption kinetics as measured by multiple harmonic analyses, it is possible to discriminate whether the response is due to methane or carbon dioxide. In summary, we demonstrate data relevant toward new applications of metal-organic frameworks and microporous hybrid materials in sensing.

A Multi-Enzyme Cascade Response for the Colorimetric Recognition of Organophosphorus Pesticides Utilizing Core-Shell Pd@Pt Nanoparticles with High Peroxidase-like Activity

In modern agricultural practices, organophosphorus pesticides or insecticides (OPs) are regularly used to restrain pests. Their limits are closely monitored since their residual hinders the capability of acetylcholinesterase (AChE) and brings out a threatening accumulation of the neurotransmitter acetylcholine (ACh), which affects human well-being. Therefore, spotting OPs in food and the environment is compulsory to prevent human health. Several techniques are available to identify OPs but encounter shortcomings like time-consuming, operating costs, and slow results achievement, which calls for further solutions. Herein, we present a rapid colorimetric sensor for quantifying OPs in foods using TMB as a substrate, a multi-enzyme cascade system, and the synergistic property of core-shell Palladinum@Platinum (Pd@Pt) nanoparticles. The multi-enzyme cascade response framework is a straightforward and effective strategy for OPs recognition and can resolve the previously mentioned concerns. Numerous OPs, including Carbofuran, Malathion, Parathion, Phoxim, Rojor, and Phosmet, were successfully quantified at different concentrations. The cascade method established using Pd@Pt had a simple and easy operation, a lower detection limit range of (1-2.5 ng/mL), and a short detection time of about 50 min. With an R2 value of over 0.93, OPs showed a linear range of 10-200 ng/mL, portraying its achievement in quantifying pesticide residue. Lastly, the approach was utilized in food samples and recovered more than 80% of the residual OPs.

Machine Learning-Mediated Ultrasensitive Detection of Citrinin and Associated Mycotoxins in Real Food Samples Discerned from a Photoluminescent Carbon Dot Barcode Array

Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.

ROS-Responsive Fluorescent Sensor Array for Precise Diagnosis of Cancer via pH-Controlled Multicolor Gold Nanoclusters

Intracellular reactive oxygen species (ROS) are closely associated with cancer cell types. Therefore, ROS-based pattern recognition is a promising strategy for precise diagnosis of cancer, but such a possibility has never been reported yet. Herein, we proposed an ROS-responsive fluorescent sensor array based on pH-controlled histidine-templated gold nanoclusters (AuNCs@His) to distinguish cancer cell types and their proliferation states. In this strategy, three types of AuNCs@His with diverse fluorescence profiles were first synthesized by only adjusting the pH value. Upon the addition of various ROS, fluorescence quenching of three types of AuNCs@His occurred with different degrees, thereby forming unique optical "fingerprints", which were well-clustered into several separated groups without overlap by principal component analysis (PCA). The sensing mechanism was attributable to the oxidation of AuNCs@His by ROS, as revealed by X-ray photoemission spectroscopy, Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, and electrospray ionization mass spectrometry. Based on the ROS-responsive sensing pattern, cancer cell types were successfully differentiated via PCA with 100% accuracy. Additionally, the proposed sensor array exhibited excellent performance in distinguishing the proliferation states of cancer cells, which was supported by the results of the Ki-67 immunohistochemistry assay. Overall, the ROS-responsive fluorescent sensor array can serve as a promising tool for precise diagnosis of cancer, indicating great potential for clinical application.

An enzymatic activity regulation-based clusterzyme sensor array for high-throughput identification of heavy metal ions

The accurate identification and sensitive quantification of heavy metal ions are of great significance, considering that pose a serious threat to environment and human health. Most array-based sensing platforms, to date, utilize nanozymes as sensing elements, but few studies have explored the application of the peroxidase-like activity of clusterzymes in identification of multiple analytes. Herein, for the first time, we developed a clusterzyme sensor array utilizing gold nanoclusters (AuNCs) as sensing elements for five heavy metal ions identification including Hg²⁺, Pb²⁺, Cu²⁺, Cd²⁺ and Co²⁺. The heavy metal ions can differentially regulate the peroxidase-like activity of AuNCs, and that can be converted into colorimetric signals with 3,3',5,5'-tetramethylbenzidine (TMB) as the chromogenic substrate. Subsequently, the generated composite responses can be interpreted by combining pattern recognition algorithms. The developed clusterzyme sensor array can identify five heavy metal ions at concentrations as low as 0.5 μM and their multi-component mixtures. Especially, we demonstrated the successful identification of multiple heavy metal ions in tap water and traditional Chinese medicine, with an accuracy of 100% in blind test. This study provided a simple and effective method for identification and quantification of heavy metal ions, rendering a promising technique for environmental monitoring and drug safety assurance.

Perspectives of different colour-emissive nanomaterials in fluorescent ink, LEDs, cell imaging, and sensing of various analytes

In the past 2 decades, multicolour light-emissive nanomaterials have gained significant interest in chemical and biological sciences because of their unique optical properties. These materials have drawn much attention due to their unique characteristics towards various application fields. The development of novel nanomaterials has become the pinpoint for different application areas. In this review, the recent progress in the area of multicolour-emissive nanomaterials is summarized. The different emissions (white, orange, green, red, blue, and multicolour) of nanostructure materials (metal nanoclusters, quantum dots, carbon dots, and rare earth-based nanomaterials) are briefly discussed. The potential applications of different colour-emissive nanomaterials in the development of fluorescent inks, light-emitting diodes, cell imaging, and sensing devices are briefly summarized. Finally, the future perspectives of multicolour-emissive nanomaterials are discussed.

Engineering Nano-Au-Based Sensor Arrays for Identification of Multiple Ni(II) Complexes in Water Samples

Advanced techniques for nickel (Ni(II)) removal from polluted waters have long been desired but challenged by the diversity of Ni(II) species (most in the form of complexes) which could not be readily discriminated by the traditional analytical protocols. Herein, a colorimetric sensor array is developed to address the above issue based on the shift of the UV-vis spectra of gold nanoparticles (Au NPs) after interaction with Ni(II) species. The sensor array is composed of three Au NP receptors modified by N-acetyl-l-cysteine (NAC), tributylhexadecylphosphonium bromide (THPB), and the mixture of 3-mercapto-1-propanesulfonic acid and adenosine monophosphate (MPS/AMP), to exhibit possible coordination, electrostatic attraction, and hydrophobic interaction toward different Ni(II) species. Twelve classical Ni(II) species were selected as targets to systematically demonstrate the applicability of the sensor array under various conditions. Multiple interactions with Ni(II) species were evidenced to trigger the diverse Au NP aggregation behaviors and subsequently produce a distinct colorimetric response toward each Ni(II) species. With the assistance of multivariate analysis, the Ni(II) species, either as the sole compound or as mixtures, can be unambiguously discriminated with high selectivity in simulated and real water samples. Moreover, the sensor array is very sensitive with the detection limit in the range of 4.2 to 10.5 μM for the target Ni(II) species. Principal component analysis signifies that coordination dominates the response of the sensor array toward different Ni(II) species. The accurate Ni(II) speciation provided by the sensor array is believed to assist the rational design of specific protocols for water decontamination and to shed new light on the development of convenient discrimination methods for other toxic metals of concern.

Visual Sensor Arrays for Distinction of Phenolic Acids Based on Two Single-Atom Nanozymes

Although great achievements have been made in the study of artificial enzymes, the design of nanozymes with high catalytic activities of natural enzymes and the further establishment of sensitive biosensors still remain challenging. Here, two nanozymes, i.e., ZnCoFe three-atom nanozyme (TAzyme) and Sn single-atom nanozyme (SAzyme)/Ti₃C₂T_x, are developed, which show peroxidase-like catalytic activities by catalyzing the reaction of hydrogen peroxide (H₂O₂), 4-aminoantipyrine (4-AAP), and phenolic acids to generate colorimetric reactions. The involvement of different phenolic acids leads to the generation of different color products. These subtle color-variation profiles between these phenolic acids prompt us to exploit an electronic tongue based on the two nanozymes to distinguish phenolic acids. Data interpretation by the pattern recognition method, such as linear discriminant analysis (LDA), displays good clustering separation of six different phenolic acids at concentrations of 0.1 μM to 1 mM, validating the effectiveness of the colorimetric nanozyme sensor array.

Smartphone-assisted sensor array constructed by copper-based laccase-like nanozymes for specific identification and discrimination of organophosphorus pesticides

Accurate pesticide identification is of great importance for regulating food safety. However, the discrimination between organophosphorus pesticides (OPs) and carbamate pesticides (CPs) is still a challenge for existing analytical methods based on cholinesterase inhibition. It mainly because of the similar inhibitory effect of OPs and CPs on cholinesterase. Herein, we found that OPs and CPs differentially affected nanozymes with laccase-like activity, which would be interfered by OPs in different degrees rather than CPs. Thus, we fabricated a nanozyme sensor array and successfully achieved the OPs identification and similar individual discrimination, ignoring the interference from CPs or other potential interferents (antibiotics, ions, other pesticides). On the basis of nanozyme sensor array, a portable method using smartphone was constructed and utilized to determine OPs in fruits and vegetables. This work would contribute to the development of portable sensors and the highly selective identification and discrimination of OPs in complex samples.

Fluorimetric identification of sulfonamides by carbon dots embedded photonic crystal molecularly imprinted sensor array

Identification of sulfonamides (SAs) residues in food is vital for human health. A set of 4-channel sensor array was constructed by carbon dots (CDs) embedded in photonic crystal molecularly imprinted (PCMIP@CDs) film which included 3 PCMIP@CDs units and 1 PCNIP@CDs unit to determine typical SAs: sulfadimethoxine, sulfathiazole, sulfaguanidine, sulfamethazine, sulfadiazine. Under the optimal conditions, the response time of the sensor array was only 200 s. Moreover, 300 fluorescence response signals (4 sensor units × 5 sulfonamides × 3 concentrations × 5 repeats) were processed by pattern recognition technique to analyze the ability of the sensor array to recognize 5 kinds of SAs. Subsequently, the linear discrimination analysis (LDA) method was used to identify the five SAs simultaneously with 100 % classification accuracy and the limit of detection was 0.01-0.26 nmol/L. Moreover, the proposed method can effectively identify-five SAs in water and fish samples.

Leveraging Coupled Solvatofluorochromism and Fluorescence Quenching in Nitrophenyl-Containing Thiazolothiazoles for Efficient Organic Vapor Sensing

Solvatofluorochromic molecules provide strikingly high fluorescent outputs to monitor a wide range of biological, environmental, or materials-related sensing processes. Here, thiazolo[5,4-d]thiazole (TTz) fluorophores equipped with simple alkylamino and nitrophenyl substituents for solid-state, high-performance chemo-responsive sensing applications are reported. Nitroaromatic substituents are known to strongly quench dye fluorescence, however, the TTz core subtly modulates intramolecular charge transfer (ICT) enabling strong, locally excited-state fluorescence in non-polar conditions. In polar media, a planar ICT excited-state shows near complete quenching, enabling a twisted excited-state emission to be observed. These unique fluorescent properties (spectral shifts of 0.13 - 0.87 eV and large transition dipole moments Δµ = 20.4 - 21.3 D) are leveraged to develop highly sought-after chemo-responsive, organic vapor optical sensors. The sensors are developed by embedding the TTz fluorophores within a poly(styrene-isoprene-styrene) block copolymer to form fluorescent dye/polymer composites (Φ_F = 70 - 97%). The composites respond reversibly to a comprehensive list of organic solvents and show low vapor concentration sensing (e.g., 0.04% solvent saturation vapor pressure of THF - 66 ppm). The composite films can distinguish between solvent vapors with near complete fluorescent quenching observed when exposed to their saturated solvent vapor pressures, making this an extremely promising material for optical chemo-responsive sensing.

Chrono-colorimetric sensor array for detection and discrimination of halide ions using an all-in-one plasmonic sensor element

Most nanoparticle based colorimetric sensor array utilize several sensor elements and static response for discrimination of target analytes. This approach can be complicated and costly to synthesize or functionalize different nanoparticles for providing wide color variation. Herein, triangular silver nanoparticles (TSNPs) were used to develop a colorimetric sensor array by time-dimension responses. The principle of this sensor array is based on the diverse etching process of TSNPs in the presence of three halide ions, including bromide (Br^-), iodide (I^-) and chloride (Cl^-). Various etchings of TSNPs induced color changes at different reaction time intervals, which produced a colorimetric pattern for each ion. Therefore, using time dependent etching responses of TSNPs as a single sensing component can produce a wide color variation which can be distinguished by naked eyes. The colorimetric responses of TSNPs upon the addition of different concentrations of halide ions have been analyzed by PLS regression (PLS-R) and PLS discriminant analysis (PLS-DA). The analytical figures of merit confirmed that the developed chrono-colorimetric TSNPs -based sensor array is successful in both the discrimination and quantitative detection of halide ions. At the final step, the three halide ions were accurately determined in a real water sample, which verified the potential of the developed sensor in a real sample.

Field Effect Transistor Gas Sensors Based on Mechanically Exfoliated Van der Waals Materials

The high surface-to-volume ratio and flatness of mechanically exfoliated van der Waals (vdW) layered materials make them an ideal platform to investigate the Langmuir absorption model. In this work, we fabricated field effect transistor gas sensors, based on a variety of mechanically exfoliated vdW materials, and investigated their electrical field-dependent gas sensing properties. The good agreement between the experimentally extracted intrinsic parameters, such as equilibrium constant and adsorption energy, and theoretically predicted values suggests validity of the Langmuir absorption model for vdW materials. Moreover, we show that the device sensing behavior depends crucially on the availability of carriers, and giant sensitivities and strong selectivity can be achieved at the sensitivity singularity. Finally, we demonstrate that such features provide a fingerprint for different gases to quickly detect and differentiate between low concentrations of mixed hazardous gases using sensor arrays.

The protein corona from nanomedicine to environmental science

The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.

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 sensor arrays for the detection and discrimination of natural products

Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.

A susceptible coordination hybrid based terbium sensibilization coupled ESIPT effects for pattern discrimination of analogues

Multianalyte detection and analogue discrimination are extremely valuable frontier areas for their wide applications in environmental, medical, clinical and industrial analyses. Nowadays, researchers rack their brains on how to develop excellent multianalyte chemosensors that have presented huge challenges in designing high-efficient fluorescent sensing materials and constructing high-throughput detection methods. In this paper, we propose a novel strategy to utilize the dual-emission fluorescent detection platform as a lab-on-a-molecule, arising from the disalicylaldehyde-coordinated hybrid H₂Q_j3/Tb based terbium sensibilization coupled excited-state intramolecular proton transfer effects. Using the statistical analysis (PCA and HCA) for sensing signals of three fluorescence channels (431, 543 and 583 nm), we demonstrate this elaborate chemosensor with multianalyte detection of three species (solvents, anions and cations) and pattern discrimination of analogues. As a result, the H₂Q_j3/Tb shows great lab-on-a-molecule characters for each set of species, resulting in the easier identification of many critical analytes (e.g., H₂O, NO₂^- and Fe³⁺) and discrimination of analogues. In addition, it is also proven to be able to provide reliable content determination for an analyte, especially the NO₂^- (LOD = 0.37 μM), and discrimination for mixed analogues. A combination of easy-to-implement preparation procedure and data analysis technique makes this work promising for not only designing similar lanthanide-based materials but also realizing more high-efficient multianalyte sensing systems towards various potential applications.

Machine Learning-Assisted Nanoenzyme/Bioenzyme Dual-Coupled Array for Rapid Detection of Amyloids

Array-based sensing methods offer significant advantages in the simultaneous detection of multiple amyloid biomarkers and thus have great potential for diagnosing early-stage Alzheimer's disease. Yet, detecting low concentrations of amyloids remains exceptionally challenging. Here, we have developed a fluorescent sensor array based on the dual coupling of a nanoenzyme (AuNPs) and bioenzyme (horseradish peroxidase) to detect amyloids. Various ss-DNAs were bound to the nanoenzyme for regulating enzymatic activity and recognizing amyloids. A simplified sensor array was generated from a screening model via machine learning algorithms and achieved signal amplification through a two-step enzymatic reaction. As a result, our sensing system could discriminate the aggregation species and aggregation kinetics at 200 nM with 100% accuracy. Moreover, AD model mice and healthy mice were distinguished with 100% accuracy through the sensor array, providing a powerful sensing platform for diagnosing AD.

Machine-learning assisted multiplex detection of catecholamine neurotransmitters with a colorimetric sensor array

Catecholamine neurotransmitters (CNs), such as dopamine (DA), epinephrine (EP), norepinephrine (NEP), and levodopa (LD), are recognized as the primary biomarkers of a variety of neurological illnesses. Therefore, simultaneous monitoring of these biomarkers is highly recommended for clinical diagnosis and treatment. In this study, a high-performance colorimetric artificial tongue has been proposed for the multiplex detection of CNs. Different aggregation behaviors of gold nanoparticles in the presence of CNs under various buffering conditions generate unique fingerprint response patterns. Under various buffering conditions, the distinct acidity constants of CNs, and consequently their predominant species at a given pH, drive the aggregation of gold nanoparticles (AuNPs). The utilization of machine learning algorithms in this design enables classification and quantification of CNs in various samples. The response profile of the array was analyzed using the linear discriminant analysis algorithm for classification of CNs. This colorimetric sensor array is capable of accurately distinguishing between individual neurotransmitters and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit (FOMs) and linear ranges of 0.6-9 μM (R² = 0.99) for DA, 0.1-10 μM (R² = 0.99) for EP, 0.1-9 μM (R² = 0.99) for NEP and 1-70 μM (R² = 0.99) for LD demonstrated the potential applicability of the developed sensor array in precise and accurate determination of CNs. Finally, the feasibility of the array was validated in human urine samples as a complex biological fluid with LODs of 0.3, 0.5, 0.2, and 1.9 μM for DA, EP, NEP, and LD, respectively.

Instant Fingerprint Discrimination for Military Explosive Vapors by Dy3+ Doping a La2O3-Based Cataluminescence Sensor System

With the intensification of explosive-based terrorism attack and environmental concerns, the innovation of high-efficiency and portable sensors for facile, rapid, and reliable monitoring of explosives has become one of the major demands in societies. Herein, a reliable and easy-to-use cataluminescence sensor system based on Dy³⁺ doping La₂O₃ nanorod catalysts has been developed for the identification and detection of six types of military explosive vapors, including homologous compounds and even isomers. The efficient discrimination is to make full use of the thermodynamic and kinetic information that can be extracted from the catalytic oxidation process of explosive molecules on various sensing materials, that is, the response signal and response time to generate the fingerprint of each target compound, while the rapid detection of the strategy can be manifested in response toward six military explosive vapors within 2.5 s and recover within 4 s. Meanwhile, the quantitative analysis of the explosives by the sensor system was realized based on 0.8%Dy:La₂O₃ with optimal catalytic activity, and the detection limits of NB, m-MNT, m-DNB, PNT, DNT, and TNT can reach 0.62, 0.49, 0.63, 0.38, 0.023, and 0.067 μg mL^-1. In this research, we also constructed a novel sensor device and detection platform for explosive monitoring, which is of great significance for providing a new sensing principle for the efficient identification of explosives.

Single-Probe-Based Colorimetric and Photothermal Dual-Mode Identification of Multiple Bacteria

Effective identification of multiple pathogenic bacteria in unknown samples is important for disease prevention and control but remains a challenge yet. A single-mode array-based sensing approach is simple and sensitive, but it usually relies on the use of multiple cross-reactive receptors to construct sensor arrays, which is cumbersome and insufficiently accurate. Here, we developed a sensor array with colorimetric and photothermal dual mode of differentiating multiple pathogenic bacteria. The sensor array was based on boronic acid-functionalized Au-Fe₃O₄ nanoparticles (BA-GMNPs), which not only possess localized surface plasmon resonance properties, showing a burgundy color similar to that of AuNPs, but also exhibit mild superparamagnetism, allowing for the differentiation of bacteria before and after binding to the nanoparticles. Immobilization of BA-GMNPs on the bacterial cell surface by covalent bonding would diminish NaCl-induced assembly of BA-GMNPs. Different BA-GMNPs@bacterial complexes differed in their ability to resist assembly and produced different colorimetric and photothermal response signals. A unique molecular fingerprint of each bacterium was obtained by linear discriminant analysis of the response patterns, demonstrating an effective differentiation among the six species studied. Compared with single-mode sensing arrays based on multiple receptors, this method only requires the preparation of a single nanomaterial, which produces two signal outputs for the identification of multiple bacteria with better differentiation. It can distinguish not only multiple pathogenic bacteria but also Gram-negative and Gram-positive bacteria, and, more importantly, it can perform preliminary discrimination of unknown samples.

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.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.