This work investigated the feasibility of applying headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) combining olfactory visualization for flavor characterization of black garlic. Volatile organic compounds (VOCs) analysis was performed to select important differential VOCs during black garlic processing. A multi-channels nanocomposite CSA assembled with two porous metal-organic frameworks was then developed to characterize flavor profiles changes during black garlic processing, and garlic samples during processing could be divided into five clusters, consistent with VOCs analysis. Artificial neural network (ANN) model outperformed other pattern recognition methods in discriminating processing stages. Furthermore, SVR model for odor sensory scores with the correlation coefficient for prediction set of 0.8919 exhibited a better performance than PLS model, indicating a preferable prediction ability for odor quality. This work demonstrated that the nanocomposite CSA combining appropriate chemometrics can offer an effective tool for objectively and rapidly characterizing flavor quality of black garlic or other food matrixes.

Rational design of peroxidase (POD)-like nanozymes with high activity and specificity still faces a great challenge. Besides, the investigations of nanozymes inhibitors commonly focus on inhibition efficiency, the interaction between nanozymes-involved catalytic reactions and inhibitors is rarely reported. In this work, we design a p-block metal Sn-doped Pt (p-d/PtSn) nanozymes with the selective enhancement of POD-like activity. The p-d orbital hybridization interaction between Pt and Sn can effectively optimize the electronic structure of PtSn nanozymes and thus selectively enhance POD-like activity. In addition, the antioxidants as nanozymes inhibitors can effectively inhibit the POD-like activity of p-d/PtSn nanozymes, which results in the fact that antioxidants absorbed on the p-d/PtSn surface can hinder the adsorption of hydrogen peroxide. The inhibition type (glutathione as a model molecule) is reversible mixed-inhibition with inhibition constants (Ki\' and Ki) of 0.21 mM and 0.03 mM. Finally, based on the varying inhibition levels of antioxidant molecules, a colorimetric sensor array is constructed to distinguish and simultaneously detect five antioxidants. This work is expected to design highly active and specific nanozymes through p-d orbital hybrid engineering, and also provides insights into the interaction between nanozymes and inhibitors.

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1-10 μM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Baijiu authenticity has been a frequent problem driven by economic interests in recent years, so it is important to discriminate against baijiu with different origins. Herein, we proposed a simple and efficient esters-targeted colorimetric sensor array mediated by hydroxylamine hydrochloride. Esters undergo a nucleophilic addition reaction with hydroxylamine hydrochloride to form hydroxamic acid, which rapidly forms a purplish red ferric hydroxamate under FeCl3·6H2O. Bromophenol blue and rhodamine B enrich the color effects. The array detected 12 esters with a detection limit on the order of 10-5 of most esters and 16 mixed esters with R2 > 0.999 and recoveries close to 100%. Otherwise, for discriminating 34 strong-aroma baijius (SABs), the array has an accuracy of 98% according to the origin, and 95% according to the grades, with a response time of 1 min. This study provides a new strategy for authenticity determination and quality control of baijiu.

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.

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.

Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92 μ g ∙ kg-1 and a coefficient of determination on the prediction (RP2) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool.

Developing methods for the accurate identification and analysis of sulfur-containing compounds (SCCs) is of great significance because of their essential roles in living organisms and the diagnosis of diseases. Herein, Se-doping improved oxidase-like activity of iron-based carbon material (Fe-Se/NC) was prepared and applied to construct a four-channel colorimetric sensor array for the detection and identification of SCCs (including biothiols and sulfur-containing metal salts). Fe-Se/NC can realize the chromogenic oxidation of 3,3\',5,5\'-tetramethylbenzidine (TMB) by activating O2 without relying on H2O2, which can be inhibited by different SCCs to diverse degrees to produce different colorimetric response changes as \"fingerprints\" on the sensor array. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that nine kinds of SCCs could be well discriminated. The sensor array was also applied for the detection of SCCs with a linear range of 1-50 μM and a limit of detection of 0.07-0.2 μM. Moreover, colorimetric sensor array inspired by the different levels of SCCs in real samples were used to discriminate cancer cells and food samples, demonstrating its potential application in the field of disease diagnosis and food monitoring. ENVIRONMENTAL IMPLICATIONS: In this work, a four-channel colorimetric sensor array for accurate SCCs identification and detection was successfully constructed. The colorimetric sensor array inspired by the different levels of SCCs in real samples were also used to discriminate cancer cells and food samples. Therefore, this Fe-Se/NC based sensor array is expected to be applied in the field of environmental monitoring and environment related disease diagnosis.

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.