A colorimetric sensor array for the discrimination of Chinese liquors

Fast and non-invasive identification of Baijiu based on Tyndall effect and chemometrics

The value of Baijiu is affected by its flavor, age, and adulteration. Therefore, a simple and rapid identification method is crucial for the market. In this study, we present a rapid, non-intrusive identification technique for Baijiu utilizing the Tyndall effect combined with chemometrics analysis. Our experiment begins illuminating Baijiu with a 405 nm wavelength laser and recording the resulting bright light path due to the Tyndall effect. To further analyze the color and brightness information, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Hierarchical Cluster Analysis (HCA), and Multilayer Perceptron (MLP) were employed. This study establishes correlations between the brightness of the Tyndall light path and seven trace flavor compounds in Baijiu. The findings demonstrate that this method effectively identifies the flavor, age cellar, and adulteration of Baijiu and also quantitatively detects the concentrations of flavor compounds. Additionally, an analysis platform was developed to enable the rapid identification of Baijiu.

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.

Pattern-based colorimetric sensor array chip for discrimination of Baijiu aromas

Gas mixtures are comprised of numerous complex components, making the accurate identification a continuing challenge due to the significant limitations of existing detection methods. Herein, we developed a low-cost and sensitive pattern-based colorimetric sensor array chip for the identification of typical gas mixtures - Baijiu aroma. Specifically, three nanomaterials (AuNPs, MoS2 and ZIF-8) were prepared to adsorb gas molecules and enhance the reaction of trace gases with sensor arrays. The colorimetric sensor array chip took only 5 min to complete the recognition of Baijiu aromas and effectively avoided recognition errors caused by sommelier olfactory fatigue. Notably, the hierarchical cluster analysis (HCA) revealed no confusion or errors in the results of 80 tests across the five trials involving 16 commercial Baijius. Even fake Baijius with similar ingredients could be easily identified, demonstrating the excellent analytical capabilities of the system in Baijiu identification and its significant potential for quality control of Baijius.

Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat

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 (R P 2 ) 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.

A multi-wavelength cross-reactive fluorescent sensor ensemble for fingerprinting flavonoids in serum and urine

Flavonoids are a kind of natural polyphenols which are closely related to human health, and the identification of flavonoids with similar structures is an important but difficult issue. We herein easily constructed a powerful fluorescent sensor ensemble by using surfactant cetyltrimethylammoniumbromide (CTAB) encapsulating two commercially available fluorescent probes (F1 and F2) with multi-wavelength emission. Fluorescence measurements illustrate the present sensor ensemble exhibits turn-off responses to flavones and flavonols but ratiometric responses to isoflavones, owing to different FRET processes. The heat map and linear discriminant analysis (LDA) results show that this single sensor can effectively distinguish 6 flavonoids belong to three subgroups by collecting the fluorescence variation at four typical wavelengths. Moreover, it can be applied to identify different flavonoids even in biofluids like serum and urine, providing potential practical application.

A paper-based chemical tongue based on the charge transfer complex of ninhydrin with an array of metal-doped carbon dots discriminates natural amino acids and several of their enantiomers

Simultaneous detection of multiple amino acids (AAs) instead of individual AAs is inherently worthwhile for improving diagnostic accuracy in clinical applications. Here, a facile and reliable colorimetric microfluidic paper-based analytical device (μPAD) using carbon dots doped with transition metals (Cr3+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, and Zn2+) has been provided to detect and discriminate 20 natural amino acids. To make the colourless metal-doped carbon dots suitable for colorimetric assays, they were mixed with ninhydrin to form a charge transfer complex. This optical tongue system, which was constructed by dropping mixtures of ninhydrin with a series of metal-doped carbon dots on a paper substrate in an array format, represented obvious but different colorimetric signatures for every examined amino acid. Since bovine serum albumin was used as a chiral selector reagent for synthesizing the CDs, the sensor device represented excellent selectivity to identify enantiomeric species of AAs. This is the first optical array device that can simultaneously discriminate AAs and several of their enantiomers. We employed various statistical and chemometric methods to analyze the digital data library collected by Image J software, including principal component analysis (PCA), linear discriminant analysis (LDA), and hierarchical cluster analysis (HCA). Twenty AAs could be well distinguished at various concentrations (10.00, 5.00, 2.50, and 1.25 mM). The colorimetric patterns were highly repeatable and were characteristic of individual AAs. Besides qualitative analysis, the designed μPAD-based optical tongue represented quantitative analysis ability, e.g., for lysine in the concentration ranges of 0.005-20.0 mM with a detection limit of 1.0 × 10-6 M and for arginine in the concentration range of 0.12-20.00 mM with a detection limit of 80.0 × 10-6 M. In addition, the binary, ternary, and quaternary mixtures of AAs could also be well recognized with this sensor.

Colorimetric identification of multiple terpenoids based on bimetallic FeCu/NPCs nanozymes

Nanozymes have been relatively well explored, and bimetal-doped nanozymes have attracted much exploration due to their superior catalytic activity. We developed bimetallic FeCu/NPCs and Cu/NPCs nanozymes, which have good catalytic properties due to the coordination of Fe and Cu with N and P. The nanozymes acted as sensing elements in a cascade reaction system to effectively recognize seven terpenoids, including menthol (Men), paeoniflorin (Pae), camphor (Cam), paclitaxel (Pac), andrographolide (Andro), ginkgolide A (Gin A), and piperone (Pip). Terpenoids act as inhibitors of acetylcholinesterase (AChE) and reduce the hydrolysis of acetylcholine (ATCh), providing insight into establishing a simple and distinct assay for terpenoids. Notably, the sensor array distinguished seven terpenoids with concentrations as low as 10 ng/mL and achieved high-precision detection of mixed samples with different molar ratios and 21 unknown samples. Finally, the sensor array successfully distinguished and identified multiple terpenoids in herbal samples.

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.

Identification of Trace Components in Sauce-Flavor Baijiu by High-Resolution Mass Spectrometry

Sauce-flavor Baijiu is one of the most complex and typical types of traditional Chinese liquor, whose trace components have an important impact on its taste and quality. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is one of the most favorable analytical tools to reveal trace molecular components in complex samples. This study analyzed the chemical diversity of several representative sauce-flavor Baijiu using the combination of electrospray ionization (ESI) and FT-ICR MS. The results showed that ESI+ and ESI- exhibited different chemical features characteristic of trace components. Overall, sauce-flavor Baijiu was dominated by CHO class compounds, and the main specific compound types were aliphatic, highly unsaturated with low oxygen, and peptide-like compounds. The mass spectral parameters resolved by FT-ICR MS of several well-known brands were relatively similar, whereas the greatest variability was observed from an internally supplied brand. This study provides a new perspective on the mass spectrometry characteristics of trace components of sauce-flavor Baijiu and offers a theoretical foundation for further optimization of the gradients in Baijiu.

Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method

Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation.

Growth of Plasmonic Nanoparticles for Aging Cask-Matured Whisky

The maturation of spirit in wooden casks is key to the production of whisky, a hugely popular and valuable product, with the transfer and reaction of molecules from the wooden cask with the alcoholic spirit imparting color and flavor. However, time in the cask adds significant cost to the final product, requiring expensive barrels and decades of careful storage. Thus, many producers are concerned with what "age" means in terms of the chemistry and flavor profiles of whisky. We demonstrate here a colorimetric test for spirit "agedness" based on the formation of gold nanoparticles (NPs) by whisky. Gold salts were reduced by barrel-aged spirit and produce colored gold NPs with distinct optical properties. Information from an extinction profile, such as peak position, growth rate, or profile shape, was analyzed, and our assay output was correlated with measurements of the whisky sample makeup, assays for key functional groups, and spiking experiments to explore the mechanism in more detail. We conclude that age is not just a number, that the chemical fingerprint of key flavor compounds is a useful marker for determining whisky "age", and that our simple reduction assay could assist in defining the aged character of a whisky and become a useful future tool on the warehouse floor.

A novel colorimetric sensor array for real-time and on-site monitoring of meat freshness

Food quality control is essential in industry and daily life. In this work, we developed a novel colorimetric sensor array composed of several pH-sensitive dyes for monitoring meat freshness. A color change in the sensor array was seen after exposure to volatile organic compounds (VOCs), and the images were captured for precise quantification of the VOCs. In conjunction with pattern recognition, meat freshness at different storage periods was readily discerned, revealing that the as-fabricated colorimetric sensor array possessed excellent discrimination ability. The linear range for quantitative analysis of volatiles related to meat spoilage was from 5 ppm to 100 ppm, with a limit of detection at the ppb level (S/N = 3). Furthermore, the testing results obtained by the sensor in assessing meat freshness were validated by a standard method for measuring the total volatile basic nitrogen (TVB-N). The sensing signals showed good agreement with the results obtained in TVB-N when measuring real food samples. The sensor also displayed good reproducibility (RSD < 5%) and long-term stability. The sensor was successfully used for on-site and real-time determination of volatiles emitted from rotting meat, demonstrating its potential application in monitoring the quality and safety of meat products.

Plasma colorimetric aptasensor for the detection of chloramphenicol in honey based on cage Au@AuNPs and cascade hybridization chain reaction

A plasma colorimetric aptasensor was developed for rapid determination of chloramphenicol (CAP) in honey on site. Herein, cage gold shell@core nanoparticles (Au@AuNPs) were synthesized to enhance signal response and broaden the linear range. In addition, aptamer-based cascade hybridization chain reaction (cHCR), consisting of HP1, HP2, HP3, and HP4, was also designed for signal amplification and specific analysis. In this assay, HP1 and HP4 were immobilized on the surface of cage Au@AuNPs. In the presence of CAP, cHCR was triggered, and frond-like DNA products were formed, which made the distance among the cage Au@AuNPs closer and the system color changed from red to deep purple. Qualitative and quantitative analysis were carried out according to color changes and UV-Vis spectra. Under the optimized conditions, the wavelength of UV-Vis absorption peak exhibited a good linear relationship with CAP concentration in the range of 5.0 to 500 nmol/L with the detection limit of 1.2 nmol/L (S/N = 3). This aptasensor also showed good specificity for CAP detection compared with other antibiotics similar to the target analyte. Furthermore, the colorimetric aptasensor was successfully applied to the detection of CAP in honey with recoveries of 88.0-107.6%. This cHCR-based aptasensing for CAP possesses high sensitivity, good selectivity, low cost and excellent stability, and could be extended to detect a wide variety of other small molecular analytes, nucleic acids or proteins. Therefore, the versatile method might become a potential alternative tool in food analysis and environmental monitoring.

Printed 384-Well Microtiter Plate on Paper for Fluorescent Chemosensor Array in Food Analysis

We propose a printed 384-well microtiter paper-based fluorescent chemosensor array device (384-well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur-containing amino acids for food analysis. The 384-well microtiter PCAD required 1 μL/4 mm 2 of each well can allow high-throughput sensing. The device embedded with self-assembled fluorescence chemosensors displayed a fingerprint-like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper-based chemosensor array system combined with imaging analysis and pattern recognition techniques successfully not only categorized saccharides and sulfur-containing amino acids but also classified mono- and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384-well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi-analytes at several categorized groups in real samples.

Detection and discrimination of sulfur dioxide using a colorimetric sensor array

Discrimination and detection of sulfur dioxide residues in foods using a simple colorimetric array have been achieved. The difference maps before and after the reaction showed that the specific color fingerprint was related to the amount of sulfur dioxide. The results of principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) demonstrated that the as-fabricated colorimetric sensor array have good performance for the discrimination of sulfur dioxide and other interferents, as well as different concentrations of sulfur dioxide. Moreover, the array has been successfully applied to determine the concentration of sulfur dioxide residues in real samples and revealed good accuracy, precision and repeatability.

In this work, a colorimetric sensor array based on six specific color reactions was developed and used for the determination of sulfur dioxide content. The qualitative and quantitative analysis of sulfur dioxide residues in real samples was achieved.

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.