Carbonyl flavor compound-targeted colorimetric sensor array based on silver nitrate and o-phenylenediamine derivatives for the discrimination of Chinese Baijiu

The Identification of Oral Cariogenic Bacteria through Colorimetric Sensor Array Based on Single-Atom Nanozymes

Effective identification of multiple cariogenic bacteria in saliva samples is important for oral disease prevention and treatment. Here, a simple colorimetric sensor array is developed for the identification of cariogenic bacteria using single-atom nanozymes (SANs) assisted by machine learning. Interestingly, cariogenic bacteria can increase oxidase-like activity of iron (Fe)─nitrogen (N)─carbon (C) SANs by accelerating electron transfer, and inversely reduce the activity of Fe─N─C further reconstruction with urea. Through machine-learning-assisted sensor array, colorimetric responses are developed as "fingerprints" of cariogenic bacteria. Multiple cariogenic bacteria can be well distinguished by linear discriminant analysis and bacteria at different genera can also be distinguished by hierarchical cluster analysis. Furthermore, colorimetric sensor array has demonstrated excellent performance for the identification of mixed cariogenic bacteria in artificial saliva samples. In view of convenience, precise, and high-throughput discrimination, the developed colorimetric sensor array based on SANs assisted by machine learning, has great potential for the identification of oral cariogenic bacteria so as to serve for oral disease prevention and treatment.

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

Organic-Acid-Sensitive Visual Sensor Array Based on Fenton Reagent-Phenol/Aniline for the Rapid Species and Adulteration Assessment of Baijiu

Baijiu is an ancient, distilled spirit with a complicated brewing process, unique taste, and rich trace components. These trace components play a decisive role in the aroma, taste, and especially the quality of baijiu. In this paper, the redox reaction between the Fenton reagent and four reducing agents, including o-phenylenediamine (OPD), p-phenylenediamine (PPD), 4-aminophenol (PAP), and 2-aminophenol (OAP), was adopted to construct a four-channel visual sensor array for the rapid detection of nine kinds of common organic acids in baijiu and the identification of baijiu and its adulteration. By exploiting the color-changing fingerprint response brought by organic acids, each organic acid could be analyzed accurately when combined with an optimized variable-weighted least-squares support vector machine based on a particle swarm optimization (PSO-VWLS-SVM) model. What is more, this novel sensor also could achieve accurate semi-quantitative analysis of the mixed organic acid samples via partial least squares discriminant analysis (PLSDA). Most importantly, the sensor array could be further used for the identification of baijiu with different species through the PLSDA model and the adulteration assessment with the one-class partial least squares (OCPLS) model simultaneously.

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.

Gold nanobipyramid colorimetric sensing array for the differentiation of strong aroma-type baijiu with different geographical origins

It is of great significance to quickly and effectively distinguish strong aroma-type baijiu (SAB) with the largest baijiu market share and the most extensive production regions. Colorimetric sensor arrays based on gold nanobipyramids (AuNBPs) with extraordinary plasmonic properties were constructed for the differentiation of SAB from different geographical origins. The sensing strategy was based on silver deposition on different morphologies of AuNBPs under different reducing conditions containing amino or hydroxyl groups. The deposition process can be effective for distinguishing differences in baijiu due to the chemical interaction between the trace ingredients in baijiu and reductants. The colorimetric sensor arrays were implemented for the response of the main ingredients and further used for the differentiation of SAB from different regions by linear discriminant analysis. The results showed that the sensing strategy had excellent performance in distinguishing SAB from different origins, and provides a promising application strategy for baijiu quality control.

Nanozyme colorimetric sensor array based on monatomic cobalt for the discrimination of sulfur-containing metal salts

The identification of sulfur-containing metal salts (SCMs) is of great interest because they play an important role in many biological processes and diseases. Here, we constructed a ternary channel colorimetric sensor array to detect multiple SCMs simultaneously, relying on monatomic Co embedded in nitrogen-doped graphene nanozyme (CoN₄-G). Due to the unique structure, CoN₄-G exhibits activity similar to native oxidases, capable of catalysing directly the oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) by O₂ molecules independent of H₂O₂. Density functional theory (DFT) calculations suggest that CoN₄-G has no potential barrier in the whole reaction route, thus presenting higher oxidase-like catalytic activity. Based on different degrees of TMB oxidation, different colorimetric response changes are obtained as "fingerprints" on the sensor array. The sensor array can discriminate different concentrations of unitary, binary, ternary, and quaternary SCMs and has been successfully applied to detect six real samples (soil, milk, red wine and egg white). To advance the field detection of the above four types of SCMs, we creatively propose a smartphone-based autonomous detection platform with a linear range of 1.6-320 μM and a limit of detection of 0.0778-0.218 μM, which demonstrates the potential use of sensor arrays in the application of disease diagnosis and food and environment monitoring.

Machine-learning-assisted SERS nanosensor platform toward chemical fingerprinting of Baijiu flavors

A novel fingerprinting platform for multiplex detection of flavor molecules in Baijiu was developed by using a surface-enhanced Raman scattering (SERS) nanosensor array in combination with machine learning. The SERS sensors were constructed by core-shell Fe₃O₄@Ag nanoparticles modified with molecules carrying end-groups of hydroxyl, pyridyl, methyl, and amino, respectively, which interacted with flavors and led to changes in the sensors' spectra. All the Raman spectra acquired from the nanosensor array contacting with the sample were concatenated into a single SERS super-spectrum, representing the flavor fingerprint which was recognized through machine learning. Principal component analysis, support vector machine, and partial least squares were utilized to build classification and quantitation models for predictive analyses. The SERS nanosensor array was successfully used for fingerprinting ten typical flavors in Baijiu including four esters, three alcohols, and three acids, with an accuracy of 100%, linear detection ranges over two orders of magnitude, and limits of detection ranging from 3.45 × 10^-3 mg/L of phenylethyl acetate to 1.21 × 10^-2 mg/L of ethyl hexanoate. It was also demonstrated that satisfactory accuracies (recoveries) ranging from 96.2 to 104% and relative standard deviations ranging from 0.65 to 2.78% were obtained for the simultaneous quantification of 3-methylbutyl acetate and phenylethyl acetate in eighteen Baijiu samples of three flavor types including sauce flavor, strong flavor, and light flavor. Compared with the existing detection techniques, this chemical fingerprinting platform is easy to use, highly sensitive, and can perform multiplex detection, which has great potential for practical applications.

Synergy of physicochemical reactions occurred during aging for harmonizing and improving flavor

Numerous counterfeit vintage Baijiu are widely distributed in the market driven by economic interest which disturb the market economic rules and damage the reputation of particular Baijiu brand. Found on the situation, the Baijiu system variation during aging period, aging mechanisms and discrimination strategies for vintage Baijiu are systematically illuminated. The aging mechanisms of Baijiu cover volatilization, oxidation, association, esterification, hydrolysis, formation of colloid molecules and catalysis by metal elements or other raw materials dissolved from storage vessels. The discrimination of aged Baijiu has been performed by electrochemical method, colorimetric sensor array or component characterization coupled with multivariate analysis. Nevertheless, the characterization of non-volatile compounds in aged Baijiu is deficient. Further research on the aging principles, more easy-operation and low-cost discrimination strategies for aged Baijiu are imperative. The above information is favorable to better understand the aging process and mechanisms of Baijiu, and promote the development of artificial aging techniques.

Metabolite-based cell sorting workflow for identifying microbes producing carbonyls in tobacco leaves

Carbonyl compounds represented by aldehydes and ketones make an important contribution to the flavor of tobacco. Since most carbonyl compounds are produced by microbes during tobacco fermentation, identifying their producers is important to improve the quality of tobacco. Here, we created an efficient workflow that combines metabolite labeling with fluorescence-activated cell sorting (ML-FACS), 16S rRNA gene sequencing, and microbial culture to identify the microbes that produce aldehydes or ketones in fermented cigar tobacco leaves (FCTL). Microbes were labeled with a specific fluorescent dye (cyanine5 hydrazide) and separated by flow cytometry. Subsequently, the sorted microbes were identified and cultured under laboratory conditions. Four genera, Acinetobacter, Sphingomonas, Solibacillus, and Lysinibacillus, were identified as the main carbonyl compound-producing microbes in FCTL. In addition, these microorganisms could produce flavor-related aldehydes and ketones in a simple synthetic medium, such as benzaldehyde, phenylacetaldehyde, 4-hydroxy-3-ethoxy-benzaldehyde, and 3,5,5-trimethyl-2-cyclohexene-1-one. On the whole, this research has developed a new method to quickly isolate and identify microorganisms that produce aldehydes or ketones from complex microbial communities. ML-FACS would also be used to identify other compound-producing microorganisms in other systems. KEY POINTS: • An approach was developed to identify target microbes in complex communities. • Microbes that produce aldehyde/ketone flavor compounds in fermented cigar tobacco leaves were identified. • Functional microbes that produce aldehyde/ketone flavor compounds from the native environment were captured in pure cultures.