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Volatile organic compounds (VOCs) are an important indicator for fungal-infected wheat identification. This work proposes a novel approach for toxigenic Aspergillus flavus infected wheat identification through characteristic VOCs analyzed by nano-composite colorimetric sensors. Nanoparticles of poly styrene-co-acrylic acid (PSA), porous silica nanoparticles (PSN), and metal-organic framework (MOF) were combined with boron dipyrromethene (BODIPY) to fabricate nano-composite colorimetric sensors. The combination mechanisms for nanoparticles and the information extracted from nano-colorimetric sensors by digital images were analyzed in the current work. Furthermore, linear discriminant analysis (LDA) and k-nearest neighbor (KNN) were used comparatively to analyze the data from images, and toxigenic Aspergillus flavus infected wheat samples could be 100.00% correctly identified when using the optimal KNN model. This research contributes to the practical analysis of VOCs and the detection of toxigenic Aspergillus flavus infected wheat.

Flavor instability of ready-to-drink (RTD) coffee during storage negatively impacts product quality. Untargeted liquid chromatography/mass spectrometry (LC/MS) analysis was applied to identify chemical compounds that degraded during storage and impacted the flavor attributes of RTD coffee. LC/MS chemical profiles of non-aged and aged coffee samples were modeled against the degree of difference sensory scores by orthogonal partial least squares with good fit (R

Edible crude palm oil (CPO) is a vital oil utilized in various industries, including food, pharmaceuticals, and domestic cooking. Unfortunately, reports of CPO adulteration with harmful Sudan dyes have surfaced over the years. Surface-enhanced Raman spectroscopy (SERS) and chemometrics were employed to detect Sudan dyes adulteration in CPO within 900 - 1800 cm

Current work presented a novel method based on colorimetric sensor (CS) combined with visible/near-infrared spectroscopy (VNIRs) for the detection of volatile markers in wheat infected by Aspergillus glaucus. Wheat samples with different mouldy degree was cultivated for backup under temperature of 25–28 °C in incubator. The total colony number was determined by flat colony counting method. Through employing chemo-responsive dyes including 8-(4-nitrophenyl)-4, 4-difluoro-BODIPY (NO2BDP), 8-(4-bromophenyl)-4,4-difluoro-BODIPY(BrBDP) and 8-phenyl-4,4-difluoro- BODIPY(HBDP) as capture probes of colorimetric sensor for volatile organic compounds (VOCs). The spectral data of CS-VNIRs were scanned and used to build synergic interval partial least squares (Si-PLS) models. The optimized Si-PLS model based on HBDP sensor gave a better detection performance, and the correlation coefficient of the prediction set Rp = 0.9387. The achieved high correlation rates imply that the technique may be deployed as a panacea to identify and quantify the colony number of different mouldy wheat.

A label-free colorimetric method based on exonuclease I (Exo I)-assisted signal amplification with protamine as a medium was developed for analysis of kanamycin. In this study, a double-stranded DNA (dsDNA) probe was tailored by manipulating an aptamer and its complementary DNA (cDNA) ensuring detection of target with high selectivity and excellent sensitivity. Herein, protamine could not only combine with negatively charged gold nanoparticles but also interaction with polyanion DNA. Upon addition of target kanamycin, the target-aptamer complex was formed and the cDNA was released. Thus, both aptamer and cDNA could be digested by Exo I, and the captured kanamycin was liberated for triggering target recycling and signal amplification. Under optimized conditions, the proposed colorimetric method realized a low detection limit of 2.8 × 10−14 M along with a wide linear range plus excellent selectivity. Our strategy exhibited enormous potentials for fabricate various kinds of biosensors based on target-induced aptamer configuration changes.

A LC-Q-Orbitrap HRMS analytical method for both qualitative screening and quantitative determination of 90 synthetic dyes including ten groups of isomers in foods has been established. An in-house synthetic dyes database and characteristic ions were also developed. Based on Q-Orbitrap HRMS, mass spectrum and fragmentation patterns of synthetic dyes were studied, which indicated that double charged ions were usually the main precursor ions. Matrix effects were successfully eliminated by the C18 d-SPE clean-up coupled with dilute and shoot approach with methanol–water (1:4, v/v) in 100-fold. For most of the compounds, mean recoveries were satisfactory between 70% and 120% with RSD

Current work proposed a novel quantitative method of volatile aldehydes (VAs) using chemoselective response dyes (CRDs) combined with multivariate data analysis. Multivariate spectral data of selected CRDs was obtained by visible near-infrared spectroscopy. The Synergy-interval Partial Least Squares (Si-PLS) algorithm processed multivariate spectral data to establish VAs quantitative prediction models at the level of 0.0002 v/v to 0.18 v/v. The prediction coefficient (Rp) values of models ranged from 0.8399 to 0.9886, and the Root Mean Square Error of Prediction (RMSEP) values were less than 0.01. These models were verified by classification of aging rice samples, and 93% samples were correctly identified in prediction set. In addition, Density Functional Theory (DFT) calculations explored the interaction mechanism between selected CRDs and VAs. The optimized Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital (HOMO-LUMO) energy levels, dipole moment, distance between molecules were found to have strong correlations with the interaction.

In this study, an efficient, fast and sensitive method for the simultaneous determination of eleven synthetic color additives (Allura red, Amaranth, Azo rubine, Brilliant blue, Erythrosine, Indigotine, Ponceau 4R, New red, Sunset yellow, Quinoline yellow and Tartrazine) in flour and meat foodstuffs is developed and validated using HPLC coupled with DAD and MS/MS. The color additives were extracted with ammonia-methanol and was further purified with SPE procedure using Strata-AW column in order to reduce matrix interference. This HPLC-DAD method is intended for a comprehensive survey of color additives in foods. HPLC-MS/MS method was used as the further confirmation and identification. Validation data showed the good recoveries in the range of 75.2-113.8%, with relative standard deviations less than 15%. These methods are suitable for the routine monitoring analysis of eleven synthetic color additives due to its sensitivity, reasonable time and cost.

A novel colorimetric sensor array based on boron-dipyrromethene (BODIPY) dyes was developed to monitor the volatile organic compounds (VOCs) of rice at different storage times. The VOCs of rice at different storage times were analyzed through GC-MS combined with multivariate analysis, and the compound 18-crown-6 was found significantly changed during rice aging process. Aimed at 18-crown-6 with particular macrocyclic structure, a series of BODIPYs were targeted synthesized for the selection of sensitive chemically responsive dyes. Four dyes were chosen to construct colorimetric sensor array based on sensitivity to VOCs of aged rice samples. Data acquired from the interactions of dyes and rice VOCs were subjected to the principal components analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained by the LDA model was 98.75% in prediction set. Application of BODIPYs in this work has improved the sensitivity and expanded the choices of colorimetric dyes for the specific detection.

A novel artificial olfactory technique based on colorimetric sensor array was developed for the classification of Chinese rice wine according to different marked ages. The sensor array was composed of nine porphyrins or metalloporphyrins materials and six pH indicators. When the sensor array was exposed to rice wine for several minutes, a colour change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to the head-gas of the sample. The values of RGB (i.e., red, green, and blue) colour components were extracted from each dye in colour change profiles, and they were analysed using principal component analysis (PCA) and linear discriminant analysis (LDA). In contrast to PCA, LDA obtained an optimum classification result. This research shows that the artificial olfactory technique based on colorimetric sensor array has a powerful potential in the quality evaluation of rice wine.