A Novel Nanoscaled Chemo Dye–Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat

Co-MOF-derived Co3O4 sensors for efficient 3-octanone biomarker monitoring in wheat mildew

3-octanone has been widely identified as a primary biomarker for mold and insect infestation in wheat. Nevertheless, to date, no chemiresistive sensor based on a metal oxide semiconductor with excellent sensing properties for 3-octanone has been developed. In light of the extensively reported superior efficacy of Co3O4-based sensors for ketone detection, we designed Co3O4 samples with various hierarchical morphologies (hollow sphere, multi-wall sphere, flower-like, and urchin-like), which were determined by regulating solvent and organic ligand. The sensing properties of four as-fabricated Co3O4-based sensors were systematically evaluated, verifying their potential for efficient 3-octanone monitoring. The adsorption behavior of 3-octanone was studied by DFT simulation, and the preferential adsorption of the hydroxyl functional group in 3-octanone at Co sites was verified. Among the four samples, the hollow spherical Co3O4 demonstrated the highest sensitivity for 3-octanone (173.88 ± 5.59@50 ppm), which can be attributed to the larger specific surface area (SSA, 60.164 m2/g), lower energy gap (1.306 eV), and the superior concentration of chemisorbed oxygen (23.8 %). Furthermore, the practical value of this sensor in agricultural product inspection and environmental monitoring applications was validated by testing its response to the complex gases emitted from wheat stored for different periods.

Comparative transcriptomic analysis and volatile compound characterization of Aspergillus tubingensis and Penicillium oxalicum during their infestation of Japonica rice

Volatile organic compounds (VOCs), a byproduct of mold metabolism, have garnered increasing interest because the VOCs can be used to detect food early contamination. So far, the use of VOCs as indicators of rice mildew, specifically caused by Aspergillus tubingensis and Penicillium oxalicum, and the mechanisms of their generation are not well investigated. This study examines the VOCs produced by these molds during paddy storage, utilizing headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). We further elucidate the mechanisms underlying the formation of these VOCs through a comparative transcriptomic analysis. The VOCs characteristic to A. tubingensis and P. oxalicum, identified with a VIP value > 1 in the partial least squares discriminant analysis (PLS-DA) model, are primarily alkenes. Our transcriptome analysis uncovers key metabolic pathways in both molds, including energy metabolism and pathways related to volatile substance formation, and identifies differentially expressed genes associated with alkane and alcohol formation.

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.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality

The sensory quality of matcha is a pivotal factor in determining consumer acceptance. However, the human sensory panel test is difficult to popularize by virtue of professional requirements and inability to evaluate large samples. The analysis showed that physicochemical indicators of matcha were significantly related to sensory quality. Hence, principal component analysis (PCA) based on selected key physicochemical indicators was proposed to evaluate the sensory quality of matcha in this research. The eight key indicators were selected from twenty-four physicochemical indicators based on least absolute shrinkage and selection operator (LASSO) for the establishment of the PCA comprehensive evaluation model. The results demonstrated that the PCA comprehensive evaluation model achieved superior performance, with -0.895 r_c (correlation coefficient in calibration set) and -0.883 r_p (correlation coefficient in prediction set) for overall sensory quality. This work demonstrated that LASSO-PCA comprehensive evaluation as an objective protocol has great potential in predicting matcha sensory quality.

Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features

The fatty acid content of flour is an important indicator for determining the deterioration of flour. We propose a novel rapid measurement method for fatty acid content during flour storage based on a self-designed color-sensitive gas sensor array. First, a color-sensitive gas sensor array was prepared to capture the odor changes during flour storage. The sensor features were then optimized using genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). Finally, back propagation neural network (BPNN) models were established to measure the fatty acid content during flour storage. Experimental results showed that the optimization effects of the three algorithms improved in the following order: GA < ACO < PSO, for the sensor features optimization. In the validation set, the determination coefficient of the best PSO-BPNN model was 0.9837. The overall results demonstrate that the models established on the optimized features can realize rapid measurements of fatty acid content during flour storage.