Fabricating a mechanochromic AIE luminogen into a wearable sensor for volatile organic compound (VOC) detection

High-precision recognition of wheat mildew degree based on colorimetric sensor technique combined with multivariate analysis

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.

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

Non-destructive detection of multi-component heavy metals in corn oil using nano-modified colorimetric sensor combined with near-infrared spectroscopy

In this work, two methods, which were visible near-infrared spectroscopy (VNIRS) and visible near-infrared spectroscopy combined with colorimetric sensor array (VNIRS-CSA), were used to identify the volatile compound changes of rice samples stored for 0 to 6 months. Principal component analysis (PCA), interval partial least squares (iPLS), and synergy interval partial least squares (SiPLS) were used for qualitative classification. A prediction model was established by linear discriminant analysis (LDA), which was compared with the traditional VNIRS detection technology. The results revealed that the VNIRS-CSA got better performance than VNIRS and exhibited a good result based on iPLS/SiPLS-PCA/LDA models. Furthermore, spectral data from VNIRS-CSA were the best for LDA with a high prediction value of 0.925 after standard normal variate (SNV) processing and variable selection by SiPLS. The research demonstrated that VNIRS-CSA is a quick, accurate, and non-destructive method for monitoring the storage time of rice. The strategy also has the potential for volatile organic components analysis.

Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array

2,4,5-Trimethyloxazole, an important volatile organic compound, is widely detected in food products such as wine and vinegar. The current study presents a colorimetric sensor array (CSA) made of porphyrins and pH indicator for analysing the 2,4,5-trimethyloxazole in complex volatile organic compounds (VOCs). Based on density functional theory (DFT), time-dependent density functional theory (TDDFT) and UV spectral analysis, porphyrin complexes were synthesized and optimized specifically for the discriminating of 2,4,5-trimethyloxazole. We explored the possibility of porphyrins binding with VOCs, and the binding energies, dipole moments or equilibrium constants of optimized porphyrins have an upward trend after combining with 2,4,5-trimethyloxazole. The colorimetric dye of 5,10,15,20-tetrakis(4-fluorophenyl)-21H,23H-porphine zinc (TPP-Zn-F) is found specifically sensitive to the compound of 2, 4, 5-trimethyloxazole. The optimized porphyrin can resist the interference of complex VOCs, and the accuracy of characterization of 2,4,5-trimethyloxazole was improved. Finally, CSA composed of TPP-Zn-F and two chemoselective dyes shows a great ability to identify the storage time of vinegar, and the rates of LDA and KNN algorithms to predict vinegar samples are 92.5% and 100%, respectively.

Development of Colorimetric Detection of 2,4,5-Trimethyloxazole in Volatile Organic Compounds Based on Porphyrin Complexes for Vinegar Storage Time Discrimination

An olfactory visualization system conducts a qualitative or quantitative analysis of volatile organic compounds (VOCs) by utilizing the sensor array made of color sensitive dyes. The reaction chamber is important to the sensor array’s sufficient and even exposure to VOCs. In the current work, a reaction chamber with an arc baffle embedded in the front of the air inlet for drainage effect was designed. The velocity of field and particle distribution of flow field in the reaction chamber was simulated by COMSOL Multiphysics. Through repeated simulation, the chamber achieved optimal result when the baffle curvature was 3.1 and the vertical distance between the baffle front end and the air inlet was 1.6 cm. Under the new reaction chamber, principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to identify vinegar samples with different storage time through analyzing their VOCs. The LDA model achieved optimal performance when 8 principal components (PCs) were used, and the recognition rate was 95% in both training and prediction sets. The new reaction chamber could improve the stability and precision of an olfactory visualization system for VOCs analysis, and achieve the accurate differentiation and rapid discrimination of Zhenjiang vinegar with different storage time.

https://www.mdpi.com/2304-8158/10/3/532/pdf

Jinjin Lin

Papers

Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array

Development of Colorimetric Detection of 2,4,5-Trimethyloxazole in Volatile Organic Compounds Based on Porphyrin Complexes for Vinegar Storage Time Discrimination

Non-destructive detection of multi-component heavy metals in corn oil using nano-modified colorimetric sensor combined with near-infrared spectroscopy

Simulation and Non-Invasive Testing of Vinegar Storage Time by Olfaction Visualization System and Volatile Organic Compounds Analysis.

Detection of wheat toxigenic Aspergillus flavus based on nano-composite colorimetric sensing technology.