HS-GC-IMS identification of volatile aromatic compounds of freshly-cooked rice packaged with different disposable lunchboxes

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.

Evaluation of Fourier deconvolution ion mobility spectrometer as high-performance gas chromatography detector for the analysis of plant extract flavors

The Fourier deconvolution ion mobility spectrometer (FDIMS) offers multiplexing and improves the resolving power and signal-to-noise ratio. To evaluate the FDIMS as a detector for gas chromatography for the analysis of complex samples, we connected a drift tube ion mobility spectrometer to a commercial gas chromatograph and compared the performance including resolving power, sensitivity, and linear range using 2,6-di‑tert-butylpyridine. Mixed standards were also injected into the tandem system to evaluate the performance under optimized conditions. A complex plant extract sample used as natural flavoring was investigated using the resulting system. The results show that the instrument implemented with the Fourier deconvolution multiplexing method demonstrated higher performance over the traditional signal averaging method including higher resolving power, better limit of detection, and wider linear range for a variety of compounds and natural plant extract flavorings.

Discrimination of toxigenic and non-toxigenic Aspergillus flavus in wheat based on nanocomposite colorimetric sensor array

A novel method was developed for the early detection of wheat infected with Aspergillus flavus (A. flavus) using a nanocomposite colorimetric sensors array (CSA). LC-MS analysis revealed the presence of Aflatoxin B1 (AFB1) and Aflatoxin B2 (AFB2) on day seven, demonstrating mycotoxin variabilities in infected wheat. HS-SPME-GC-MS analysis detected 2-methylbutyral, a gas exclusively associated with toxigenic A. flavus. The CSA was modified using three nanoparticles of MOF and successfully used to detect the wheat infected with A. flavus. Discrimination of different types of infected wheat samples was achieved using the RGB difference map and Principal Component Analysis (PCA) model. Additionally, the Linear Discriminant Analysis (LDA) model accurately predicted the presence of toxigenic A. flavus at various stages of infection. These findings highlight the promising capabilities of nanocomposite CSA for early-stage detection of A. flavus infection in wheat.

Sugarcane Pulp Take-Out Containers Produce More Microparticles in Acidic Foods

In the current study, the production of microparticles released from fifteen commercial sugarcane pulp (SCP) take-out containers into different food simulants under different conditions was investigated, where deionized water (DI water), 4% acetic acid (4% HAc), and 95% ethanol (95% EtOH) were used to simulate aqueous, acidic, and fatty foods, respectively. Results showed that compared with DI water and 95% EtOH, 4% HAc caused the degradation of sugarcane fibers, thereby releasing the highest number of microparticles. The overall migration values of the sugarcane pulp take-out containers in 4% HAc were above the prescribed limit of 10 mg/dm². Furthermore, it was estimated that consumers may intake 36,400-231,700 microparticles in a take-out container at one time, of which the proportion of particles with a particle size between 10 and 500 μm was the highest, ranging from 26,470 to 216,060 items. Moreover, the Al and Fe are the main metals in these take-out containers, ranging between 35.16 and 1244.04 and 44.71 and 398.52 mg/kg, respectively, followed by Pb, Ti, and Sr. This study provides important information that the safety of both the production of microparticles and the metallic elements should be considered for SCP take-out containers when in contact with food.