A multiplexed FRET aptasensor for the simultaneous detection of mycotoxins with magnetically controlled graphene oxide/Fe3O4 as a single energy acceptor.

Aflatoxin B1 (AFB1) and fumonisin B1 (FB1) are the most common mycotoxins and often coexist in agricultural products, and are known to form a toxic superposition and even have carcinogenic effects on humans. We propose a multiplexed fluorescence resonance energy transfer (FRET) aptasensor for the simultaneous detection of mycotoxins with magnetically controlled graphene oxide (GO)/Fe3O4 as the single energy acceptor. CdTe quantum dots emitting green (GQDs) and red (RQDs) fluorescence were modified by aptamers that are specific for AFB1 and FB1 and used as dual energy donors. Compared with conventional FRET systems based on a GO quencher, GO/Fe3O4, as a single energy acceptor, not only simultaneously quenches the different fluorescence emission peaks of the aptamer-modified GQDs and RQDs but also can be effectively removed by magnetic separation to eliminate background interference. In the absence of the GO/Fe3O4 nanocomposites, the aptamer-modified GQDs and RQDs emit strong fluorescence under ultraviolet radiation. The fluorescence of the GQDs and RQDs is quenched when the GO/Fe3O4 nanocomposites are added to the system owing to the π-π stacking interactions between the GO/Fe3O4 nanocomposites and the GQD- and RQD-labeled aptamers. However, in the presence of AFB1 and FB1, the binding of aptamers to their specific targets will fold their single stranded structures and hinder the contact between the base group in the aptamers and GO/Fe3O4, which will cause the fluorescence recovery of GQDs and RQDs. With the help of a one-step magnetic separation, the supernatants can be collected for fluorescence analysis. After the optimization of detection conditions, the developed method had a wide linear range of 10 pg mL-1-100 ng mL-1 for AFB1 and 50 pg mL-1-300 ng mL-1 for FB1 and showed no cross-reactivity with other closely related mycotoxins. The limit of detection for AFB1 and FB1 were calculated to be 6.7 and 16.2 pg mL-1 based on S/N = 3, respectively. The detection of mycotoxins was successfully realized in peanut samples, indicating the successful application of this new FRET system for various future targets.

Study on the Quality Variation and Internal Mechanisms of Frozen Oatmeal Cooked Noodles during Freeze-Thaw Cycles.

Frozen staple food, attributed to its favorable taste and convenience, has a promising development potential in the future. Frequent freezing and thawing, however, will affect its quality. This study simulated several freeze-thaw cycles (FTC) that may occur during the cold chain process of frozen oatmeal cooked noodles (FOCN) production to consumption. The quality changes and their mechanisms were elucidated using methods such as differential scanning calorimetry (DSC), low-field nuclear magnetic resonance (LF-NMR), Fourier-transform infrared spectroscopy (FTIR), confocal laser scanning microscopy (CLSM), texture analysis, and sensory evaluation. The freezable water content of the FOCN decreased because of the FTC treatment, and the relative content of total water in FOCN also decreased accordingly. The increase in ß-Turn after FTC induced disorder in the secondary structure of proteins, causing the protein microstructure to become loose and discontinuous, which in turn reduced the water-holding capacity of FOCN. Additionally, FTC reduced the chewiness and sensory score of FOCN. This research will contribute a theoretical foundation for optimizing the cold chain process.

Characterization of the volatile flavor profiles of black garlic using nanomaterial-based colorimetric sensor array, HS-SPME-GC/MS coupled with chemometrics strategies.

This work investigated the feasibility of applying headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) combining olfactory visualization for flavor characterization of black garlic. Volatile organic compounds (VOCs) analysis was performed to select important differential VOCs during black garlic processing. A multi-channels nanocomposite CSA assembled with two porous metal-organic frameworks was then developed to characterize flavor profiles changes during black garlic processing, and garlic samples during processing could be divided into five clusters, consistent with VOCs analysis. Artificial neural network (ANN) model outperformed other pattern recognition methods in discriminating processing stages. Furthermore, SVR model for odor sensory scores with the correlation coefficient for prediction set of 0.8919 exhibited a better performance than PLS model, indicating a preferable prediction ability for odor quality. This work demonstrated that the nanocomposite CSA combining appropriate chemometrics can offer an effective tool for objectively and rapidly characterizing flavor quality of black garlic or other food matrixes.

Stability and Bioaccessibility of Quercetin-Enriched Pickering Emulsion Gels Stabilized by Cellulose Nanocrystals Extracted from Rice Bran.

To investigate the optimal delivery system of quercetin, in this paper, cellulose nanocrystals (CNCs) extracted from rice bran were used to stabilize the Pickering emulsion and Pickering emulsion gels (PEGs) with quercetin. To compare the emulsion properties, stability, antioxidation activity, encapsulation rate, and bioaccessibility of the quercetin, four emulsions of CNC Pickering emulsion (C), CNC Pickering emulsion with quercetin (CQ), CNC Pickering gel emulsion (CG), and CNC Pickering gel emulsions with quercetin (CQG) were prepared. All four emulsions exhibited elastic gel network structure and good stability. The quercetin significantly reduced the particle size, increased the stability, and improved the antioxidant capacity of CQ and CQG. Compared to C and CG, the ABTS+ radical scavenging capacities of CQ and CQG were respectively enhanced by 46.92% and 3.59%. In addition, CQG had a higher encapsulation rate at 94.57% and higher bioaccessibility (16.17) compared to CQ. This study not only indicated that CNC from rice bran could be exploited as an excellent stabilization particle for Pickering emulsions, but also provided a highly stable and bioaccessible delivery system for water-insoluble functional active factors.

Qualitative and quantitative assessment of flavor quality of Chinese soybean paste using multiple sensor technologies combined with chemometrics and a data fusion strategy.

Multiple sensor technologies including electronic nose (E-nose), electronic tongue (E-tongue), colorimeter and texture analyzer combined with chemometrics and dada fusion strategies were applied to characterize the flavor quality of traditional Chinese fermented soybean paste. Principal components analysis (PCA) was performed to divide the selected soybean pastes into three clusters which was not completely consistent with geographical regions of selected samples. Support vector machine regression (SVR) outperformed partial least squares regression (PLSR) in quantitatively predicting sensory attributes. Additionally, prediction of overall flavor of soybean paste based on data fusion of multiple sensor information, with a correlation coefficient of prediction (Rp) of 0.9636 based on SVR, was better than prediction of E-nose and E-tongue data fusion (Rp = 0.9267). This study suggested multiple sensor technologies coupled with chemometrics can be a promising tool for flavor assessment and characterization of fermented soybean paste or other food matrixes.

Ultrasonic-assisted resting of Tartary buckwheat dough: Study on its effect and mechanism.

Utilizing natural hypoglycemic ingredients in staple foods is a safe and effective way to improve diabetes. High Tartary buckwheat noodles have garnered research interest due to their hypoglycemic properties. However, increasing the Tartary buckwheat content poses challenges in noodle processing and affects their edible quality. Effective resting is a critical link to improve the processing performance of noodle and edible quality of noodle. Therefore, research was conducted on ultrasound assisted resting of Tartary buckwheat dough (TBD) to explore its feasibility and mechanism in improving the quality of Tartary buckwheat noodle. The results indicated that ultrasound treatment effectively promoted the migration of weakly-bound water towards strongly-bound water, thereby enhancing the gluten protein network structure and increasing the α-helix and ß-sheet contents significantly (p < 0.05). Furthermore, Texture analysis indicated decreased hardness and adhesion, and increased elasticity and stretching distance in the final noodles. Ultrasound-assisted maturation pre-treatment shortens TBD's dough's resting time and improves noodle quality, according to this study.

Multi-scale structure characterization of ozone oxidized waxy rice starch.

The elucidation of multi-scale structural variation and oxidation reaction mechanism of ozone oxidized waxy rice starch molecules remains a big challenge, limiting its development of intensive processing. In the present work, the changes in the structure of waxy rice starch after ozone treatment were systematically researched by various characterization methods. The study has shown that with the increase in ozone oxidation time, the granules of oxidized starch were polygons with multiple face depressions. It was also observed that ozone first attacked the amorphous zone of the starch granules and then penetrated the crystalline zone. Combining 1D and 2D NMR (1H NMR, 13C NMR, HSQC and HMBC) and other methods, it was proved that ozone oxidation led to ring splitting between C2 and C3 of the glucose unit. The resulting hemiacetal groups showed different types of structures. Among them, the main structures were intramolecular acetals and intermolecular hemiacetals. This research offered theoretical guidance for the utilization of ozone oxidation technology for starch modification and the development of waxy rice new foods.

Applications of surface functionalized Fe3O4 NPs-based detection methods in food safety.

Food safety has always been an issue of great concern to people. The development of rapid, sensitive and specific detection technology of food pollutants is one of the hot issues in food science field. The rapid development of functionalized Fe3O4 nanoparticles (NPs) provides unprecedented opportunities and technical support for the innovation of food safety detection. The surface functionalized Fe3O4 NPs, which combine superparamagnetic with nanoscale feature, have become an excellent tool for food quality and safety detection. This review highlights the mechanism, principles, and applications of surface functionalized Fe3O4 NPs-based detection technique in the agrifood industry. Then the relevant characteristics, functional roles and general mechanisms of nanomaterial-based detection of various endogenous components and exogenous pollutants in foods are discussed in detail. Ultimately, this review is expected to promote the optimization of functionalized Fe3O4 NPs and provide direction for the diversity of signal recognition and the sustainability of detection methods.

Application of volatile and spectral profiling together with multimode data fusion strategy for the discrimination of preserved eggs.

The maturity level of eggs during pickling is conventionally assessed by choosing few eggs from each curing batch to crack open. Yet, this method is destructive, creates waste and has consequences for financial losses. In this work, the feasibility of integrating electronic nose (EN) with reflectance hyperspectral (RH) and transmittance hyperspectral (TH) data for accurate classification of preserved eggs (PEs) at different maturation periods was investigated. Classifier models based solely on RH and TH with EN achieved a training accuracy (93.33%, 97.78%) and prediction accuracy (88.89%; 93.33%) respectively. The fusion of the three datasets, (EN + RH + TH) as a single classifier model yielded an overall training accuracy of 98.89% and prediction accuracy of 95.56%. Also, 52 volatile compounds were obtained from the PE headspace, of which 32 belonged to seven functional groups. This study demonstrates the ability to integrate EN with RH and TH data to effectively identify PEs during processing.

Dynamic characteristics of dough during the fermentation process of Chinese steamed bread.

The fermentation process is crucial to the production of Chinese steamed bread (CSB). In order to select suitable indicators as the basis for further research of establishing intelligent monitoring method for dough fermentation state, this study investigated the dynamic characteristics of dough during fermentation. Indicators included water mobility and distribution, starch-pasting properties, content of free amino acid (FAA), volatile organic compounds (VOCs) and electronic nose (E-nose) response value. Starch-pasting properties of dough and relaxation time (T21, T22) did not change significantly during the fermentation process (p < 0.05). The VOCs and FAAs of the dough had significant differences (p < 0.05) in different fermentation times, but no rule was established. The E-nose response value to headspace was most suitable to monitor the fermentation of dough. Principal component analysis (PCA) was performed on E-nose data from 75 samples and the results indicated that samples of different fermentation states were accurately classified.