Near-infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

Smartphone-Integrated Photoacoustic Analytical Device for Point-of-Care Testing of Food Contaminant Azodicarbonamide

Azodicarbonamide (ADA) is widely used as a flour additive due to its oxidizing and bleaching properties, but it reacts with wet flour during heat processing and is easily decomposed into semicarbazide with genotoxicity and carcinogenicity. In order to improve the efficiency of food safety supervision and expand the scope of food safety control, it is of great significance to develop a facile method for point-of-care testing (POCT) of ADA. Herein, a field-portable and universal smartphone-based photoacoustic (PA) integration device is constructed for quantitative POCT of ADA in flour. The recognition probe Prussian blue with favorable stability is loaded on a flexible substrate for fabricating a portable test strip. In the presence of target ADA, the PA signal changes driven by a modulated 808 nm laser beam can be conveniently collected through the recording application (Audio Lab) of the smartphone. By combining the economic test strip and portable PA device with smartphone readout, it not only greatly simplifies the operation steps but also dramatically reduces the size and cost of the instrument. There is a favorable linear relationship between the PA signal and ADA concentration in the range of 10-200 μmol L^-1 (R² = 0.9928), and a detection limit of 5 μmol L^-1 obtained is much lower than the maximum allowable ADA level in the extract of flour (388 μmol L^-1). The present miniature PA device with strong POCT ability holds enormous public health significance and economic value in the field of food safety, especially in resource-limited settings.

A handheld multifunctional smartphone platform integrated with 3D printing portable device: On-site evaluation for glutathione and azodicarbonamide with machine learning

Azodicarbonamide (ADA) in flour can be easily decomposed to semi-carbazide and biuret, exhibiting strong genotoxicity in vitro and carcinogenicity. Glutathione (GSH) can be conjugated with some ketone-containing compounds and unsaturated aldehydes to form toxic metabolites. Here, a novel ratio fluorescence probe based on blue emitting biomass-derived carbon dots (BCDs) and yellow emitting 2,3-diaminophenazine (OxOPD) was prepared for the bifunctional determination of glutathione (GSH) and ADA. This strategy includes three processes: (1) Ag⁺ oxidizes o-phenylenediamine (OPD) to produce OxOPD. The peak at 562 nm was enhanced, and the peak at 442 nm was reduced due to fluorescence resonance energy transfer (FRET), (2) glutathione binds Ag⁺ and inhibits the production of OxOPD, (3) ADA oxidizes GSH to form GSSG, resulting in the release of Ag⁺ by GSH. Therefore, the newly designed ratio fluorescence probe can be based on the intensity ratio (I₄₄₂/I₅₆₂) changes and significant fluorescent color changes to detect GSH and ADA. Moreover, a smartphone WeChat applet and a yolov3-assisted deep learning classification model have been developed to quickly detect GSH and ADA on-site based on an image processing algorithm. These results indicate that smartphone ratiometric fluorescence sensing combined with machine learning has broad prospects for biomedical analysis.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging

Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082 nm) and short wave infrared (SWIR, 930-2548 nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3 days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R² results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.

Visual determination of azodicarbonamide in flour by label-free silver nanoparticle colorimetry

A very practical and competitive sensing strategy for the detection of azodicarbonamide in flour samples was developed by using label-free Ag NPs as a colorimetric probe. Well-dispersed Ag NPs in suspension can form aggregates upon reacting with glutathione (GSH) via Ag-SH covalent bonds and electrostatic attraction, with the color changing from bright yellow to red. However, azodicarbonamide can oxidize the -SH of GSH, preventing the aggregation of Ag NPs. Under the optimum conditions, the A₅₅₀/A₃₉₈ of Ag NPs is linearly related to the concentration of azodicarbonamide in the range of 0.33 μM to 1.7 μM. The proposed method can be used for the detection of azodicarbonamide in flour, with a detection limit of 0.09 μM and recovery between 95% and 97.4% (RSD < 6%). When the azodicarbonamide concentration reaches 0.33 μM, the color change can be detected by the naked eye.

Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging

Near-infrared microscopy (NIRM) technology can analyze different components within a sample while also obtaining spatial information about the sample. No rapid detection methods are available for effectively identifying antibiotic mycelia residues (AMRs) in protein feeds materials to date. In this study, the feasibility of using NIRM to identify AMRs (oxytetracycline residue, streptomycin sulfate residue and clay colysin sulfate residue) mixed in cottonseed meals was studied. The samples were scanned by NIRM, then the spectra of images were analyzed by principal component analysis (PCA) to select characteristic bands for further identification with one-class partial least squares analysis (OCPLS). The results showed that: a) AMRs were effectively identified in cottonseed meal; b) screening characteristic bands and increasing the spectral number of the calibration set improved the identification results of the model; and c) the sensitivity, specificity, accuracy and class error of the method were 100%, 95.93%, 99.01% and 2.03%, respectively.