Rapid determination and chemical change tracking of benzoyl peroxide in wheat flour by multi-step IR macro-fingerprinting

A novel long-acting antimicrobial nanomicelle spray

Contaminated surfaces play a major role in disease transmission to humans. The vast majority of commercial disinfectants provide short-term protection of surfaces against microbial contamination. The Covid-19 pandemic has attracted attention to the importance of long-term disinfectants as they would reduce the need for staff and save time. In this study, nanoemulsions and nanomicelles containing a combination of benzalkonium chloride (BKC; a potent disinfectant and a surfactant) and benzoyl peroxide (BPO; a stable form of peroxide that is activated upon contact with lipid/membranous material) were formulated. The prepared nanoemulsion and nanomicelle formulas were of small sizes <80 nm and high positive charge >45 mV. They showed enhanced stability and prolonged antimicrobial efficacy. The antibacterial potency was evaluated in terms of long-term disinfection on surfaces as verified by repeated bacterial inoculums. Additionally, the efficacy of killing bacteria upon contact was also investigated. A nanomicelle formula (NM-3) consisting of 0.8% BPO in acetone and 2% BKC plus 1% TX-100 in distilled water (1 : 5 volume ratio) demonstrated overall surface protection over a period of 7 weeks upon a single spray application. Furthermore, its antiviral activity was tested by the embryo chick development assay. The prepared NM-3 nanoformula spray showed strong antibacterial activities against Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus as well as antiviral activities against infectious bronchitis virus due to the dual effects of BKC and BPO. The prepared NM-3 spray shows great potential as an effective solution for prolonged surface protection against multiple pathogens.

A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin

We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (~20 min, cf. ELISA ~4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.

Integrated recognition and quantitative detection of starch in surimi by infrared spectroscopy and spectroscopic imaging

Surimi products have become increasingly-consumed food with prominent characteristics of high nutrition and convenience and its supply falls short of demand. However, due to exhausted fishery resource in recent years, surimi adulteration, such as addition of plant proteins, starch and other animal origin meat, is becoming serious, so recognition of these exogenous substances has become an urgent issue. In this study, Fourier transform infrared spectroscopy (FT-IR) combined with infrared spectroscopic imaging could distinguish heterogeneity in surimi qualitatively and quantitatively and obtain integral chemical images so that spatial distribution of each component in surimi could be visually displayed, thus a rapid recognition method and a prediction model were developed. The different starch contents in surimi had been primarily identified through intensity change of infrared absorption peaks at 1045cm^-1 and 988cm^-1, specifically with peak shifts to 1041cm^-1 and to 992cm^-1, respectively. In infrared imaging analysis, principal components (PCs) were separated and one key PC was confirmed as starch by characteristic peaks comparison at 1147cm^-1, 1075cm^-1, 997cm^-1 and 930cm^-1. Meanwhile, an established statistic model could predict starch content in surimi correctly with a reliable correlation coefficient (R=0.9856) and root mean square error of prediction (RMSEP=5.64). Therefore, FT-IR combined with infrared spectroscopic imaging could be applicable to integrally recognize and quantitatively detect starch in surimi.

Enhanced chemical and spatial recognition of fish bones in surimi by Tri-step infrared spectroscopy and infrared microspectroscopic imaging

Surimi is an intermediate product with an increasing popularity worldwide. Discrimination of impurities like fish bones in surimi has become an urgent issue owing to the food safety and the improved requirements for assessment methods in identification of surimi quality and grades. A Tri-step infrared spectroscopy, including Fourier transform infrared spectroscopy (FT-IR), second derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR) has been applied to integrally discriminate different contents (1%-8%) of fish bones in surimi at macro-scale. Meanwhile, attenuated total reflection infrared spectroscopy (ATR-IR) microspectroscopic imaging has been employed to recognize and identify the location of fish bones (less than 1.0 mm in size) in micro-scale. Fishbone characteristic infrared absorption peak at 1011 cm^-1 contributes to surimi peaks at 1045 cm^-1 and 988 cm^-1 confirmed by calculation of their peak heights and ratios of peak areas in original spectra. SD-IR spectra enhance the difference in range of 1440-500 cm^-1, and specifically peak intensity at 599 cm^-1 is significantly increased in surimi with 3%-8% fish bones. Moreover, 2DCOS-IR spectra reveal that surimi containing fish bones have increased intensity of auto-peaks at 525 cm^-1, 519 cm^-1, 512 cm^-1 and 505 cm^-1 mainly contributed by hydroxyapatite and collagen. In ATR-IR microspectroscopic images, a clear fishbone shape (800 × 200 μm) corresponding to its visible image is clearly observed in principal component (PC) score image, which is confirmed as a fish bone by corresponding pixel spectra. Furthermore, the single-wavenumber image shows the spatial chemical distribution of various components for both the fish bone and surimi. Consequently, fish bones can be integrally recognized by physical and chemical imaging manners. It has been demonstrated that the developed Tri-step infrared spectroscopy and ATR-IR microspectroscopic imaging could be applicable for rapidly recognizing impurities and adulterants in surimi.

A Colorimetric Sensor for the Visual Detection of Azodicarbonamide in Flour Based on Azodicarbonamide-Induced Anti-Aggregation of Gold Nanoparticles

Azodicarbonamide (ADA) in flour products can be converted into carcinogenic biurea and semicarbazide hydrochloride after baking. Thus, it is mandatory to determine ADA in flour. We herein developed a colorimetric method for the rapid and visual detection of ADA in flour based on glutathione (GSH)-induced gold nanoparticles (AuNPs) aggregation and specific reaction between ADA and GSH. The GSH can react to AuNPs via Au-SH covalent bond to form a network structure, which leads to AuNPs aggregation to produce color change, whereas ADA can specifically react with GSH to lead to the coupling of two GSH molecules, which makes GSH lose a -SH group and thus decreases the aggregation degree of AuNPs induced by GSH. This provided a platform for field-portable colorimetric detection of ADA. The colorimetric sensor can be used to detect as little as 0.33 μM (38.3 ppb) of ADA by naked eye observation and 0.23 μM (26.7 ppb) of ADA by spectrophotometry within 2 h. The method was successfully used to detect ADA in flour with a recovery of 91-104% and a relative standard deviation (RSD) < 6%. The visual detection limit of sensor is lower than the ADA limitation in flour (45 mg/kg), which makes the sensor a potential approach for the instrument-free visual and on-site detection of ADA in flour.

Rapid identification of pearl powder from Hyriopsis cumingii by Tri-step infrared spectroscopy combined with computer vision technology

Pearl powder, an important raw material in cosmetics and Chinese patent medicines, is commonly uneven in quality and frequently adulterated with low-cost shell powder in the market. The aim of this study is to establish an adequate approach based on Tri-step infrared spectroscopy with enhancing resolution combined with chemometrics for qualitative identification of pearl powder originated from three different quality grades of pearls and quantitative prediction of the proportions of shell powder adulterated in pearl powder. Additionally, computer vision technology (E-eyes) can investigate the color difference among different pearl powders and make it traceable to the pearl quality trait-visual color categories. Though the different grades of pearl powder or adulterated pearl powder have almost identical IR spectra, SD-IR peak intensity at about 861cm^-1 (v₂ band) exhibited regular enhancement with the increasing quality grade of pearls, while the 1082cm^-1 (v₁ band), 712cm^-1 and 699cm^-1 (v₄ band) were just the reverse. Contrastly, only the peak intensity at 862cm^-1 was enhanced regularly with the increasing concentration of shell powder. Thus, the bands in the ranges of (1550-1350cm^-1, 730-680cm^-1) and (830-880cm^-1, 690-725cm^-1) could be exclusive ranges to discriminate three distinct pearl powders and identify adulteration, respectively. For massive sample analysis, a qualitative classification model and a quantitative prediction model based on IR spectra was established successfully by principal component analysis (PCA) and partial least squares (PLS), respectively. The developed method demonstrated great potential for pearl powder quality control and authenticity identification in a direct, holistic manner.