Integrated molybdenum single atom array sensors with multichannels for nitrite detection in foods

Nitrite (NO2-) is present in a variety of foods, but the excessive intake of NO2- can indirectly lead to carcinogenic, teratogenic, mutagenicity and other risks to the human body. Therefore, the detection of NO2- is crucial for maintaining human health. In this study, an integrated array sensor for NO2- detection is developed based on molybdenum single atom material (IMSMo-SAC) using high-resolution electrohydrodynamic (EHD) printing technology. The sensor comprises three components: a printed electrode array, multichannels designed on polydimethylsiloxane (PDMS) and an electronic signal process device with bluetooth. By utilizing Mo-SAC to facilitate electron transfer during the redox reaction, rapid and efficient detection of NO2- can be achieved. The sensor has a wide linear range of 0.1 μM-107.8 mM, a low detection limit of 33 nM and a high sensitivity of 0.637 mA-1mM-1 cm-2. Furthermore, employing this portable array sensor allows simultaneously measurements of NO2- concentrations in six different foods samples with acceptable recovery rates. This array sensor holds great potential for detecting of small molecules in various fields.

Hemicyanine-Phenothiazine Based Highly Selective Ratiometric Fluorescent Probes for Detecting Hypochlorite Ion in Fruits, Vegetables and Beverages

Hypochloric acid (HClO) is a reactive oxygen species (ROS) that functions as a bacteriostatic and disinfectant in food production. Excessive levels of ClO-, however, have been linked to various health issues, including cardiovascular diseases (Halliwell and Gutteridge in Oxford University press, USA, 2015), arthritis, and neurodegenerative diseases (Heinzelmann and Bauer in Biol Chem. 391(6):675-693, 2010). Therefore, synthesizing highly selective and sensitive probes for rapidly detecting endogenous ClO- in daily foods is currently a popular research topic (Kalyanaraman et al. in Redox Biol. 15:347-362, 2018; Winterbourn in Nat Chem Biol. 4(5):278-286, 2008; Turrens in J Physiol. 552(2):335-344, 2003). Thus, we have developed two highly selective ratiometric fluorescent probes (Probe1 and Probe2) based on indole-phenothiazine to detect ClO- in common vegetables, fruits and beverages qualitatively and quantitatively. Moreover, Both Probe1 and Probe2 have shown good specificity and stability, with high fluorescence intensity and long duration (Feng et al. in Adv Sci. 5:1800397, 2018; Wei et al. in Angew Chem. 131(14):4595-4599, 2019; Baruah et al. in J Mater Chem B, 2022).

Novel Ag-coated nanofibers prepared by electrospraying as a SERS platform for ultrasensitive and selective detection of nitrite in food

Nitrite is commonly used as a preservative and color fixative in the meat industry. However, the risk of it transforming into N-nitrosamine restricts its intake. Herein, a novel sensitive Ag-coated nanofiber surface-enhanced Raman scattering (SERS) platform was developed for rapid nitrite detection. The electrospraying technique was firstly used to assemble Ag nanoparticles (NPs) on the nanofibers to obtaine SERS platform. The homogeneity and long-term stability of the SERS platform were evaluated. The limit of detection (LOD) of the SERS platform was estimated to be 2.216 × 10^-12 mol/L, corresponding to 15.29 ng·L^-1 and good linearity was shown between the relative SERS intensity and nitrite concentration range of 10-1 to 10^-4 mol/L. The Ag-coated nanofiber SERS platform was utilized to assay-five common nitrite foods, and the results provided valid evidence for the compatibility of SERS platform in quantitative nitrite detection.

Rapid detection of I- in food samples by cholesteric chiral artificial receptor L5

In this study, a cholesteric chiral artificial receptor L5 was synthesized and a rapid method for the detection of iodide ions was established. The addition of L5 to Ethanol/Tris (V/V = 1/1, pH 7.0) solution changed the iodide ion solution from colorless to light yellow to achieve the naked-eye identification. The identification pattern was confirmed with the help of spectral titration, job test and anti-interference test, and it was found that L5 bound to iodide ion in a 1:1 coordination ratio. It was also confirmed that the hydrogen atoms of the amide group of MeO-PhCONH in L5 and the hydrogen atoms of -COOCH₃ in L5 interacted with I^- through hydrogen bonding force by ¹H NMR titration, infrared spectroscopy, scanning electron microscopy and computer molecular simulation. The detection limit of L5 was 0.025 µmol/L, which could efficiently and conveniently detect iodide ions in food samples.

Colorimetric and fluorescent dual-signals probes for naked-eye detection of hydrogen peroxide and applications in milk samples and in vivo

Excessive residual hydrogen peroxide (H₂O₂) disinfectant in food is harmful to human health. Therefore, it is necessary to develop efficient detection methods for H₂O₂ detection. In this work, we designed and synthesized five D-A molecules 3a-3e by introducing electron-donor substituents (-OCH₃ and -CH₃) to the electron-acceptor dicyanoisophorone skeleton in order to find out the suitable probes for H₂O₂ detection. Among them, two promising probes, 3a and 3c, are screened out according to structure-property relationships. Based on the principle of intramolecular charge transfer (ICT), 3a and 3c express colorimetric and fluorescent dual-signals towards H₂O₂ with low detection limits (0.20 μM and 0.14 μM) and rapid response (within 20 mins). The reaction mechanism between probes and H₂O₂ is determined by ¹H NMR and HRMS. Density functional theory (DFT) calculations are measured to study the regulation mechanism of structure adjustment on probs performance. Furthermore, a smartphone RGB analysis is utilized as a portable platform for the quantitative detection of H₂O₂ without complicated instruments, indicating a high efficiency and on-site detection method for H₂O₂. In addition, probes are applied to detect H₂O₂ in milk samples, HepG-2 cells and zebrafish, suggesting the promising applications in food samples and physiological systems.

Design of a naphthalimide-based probe for acrolein detection in foods and cells

Acrolein is a highly toxic agent that can be generated exogenously and endogenously. Therefore, a highly specific and sensitive probe for acrolein with potential applications in acrolein detection must be developed. In this research, a novel fluorescent probe named "probe for acrolein detection" (Pr-ACR) was designed and synthesized based on a naphthalimide fluorophore skeleton, and a thiol group (-SH) was introduced into its structure for acrolein recognition. The -SH traps acrolein via Michael addition and the resultant interaction product of the probe inhibits the photoinduced electron transfer process and produce a strong fluorescence at 510 nm. The probe showed high sensitivity and specificity for acrolein. HPLC-MS/MS analysis verified that it can be used to quantify acrolein in foods, such as soda crackers, red wine, and baijiu, with a fluorescence spectrophotometer. After methyl esterification, the methyl esterified probe (mPr-ACR) successfully visualised acrolein in Hela cells under a laser scanning confocal microscope. This finding proved that Pr-ACR and mPr-ACR are potential tools for the detection and visualisation of acrolein from different sources.

Ocean freshening and acidification differentially influence mortality and behavior of the Antarctic amphipod Gondogeneia antarctica

The Western Antarctic Peninsula (WAP) has experienced rapid atmospheric and ocean warming over the past few decades and many marine-terminating glaciers have considerably retreated. Glacial retreat is accompanied by fresh meltwater intrusion, which may result in the freshening and acidification of coastal waters. Marian Cove (MC), on King George Island in the WAP, undergoes one of the highest rates of glacial retreat. Intertidal and shallow subtidal waters are likely more susceptible to these processes, and sensitive biological responses are expected from the organisms inhabiting this area. The gammarid amphipod Gondogeneia antarctica is one of the most abundant species in the shallow, nearshore Antarctic waters, and it occupies an essential ecological niche in the coastal marine WAP ecosystem. In this study, we tested the sensitivity of G. antarctica to lowered salinity and pH by meltwater intrusion following glacial retreat. We exposed G. antarctica to four different treatments combining two salinities (34 and 27 psu) and pH (8.0 and 7.6) levels for 26 days. Mortality, excluding cannibalized individuals, increased under low pH but decreased under low salinity conditions. Meanwhile, low salinity increased cannibalism, whereas low pH reduced food detection. Shelter use during the daytime decreased under each low salinity and pH condition, indicating that the two stressors act as disruptors of amphipod behavior. Under low salinity conditions, swimming increased during the daytime but decreased at night. Although interactions between low salinity and low pH were not observed during the experiment, the results suggest that each stressor, likely induced by glacial melting, causes altered behaviors in amphipods. These environmental factors may threaten population persistence in Marian Cove and possibly other similar glacial embayments.

Automatic food detection in egocentric images using artificial intelligence technology

OBJECTIVE

To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.

DESIGN

To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.

RESULTS

A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.

CONCLUSIONS

The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.