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The ability of anuran larvae to efficiently locate food is essential for their growth and development, but little is known about the sensory modalities they use to find food. We experimentally assessed the role of visual and chemical cues in finding food in tadpoles of the invasive species Lithobates catesbeianus. In the two‐choice experiment, using rectangular containers that offer visual and/or chemical food cues on opposite ends, tadpoles chose the side where the chemical cues of the food were present consistently. The visual cues alone showed an initial transient attraction of tadpoles, which disappeared after a few minutes, suggesting that tadpoles are potentially capable of seeing contrasting objects, at least, at a close range and in clear waters and daylight conditions. We also evaluated the foraging behaviour and the capability of tadpoles to find food in an open field experiment under two different lighting conditions: natural daylight and darkness. Results showed that tadpoles find food faster in dark conditions, confirming that chemical cues alone are sufficient for tadpoles of this species to find food. Moreover, the time spent feeding and the number of tadpoles simultaneously exploiting the food patch were greater in darkness suggesting that environments with low visibility conditions could favour the establishment of L. catesbeianus. Understanding the sensory modalities used by tadpoles of this invasive species to find food could help to understand the potential abilities to expand and establish in novel environments. [ABSTRACT FROM AUTHOR]

Food is closely related to human life. With the development of the times, the human demand for food has changed dramatically. People pay closer attention to the safety, health, composition, brand, origin, and processing method of food, which is precisely inseparable from food testing technology. Currently, there are many food inspection technologies, and the electronic nose (E-nose), as an efficient, fast, non-destructive, and promising technology, has been successfully applied in many aspects of the food industry and has shown promising results. This paper first introduces the basic principle and composition of the E-nose. Then it describes in detail the key elements, including gas sensor selection, sampling method design, data acquisition and information processing. Further summarizes the various typical applications of E-nose technology in the food industry in recent years, including six application directions: freshness assessment, process monitoring, flavor evaluation, authenticity evaluation, quality control, origin traceability and pesticide residue detection. Finally, the critical problems that need to be solved in the current application of E-nose technology in the food industry are discussed, and the potential future research directions in this field are foreseen. [ABSTRACT FROM AUTHOR]

From farm to plate, food meets various challenges and pollution. Food detection is essential to ensure food safety. Although detection techniques have obtained great development, the accuracy, sensitivity and operating conditions still have room for improvement. Composed of dielectric media with different refractive indices to form a periodic structure, an optical material named photonic crystal (PC) can offer a breakthrough. Owing to the periodic structure, Bragg diffraction happens with optical waves propagating in a PC and photonic band gap (PBG), a characteristic parameter of the PC, will form. The properties of PBG are widely used in the field of detection. Combined with the techniques of biology, immunology, optics and fluorescence etc., PCs can play the role of specific, efficient, sensitive and convenient sensors in food detection. At present, PCs have been successfully applied to analyze hazardous substances in food, such as microorganisms, biotoxins, veterinary drug residues, pesticide residues, excessive additives, illegal additives, environmental pollutants and heavy metal composition. This article introduces PCs about the fabrication methods, principles and techniques applied in detection and research progress in food detection. [ABSTRACT FROM AUTHOR]

In recent years, developing rapid, simple, and effective new methods to monitor food safety is standing out enough to be noticed. Electrospinning is an easy, versatile, and low-cost technique to fabricate multifunctional micro-and nanomaterials. The combination of electrospinning and biosensors is one of the most promising strategies and is expected to replace traditional detection materials. This paper reviews the research progress of electrospinning in food detection, including electrospinning materials generally used in the food field with their benefits of high aspect ratio, large specific surface area, tunable porosity, easy functionalization, easy handling, mechanical stability, such as natural polymers, synthetic polymers or the mixture of both. Besides, we also emphatically introduced the application of their intrinsic characteristics combined with biosensing platforms and list the sensitivity and detection limit of various methods and materials in detection, as well as their application potential, and the challenges faced by the on-site detection and the large-scale commercial applications in the food industry as future exploration headings, giving new thinking of nanomaterial applications for food safety detection. [ABSTRACT FROM AUTHOR]

Automatic food classification systems have several interesting applications ranging from detecting eating habits, to waste food management and advertisement. When a food image has multiple food items, the food detection step is necessary before classification. This work challenges the food detection issue and it introduces to the research community the Segmented UEC Food-100 dataset, which expands the original UEC Food-100 database with segmentation masks. In the semantic segmentation experiment, the performance of YOLAC and DeeplabV3+ has been compared and YOLAC reached the best accuracy of 64.63% mIoU. In the instance segmentation experiment, YOLACT has been used due to its speed and high accuracy. The benchmark performance on the newly released Segmented UEC Food-100 dataset is 68.83% mAP. For comparison purpose, experiments have been run also on the UEC FoodPix Complete dataset of Okamoto et al. The database and the code will be available after publication. [ABSTRACT FROM AUTHOR]

Carbon quantum dots (CQDs) are category of novel carbon nanomaterials, which have gradually attracted attention due to their unique properties. CQDs have obvious advantages such as water solubility, modifiability, photochemical property, easy surface modification, antibacterial activity and good biocompatibility. In this paper, the formation mechanism and doping synthesis methods of CQDs are summarized. This review also highlights the biological toxicity, antibacterial properties and application of CQDs in the detection of heavy metals, antibiotics, pathogens, pesticide residues and prohibited additives in food, nutritional functional components, and fresh-keeping of fruits and vegetables. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture inter‐ ference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecu‐ lar composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Ra‐ man spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new pro‐ gresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, re‐ spectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil col‐ ony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organ‐ isms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, por‐ table and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the character‐ ization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wid‐ er range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more in‐ spiration to future agricultural production and research. [ABSTRACT FROM AUTHOR]

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance. [ABSTRACT FROM AUTHOR]

The development of techniques and methods for rapidly and reliably detecting and analysing food quality and safety products is of significance for the food industry. Traditional machine learning algorithms based on handcrafted features normally have poor performance due to their limited representation capacity for complex food characteristics. Recently, the convolutional neural network (CNN) emerges as an effective and potential tool for feature extraction, which is considered the most popular architecture of deep learning and has been increasingly applied for the detection and analysis of complex food matrices. In the current review, the structure of CNN, the method of feature extraction based on 1-D, 2-D and 3-D CNN models, and multi-feature aggregation methods are introduced. Applications of CNN as a depth feature extractor for detecting and analyzing complex food matrices are discussed, including meat and aquatic products, cereals and cereal products, fruits and vegetables, and others. In addition, data sources, model architecture and overall performance of CNN with other existing methods are compared, and trends of future studies on applying CNN for food detection and analysis are also highlighted. CNN combined with nondestructive detection techniques and computer vision system show great potential for effectively and efficiently detecting and analysing complex food matrices, and the features based on CNN show better performance and outperform the features handcrafted or those extracted by machine learning algorithms. Although there still remains some challenges in using CNN, it is expected that CNN models will be deployed on mobile devices for real-time detection and analysis of food matrices in future. • The principle and architectures of convolutional neural network are presented. • Feature extraction methods based on convolutional neural network are investigated. • Recent applications of convolutional neural network in food are summarized. • Challenges and future work of convolutional neural network are discussed. [ABSTRACT FROM AUTHOR]

Based on the demonstration of molecular imprinting technology, photonic crystal technology and the technology of molecularly imprinted photonic crystal, the relevant researches on the application of molecularly imprinted photonic crystal technology in the detection of toxic and harmful substances in food (veterinary drug residues, pesticide residues, biological toxins, endocrine disruptors, illegal additives) and nutrients was combed. It pointed out that molecularly imprinted photonic crystal technology combined the high selectivity of molecular imprinting technology to the target and the unique optical properties of photonic crystals, and was suitable for food detection as a new optical sensor. Compared with the traditional food detection methods, molecularly imprinted photonic crystal technology greatly improved the detection efficiency, shortened the detection time, improved the visual detection degree, and was more conducive to the development of field detection. However, molecularly imprinted photonic crystal technology also had the disadvantages of complex synthesis process, large amount of organic reagents, narrow detection range and poor repeatability. Researchers could make further research in the future on developing environment-friendly green synthesis methods, enhancing the stability and controllability of photonic crystals, reducing the nonspecific adsorption of molecularly imprinted photonic crystals, developing high-throughput detection methods suitable for simultaneous detection of multiple targets, and expanding the application in the field of microbial detection to optimize and develop molecularly imprinted photonic crystal technology. [ABSTRACT FROM AUTHOR]

Most of existing methods mainly focus on the food image recognition which assumes that one food image contains only one food item. However, in this paper, we present a system to detect a diversity of foods in refrigerator where multiple food items may exist. In view of the refrigerator environment, we propose a food detection framework based on the supervised transformer network. More specifically, the supervised transformer network, dotted as RectNet, is first proposed to automatically select the irregular food regions and transform them to the frontal views. Then, based on the rectified food images, we further propose an end-to-end detection network that predicts the categories and locations of food items. The proposed detection network, called Lite Fully Convolutional Network (LiteFCN), is evolved from the advanced object detection algorithm Faster R-CNN while several significant improvements are tailored to achieve a higher accuracy and keep inference time efficiency. To validate the effectiveness of each component of our method, we build a real-world refrigerator dataset with 80 classes. Extensive experiments demonstrate that our methods achieve the state-of-the-art results, which improves the baseline by a large margin, e.g. , 3–5% in terms of F-measure. We also show that the proposed detection network achieve a competitive result on the public PASCAL VOC2007 dataset, which outperforms the Faster R-CNN by 2.3% with a higher speed. [ABSTRACT FROM AUTHOR]

[Display omitted] • 3a and 3c are screened out from five D-A type dicyanoisophorone derivatives, which show high selectivity towards H 2 O 2. • 3a and 3c express colorimetric and fluorescent dual-signals towards H 2 O 2 with low detection limits (0.20 μM and 0.14 μM). • 3a and 3c are capable of H 2 O 2 detection in milk samples. • Low cytotoxicity allows 3a to be utilized as a H 2 O 2 fluorescence sensor in living HepG-2 cells and zebrafish. • A smartphone-based analysis method is utilized for the rapid quantitative detection of H 2 O 2 by 3c. Excessive residual hydrogen peroxide (H 2 O 2) disinfectant in food is harmful to human health. Therefore, it is necessary to develop efficient detection methods for H 2 O 2 detection. In this work, we designed and synthesized five D-A molecules 3a - 3e by introducing electron-donor substituents (–OCH 3 and –CH 3) to the electron-acceptor dicyanoisophorone skeleton in order to find out the suitable probes for H 2 O 2 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 H 2 O 2 with low detection limits (0.20 μM and 0.14 μM) and rapid response (within 20 mins). The reaction mechanism between probes and H 2 O 2 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 H 2 O 2 without complicated instruments, indicating a high efficiency and on-site detection method for H 2 O 2. In addition, probes are applied to detect H 2 O 2 in milk samples, HepG-2 cells and zebrafish, suggesting the promising applications in food samples and physiological systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A novel, integrated, real‐time electrochemical loop‐mediated isothermal amplification (LAMP) device for pathogen detection in food was developed. The electrochemical LAMP device for detection of the vt gene in E. coli O157 : H7 and invA gene in Salmonella enterica was implemented with DNA intercalating probes‐methylene blue. To improve assay efficiency, the optimal concentration of MB was determined to be 30 μM. The real‐time electrochemical LAMP results of gene serial dilutions were compared to the fluorescence‐based detection method. For E. coli O157 and Salmonella, the assay detection limits were 10 DNA copies and 1 DNA copy, respectively, in a reaction time of less than 30 min. The electrochemical and optical LAMP method analyses were realized with 10 % milk to simulate the turbid environment of real food samples. In real‐time LAMP of Salmonella in a turbid environment, the detection limit of 10 copies without pretreatment was possible using the electrochemical method only. Our electrochemical device was used for bacterial detection in 150 turbid food samples, including juice, milk, and soy milk. This electrochemical device has many advantages, including size, portability, high specificity, and high sensitivity, while also being a user‐friendly device that provides stable signals. Thus, to the best of our knowledge, this new electrochemical device demonstrates the best performance for bacterial detection in the rapid contaminated food detection category. [ABSTRACT FROM AUTHOR]

• An Ag-coated nanofiber SERS platform was prepared for rapid nitrite detection. • Electrospraying technique was firstly used to assemble SERS platform. • The Ag-coated nanofiber SERS platform has good homogeneity and reproducibility. • The EF and the LOD was 2.635 × 108, 2.216 × 10−12 M, respectively. • The SERS platform shows good compatibility in nitrite detection for real samples. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] We report herein a new highly selective fluorescent probe QVPB-DNP for selective evaluation of H 2 S in the solid states and food samples. In this probe, a quinoline-phenol vinylic conjugated skeleton as well as the expanded benzothiazole moiety (QVPB-OH) is selected as the fluorophore and a 2,4‑dinitrophenyl (DNP) ether is chosen as binding site of H 2 S. The probe shows significant variations in UV/visible absorption (Stokes Shift = 160 nm) and fluorescence emission enhancement at 605 nm for selective detection of H 2 S in DMSO/PBS system (v/v , 7:3, PBS buffer; pH 7.4). Moreover, probe QVPB-DNP -loaded test strips are successfully applied for the visual and real-time detection of gaseous H 2 S in practical food samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • L5 can achieve naked-eye recognition for I−. • The mechanism of subject-object recognition was elucidated. • The method can be used for I− detection in food. • L5 has lower detection limit and higher sensitivity. 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 –COOCH 3 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. [ABSTRACT FROM AUTHOR]

Formaldehyde (FA) is an environmental toxin and widely used in building and decorating materials, which is listed as a class 1 carcinogen. Silver ion (Ag+) has a beneficial bactericidal effect, while it can also cause silver poisoning and skin damage. Therefore, developing a quantitative detection technique for formaldehyde and Ag+ is necessary. In this study, a novel difunctional fluorescent probe (DBP) was designed and synthesized based on the natural product dehydroabietic acid, which could selectively detect formaldehyde and Ag+ through "turn-on" and "turn-off" fluorescence response, respectively. The probe DBP performed satisfactory nanomolar-level detection limit to formaldehyde (38 nM) and Ag+ (19 nM) in ethanol/PBS (3/7, v/v, pH = 7.4) solution. Furthermore, DBP could effectively recognize formaldehyde and Ag+ within a short response time (FA: 15 min, Ag+: 100 s) and a wide pH range (5–10). The sensing mechanisms of DBP to FA and Ag+ were well verified by 1H NMR titration, ESI-MS, DFT computation, and Job's plot analysis. What is more, the applications in bioimaging were carried out in MCF-7 cells and living zebrafish. Besides, the probe DBP was successfully applied in detecting FA and Ag+ in actual food and water samples, and the DBP -loaded filter paper was prepared for convenient tracking gaseous FA and Ag+. • A novel fluorescent probe DBP for detecting formaldehyde and Ag+ was synthesized. • DBP exhibits low LOD, short response time and wide pH range towards FA and Ag+. • DBP was utilized in food and water samples, filter paper test, and bioimaging. [ABSTRACT FROM AUTHOR]

The high quantum-yield (QY) lignin fluorescence material at solid state was obtained via the polymer confinement strategy, using polyvinyl alcohol (PVA) as polymer matrix and organic solvent lignin (OL) as luminescent material. The absolute QY of lignin/PVA film could reach 13.68 % by optimizing the raw material of OL, of which was even higher than that in THF medium due to their hydrogen bond interaction for inhibiting aggregation and rigid environment for decreasing molecular rotation and vibration. In addition, under the encapsulation of PVA, the resistance of photobleaching of lignin fluorescence was also enhanced. Finally, OL was proved as a natural ratiometric fluorescence pH sensor from the electronegativity change of phenylpropane unit, and the fluorescence of lignin/PVA film showed a naked-eye recognition for spoiled pork detection. This work provided a new perspective to lignin fluorescence for really using as a feedstock for luminescent materials. • High QY lignin fluorescent material at solid-state is obtained by polymer confinement. • Absolute QY of doping film could reach 13.68% by optimizing the raw material of OL. • A naked-eye recognition displays from the natural ratiometric pH response of lignin. [ABSTRACT FROM AUTHOR]

The senses involved in food detection in primates in general, and lemurs in particular, remain poorly investigated. However, as lemurs include diurnal, nocturnal, and cathemeral species they represent a good model to test whether prey detection is dependent on activity pattern. As both diurnal and nocturnal species have been investigated previously we here aim to quantify the relative importance of different sensory modalities during prey detection in a cathemeral species, the red-bellied lemur (Eulemeur rubriventer). A series of experiments was performed using a group of four Eulemur rubriventer (Zoo de La Londe les Maures, France) to test the role of visual, olfactory and acoustic cues in prey detection. Both unimodal and multimodal cues were tested. the responses obtained in the different experiments show that visual cues are essential for prey detection in this species, at least in captivity. However, the use of multiple sensory modalities improves the success of detection suggesting that cathemeral species may benefit from the use of multiple sensory modalities. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • A napthalimide-based fluorescent probe was developed for sensitive detection of acrolein. • It recognized acrolein through thiol participation in Michael addition. • It was used to quantitatively determine acrolein in different foods. • It successfully tracked acrolein in Hela cells. [ABSTRACT FROM AUTHOR]

Cr3+-doped Ca 3 Y 2 Ge 3 O 12 broadband near-infrared phosphor was synthesized by the high temperature solidstate reaction method. Under the excitation of 475 nm, the emission peak of Ca 3 Y 2 Ge 3 O 12 :Cr3+ is at 823 nm and the full width at half maximum (FWHM) is 155 nm. The introduction of Ce3+ as a sensitizer leads to the enhancement of CYG:Cr3+ emission intensity. The emission range of CYG:Ce3+, Cr3+ can perfectly cover the frequency doubling absorption band of C–H, O–H and other hydrogen groups. There is an energy transfer process between Ce3+ and Cr3+, and the energy transfer efficiency is as high as 94.21%. According to the near-infrared response to hydrogen group and Lambert-Beer law, the dependence of absorbance Lg(I 0 / I) on sugar content was explored, and then the reliability and accuracy of the method were verified. The results show that the phosphor Ca 3 Y 2 Ge 3 O 12 :Ce3+, Cr3+ phosphor has prospects for a potential application in nondestructive food detection. • There is the energy transfer from Ce3+ to Cr3+. • CYG:Ce3+, Cr3+ has certain application value in nondestructive food detection. • The best excitation wavelength of the sample is 475 nm, which can be effectively excited by the blue light chip, and the FWHM is 155 nm. [ABSTRACT FROM AUTHOR]

[Display omitted] • A unique SERS substrate processed by one-step femtosecond laser ablation in Argon. • S-Ag-Ar can detect 10-8mol/L R6G with enhancement factor up to 5.3 × 106. • S-Ag-Ar has high uniformity with relative standard deviation less than 11.97%. • S-Ag-Ar can be stored for more than seven months and shows good universality. • This unique SERS substrate demonstrates good potentials for food safety evaluation. Surface Enhanced Raman Scattering (SERS) is a reliable tool for detection of food additives due to its advantages including rapid detection, non-destructive and excellent sensitivity. Currently, the commercial SERS substrates are not yet satisfactory referring to cost, sensitivity, uniformity and stability. Herein, we report a unique SERS substrate processed by one-step femtosecond laser ablation in Argon. The substrate shows high sensitivity with limit of detection up to 10-8mol/L for Rhodamine 6G (R6G) and enhancement factor up to 5.3 × 106 due to laser induced more plentiful nanostructures and less oxidation in Argon, in addition to high uniformity with relative standard deviation less than 11.97% and long storage time more than seven months. This SERS substrate is capable of detecting the common food additives including malachite green (1 ppm), crystal violet (10-7mol/L), Sudan I (1 ppm), melamin (1 ppm), brilliant blue (1 ppm) and tartrazine (1 ppm). This unique Ag SERS substrate demonstrates good potentials for food safety evaluation. [ABSTRACT FROM AUTHOR]

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. Image 1 • Glacial retreat induced by global warming can decrease salinity and pH of the Antarctic ocean. • The Antarctic amphipod Gondogeneia antarctica was exposed to low salinity (27 psu) and low pH (7.6) conditions. • Low salinity increased cannibalism and induced adjusted swimming. • Low pH increased mortality, impaired food detection and reduced daytime shelter use. • Ocean freshening and acidification act as independent stressors influencing behavior and physiology of Antarctic amphipods. [ABSTRACT FROM AUTHOR]