Self-Responsive Fluorescence Aptasensor for Lactoferrin Determination in Dairy Products.

In this study, a self-responsive fluorescence aptasensor was established for the determination of lactoferrin (Lf) in dairy products. Herein, the aptamer itself functions as both a recognition element that specifically binds to Lf and a fluorescent signal reporter in conjunction with fluorescent moiety. In the presence of Lf, the aptamer preferentially binds to Lf due to its specific and high-affinity recognition by folding into a self-assembled and three-dimensional spatial structure. Meanwhile, its reduced spatial distance in the aptamer-Lf complex induces a FRET phenomenon based on the quenching of 6-FAM by amino acids in the Lf protein, resulting in a turn-off of the fluorescence of the system. As a result, the Lf concentration can be determined straightforwardly corresponding to the change in the self-responsive fluorescence signal. Under the optimized conditions, good linearities (R2 > 0.99) were achieved in an Lf concentration range of 2~10 µg/mL for both standard solutions and the spiked matrix, as well as with the desirable detection limits of 0.68 µg/mL and 0.46 µg/mL, respectively. Moreover, the fluorescence aptasensor exhibited reliable recoveries (89.5-104.3%) in terms of detecting Lf in three commercial samples, which is comparable to the accuracy of the HPCE method. The fluorescence aptasensor offers a user-friendly, cost-efficient, and promising sensor platform for point-of-need detection.

Chemical Composition of Fat Bloom on Chocolate Products Determined by Combining NMR and HPLC-MS.

To reduce unwanted fat bloom in the manufacturing and storage of chocolates, detailed knowledge of the chemical composition and molecular mobility of the oils and fats contained is required. Although the formation of fat bloom on chocolate products has been studied for many decades with regard to its prevention and reduction, questions on the molecular level still remain to be answered. Chocolate products with nut-based fillings are especially prone to undesirable fat bloom. The chemical composition of fat bloom is thought to be dominated by the triacylglycerides of the chocolate matrix, which migrate to the chocolate's surface and recrystallize there. Migration of oils from the fillings into the chocolate as driving force for fat bloom formation is an additional factor in the discussion. In this work, the migration was studied and confirmed by MRI, while the chemical composition of the fat bloom was measured by NMR spectroscopy and HPLC-MS, revealing the most important triacylglycerides in the fat bloom. The combination of HPLC-MS with NMR spectroscopy at 800 MHz allows for detailed chemical structure determination. A rapid routine was developed combining the two modalities, which was then applied to investigate the aging, the impact of chocolate composition, and the influence of hazelnut fillings processing parameters, such as the degree of roasting and grinding of the nuts or the mixing time, on fat bloom formation.

A Cost-Effective and Sensitive Method for the Determination of Lincomycin in Foods of Animal Origin Using High-Performance Liquid Chromatography.

BACKGROUND: Lincomycin (LIN) is extensively used for treating diseases in livestock and promoting growth in food animal farming, and it is frequently found in both the environment and in food products. Currently, most of the methods for detecting lincomycin either lack sensitivity and precision or require the use of costly equipment such as mass spectrometers. RESULT: In this study, we developed a reliable high-performance liquid chromatography-ultraviolet detection (HPLC-UVD) method and used it to detect LIN residue in 11 types of matrices (pig liver and muscle; chicken kidney and liver; cow fat, liver and milk; goat muscle, liver and milk; and eggs) for the first time. The tissue homogenates and liquid samples were extracted via liquid-liquid extraction, and subsequently purified and enriched via sorbent and solid phase extraction (SPE). After nitrogen drying, the products were derivatized with p-toluene sulfonyl isocyanic acid (PTSI) (100 µL) for 30 min at room temperature. Finally, the derivatized products were analyzed by HPLC at 227 nm. Under the optimized conditions, the method displayed impressive performance and demonstrated its reliability and practicability, with a limit of detection (LOD) and quantification (LOQ) of LIN in each matrix of 25-40 µg/kg and 40-60 µg/kg, respectively. The recovery ranged from 71.11% to 98.30%. CONCLUSIONS: The results showed that this method had great selectivity, high sensitivity, satisfactory recovery and cost-effectiveness-fulfilling the criteria in drug residue and actual detection requirements-and proved to have broad applicability in the field of detecting LIN in animal-derived foods.

A rapid and improved method for the determination of ethyl carbamate in foodstuffs of different matrices.

This work deals with the rapid and simple determination of the probable carcinogen ethyl carbamate (EC), which is naturally present in fermented food products. An undemanding, robust, and rapid pre-column derivatization utilizing a 9-xanthydrol reagent has been developed. The resulting derivative was subsequently analysed by reversed-phase high-performance liquid chromatography coupled with fluorescence detection. As a result of the thorough optimisation of the chromatographic conditions, the run was completed in just 5 minutes, considerably speeding up the usual time of EC separation (30-60 min). Thanks to the fast separation, satisfactory yields (around 90%), negligible matrix effects, no interfering peaks, very low detection limit, and simple sample pre-treatment (for the very first time, the derivatization was performed in the presence of light and without any extraction step), the proposed method represents a significant improvement of the EC determination protocol used so far. After method validation, a total of fifty food samples were subjected to analysis without any additional sample pre-treatment despite their diverse matrix. Due to its robustness, simplicity, and low time, cost, and manual demands, this method is suitable for rapid screening of EC in both final food products and during their production.

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning.

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC­HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Innovations in nondestructive assessment of baked products: Current trends and future prospects.

Rising consumer awareness, coupled with advances in sensor technology, is propelling the food manufacturing industry to innovate and employ tools that ensure the production of safe, nutritious, and environmentally sustainable products. Amidst a plethora of nondestructive techniques available for evaluating the quality attributes of both raw and processed foods, the challenge lies in determining the most fitting solution for diverse products, given that each method possesses its unique strengths and limitations. This comprehensive review focuses on baked goods, wherein we delve into recently published literature on cutting-edge nondestructive methods to assess their feasibility for Industry 4.0 implementation. Emphasizing the need for quality control modalities that align with consumer expectations regarding sensory traits such as texture, flavor, appearance, and nutritional content, the review explores an array of advanced methodologies, including hyperspectral imaging, magnetic resonance imaging, terahertz, acoustics, ultrasound, X-ray systems, and infrared spectroscopy. By elucidating the principles, applications, and impacts of these techniques on the quality of baked goods, the review provides a thorough synthesis of the most current published studies and industry practices. It highlights how these methodologies enable defect detection, nutritional content prediction, texture evaluation, shelf-life forecasting, and real-time monitoring of baking processes. Additionally, the review addresses the inherent challenges these nondestructive techniques face, ranging from cost considerations to calibration, standardization, and the industry's overreliance on big data.

Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives.

Commercial applications of nanotechnology in the food industry are rapidly increasing. Accordingly, there is a simultaneous increase in the amount and diversity of nanowaste, which arise as byproducts in the production, use, disposal, or recycling processes of nanomaterials utilized in the food industry. The potential risks of this nanowaste to human health and the environment are alarming. It is of crucial significance to establish analytical methods and monitoring systems for nanowaste to ensure food safety. This review provides comprehensive information on nanowaste in foods as well as comparative material on existing and new analytical methods for the detection of nanowaste. The article is specifically focused on nanowaste in food systems. Moreover, the current techniques, challenges as well as potential use of new and progressive methods are underlined, further highlighting advances in technology, collaborative efforts, as well as future perspectives for effective nanowaste detection and tracking. Such detection and tracking of nanowaste are required in order to effectively manage this type ofwasted in foods. Although there are devices that utilize spectroscopy, spectrometry, microscopy/imaging, chromatography, separation/fractionation, light scattering, diffraction, optical, adsorption, diffusion, and centrifugation methods for this purpose, there are challenges to be overcome in relation to nanowaste as well as food matrix and method characteristics. New technologies such as radio-frequency identification, Internet of things, blockchain, data analytics, and machine learning are promising. However, the cooperation of international organizations, food sector, research, and political organizations is needed for effectively managing nanowaste. Future research efforts should be focused on addressing knowledge gaps and potential strategies for optimizing nanowaste detection and tracking processes.

Deep eutectic solvent for the extraction of polycyclic aromatic compounds in fuel, food and environmental samples.

Polycyclic aromatic compounds (PACs) encompass a wide variety of organic analytes that have mutagenic and carcinogenic potentials for human health and are recalcitrant in the environment. Evaluating PACs levels in fuel (e.g., gasoline and diesel), food (e.g., grilled meat, fish, powdered milk, fruits, honey, and coffee) and environmental (e.g., industrial effluents, water, wastewater and marine organisms) samples are critical to determine the risk that these chemicals pose. Deep eutectic solvents (DES) have garnered significant attention in recent years as a green alternative to traditional organic solvents employed in sample preparation. DES are biodegradable, have low toxicities, ease of synthesis, low cost, and a remarkable ability to extract PACs. However, no comprehensive assessment of the use of DESs for extracting PACs from fuel, food and environmental samples has been performed. This review focused on research involving the utilization of DESs to extract PACs in matrices such as PAHs in environmental samples, NSO-HET in fuels, and bisphenols in foods. Chromatographic methods, such as gas chromatography (GC) and high-performance liquid chromatography (HPLC), were also revised, considering the sensibility to quantify these compound types. In addition, the characteristics of DES and advantages and limitations for PACs in the context of green analytical chemistry principles (GAC) and green profile based on metrics provide perspective and directions for future development.

Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin.

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Direct and Sensitive Electrochemical Determination of Total Antioxidant Capacity in Foods Using Nanochannel-Based Enrichment of Redox Probes.

Simple and sensitive determination of total antioxidant capacity (TAC) in food samples is highly desirable. In this work, an electrochemical platform was established based on a silica nanochannel film (SNF)-modified electrode, facilitating fast and highly sensitive analysis of TAC in colored food samples. SNF was grown on low-cost and readily available tin indium oxide (ITO) electrode. Fe3+-phenanthroline complex-Fe(III)(phen)3 was applied as the probe, and underwent chemical reduction to form Fe2+-phenanthroline complex-Fe(II)(phen)3 in the presence of antioxidants. Utilizing an oxidative voltage of +1 V, chronoamperometry was employed to measure the current generated by the electrochemical oxidation of Fe(II)(phen)3, allowing for the assessment of antioxidants. As the negatively charged SNF displayed remarkable enrichment towards positively charged Fe(II)(phen)3, the sensitivity of detection can be significantly improved. When Trolox was employed as the standard antioxidant, the electrochemical sensor demonstrated a linear detection range from 0.01 µM to 1 µM and from 1 µM to 1000 µM, with a limit of detection (LOD) of 3.9 nM. The detection performance is better that that of the conventional colorimetric method with a linear de range from 1 µM to 40 µM. Owing to the anti-interfering ability of nanochannels, direct determination of TAC in colored samples including coffee, tea, and edible oils was realized.

Synthesis of Boronate Affinity-Based Oriented Dummy Template-Imprinted Magnetic Nanomaterials for Rapid and Efficient Solid-Phase Extraction of Ellagic Acid from Food.

Ellagic acid (EA) is a natural polyphenol and possesses excellent in vivo bioactivity and antioxidant behaviors, which play an important role in the treatment of oxidative stress-related diseases, such as cancer. Additionally, EA is also known as a skin-whitening ingredient. The content of EA would determine its efficacy. Therefore, the accurate analysis of EA content can provide more information for the scientific consumption of EA-rich foods and cosmetics. Nevertheless, the analysis of EA in these samples is challenging due to the low concentration level and the presence of interfering components with high abundance. Molecularly imprinted polymers are highly efficient pretreatment materials in achieving specific recognition of target molecules. However, the traditional template molecule (EA) could not be absolutely removed. Hence, template leakage continues to occur during the sample preparation process, leading to a lack of accuracy in the quantification of EA in actual samples, particularly for trace analytes. In addition, another drawback of EA as an imprinting template is that EA possesses poor solubility and a high price. Gallic acid (GA), called dummy templates, was employed for the synthesis of MIPs as a solution to these challenges. The approach used in this study was boronate affinity-based oriented surface imprinting. The prepared dummy-imprinted nanoparticles exhibited several significant advantages, such as good specificity, high binding affinity ((4.89 ± 0.46) × 10-5 M), high binding capacity (6.56 ± 0.35 mg/g), fast kinetics (6 min), and low binding pH (pH 5.0) toward EA. The reproducibility of the dummy-imprinted nanoparticles was satisfactory. The dummy-imprinted nanoparticles could still be reused even after six adsorption-desorption cycles. In addition, the recoveries of the proposed method for EA at three spiked levels of analysis in strawberry and pineapple were 91.0-106.8% and 93.8-104.0%, respectively, which indicated the successful application to real samples.

Trends in authentication of edible oils using vibrational spectroscopic techniques.

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Recent advances and trends in innovative biosensor-based devices for heavy metal ion detection in food.

Low-cost, reliable, and efficient biosensors are crucial in detecting residual heavy metal ions (HMIs) in food products. At present, based on distance-induced localized surface plasmon resonance of noble metal nanoparticles, enzyme-mimetic reaction of nanozymes, and chelation reaction of metal chelators, the constructed optical sensors have attracted wide attention in HMIs detection. Besides, based on the enrichment and signal amplification strategy of nanomaterials on HMIs and the construction of electrochemical aptamer sensing platforms, the developed electrochemical biosensors have overcome the plague of low sensitivity, poor selectivity, and the inability of multiplexed detection in the optical strategy. Moreover, along with an in-depth discussion of these different types of biosensors, a detailed overview of the design and application of innovative devices based on these sensing principles was provided, including microfluidic systems, hydrogel-based platforms, and test strip technologies. Finally, the challenges that hinder commercial application have also been mentioned. Overall, this review aims to establish a theoretical foundation for developing accurate and reliable sensing technologies and devices for HMIs, thereby promoting the widespread application of biosensors in the detection of HMIs in food.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.

Rapidly responsive and highly selective NIR fluorescent probe for detecting hydrogen sulfide in food samples and living cells.

Hydrogen sulfide (H2S) is a pungent gas that is one of the key mediators of signal transduction in biological systems, and its presence is related to the freshness of some protein foods. Using phenothiazine derivatives as fluorophores and 2, 4-dinitrobenzene sulfonate (DNBS) fragments as reaction groups, a near-infrared (NIR) probe WX-HS for H2S identification was designed. With the addition of H2S, WX-HS appeared a strong fluorescence signal at 660 nm with short reaction time (90 s) and high sensitivity, and fluorescence state change from non-fluorescent to orange-red. In addition, WX-HS could effectively detect H2S produced during food oxidation. Based on its low cytotoxicity, the WX-HS probe further enabled the detection and imaging of H2S in A549 cells.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

Portable sensing methods based on carbon dots for food analysis.

Food analysis is significantly important in monitoring food quality and safety for human health. Traditional methods for food detection mainly rely on benchtop instruments and require a certain amount of analysis time, which promotes the development of portable sensors. Portable sensing methods own many advantages over traditional techniques such as flexibility and accessibility in diverse environments, real-time monitoring, cost-effectiveness, and rapid deployment. This review focuses on the portable approaches based on carbon dots (CDs) for food analysis. CDs are zero-dimensional carbon-based material with a size of less than 10 nm. In the manner of sensing, CDs exhibit rich functional groups, low biotoxicity, good biocompatibility, and excellent optical properties. Furthermore, there are many methods for the synthesis of CDs using various precursor materials. The incorporation of CDs into food science and engineering for enhancing food safety control and risk assessment shows promising prospects.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis.

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

An electrochemical aptasensor for zearalenone detection based on the Co3O4/MoS2/Au nanocomposites and hybrid chain reaction.

An electrochemical aptasensor was used for the fast and sensitive detection of zearalenone (ZEN) based on the combination of Co3O4/MoS2/Au nanocomposites and the hybrid chain reaction (HCR). The glassy carbon electrode was coated with Co3O4/MoS2/Au nanomaterials to immobilize the ZEN-cDNA that had been bound with ZEN-Apt by the principle of base complementary pairing. In the absence of ZEN, the HCR could not be triggered because the ZEN-cDNA could not be exposed. After ZEN was added to the surface of the electrode, a complex structure was produced on the modified electrode by the combination of ZEN and ZEN-Apt. Therefore, the ZEN-cDNA can raise the HCR to produce the long-strand dsDNA structure. Due to the formation of dsDNA, the methylene blue (MB) could be inserted into the superstructure of branched DNA and the peak currents of the MB redox signal dramatically increased. So the concentration of ZEN could be detected by the change of signal intensity. Under optimized conditions, the developed electrochemical biosensing strategy showed an outstanding linear detection range of 1.0×10-10 mol/L to 1.0×10-6 mol/L, a low detection limit (LOD) of 8.5×10-11 mol/L with desirable selectivity and stability. Therefore, the fabricated platform possessed a great application potential in fields of food safety, medical detection, and drug analysis.