•Carbon dots (CDs) was synthesized from Rosemary leaves as a carbon source.•Silica based-molecularly imprinted polymer was stabilized on the surface of the CDs.•A new optical sensor is developed to ...measure trace amount of thiabendazole.•Photoluminescence of the nanocomposite could be enhanced by thiabendazole.•Thiabendazole is measured from 0.03 to 1.73 μg/mL with a LOD of 8 ng/mL.
An eco-friendly method was used to synthesize carbon dots (CDs) from Rosemary leaves, as a carbon source. The as-synthesized CDs was applied as a fluorophore in an optical sensor after modification with molecularly imprinted polymers (MIPs) for determination of thiabendazole (TBZ). For this purpose, a silica shell using tetraethoxysilane (TEOS), as a Si source, was stabilized on the surface of CDs via reverse microemulsion technique. Following, MIPs were synthesized in the presence of TBZ as a template, using 3-aminopropyl triethoxysilane and TEOS as a functional monomer and a crosslinker, respectively. After optimization of the experimental parameters, a linear dynamic range of 0.03–1.73 μg/mL TBZ with a detection limit as 8 ng/mL were obtained for the suggested method. Finally, the proposed sensor was successfully applied for the determination of TBZ in apple, orange, and tomato juices. This sensor is a simple, rapid, selective, and non-expensive method for TBZ measurement.
•Single-walled carbon nanohorns modified sensor for heavy metal ions detection was developed.•Disposable sensor exhibits distinct and detached stripping peaks towards Cd(II) and Pb(II) ions.•The ...developed electrochemical sensor possesses high sensitivity, easy operation, and low cost.
In view of the significant risk of heavy metal ions on human health, effective determination method is quite urgent. Herein, a single-walled carbon nanohorns modified screen-printed electrode was proposed as a disposable electrochemical sensor. The electrochemical study reveals that the developed electrode possesses excellent electrochemical activity. By combination of bismuth film, the electrochemical sensor exhibits distinct and detached stripping peaks towards cadmium and lead. Under the optimized conditions, the linear range of the single-walled carbon nanohorns film modified electrode for both heavy metal ions varied from 1.0 to 60.0 μg L−1. The detection limit of cadmium (II) and lead (II) ions was 0.2 μg L−1 and 0.4 μg L−1. Furthermore, the determination of cadmium (II) and lead (II) ions in honey and milk samples illustrates the prepared electrochemical sensor possesses excellent practicability for determining cadmium (II) and lead (II) ions in a low levels (μg L−1).
•Metal-polydopamine framework (MPF) based lateral flow assay (LFA) was developed.•MPF displayed excellent affinity with antibodies to detect tetracycline.•Assay performance of this LFA was 66-fold ...higher than that of traditional AuNP-LFA.•This work expands the application of MOFs as a novel label in immunoassays.
Gold nanoparticles (AuNPs)-based lateral flow assay (LFA) enables a rapid detection of tetracycline (TET) in food samples but suffers from low sensitivity. Herein, metal-polydopamine framework (MPF), as a label, was employed to load monoclonal antibodies (mAbs) directly as the probe in LFA for highly sensitive sensing of TET. Combining zeolitic imidazolate framework (ZIF-67) and polydopamine (PDA), a stable MPF with large size, well water-dispersible, excellent affinity and optical properties was prepared through a versatile layer-by-layer assembly (LLA) strategy. Under optimized conditions, the biosensor (MPF-LFA) exhibited a great linear relationship in the range of 0.09–6 ng/mL and a detection limit of 0.045 ng/mL for TET detection, which was over 66-fold more sensitive than traditional AuNPs based LFA. Furthermore, the MPF-LFA was successfully applied to the screening of TET in fish, chicken, milk and shrimp samples with satisfied recoveries from 91% to 114%.
•For the first time, GC×GC-TOFMS was used to classify strong aroma-type baijiu (SAB) from different regions.•The SAB samples from two different basins in China were clearly separated in PCA score ...plots.•The predictive ability of the PLS-DA model was excellent, correctly classifying 100% of the unknown SAB samples.•Twenty-three potential markers were identified using standard compounds.•The HCA result showed that the selected markers offered strong discrimination power.
A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid–liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples.
Deep Eutectic Solvents Application in Food Analysis Ortega-Zamora, Cecilia; González-Sálamo, Javier; Hernández-Borges, Javier
Molecules (Basel, Switzerland),
11/2021, Letnik:
26, Številka:
22
Journal Article
Recenzirano
Odprti dostop
Current trends in Analytical Chemistry are focused on the development of more sustainable and environmentally friendly procedures. However, and despite technological advances at the instrumental ...level having played a very important role in the greenness of the new methods, there is still work to be done regarding the sample preparation stage. In this sense, the implementation of new materials and solvents has been a great step towards the development of "greener" analytical methodologies. In particular, the application of deep eutectic solvents (DESs) has aroused great interest in recent years in this regard, as a consequence of their excellent physicochemical properties, general low toxicity, and high biodegradability if they are compared with classical organic solvents. Furthermore, the inclusion of DESs based on natural products (natural DESs, NADESs) has led to a notable increase in the popularity of this new generation of solvents in extraction techniques. This review article focuses on providing an overview of the applications and limitations of DESs in solvent-based extraction techniques for food analysis, paying especial attention to their hydrophobic or hydrophilic nature, which is one of the main factors affecting the extraction procedure, becoming even more important when such complex matrices are studied.
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition ...tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
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•Alternative machine learning methods to perform the authentication of foods are described•Chemometric multivariate tools are similar to data mining methods•The terms used in different work areas are discussed and defined•RF and SVM methods provide better results than the traditional chemometrics in the food quality field
Food quality and safety are closely related to human health. In the face of unceasing food safety incidents, various analytical techniques, such as mass spectrometry, chromatography, spectroscopy, ...and electrochemistry, have been applied in food analysis. High sensitivity usually requires expensive instruments and complicated procedures. Although these modern analytical techniques are sensitive enough to ensure food safety, sometimes their applications are limited because of the cost, usability, and speed of analysis. Electrochemiluminescence (ECL) is a powerful analytical technique that is attracting more and more attention because of its outstanding performance. In this review, the mechanisms of ECL and common ECL luminophores are briefly introduced. Then an overall review of the principles and applications of ECL sensors for food analysis is provided. ECL can be flexibly combined with various separation techniques. Novel materials (e.g., various nanomaterials) and strategies (e.g., immunoassay, aptasensors, and microfluidics) have been progressively introduced into the design of ECL sensors. By illustrating some selected representative works, we summarize the state of the art in the development of ECL sensors for toxins, heavy metals, pesticides, residual drugs, illegal additives, viruses, and bacterias. Compared with other methods, ECL can provide rapid, low-cost, and sensitive detection for various food contaminants in complex matrixes. However, there are also some limitations and challenges. Improvements suited to the characteristics of food analysis are still necessary.
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•The reliability of food fingerprinting studies has been critically reviewed.•A validation scheme for multivariate statistical models is proposed.•Recommendations to improve reporting ...quality are provided.•Gaps of current validation practice are identified.
Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.
Nanotechnology-adapted detection technologies could improve the safety and quality of foods, provide new methods to combat fraud and be useful tools in our arsenal against bioterrorism. Yet despite ...hundreds of published studies on nanosensors each year targeted to the food and agriculture space, there are few nanosensors on the market in this area and almost no nanotechnology-enabled methods employed by public health agencies for food analysis. This Review shows that the field is currently being held back by technical, regulatory, political, legal, economic, environmental health and safety, and ethical challenges. We explore these challenges in detail and provide suggestions about how they may be surmounted. Strategies that may have particular effectiveness include improving funding opportunities and publication venues for nanosensor validation, social science and patent landscape studies; prioritizing research and development of nanosensors that are specifically designed for rapid analysis in non-laboratory settings; and incorporating platform cost and adaptability into early design decisions.
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