High crystalline magnetic covalent organic framework with three-dimensional grapevine structure for ultrasensitive extraction of nitro-polycyclic aromatic hydrocarbons in food and environmental samples.

Developing and establishing an efficient pre-treatment approach for the precise extraction of nitrated-polycyclic aromatic hydrocarbons (N-PAHs) from real-life samples is critical for ensuring their safety. In this study, a novel crystalline magnetic covalent organic framework with a grapevine structure not a single core-shell, Fe3O4@TAPT-DMTA-COF, was fabricated via chemical bonding. Unchanging the reticulated structure and high crystallinity of TAPT-DMTA-COF, the combination made this material possess not only simple operation via magnetic decantation but also remarkable chemical stability. Fe3O4@TAPT-DMTA-COF had a large surface area (1578.45 m2/g), and rich electronegative triazine-groups, which makes it become a superior magnetic enrichment material for trace N-PAHs. For N-PAHs analysis, low limits of detection (LODs) (1.43-17.24 ng/L), excellent relative standard deviations (RSDs ≤ 11.52%), and wide linearity (10-5000 ng/L) were obtained. Real-life applications based on this composite have been successfully explored by capturing the N-PAHs emitted from food and environmental samples.

Gel electromembrane microextraction followed by ion chromatography for direct determination of iodine in supplements and fortified food samples: Green chemistry for food analysis.

In this study, a sensitive, selective, and environmentally friendly analytical method for direct extraction and preconcentration of iodine was developed. Iodine, as an iodate ion or iodide ion, was simultaneously extracted and preconcentrated by gel electromembrane microextraction (G-EME) and analyzed for total iodine by ion chromatography. The total iodine was determined by combining the peak areas of both iodate and iodide ions. Under the optimized conditions, linear calibration for iodine using a mixture of iodate and iodide ions was obtained from 10 to 100 µg L-1 (r2 > 0.996). The detection limit was 7.0 µg L-1. Recoveries of spiked iodine (as iodate) in the samples were greater than 90%. The method was applied for the determination of iodine in dietary supplements and fortified food samples, i.e., iodine-enriched eggs. Our developed method could be directly applied for the determination of iodine in different matrix samples including eggs without a pretreatment step.

Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system.

Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.

Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer.

Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.

Electrochemical flow injection analysis for the rapid determination of reducing sugars in potatoes.

Non-enzymatic electrochemical sensors for the monitoring of reducing sugars in foods has great potential as a rapid in-situ detection method. This development involved the assembly of a nanoporous platinum structure on a screen-printed carbon electrode (SPCE). The modified electrode was then employed as an amperometric sensing element in a flow injection analysis (FIA) manifold. The system was successfully applied to the rapid detection of reducing sugars in potatoes, without the need for sample preparation. Optimal signals were achieved in phosphate buffer (pH 7.4) at a flow rate of 0.5 mL min-1 and an applied potential of 0.6 V. Experimental results demonstrated the sensor's long-term stability and high selectivity for reducing sugars. This method provides high sample throughput due to a rapid response time of less than five seconds. Reducing sugar values determined were in good agreement with those recorded using a commercially available enzymatic assay kit.

Signal optimized rough silver nanoparticle for rapid SERS sensing of pesticide residues in tea.

Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 °C of 1.39 × 106 to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 × 10-4, 1.88 × 10-4 and 4.72 × 10-3 µg/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea.

Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis.

BACKGROUND: The edible oil storage period is one of the important indicators for evaluating the intrinsic quality of edible oil. The present study aimed to develop a portable electronic nose device for the qualitative identification of the edible oil storage period. First, four metal oxide semiconductor gas sensors, comprising TGS2600, TGS2611, TGS2620 and MQ138, were selected to prepare a sensor array to assemble a portable electronic nose device. Second, the homemade portable electronic nose device was used to obtain the odor change information of edible oil samples during different storage periods, and the sensor features were extracted. Finally, three pattern recognition methods, comprising linear discriminant analysis (LDA), K-nearest neighbors (KNN) and support vector machines (SVM), were compared to establish a qualitative identification model of the edible oil storage period. The input features and related parameters of the model were optimized by a five-fold cross-validation during the process of model establishment. RESULTS: The research results showed that the recognition performance of the non-linear SVM model was significantly better than that of the linear LDA and KNN models, especially in terms of generalization performance, which had a correct recognition rate of 100% when predicting independent samples in the prediction set. CONCLUSION: The overall results demonstrate that it is feasible to apply the homemade portable electronic nose device with the help of the appropriate pattern recognition methods to achieve the fast and efficient identification of the edible oil storage period, which provides an effective analysis tool for the quality detection of the edible oil storage. © 2020 Society of Chemical Industry.

Rapid measurement of total polyphenol content in tea by kinetic matching approach on microfluidic paper-based analytical devices.

In this work, a facile kinetic matching approach for total polyphenol content (TPC) measurement was developed based on the adoption of microfluidic paper-based analytical devices with symmetric channel distribution. A set of Folin-Ciocalteu reactions performed on the same paper chip were activated all at the same time through synchronized filling of sodium carbonate solution among individual channels. Gallic acid was found valid as a standard compound for kinetic matching measurement of tea samples. TPC of tea infusions was successfully measured within ten minutes without any complexed time control procedure needed. Under the optimized conditions, the new developed method showed good linearity in the TPC range of 10-100 mg/L (r > 0.9955) and the inter-chip precision was 5.6% (n = 11). The results measured with the new developed approach were in good agreement with those with the conventional FC assay.

Determination of l-theanine in tea water using fluorescence-visualized paper-based sensors based on CdTe quantum dots/corn carbon dots and nano-porphyrin with chemometrics.

BACKGROUND: The quality of tea is influenced by numerous factors, especially l-theanine, which is one of the important markers used to evaluate the sweetness and freshness of tea. Sensitive, rapid, and accurate detection of l-theanine is therefore useful to identify the grade and quality of tea. RESULTS: A high-sensitivity, paper-based fluorescent sensor combined with chemometrics was established to detect l-theanine in tea water based on CdTe quantum dots / corn carbon dots and nano tetra pyridel-porphine zinc (ZnTPyP). To verify the reliability of this method, fluorescence spectra and fluorescence-visualized paper-based sensors were compared. The fluorescence spectrum method demonstrated a linear range of 1 to 10 000 nmol L-1 and a limit of detection (LOD) of 0.19 nmol L-1 . In the fluorescence-visualized paper-based sensors there was a linear range of 10-1000 nmol L-1 , and the LOD was 10 nmol L-1 . Partial least squares discriminant analysis (PLSDA) and partial least squares regression analysis (PLSR) were used successfully to determine l-theanine accurately in tea water with this approach. The accuracy of the PLSDA model was 100% both in the training set and the predicting set, and the correlation coefficient between the actual concentration and the predicted concentration was greater than 0.9997 in the PLSR model. CONCLUSION: This fluorescence-visualized paper-based sensor, combined with chemometrics, could be applied efficiently to the practical analysis of tea water samples, which provides a new idea to ensure the flavor and quality of tea. © 2020 Society of Chemical Industry.

Simultaneous determination of total phenolic acids and total flavonoids in tea and honey samples using an integrated lab on a chip device.

A microfluidic device for simultaneous analysis of total flavonoids and total phenolic acids is presented for the first time. The process was based on the utilization of magnetic zinc-imidazole frameworks (ZIF-4), as a highly efficient media for on-line separation of phenolic acids and flavonoids, which were subsequently detected by a sensitive chemiluminescence (CL) method. Acrylate-based polymeric microchips containing a separation column (12.5 mm length, 3 mm width and 1 mm depth) were fabricated using a 3D-printer, and magnetic ZIF-4 was fixed into the column. The high porosity of the magnetic ZIF-4 made it a suitable adsorbent, guaranteeing an effective separation. The detection limits were in the range of 0.04-0.10 µg mL-1 with relative standard deviation values of (2.19-4.38%). The system was successfully applied for the analysis of flavonoids and phenolic acids in tea and honey samples. The recovery was from 95.4 to 104.1%, indicating a good accuracy of the developed method.

Bitterness compounds in coffee brew measured by analytical instruments and taste sensing system.

We investigated the bitter compounds in coffee brews using multivariate analysis of the data obtained from analytical instrument and electronic taste sensor experiments. Coffee brews were prepared from coffee beans roasted to four different degrees. Each brew was fractionated into four fractions by liquid-liquid extraction. The relative amounts of 30 compounds in each fraction were analyzed by analytical instruments, and the bitterness response value of each fraction was analyzed by a taste sensor. Candidate bitter compounds in the coffee brews were identified with reference to their variable importance in projection and by coefficient of projection to latent structure regression (PLS-R) analysis. PLS-R analysis suggested that nicotinic acid, l-lactic acid, and nicotinamide contributed to the bitterness of the coffee brews. In fact, the coffee brews with added nicotinic acid, l-lactic acid, and nicotinamide had an increased bitterness response value compared to those without.

A filtration-assisted approach to enhance optical detection of analytes and its application in food matrices.

Analysis of target analytes in food and environmental samples often required sophisticated instrumentation, which restrains the accessibility and portability of the analysis. Herein, we developed an instrument-free approach for rapid quantification of target analytes. The reported filtration-assisted approach enables image analysis of aggregates formed via interaction between analytes and silver nanoparticles (AgNPs). Two model analytes were chosen for aggregating AgNPs, potassium phosphate for neutralizing the charges and a di-thiol molecule (2,2'-(ethylenedioxy) diethanethiol (EDT)) for cross-linking. The mixtures of AgNPs and analytes were filtered onto filter membranes and analyzed using grey color intensity analysis. Based on the AgNPs-EDT platform, we demonstrated the detection of 1 µg/mL acrylamide in instant coffee and biscuit matrices was achievable. The filtration-assisted method provides a simple, fast and inexpensive approach for optical detection and quantification of analytes in food matrices.

Fluorescence detection of malachite green in fish tissue using red emissive Se,N,Cl-doped carbon dots.

Facile detection of malachite green (MG), a toxic dye, in aquaculture is urgently demanded for environment and food safety. Herein, we design a novel fluorescent probe, namely red emissive Se,N,Cl-doped carbon dots (CDs), to accurately determinate MG. CDs are prepared by hydrothermal treatment of selenourea and o-phenylenediamine in HCl solution. This material exhibits excitation-independent dual emissions at 625 and 679 nm, with a high quantum yield of 23.6%. A selective and sensitive fluorescent sensor toward MG is established based on inner filter effect, because both the excitation and emission light of CDs can be strongly absorbed by MG. The fluorescence quenching of CDs is linear to the MG concentration over the range of 0.07-2.50 µM with a low detection limit of 21 nM. Trace-level analysis of MG in fish tissue is successfully explored, demonstrating the great potential of the proposed sensor for MG monitoring in aquatic products.

In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor.

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02-3.07%), and high predictive performance (RPD range 2.4-8.8, RER range 7.5-29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

Visual determination of oxidation of edible oil by a nanofiber mat prepared from polyvinyl alcohol and Schiff's reagent.

A fiber mat was developed to visually determine the oxidation of edible oils, based on the colorimetric reaction of Schiff's reagent and aldehydes - the major volatile formed during lipid oxidation. The mixtures of polyvinyl alcohol (PVA) and Schiff's reagent containing various amounts of glycerol were electrospun to form the fiber mats. The response of the PVA/Schiff's reagent fiber mats to gaseous hexanal (model aldehyde) was investigated. Oxidized soybean oils were used to evaluate the effectiveness of the PVA/Schiff's reagent fiber mat for indicating oxidation of the oils. The results showed that the fiber mats obtained had average fiber diameters of less than 100 nm. Upon hexanal exposure, the fiber mats turned from white to purple. Higher amount of glycerol led to larger color change of the fiber mats and shorter response time to hexanal. A linear relationship (R2 = 0.96) was observed between the color change of the mat and hexanal concentration (15-117 µmol L-1). The visual determination limit of the mat for hexanal was 29 µmol L-1. The color change of the PVA/Schiff's reagent fiber mat was increased with an increase of soybean oil oxidation. Out of the seven soybean oils tested, the PVA/Schiff's reagent fiber mat was able to correctly indicate the oxidation states of six oils. The result suggested that the visual determination method developed is a promising method to indicate the oxidation of edible oils, which can be performed easily by non-experts.Graphical abstract.

[Trends in the development of analytical methods for determination of the quality and authenticity of foodstuffs].

The review presents several results of investigations carried out at the Federal Research Centre for Nutrition, Biotechnology and Food Safety on the development and application of modern chemical-analytical methods for determining the quality indicators, nutritional and biological value of foodstuffs. Particular attention is paid to methodological approaches to the determination of biologically active substances of dietary supplements of plant origin, to the definition of criteria for authenticity and methods to disclose falsification of food and dietary supplements. The practical application of modern analytical techniques (capillary GLC, capillary electrophoresis, HPLC with a mass spectrometric detection, UV/VIS diode-array detection, etc.), combining high efficiency and selective separation and detection for identification and qualitative and quantitative determination of components in complex matrix of foodstuffs and dietary supplements is shown.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models.

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

Trends using biological target-based assays for drug detection in complex sample matrices.

In vivo, drug molecules interact with their biological targets (e.g., enzymes, receptors, ion channels, transporters), thereby eliciting therapeutic effects. Assays that measure the interaction between drugs and bio-targets may be used as drug biosensors, which are capable of broadly detecting entire drug classes without prior knowledge of their chemical structure. This Trends article covers recent developments in bio-target-based screening assays for detecting drugs associated with the following areas: illicit products marketed as dietary supplements, food-producing animals, and bodily fluids. General challenges and considerations associated with using bio-target assays are also presented. Finally, future applications of these assays for drug detection are suggested based upon current needs.

Prediction, evaluation, confirmation, and elimination of matrix effects for lateral flow test strip based rapid and on-site detection of aflatoxin B1 in tea soups.

Mycotoxin contaminations of tea have been considered serious problems. The presence of interfering substances presents enormous challenges to accurate detection of hazardous analytes in tea soups. In this work, we have carefully predicted, evaluated, and confirmed the matrix effects in tea that have an undesired influence on the detection of aflatoxin B1 (AFB1) in tea soups by lateral flow test strips (LFTS). After pretreatment of tea samples by simple dilution to change the acidic tea soups to alkaline environments, the matrix effects can be completely eliminated and the reliability of AFB1 analysis in tea soups can be effectively guaranteed. AFB1 contaminated samples of different tea soups can be accurately measured with detection limits down to 0.05 ppb. As the first pioneering report to study the matrix effects on AFB1 monitoring in tea soups by LFTS, we definitely expect this work to further widen the application of LFTS for hazard screening in food safety.

Novel experimental approach to study aroma release upon reconstitution of instant coffee products.

This study presents an experimental approach to study the kinetics and fast release of volatile organic compounds (VOCs) upon reconstitution of instant coffee products. A sampling setup coupled to PTR-ToF-MS (Proton Transfer Reaction Time-of-Flight Mass Spectrometry) for the automated and reproducible reconstitution of instant coffee products was developed to monitor the dynamic release of VOCs. A rapid release of aroma compounds was observed in the first seconds upon hot water addition ("aroma burst"), followed by subsequent decrease in headspace (HS) intensities over the course of analysis. Differences in time-intensity release profiles of individual VOCs were correlated to their Henry's Law constant, vapor pressure and water solubility. The setup and approach proposed here have shown to be sensitive and to respond to fast dynamic changes in aroma release. It allows studying VOCs release upon reconstitution and supports the development of novel technologies and formulations for instant products with improved aroma release properties.