Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

pH-induced interaction mechanism of zearalenone with zein: Binding characteristics, conformational structure and intermolecular forces

Zein-bound zearalenone (ZEN) complexes are naturally existed in maize by their spontaneous interaction, which significantly impacts the risk assessment of ZEN. Additionally, the pH levels in processing could affect the binding or release of zein-bound ZEN. In this study, pH-induced interaction mechanism of ZEN with zein were studied. Results showed that the acid conditions increased the binding constant (Ka) from 3.46 to 10.0 × 104 L/mol, binding energy from -17.38 to -43.49 kJ mol-1. By increasing hydrophobic interaction and hydrogen bond of ZEN with zein, the binding of ZEN with zein was promoted, forming zein-bound ZEN. Whereas, alkaline conditions decreased the Ka to 1.45 × 104 L/mol and binding energy to 148.48 kJ mol-1, weakened ZEN-zein interaction and stretched zein molecules, resulting the release of ZEN from zein. This study could provide important theoretical basis for perfecting risk assessment and controlling zein-bound ZEN during processing.

Ultrasensitive detection of zearalenone based on electrochemiluminescent immunoassay with Zr-MOF nanoplates and Au@MoS2 nanoflowers

In this work, an effective competitive-type electrochemiluminescence (ECL) immunosensor was constructed for zearalenone determination by using Zr-MOF nanoplates as the ECL luminophore and Au@MoS2 nanoflowers as the substrate material. Zr-MOF have an ultra-thin sheet-like structure that accelerates the transfer of electrons, ions and co-reactant intermediates, which exhibited strong and stable anodic luminescence. The three-dimensional Au@MoS2 nanoflowers would form a thin film modification layer on the glassy carbon electrode (GCE). And its good electrical conductivity and higher specific surface area utilization further improving the sensitivity of the ECL immunosensor. Under the optimized conditions, the proposed immunosensor exhibited satisfactory stability, sensitivity and accuracy, and its ECL signal was proportional to the logarithm of ZEN concentration (0.0001-100 ng/mL) and the limit of detection (LOD) was 0.034 pg/mL. In addition, the results of recovery experiment acquired for wheat flour and pig urine samples further proved the feasibility of the immunosensor for the detection of real samples, indicating its potential for ultrasensitive detection of ZEN.

Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS)

The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Prediction performance and reliability evaluation of three ginsenosides in Panax ginseng using hyperspectral imaging combined with a novel ensemble chemometric model

Panax ginseng C. A. Meyer (PG) is a health-promoting food, and its ginsenosides (Rb1, Rg1, Re) content, as the quality indicator, is affected by the planting modes (garden or forest ginsengs) and years. Effective prediction of this content remains to be investigated. In this study, hyperspectral (HSI) combined with ensemble model (CGRU-GPR) including the convolutional neural network (CNN), gate recurrent unit (GRU), and Gaussian process regression (GPR) realized a comprehensive evaluation of the prediction performance and predictive uncertainty. With effective wavelengths, the proposed CGRU-GPR model improved operation efficiency and obtained satisfactory prediction results with relative percent deviation (RPD) values all higher than 2.70 in three ginsenosides. Meanwhile, the interval prediction with a high prediction interval coverage probability (PICP) of 0.97 - 1.0 and a low mean width percentage (MWP) of 0.7 - 1.66 indicated a low prediction uncertainty. This study provides a rapid and reliable method for predicting ginsenosides contents in PG.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system

BACKGROUND

The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables.

RESULTS

The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC  = 0.915; RMSECV = 1.39; RP  = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC  = 0.9723, RMSECV = 0.81, RP  = 0.9114, RMSEP = 1.45 and RPD = 2.59.

CONCLUSION

The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Novel Ultralow-Potential Electrochemiluminescence Aptasensor for the Highly Sensitive Detection of Zearalenone Using a Resonance Energy Transfer System

An ultralow-potential electrochemiluminescence (ECL) aptasensor has been designed for zearalenone (ZEN) assay based on a resonance energy transfer (RET) system with SnS2 QDs/g-C3N4 as a novel luminophore and CuO/NH2-UiO-66 as a dual-quencher. SnS2 QDs were loaded onto g-C3N4 nanosheets and enhanced the ECL luminescence via strong synergistic effects under an ultralow potential. The UV-vis absorption spectrum of CuO/NH2-UiO-66 exhibits considerable overlap with the ECL emission spectrum of SnS2 QDs/g-C3N4, an important consideration for the RET process. In order to stimulate RET, the ZEN aptamer and complementary DNA are introduced for conjugation between the donor and the acceptor. With the binding interaction between ZEN by its aptamer, CuO/NH2-UiO-66 is removed from the electrode surface, resulting in the inhibition of the RET system and an increase in the ECL signal. Under optimal conditions, the as-prepared aptasensor quantified ZEN from 0.5 μg·mL-1 to 0.1 fg·mL-1 with a low limit of detection of 0.085 fg·mL-1, and it exhibited good stability, excellent specificity, high reproducibility, and desirable practicality. The sensing strategy provides a method for mycotoxins assay to monitor food safety.

Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

Is chemical analysis suitable for detecting mycotoxins in agricultural commodities and foodstuffs?

The assessment of the risks of mycotoxins to humans through consuming contaminated foods resulted in specific legislation that evaluates the presence, quantities, and type of mycotoxins in agricultural commodities and foodstuffs. Thus, to ensure compliance with legislation, food safety and consumer health, the development of suitable analytical procedures for identifying and quantifying mycotoxins in the free or modified form, in low-concentration and in complex samples is necessary. This review reports the application of the modern chemical methods of analysis employed in mycotoxin detection in agricultural commodities and foodstuffs. It is reported extraction methods with reasonable accuracy and those present characteristics according to guidelines of Green Analytical Chemistry. Recent trends in mycotoxins detection using analytical techniques are presented and discussed, evaluating the robustness, precision, accuracy, sensitivity, and selectivity in the detection of different classes of mycotoxins. Sensitivity coming from modern chromatographic techniques allows the detection of very low concentrations of mycotoxins in complex samples. However, it is essential the development of more green, fast and more suitable accuracy extraction methods for mycotoxins, which agricultural commodities producers could use. Despite the high number of research reporting the use of chemically modified voltammetric sensors, mycotoxins detection still has limitations due to the low selectivity from similar chemical structures of mycotoxins. Furthermore, spectroscopic techniques are rarely employed due to the limited number of reference standards for calibration procedures.

Raman spectroscopy: Principles and recent applications in food safety

Food contaminant is a significant issue because of the adverse effects on human health and economy. Traditional detection methods such as liquid chromatography-mass spectroscopy for detecting food contaminants are expensive and time-consuming, and require highly-trained personnel and complicated sample pretreatment. Raman spectroscopy is an advanced analytical technique in a manner of non-destructive, rapid, cost-effective, and ultrasensitive sensing various hazards in agri-foods. In this chapter, we summarized the principle of Raman spectroscopy and surface enhanced Raman spectroscopy, the methods to process Raman spectra, the recent applications of Raman/SERS (surface-enhanced Raman spectroscopy) in detecting chemical contaminants (e.g., pesticides, antibiotics, mycotoxins, heavy metals, and food adulterants) and microbiological hazards (e.g., Salmonella, Campylobacter, Shiga toxigenic E. coli, Listeria, and Staphylococcus aureus) in foods.

Rapid and sensitive detection of zearalenone in corn using SERS-based lateral flow immunosensor

Zearalenone (ZEN) is a universal mycotoxin contaminant in corn and its products. A surface-enhanced Raman scattering (SERS) based test strip was proposed for the detection of ZEN, which had the advantages of simplicity, rapidity, and high sensitivity. Core-shell Au@AgNPs with embedded reporter molecules (4-MBA) were synthesized as SERS nanoprobe, which exhibited excellent SERS signals and high stability. The detection range of ZEN for corn samples was 10-1000 μg/kg with the limit of detection (LOD) of 3.6 μg/kg, which is far below the recommended tolerable level (60 μg/kg). More importantly, the SERS method was verified by HPLC in the application on corn samples contaminated with ZEN, and the coincidence rates were in the range of 86.06%-111.23%, suggesting a high accuracy of the SERS assay. Therefore, the SERS-based test strip with an analysis time of less than 15 min is a promising tool for accurate and rapid detection of ZEN-field contamination.

Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images

Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels.

Raman spectral analysis for rapid determination of zearalenone and alpha-zearalanol

Mycotoxins, including zearalenone, are important natural products produced by fungi that occasionally contaminate agricultural commodities and pose serious health risks to consumers of food and feed. Zearalenone and its metabolite, α-zearalanol, are of significant concern due to their estrogenic and anabolic steroid activity. Several governments have regulatory standards and advisory guidelines for zearalenone and α-zearalanol. Raman and ultraviolet spectroscopy were employed with density functional theory methods to evaluate spectroscopic properties to distinguish between zearalenone and α-zearalanol systematically. Raman bands were assigned based on vibrational frequency calculations. A portable Raman spectroscopy instrument (785 nm laser) distinguished between zearalenone and α-zearalanol in a label-free manner. Many vibrational bands of zearalenone and α-zearalanol are similar, including high-intensity peaks at 1315 cm^-1 and 1650 cm^-1. However, the intensities in the Raman spectra at 1465 cm^-1, 1495 cm^-1, and 1620 cm^-1 enabled the identification of zearalenone. The Raman peak at 1450 cm^-1 is associated with α-zearalanol. These vibrational bands serve as spectral indicators to differentiate between the structurally similar zearalenone and α-zearalanol.

Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics

Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.

Sensitive colorimetric aptasensor based on stimuli-responsive metal-organic framework nano-container and trivalent DNAzyme for zearalenone determination in food samples

Zearalenone (ZEN) poses a serious threat to human and animal health. The development of sensitive determination methods for ZEN is of great significance for ensuring the quality and safety of food. Herein, based on a stimuli-responsive aptamer-functionalized metal-organic framework (MOF) nano-container and trivalent DNA peroxidase mimicking enzyme (DNAzyme), an efficient aptasensor was constructed initially for the colorimetric determination of ZEN. The proposed aptasensor only required simple operations but exhibited outstanding specificity, reproducibility, storage stability and reusability simultaneously. Under the optimal conditions, there was a good linear relationship between the changed absorbance and logarithm concentration of ZEN within 0.01-100 ng mL^-1, and the limit of detection (LOD) could reach 0.36 pg mL^-1. Moreover, the proposed aptasensor was reliable in quantifying ZEN in spiked food samples. The current bioassay provides a promising scheme for constructing stable, specific and rapid colorimetric platforms with potential applications in the fields of food safety.

A facile "turn-on" fluorescent aptasensor for simultaneous detection of dual mycotoxins in traditional Chinese medicine based on graphene oxide and FRET

Mycotoxin is a common sort of harmful contaminant in traditional Chinese medicine (TCM), which is in a great demand of controlling. On this account, a facile "turn-on" fluorescent aptasensor based on fluorescence resonance energy transfer (FRET) for simultaneous detection of patulin (PAT) and zearalenone (ZEN) was developed. In this study, the aptamers of PAT and ZEN were labeled by FAM and Cy3, respectively, serving as fluorescence probes. Both aptamers could adsorb on the surface of graphene oxide (GO) via π-π stacking, which will consequently result in the occurrence of FRET between the fluorophores and GO. In the absence of the targets, the fluorescence would be quenched "off". In the presence of any of the dual mycotoxins, the corresponding aptamers would interact with the targets and release from GO due to the conformational variation, leading to a fluorescence "turn-on" effect. The limit of detection of this difunctional aptasensor was 2.29 nM for PAT and 0.037 nM for ZEN, respectively. This aptasensing platform exhibited satisfactory selectivity against interferents and reliability in real TCM sample detection. To our knowledge, it is the first aptasensor based on GO and FRET that realizes simultaneous detection of dual mycotoxin in TCM. Moreover, the measurement takes merely ∼60 min, does not need complicated pretreatment, and uses only inexpensive aptamer and GO as consuming materials. To sum up, this aptasensor exhibits great potential in fast, cost-effective and reliable simultaneous detection of multiple targets, and is expected to contribute to the quality and safety control of TCM.

Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System

The visible and near-infrared spectroscopy (Vis/NIRS) models for sheep meat quality evaluation using only one type of meat cut are not suitable for other types. In this study, a novel portable Vis/NIRS system was used to simultaneously detect physicochemical properties (pH, color L*, a*, b*, cooking loss, and shear force) for different types of sheep meat cut, including silverside, back strap, oyster, fillet, thick flank, and tenderloin cuts. The results show that the predictive abilities for all parameters could be effectively improved by spectral preprocessing. The coefficient of determination (R_p²) and residual predictive deviation (RPD) of the optimal prediction models for pH, L*, a*, b*, cooking loss, and shear force were 0.79 and 3.50, 0.78 and 2.28, 0.68 and 2.46, 0.75 and 2.62, 0.77 and 2.19, and 0.83 and 2.81, respectively. The findings demonstrate that Vis/NIR spectroscopy is a useful tool for predicting the physicochemical properties of different types of meat cut.

NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples

Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.

Ultrasensitive immunoassay for detection of zearalenone in agro-products using enzyme and antibody co-embedded zeolitic imidazolate framework as labels

Zearalenone (ZEN) has a potential hazard to human health, and is frequently found in agro-products. To minimize ZEN exposure to consumers, a novel metal-organic framework-based immunoassay system using zeolitic imidazolate framework-encapsulated horseradish peroxidase and goat anti-mouse IgG (HRP/Ab@ZIF-L) as labels was proposed for rapid and ultrasensitive detection of ZEN in agro-products. The HRP/Ab@ZIF-L not only maintained recognition ability of antibody and catalytic activity of enzyme, but also protected encapsulated proteins against high temperature, organic solvents and long term storage. Under optimal conditions, the detection limit of HRP/Ab@ZIF-L-based immunoassay reached 0.5 ng/L for ZEN, which was approximately 126-fold lower than that of conventional HRP-based immunoassay. Moreover, the proposed method showed an excellent selectivity, and a good dynamic linear detection for ZEN in the range of 0.5 ng/L to 0.476 μg/L. The recoveries of ZEN from spiked corn and wheat samples ranged from 84.50% to 96.70% with the relative standard deviation under 8.9%. In brief, the proposed immunoassay method has potential application for rapid and ultrasensitive detection of ZEN in agro-products.

Application of Raman Spectroscopic Methods in Food Safety: A Review

Food detection technologies play a vital role in ensuring food safety in the supply chains. Conventional food detection methods for biological, chemical, and physical contaminants are labor-intensive, expensive, time-consuming, and often alter the food samples. These limitations drive the need of the food industry for developing more practical food detection tools that can detect contaminants of all three classes. Raman spectroscopy can offer widespread food safety assessment in a non-destructive, ease-to-operate, sensitive, and rapid manner. Recent advances of Raman spectroscopic methods further improve the detection capabilities of food contaminants, which largely boosts its applications in food safety. In this review, we introduce the basic principles of Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and micro-Raman spectroscopy and imaging; summarize the recent progress to detect biological, chemical, and physical hazards in foods; and discuss the limitations and future perspectives of Raman spectroscopic methods for food safety surveillance. This review is aimed to emphasize potential opportunities for applying Raman spectroscopic methods as a promising technique for food safety detection.

Integrated multi-spectroscopic and molecular modeling techniques to study the formation mechanism of hidden zearalenone in maize

Hidden mycotoxins have been reported to be "protected" by macromolecular substances to escape routine determination, but release to free mycotoxins under gastrointestinal conditions. Nowadays, the hidden zearalenone (ZEN) that binding with macromolecular zein has been found in maize. However, the binding mechanism of ZEN with zein in maize has not been clarified. In this study, the formation of ZEN-zein complex was investigated applying ultrafiltration, multi-spectroscopic and molecular modeling techniques. The steady-state and transient fluorescence analysis suggested the ZEN could interact with zein to form the complex driven by hydrophobic force and hydrogen bonds, which is in accordance with the molecular modeling studies. The conformational changes of zein induced by binding with ZEN were revealed by Fourier transform infrared spectroscopy (FTIR) and circular dichroism (CD). Elucidating the binding mechanism between zein and ZEN could help the development of detecting hidden ZEN and guarantee the safety of maize products.

Low concentration noroxin detection using terahertz spectroscopy combined with metamaterial

The unreasonable use of antibiotics in poultry breeding has led to the frequent occurrence of antibiotic residues in poultry products, affecting food safety and posing a threat to human health. Accurate and rapid detection of antibiotic drug content is therefore of great significance. In this study, a new metamaterial method based on terahertz (THz) spectroscopy for the detection of the quinolone antibacterial drug noroxin in low concentrations was developed and tested. First, a new metamaterial structure was designed using a computational electromagnetics simulation tool with experimental verification. Three-dimensional full-wave electromagnetic field simulations and measured transmission spectra for the metamaterials were observed. Second, the transmission spectra of photoresists with different thicknesses (0-40 µm) on the metamaterials surface were simulated, and the transmission spectra of noroxin thin films of different concentrations on the metamaterial surface were measured. Finally, using a purchased standard sample of noroxin in ethanol (100 µg/mL) as the mother liquor, test samples of different concentrations were prepared, and experiments and multivariate data analysis were carried out. The noroxin detection limit for the method presented in this paper was determined as 0.01 µg/mL. Thus, the new metamaterial method based on terahertz spectroscopy designed in this study was shown to effectively detect low concentrations of noroxin, providing a theoretical and experimental basis for the rapid detection of quinolone antibiotic residues in food matrices.

Application of new technologies in decontamination of mycotoxins in cereal grains: Challenges, and perspectives

Emerging decontamination technologies have been attracted considerable attention to address the consumers' demand for high quality and safe food products. As one of the important foods in the human diet, cereals are usually stored for long periods, resulting in an increased risk of contamination by different hazards. Mycotoxins comprise one of the significant contaminants of cereals that lead to enormous economic losses to the industry and threats to human health. While prevention is the primary approach towards reducing human exposure to mycotoxins, decontamination methods have also been developed as complementary measures. However, some conventional methods (chemical treatments) do not fulfill industries' expectations due to limitations like safety, efficiency, and the destruction of food quality attributes. In this regard, novel techniques have been proposed to food to comply with the industry's demand and overcome conventional methods' limitations. Novel techniques have different efficiencies for removing or reducing mycotoxins depending on processing conditions, type of mycotoxin, and the food matrix. Therefore, this review provides an overview of novel mycotoxin decontamination technologies such as cold plasma, irradiation, and pulse light, which can be efficient for reducing mycotoxins with minimum adverse effects on the quality and nutritional properties of produce.

Development of a multi-channel magnetic bead micro-probe assay for high-throughput detection of zearalenone in edible and medicinal Coix seed

A multi-channel magnetic bead micro-probes assay (MBPA) based on indirect competitive principle was developed for high-throughput detection of zearalenone (ZEA) in edible and medicinal Coix seed. This strategy introduced magnetic beads as the carriers, the specific primary antibodies as the capture probes for targets and the secondary antibodies functionalized goat anti-mouse immunoglobulin G labeled fluorescein isothiocyanate as the fluorescence signal probes. Through the competitive reaction of ZEA in Coix seed samples and that covalently coupled on the surface of MBs with their specific antibodies, as well as fast magnetic separation and sensitive fluorescence detection, the developed MBPA strategy allowed low limit of detection (2.03 ng/mL) with broad dynamic range (2.03-440.67 ng/mL), as well as excellent accuracy with the average recovery rate of 96.39% and relative standard deviation (RSD) of 5.48% for ZEA. 36 samples could realize simultaneous analysis in one operation within less than 20 min only needing 50 μL of solution and 30 s of sampling, avoiding large consumption of time and organic solvents. Multiple centrifugation and cleanup steps were omitted because of magnetic separation, avoiding the loss of targets. Diverse capture and fluorescent probes can be randomly bound onto the surface of MBs, making the MBPA strategy a promising tool for on-site high-throughput monitoring of various trace hazard factors in food safety, and environmental monitoring.

Study on terahertz spectrum analysis and recognition modeling of common agricultural diseases

Diseases are critical factors that affect the yield and quality of crops. Therefore, it is of great research value to develop rapid and quantitative methods for identification of common agricultural diseases. This exploratory study involved data analysis of common fungal pathogens using identification modeling based on terahertz spectrum technology. The selected pathogens were Physalospora piricola, Erysiphe cichoracearum, and Botrytis cinerea, which are common fungal pathogens that cause apple ring rot, cucumber powdery mildew, and grape gray mold blight, respectively. Taking polyethylene as the control, the terahertz time-domain spectra, and frequency-domain spectra of samples of the three pathogens were both measured. The absorption and refraction characteristics of these samples in the range of 0.1-2.0 THz were calculated and analyzed, and samples were then divided using the KS algorithm. Terahertz spectrum-image data blocks of the pathogen samples were preprocessed, and the dimensions of data were reduced using non-local mean filtering and the SPA algorithm, respectively. K-nearest neighbors (KNN), support vector machine (SVM), and BP neural network (BPNN), and other algorithms were used for analysis of terahertz images at characteristic frequencies, and for investigating the identification model. The model was quantitatively evaluated, and its imaging visualization was studied. The results suggest that there are significant differences among P. piricola, E. cichoracearum, and B. cinerea in absorption and refraction in the terahertz band. SVM modeling identification results of the three pathogens at the frequency of 1.376 THz were satisfactory, with an R_p of 0.9649, RMSEP of 0.0273, and a high (93.8212%) comprehensive evaluation index F1-score, and a clearly identifiable visualization effect. This study demonstrated the potential of terahertz spectroscopy to be used for identification of common crop pathogens and has provided technical references for the rapid diagnosis and early warning of agricultural diseases.

Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea

Black tea like other food crops is prone to mercury ion (Hg²⁺) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg²⁺ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg²⁺. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg²⁺ standard solutions (R_c = 0.984, R_p = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (R_c = 0.979, R_p = 0.963, RMSEC = 0.181 μg/g and RMSEP = 0.210 μg/g). The limit of detection of the proposed sensor was 4.12 × 10^-7 μg/mL for Hg²⁺ standard solutions and 2.83 × 10^-5 μg/g for Hg²⁺ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg²⁺ in tea.

Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus

Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.

Application of volatile and spectral profiling together with multimode data fusion strategy for the discrimination of preserved eggs

The maturity level of eggs during pickling is conventionally assessed by choosing few eggs from each curing batch to crack open. Yet, this method is destructive, creates waste and has consequences for financial losses. In this work, the feasibility of integrating electronic nose (EN) with reflectance hyperspectral (RH) and transmittance hyperspectral (TH) data for accurate classification of preserved eggs (PEs) at different maturation periods was investigated. Classifier models based solely on RH and TH with EN achieved a training accuracy (93.33%, 97.78%) and prediction accuracy (88.89%; 93.33%) respectively. The fusion of the three datasets, (EN + RH + TH) as a single classifier model yielded an overall training accuracy of 98.89% and prediction accuracy of 95.56%. Also, 52 volatile compounds were obtained from the PE headspace, of which 32 belonged to seven functional groups. This study demonstrates the ability to integrate EN with RH and TH data to effectively identify PEs during processing.

Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.