Paper-based SERS substrate and one-class classifier to monitor thiabendazole residual levels in extracts of mango peels

Trends and challenges of fruit by-products utilization: insights into safety, sensory, and benefits of the use for the development of innovative healthy food: a review

A significant portion of the human diet is comprised of fruits, which are consumed globally either raw or after being processed. A huge amount of waste and by-products such as skins, seeds, cores, rags, rinds, pomace, etc. are being generated in our homes and agro-processing industries every day. According to previous statistics, nearly half of the fruits are lost or discarded during the entire processing chain. The concern arises when those wastes and by-products damage the environment and simultaneously cause economic losses. There is a lot of potential in these by-products for reuse in a variety of applications, including the isolation of valuable bioactive ingredients and their application in developing healthy and functional foods. The development of novel techniques for the transformation of these materials into marketable commodities may offer a workable solution to this waste issue while also promoting sustainable economic growth from the bio-economic viewpoint. This approach can manage waste as well as add value to enterprises. The goal of this study is twofold based on this scenario. The first is to present a brief overview of the most significant bioactive substances found in those by-products. The second is to review the current status of their valorization including the trends and techniques, safety assessments, sensory attributes, and challenges. Moreover, specific attention is drawn to the future perspective, and some solutions are discussed in this report.

Analysis and experimental assessment of an optimized SERS substrate used to detect thiabendazole in apples with high sensitivity

Pesticides that linger in the environment and ecosystems for an extended period can cause severe and dangerous health problems in humans. To detect pesticides in foods, the development of high-sensitivity and quick screening technologies was required. This research investigated the performance of Au@Ag NPs with varying thicknesses of the silver shell for detecting trace quantities of thiabendazole (TBZ) in apples using surface-enhanced Raman spectroscopy (SERS). The Au@Ag NPs were synthesized by coating 32 nm gold seeds with different thicknesses of silver shell ranging from 2.4 to 8.7 nm, achieved by adjusting the incorporation of AgNO3 and ascorbic acid. The optimized Au@Ag NPs with a 7.3 nm silver shell demonstrated outstanding SERS activity, high sensitivity, and a detection limit of 0.05 μg/mL for TBZ. The R2 values, representing the goodness of fit, were found to be 0.990 and 0.986 for standard and real TBZ samples, respectively, indicating a strong correlation between the measured signal and the TBZ concentration. The recovery analysis showed a reliable and accurate detection capability (96 to 105%), suggesting good reliability and accuracy of the SERS-based detection using the optimal Au@Ag NPs. Overall, this research highlights the potential of SERS with optimal Au@Ag NPs for rapid and effective monitoring of pesticides in the food industry.

Review of Characteristics and Analytical Methods for Determination of Thiabendazole

Thiabendazole (TBZ) is a fungicide and anthelmintic drug commonly found in food products. Due to its toxicity and potential carcinogenicity, its determination in various samples is important for public health. Different analytical methods can be used to determine the presence and concentration of TBZ in samples. Liquid chromatography (LC) and its subtypes, high-performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography (UHPLC), are the most commonly used methods for TBZ determination representing 19%, 18%, and 18% of the described methods, respectively. Surface-enhanced Raman spectroscopy (SERS) and fluorimetry are two more methods widely used for TBZ determination, representing 13% and 12% of the described methods, respectively. In this review, a number of methods for TBZ determination are described, but due to their limitations, there is a high potential for the further improvement and development of each method in order to obtain a simple, precise, and accurate method that can be used for routine analysis.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Automatic detection of pesticide residues on the surface of lettuce leaves using images of feature wavelengths spectrum

The inappropriate application of pesticides to vegetable crops often results in environmental pollution, which seriously impacts the environment and human health. Given that current methods of pesticide residue detection are associated with issues such as low accuracy, high equipment cost, and complex flow, this study puts forward a new method for detecting pesticide residues on lettuce leaves. To establish this method, spectral analysis was used to determine the characteristic wavelength of pesticide residues (709 nm), machine vision equipment was improved, and a bandpass filter and light source of characteristic wavelength were installed to acquire leaf image information. Next, image preprocessing and feature information extraction were automatically implemented through programming. Several links were established for the training model so that the required feature information could be automatically extracted after the batch input of images. The pesticide residue detected using the chemical method was taken as the output and modeled, together with the input image information, using the convolutional neural network (CNN) algorithm. Furthermore, a prediction program was rewritten to generalize the input images during the prediction process and directly obtain the output pesticide residue. The experimental results revealed that when the detection device and method designed in this study were used to detect pesticide residues on lettuce leaves in a key state laboratory, the coefficient of determination of the equation reached 0.883, and the root mean square error (RMSE) was 0.134 mg/L, indicating high accuracy and that the proposed method integrated the advantages of spectrum detection and deep learning. According to comparison testing, the proposed method can meet Chinese national standards in terms of accuracy. Moreover, the improved machine vision equipment was less expensive, thus providing powerful support for the application and popularization of the proposed method.

In Situ Collection and Rapid Detection of Pathogenic Bacteria Using a Flexible SERS Platform Combined with a Portable Raman Spectrometer

This study aims to develop a simple, sensitive, low-cost, environmentally friendly and flexible surface-enhanced Raman scattering (SERS) platform, combined with a portable Raman spectrometer, for the rapid and on-site SERS detection of bacteria. Commercial tobacco packaging paper (TPP) with little background interference was used as a loading medium that effectively adsorbed Au nanoparticles and provided sufficient “hot spots”. This Au-tobacco packaging paper (Au-TPP) substrate used as a flexible SERS platform can maximize sample collection by wiping irregular surfaces, and was successfully applied to the on-site and rapid detection of pathogenic bacteria. Raman fingerprints of pathogenic bacteria can be obtained by SERS detection of spiked pork using wipeable Au-TPP, which verifies its value in practical applications. The results collected by SERS were further verified by polymerase chain reaction (PCR) results. It showed several advantages in on-site SERS detection, including accurate discrimination, simple preparation, easy operation, good sensitivity, accuracy and reproducibility. This study indicates that the established flexible SERS platform has good practical applications in pathogenic bacterial identification and other rapid detections.

Recent advances of nanomaterial-based optical sensor for the detection of benzimidazole fungicides in food: a review

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Detection and quantification of toxicants in food and water using Ag-Au core-shell fractal SERS nanostructures and multivariate analysis

Here we demonstrate a facile, two step formation of silver core - gold shell (Ag-Au) nanostructures using microelectrodes and assess their performance as surface-enhanced Raman scattering (SERS) substrates to detect and quantify toxicants. Ag nanostructures, serving as the scaffolds for the bimetallic structures, were grown first by using electrochemical deposition on the edges of microelectrodes functionalized with the alkanethiol, 11-mercaptoundecanoic acid. Subsequently, different concentrations of HAuCl₄ were used to perform a galvanic reaction on the surfaces of the Ag nanostructures with aqueous droplets being placed on the microelectrodes for 10 min before the substrate was rinsed and dried. Lower HAuCl₄ concentrations were found to better preserve the fractal morphology of the formed Ag-Au nanostructures, while higher concentrations resulted in Ag-Au fragments. The SERS enhancement factor for the Ag-Au nanostructures was estimated to have a max value of 6.51 x 10⁵. Combining a data reduction technique with a linear classifier, both identification and quantification were demonstrated with 100% success. The toxicants thiram, thiabendazole, malachite green and biphenyl-4-thiol were all detected and identified at 1 ppm. Lastly, as a proof of concept, the Ag-Au nanostructures were transferred to a PDMS film resulting in a flexible SERS substrate capable of direct detection of thiram on an apple peel without any additional sample pre-treatment.

Use of color based chromatographic images obtained from comprehensive two-dimensional gas chromatography in authentication analyses

Comprehensive two-dimensional gas chromatography (GC×GC) has been an important technique used to acquire as much information as possible from a wide variety of samples. Qualitative contour plots analysis provides useful information and in daily use it ends up being handled as images of the volatile organic compounds by analysts. Cachaça samples are used in this paper to showcase the use of two-dimensional chromatographic images as the main source for authentication purposes through one-class classifiers, such as data-driven soft independent modeling of class analogy (DD-SIMCA). The proposed workflow summarizes this fast and easy process, which can be used to certify a specific brand in comparison to other brands, as well as to authenticate if samples have been adulterated. Lower quality cachaças, non-aged cachaças and cachaças aged in different wooden barrels were tested as adulterants. Chromatographic images allowed for the distinction of all brands and nearly every adulteration tested. Sensitivity was estimated at 100% for all models and specificity ranged from 96% to 100%. Different approaches were used, alternating from working with whole-sized images to working with smaller resized versions of those images. Resized chromatographic images could be potentially useful to easily compensate for slight chromatographic misalignments, allowing for faster calculations and the use of simpler software. Reductions to 50% and 25% of the original size were tested and the results did not greatly differ from whole images model. As such, 2D chromatographic images have been found to be an interesting form of evaluating a product's authenticity.

Paper-Based Microfluidic Sensors for Onsite Environmental Detection: A Critical Review

A newly developed research topic, fabricated paper-based microfluidic sensors, was discussed in the field of low-cost environmental detection. Distinguished with the traditional dipstick or lateral-flow setups, these paper-based microfluidic sensors can serve as a tool for onsite quantitative and semi-quantitative measurements, without risks to cause environmental pollution. They have attracted increasing interest since the first easy-fabricated paper-based setup reported by Whitesides group in 2007. Most of the publications utilized paper-based sensors in clinical detection. In recent years, some groups started to use these sensors in environmental measurement, leading to precise, easy operation, low-cost, and eco-friendly methods for onsite detection. In this review, paper-based microfluidic sensors were briefly introduced, followed by literatures review and discussion for future perspectives.