Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Research progress of chilled meat freshness detection based on nanozyme sensing systems

It is important to develop rapid, accurate, and portable technologies for detecting the freshness of chilled meat to meet the current demands of meat industry. This report introduces freshness indicators for monitoring the freshness changes of chilled meat, and systematically analyzes the current status of existing detection technologies which focus on the feasibility of using nanozyme for meat freshness sensing detection. Furthermore, it examines the limitations and foresees the future development trends of utilizing current nanozyme sensing systems in evaluating chilled meat freshness. Harmful chemicals are produced by food spoilage degradation, including biogenic amines, volatile amines, hydrogen sulfide, and xanthine, which have become new freshness indicators to evaluate the freshness of chilled meat. The recognition mechanisms are clarified based on the special chemical reaction with nanozyme or directly inducting the enzyme-like catalytic activity of nanozyme.

Flexible 3D Plasmonic Web Enables Remote Surface Enhanced Raman Spectroscopy

Nanoplasmonic materials concentrate light in specific regions of dramatic electromagnetic enhancement: hot spots. Such regions can be employed to perform single molecule detection via surface-enhanced Raman spectroscopy. However, this phenomenon is challenging since hot spots are expected to be highly intense/abundant and positioning of molecules within such hot spots is crucial to manage with ultrasensitive SERS. Herein, it is discovered that a 3D plasmonic web embedded within a biohybrid (3D-POWER) exhibits plasmonic transmission, spontaneously absorbs the analyte, and meets these so much needed criteria in ultrasensitive SERS. 3D-POWER is built with nanopaper and self-assembled layers of graphene oxide and gold nanorods. According to in silico experiments, 3D-POWER captures light in a small region and performs plasmonic field transmission in a surrounding volume, thereby activating a plasmonic web throughout the simulated volume. The study also provides experimental evidence supporting the plasmonic field transport ability of 3D power, which operates as a SERS signal carrier (even beyond the apparatus field of view), and the ultrasensitive behavior of this ecofriendly and flexible material facilitating yoctomolar limit of detection. Besides, 3D-POWER is proven useful in food and biofluids analysis. It is foreseen that 3D-POWER can be employed as a valuable platform in (bio)analytical applications.

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

Dual-Mode Separation and SERS Detection of Carbaryl with PA-6/AuNRs@ZIF-8 Films

Appropriate separation and enrichment steps can enhance the performance of SERS assays. For rapid, in-situ detection of carbaryl, a novel PA-6/AuNRs@ZIF-8 film that can be applied to dual-mode separation and SERS detection, has been developed. In the film, PA-6 was used as a TLC substrate for the initial separation of the substance to be measured. ZIF-8 provides chemical enhancement in SERS as well as enrichment and secondary separation of the analytes. Utilizing this film, we have successfully implemented a TLC-SERS rapid detection scheme, resulting in a detection limit for carbaryl as low as 1 × 10-9 M in lake water in 15 min, which is significantly lower than existing standards. Additionally, the manufacturing cost of one PA-6/AuNRs@ZIF-8 film can be kept within the range of $0.20-$0.40 economically, presenting substantial financial advantages. The method is highly promising for pesticide detection as well as forensic in-situ testing.

Sensitive SERS aptasensor for histamine detection based on Au/Ag nanorods and IRMOF-3@Au based flexible PDMS membrane

BACKGROUND

Histamine is a kind of biogenic amine with strong toxicity and potential carcinogenicity. Many traditional methods of detecting histamine have the disadvantages of cumbersome detection steps, expensive equipment, and high professional requirements for staff. In contrast, SERS has become the preferred method for quantitative analysis of histamine because of rich fingerprint information, rapidity and economy. However, most of SERS substrates still have technical problems, such as poor stability, low sample collection rate, and detection efficiency. Therefore, there is a great need for new strategies to develop high-performance SERS substrates based sensors.

RESULTS

In our study, a sensitive SERS aptasensor for the detection of histamine was synthesized. The assembly was formed between IRMOF-3@Au/PDMS (flexible SERS substrate) and AuNR-DTNB@Ag-HA apt (Raman signal probe with both the target capture ability) via π-π stacking interaction from HA aptamer and IRMOF-3. Consequently, the SERS signal of the assembly derived from DTNB reached highest due to the synergistic enhancement effect by AuNR@Ag and IRMOF-3@Au. Meanwhile, HA aptamer can specifically capture histamine, therefore histamine addition competitively bound to the probe, leading to a corresponding decrease in the DTNB signal value on the SERS substrate. The SERS intensity at 1331 cm-1 presented a good linear relationship towards the logarithmic value of histamine concentrations ranging from 0.0001 mg/L to 400 mg/L (R2 = 0.990) with the LOD of 3.6 × 10-5 mg/L. Furthermore, the application in wine samples demonstrated the accuracy and applicability of the developed sensor.

SIGNIFICANCE

This method effectively improves substrate stability, detection sensitivity and signal response immediacy to amplify the SERS sensor, thus satisfying the histamine detection requirements of various systems. According to this aptasensor design, our strategy can be extended to create other MOF-based SERS substrates for accurately detecting relative targets to ensure food safety.

A sea urchin-shaped nanozyme mediated dual-mode immunoassay nanoplatform for sensitive point-of-care testing histamine in food samples

Rapid detection of histamine remains a challenge due to the complexity of food matrices. Based on the high peroxidase-like activity of sea urchin-shaped Pt@Au NPs (SU-Pt@Au NPs), a novel dual-mode nanoplatform is developed for the sensitive detection of histamine utilizing an indirect competitive enzyme-linked immunosorbent assay. According to the colorimetric-based UV-vis nanoplatform, histamine is sensitively detected with a liner range from 0.5 to 100 ng/mL and a limit of detection (LOD) as low as 0.3 ng/mL. Then, a smartphone-loaded color picker APP can intelligently detect histamine in point-of-care testing (POCT) based on the R/B ratio of the color channels, with a detection range of 0.5 to 1000 ng/mL and a LOD as low as 0.15 ng/mL, significantly expanding the detection range. Such an easy-to-use and sensitive detection system is employed to quantify histamine in Pacific saury, crab, and pork samples, indicating outstanding application potential in protein-rich meat food safety.

Advancing Mycotoxin Detection: Multivariate Rapid Analysis on Corn Using Surface Enhanced Raman Spectroscopy (SERS)

Mycotoxin contamination on food and feed can have deleterious effect on human and animal health. Agricultural crops may contain one or more mycotoxin compounds; therefore, a good multiplex detection method is desirable to ensure food safety. In this study, we developed a rapid method using label-free surface-enhanced Raman spectroscopy (SERS) to simultaneously detect three common types of mycotoxins found on corn, namely aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA). The intrinsic chemical fingerprint from each mycotoxin was characterized by their unique Raman spectra, enabling clear discrimination between them. The limit of detection (LOD) of AFB1, ZEN, and OTA on corn were 10 ppb (32 nM), 20 ppb (64 nM), and 100 ppb (248 nM), respectively. Multivariate statistical analysis was used to predict concentrations of AFB1, ZEN, and OTA up to 1.5 ppm (4.8 µM) based on the SERS spectra of known concentrations, resulting in a correlation coefficient of 0.74, 0.89, and 0.72, respectively. The sampling time was less than 30 min per sample. The application of label-free SERS and multivariate analysis is a promising method for rapid and simultaneous detection of mycotoxins in corn and may be extended to other types of mycotoxins and crops.

Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

The separation and detection of multi-component mixtures has always been a challenging task. Traditional detection methods often suffer from complex operation, high cost, and low sensitivity. Surface enhanced Raman scattering (SERS) technique is a high sensitivity, powerful and rapid detection tool, which can realize the specific detection of single substance components, but it must solve the problem that multi-component mixtures cannot be accurately determined. Thin layer chromatography (TLC) technology, as a high-throughput separation technology, uses chromatographic plate as the stationary phase, and could select different developing phases for separation experiments. The advantages of TLC technology in short distance and rapid separation are widely used in protein, dye and biomedical fields. However, TLC technology has limitations in detection ability and difficulty in obtaining ideal signal intensity. The combination of TLC technology and SERS technology made the operation procedure simple and the sample size small, which can achieve rapid on-site separation and quantitative detection of mixtures. Due to the rapid development of TLC-SERS technology, it has been widely used in the investigation of various complex systems. This paper reviews the application of TLC-SERS technology in food science, environmental pollution and biomedicine.

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.

Rapid, Convenient, and Ultrasensitive Point-of-Care sensing of histamine from fish: A Portable chromatographic platform based on derivatization reaction

Food poisoning caused by histamine ingestion is one of the prevalent allergies associated with fish consumption in the world. Reliable detection of histamine from fish by a portable platform was of urgent importance to food safety. A portable technology for on-site monitoring of histamine in tuna was established through combined azo-derivatized thin-layer chromatography (TLC) with surface-enhanced Raman scattering (SERS) spectroscopy. The real tuna meat sample was directly applied onto the portable sensor for the separation of histamine and azo-derivatizing of histamine was reacted on the TLC plate. The colorless histamine was visualized by azo-derivatization after spraying Pauly reagent onto the diatomite TLC plate. The molecule information and concentration of the histamine was measured and calculated by SERS spectra. Diatomite TLC plate was capable of separating histamine with 1.32 × 10^-7 M of Au colloid for the SERS enhancement. Accordingly, the limit of detection of histamine from mixture sample could achieve 2.8 × 10^-4 ppm. These results indicated that the portable azo-derivatized TLC-SERS sensor not only visualizes the histamine but also improves the intensity of the Raman spectra. The azo-derivatized TLC-SERS sensor could be applied for rapid, convenient, and ultrasensitive point-of-care sensing of histamine in fish.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Controllable synthesis of flower-like AuNFs@ZIF-67 core-shell nanocomposites for ultrasensitive SERS detection of histamine in fish

Histamine is a significant biomarker to assess the freshness of fish products. In this study, a novel MOF-based SERS sensor for histamine determination was synthesized by wrapping PVP-capped Au nanoflowers with a ZIF-67 shell (Au NFs@ZIF-67). The highly branched Au NFs core exhibited a strong electromagnetic field enhancement effect and provided an ultra-sensitive SERS fingerprint spectrum, while ZIF-67 shell was the contributor to enrich the target and stabilize the substrate. The morphology of the core-shell structures can be easily controlled by the concentrations of the capping agent PVP and MOF precursor Co ion. Consequently, 4-MBA pre-grafted on the optimized SERS substrate can act as the Raman internal standard (IS) to eliminate signal fluctuations through standardizing all spectra against its peak at 1074 cm^-1. Moreover, as the specific receptor for histamine molecules, 4-MBA helped reach the low detection sensitivity, where the SERS intensity ratio, I₁₁₇₂/I₁₀₇₄ presented a good linear relationship towards the histamine concentrations (10^-3-10^-7 M) with the LOD of 0.87 × 10^-7 M (R² = 0.9930). Furthermore, the application in monitoring fish spoilage process demonstrated the feasibility and reliability of the developed sensor. This work provided a facile strategy to construct MOF-based SERS substrate as a potential platform for the shelf-life prediction of fish products.

Engineered nanomaterials-based sensing systems for assessing the freshness of meat and aquatic products: A state-of-the-art review

Meat and aquatic products are susceptible to spoilage during distribution, transportation, and storage, increasing the urgency of freshness evaluation. Engineered nanomaterials (ENMs) typically with the diameter in the range of 1-100 nm exhibit fascinating physicochemical properties. ENMs-based sensing systems have received extensive attention for food freshness assessment due to the advantages of being fast, simple, and sensitive. This review focuses on summarizing the recent application of ENMs-based sensing systems for food freshness detection. First, chemical indicators related to the freshness of meat and aquatic products are described. Then, how to apply the ENMs including noble metal nanomaterials, metal oxide nanomaterials, carbon nanomaterials, and metal-organic frameworks for the construction of different sensing systems were described. Besides, the recent advance in ENMs-based colorimetric, fluorescent, electrochemical, and surface-enhanced Raman spectroscopy sensing systems for assessing the freshness of meat and aquatic products were outlined. Finally, the challenges and future research perspectives for the application of ENMs-based sensing systems were discussed. The ENMs-based sensing systems have been demonstrated as effective tools for freshness evaluation. The sensing performance of ENMs employed in different sensing systems depends on their composition, size, shape, and stability of nanoparticles. For the real application of ENMs in food industries, the risks and regulatory issues associated with nanomaterials need to be further considered. With the continuous development of nanomaterials and sensing devices, the ENMs-based sensors are expected to be applied in-field for rapid detection of the freshness of meat and aquatic products in the future.

Functional cotton fabric-based TLC-SERS matrix for rapid and sensitive detection of mixed dyes

A facile cotton fabric with a built-in TLC-SERS structure was fabricated to demonstrate an integrated TLC separation and SERS identification of mixed dyes. The soft and flexible SERS fabric was firstly fabricated using a simple method in which gold nanoparticles were in-situ synthesized on cotton fabrics by heating. β-CD was then grafted onto cotton fabric through crosslinking with citric acid in presence of sodium hypophosphite monohydrate via esterification reaction. The adsorption and TLC development performance of β-CD grafted fabrics were comprehensively investigated with two organic dyes, one anionic dye and one nonionic dye. Besides, the recyclable adsorption and separation performance were tested to evaluate its sustainable application prospects. It displayed less adsorption capacity loss and reusable separation performance after several cycles than the pristine cotton fabrics. Finally, two sets of mixed dyes were successfully separated on the TLC fabrics and then identified via on-site SERS according to their different migration distance. The developed TLC-SERS fabric shows the advantage of quick, easy to handle, low-cost, sensitive, and could be exploited in on-site study of synthetic dyes in art objects, textile and packaging products or forensic applications.

Review of Thin-Layer Chromatography Tandem with Surface-Enhanced Raman Spectroscopy for Detection of Analytes in Mixture Samples

In the real world, analytes usually exist in complex systems, and this makes direct detection by surface-enhanced Raman spectroscopy (SERS) difficult. Thin layer chromatography tandem with SERS (TLC-SERS) has many advantages in analysis such as separation effect, instant speed, simple process, and low cost. Therefore, the TLC-SERS has great potential for detecting analytes in mixtures without sample pretreatment. The review demonstrates TLC-SERS applications in diverse analytical relevant topics such as environmental pollutants, illegal additives, pesticide residues, toxic ingredients, biological molecules, and chemical substances. Important properties such as stationary phase, separation efficiency, and sensitivity are discussed. In addition, future perspectives for improving the efficiency of TLC-SERS in real sample detecting are outlined.

Freeze Surface-Enhanced Raman Scattering Coupled with Thin-Layer Chromatography: Pesticide Detection and Quantification Case

Thin-layer chromatography (TLC) is widely used in various branches of chemical science to separate components in complex mixtures because of its simplicity. In most cases, analyte spots are visually detected by fluorescence, and the retention factor (R_f) is determined from the distance traveled by the analyte. Further characterizations are often necessary to identify separated chemicals because molecular information other than R_f is not available. Surface-enhanced Raman scattering (SERS) has been coupled with TLC to complement molecular information. In previously reported TLC-SERS, metal nanoparticle suspension was dropped onto analyte spots to obtain SERS spectra. This approach is simple and efficient for SERS measurements on the TLC plate but has limited sensitivity for several reasons, such as the low solubility of analytes in the dropped solution, difficult control of nanoparticle aggregation, and interference from the stationary phase. We recently showed that freezing enhances SERS sensitivity by a factor of ∼10³. Freezing simultaneously concentrates analytes and silver nanoparticles (AgNPs) in a freeze concentrated solution, where aggregation of AgNPs is facilitated, allowing sensitive freeze SERS (FSERS) measurements. Here, we discuss FSERS measurements on TLC plates to demonstrate the superiority of this combination, i.e. TLC-FSERS. Freezing enhances SERS sensitivity by freeze concentration and facilitated aggregation of AgNPs and, in addition, eliminates interference from the stationary phase. Under the optimized condition, TLC-FSERS enables the on-site detection of pesticides at the nM level. The use of the SERS signal from adenine added as the internal standard allows us to quantify pesticides. Applications to a commercial green tea beverage are also demonstrated.

Rapid Limit Test of Seven Pesticide Residues in Tea Based on the Combination of TLC and Raman Imaging Microscopy

Numerous studies have shown that pesticide residues in tea exceeding the maximum residue limits (MRL) can cause harmful effects on the human body. There are many limitations in the existing analytical methods for pesticide residues in tea, so new analytical methods need to be developed. We developed a limit test method that combines thin-layer chromatography with Raman imaging microscopy (TLC-RIM). Seven residual pesticide components in tea (Avermectin, Methomyl, Carbendazim, Imidacloprid, Chlorothalonil, Azoxystrobin, and Acetamiprid) could be preliminarily separated by TLC and then irradiated by a 532 nm laser. Raman spectra of seven pesticides obtained by Raman imaging microscopy could be used to test whether the pesticide residues in tea exceed the MRL. The limits of detection of the seven pesticides were 0.04, 0.10, 0.24, 0.20, 0.12, 0.12, and 1.0 mg/mL, respectively. The simulated positive test showed that the matrix in tea did not interfere with the test of the seven pesticides. When the pesticides were tested within 8 h, the RSD of the peak heights of the seven pesticides were 1.2%~9.6%; the test results of three batches of tea showed that the imidacloprid in one batch of tea exceeded its MRL, and the results were consistent with that by UPLC-MS/MS. The TLC-RIM is fast, sensitive, stable, specific, and reliable.

Colorimetric visualization of histamine secreted by basophils based on DSP-functionalized gold nanoparticles

Histamine released by activated basophils has become an important biomarker and therapeutic target in the development of allergic diseases. To date, several gold nanoparticle (AuNP)-based nanosensors have been reported for histamine detection in foods. However, rapid, highly sensitive and direct detection of histamine in allergic diseases is still lacking due to the complexity of the physical environment. Herein, we developed a novel nanosensor for colorimetric visualization of histamine in activated basophils by simply coupling dithiobis(succinimidylpropionate) (DSP) on the surface of AuNPs (DSP-AuNPs). The DSP moiety serves as a linker and can react with the aliphatic amino group of histamine, and the imidazole ring of histamine can selectively bind with Au by means of p-p conjugation, thus inducing the aggregation of AuNPs. In this study, we experimentally proved that DSP-AuNPs showed good sensitivity and selectivity to histamine among various amino acids, including histidine. Additionally, this nanosensor displayed a rapid response to histamine with a linear range of 0.8-2.5 μM, and the limit of detection (LOD) was 0.014 μM, which is a relatively low LOD in comparison with those of other AuNP-based nanosensors. Finally, DSP-AuNPs are used, for the first time, to successfully detect endogenous histamine changes in activated basophils. Therefore, our work may provide a promising strategy to monitor histamine levels in the basophil activation test.

Fluorescence-SERS dual-mode for sensing histamine on specific binding histamine-derivative and gold nanoparticles

Histamine (His) is used as an indicator of seafood quality, but it can be toxic at high intakes. A fluorescence (FL)-surface-enhanced Raman scattering (SERS) dual-mode assay system has been developed for His detection. The His detection method was established based on the specific binding capacity of gold nanoparticles (AuNPs) for the FL derivative of His and o-phthalaldehyde (OPA). In this strategy, His reacted with the OPA to form a Schiff base product (O-His) along with a change in FL and SERS activities. The usual nature of AuNPs could display a significant role both enhancement of SERS and quenching of FL signals. The current investigation displayed a good selectivity toward His over all other biogenic amines. Under the optimized analytical conditions, the SERS and FL intensity of the system were linearly proportional to the His concentration in the range of 0.05-4.5 mg/L and 1-20 mg/L with a detection limit of 0.04 mg/L and 0.32 mg/L, respectively. Moreover, the proposed method was successfully applied for His determination in seafood with promising results.

Rapid Detection of Five Estrogens Added Illegally to Dietary Supplements by Combining TLC with Raman Imaging Microscope

Estrogens added illegally to dietary supplements are hazardous to human health. Traditional detection and analysis methods have many limitations, and we have developed an assay that combines thin-layer chromatography with Raman imaging microscopy (TLC-RIM). The five estrogens (estrone, estradiol, estriol, ethinyl estradiol, and diethylstilbestrol) were initially separated by TLC, then detected by area scanning Raman imaging with a 532 nm laser under a microscope. Raman spectra were obtained for each estrogen, which were used for detecting estrogen illegally added to botanical dietary supplements. The LOD of each estrogen was 0.4, 1.0, 0.8, 0.2, and 0.2 mg/mL, respectively. The matrix in the real sample did not interfere with the detection of estrogens. The method was fast, sensitive, stable, specific, and reliable.

Multiplex Sensing of Complex Mixtures by Machine Vision Analysis of TLC-SERS Images

Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.

Recent Progress of Fluorescence Sensors for Histamine in Foods

Biological amines are organic nitrogen compounds that can be produced by the decomposition of spoiled food. As an important biological amine, histamine has played an important role in food safety. Many methods have been used to detect histamine in foods. Compared with traditional analysis methods, fluorescence sensors as an adaptable detection tool for histamine in foods have the advantages of low cost, convenience, less operation, high sensitivity, and good visibility. In terms of food safety, fluorescence sensors have shown great utilization potential. In this review, we will introduce the applications and development of fluorescence sensors in food safety based on various types of materials. The performance and effectiveness of the fluorescence sensors are discussed in detail regarding their structure, luminescence mechanism, and recognition mechanism. This review may contribute to the exploration of the application of fluorescence sensors in food-related work.

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Antibiotics, as veterinary drugs, have made extremely important contributions to disease prevention and treatment in the animal breeding industry. However, the accumulation of antibiotics in animal food due to their overuse during animal feeding is a frequent occurrence, which in turn would cause serious harm to public health when they are consumed by humans. Antibiotic residues in food have become one of the central issues in global food safety. As a safety measure, rapid and effective analytical approaches for detecting these residues must be implemented to prevent contaminated products from reaching the consumers. Traditional analytical methods, such as liquid chromatography, liquid chromatography mass spectrometry, and capillary electrophoresis, involve time-consuming sample preparation and complicated operation and require expensive instrumentation. By comparison, surface-enhanced Raman spectroscopy (SERS) has excellent sensitivity and remarkably enhanced target recognition. Thus, SERS has become a promising alternative analytical method for detecting antibiotic residues, as it can provide an ultrasensitive fingerprint spectrum for the rapid and noninvasive detection of trace analytes. In this study, we comprehensively review the recent progress and advances that have been achieved in the use of SERS in antibiotic residue detection. We introduce and discuss the basic principles of SERS. We then present the prospects and challenges in the use of SERS in the detection of antibiotics in food. Finally, we summarize and discuss the current problems and future trends in the detection of antibiotics in food.

Sub-Part-Per-Billion Level Sensing of Fentanyl Residues from Wastewater Using Portable Surface-Enhanced Raman Scattering Sensing

Detection of illicit drug residues from wastewater provides a new route toward community-level assessment of drug abuse that is critical to public health. However, traditional chemistry analytical tools such as high-performance liquid chromatography in tandem with mass spectrometry (HPLC-MS) cannot meet the large-scale testing requirement in terms of cost, promptness, and convenience of use. In this article, we demonstrated ultra-sensitive and portable surface-enhanced Raman scattering sensing (SERS) of fentanyl, a synthetic opioid, from sewage water and achieved quantitative analysis through principal component analysis and partial least-squares regression. The SERS substrates adopted in this application were synthesized by in situ growth of silver nanoparticles on diatomaceous earth films, which show ultra-high sensitivity down to 10 parts per trillion in artificially contaminated tap water in the lab using a commercial portable Raman spectrometer. Based on training data from artificially contaminated tap water, we predicted the fentanyl concentration in the sewage water from a wastewater treatment plant to be 0.8 parts per billion (ppb). As a comparison, the HPLC-MS confirmed the fentanyl concentration was below 1 ppb but failed to provide a specific value of the concentration since the concentration was too low. In addition, we further proved the validity of our SERS sensing technique by comparing SERS results from multiple sewage water treatment plants, and the results are consistent with the public health data from our local health authority. Such SERS sensing technique with ultra-high sensitivity down to sub-ppb level proved its feasibility for point-of-care detection of illicit drugs from sewage water, which is crucial to assess public health.

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

Influence of Sample Matrix on Determination of Histamine in Fish by Surface Enhanced Raman Spectroscopy Coupled with Chemometric Modelling

Histamine fish poisoning is a foodborne illness caused by the consumption of fish products with high histamine content. Although intoxication mechanisms and control strategies are well known, it remains by far the most common cause of seafood-related health problems. Since conventional methods for histamine testing are difficult to implement in high-throughput quality control laboratories, simple and rapid methods for histamine testing are needed to ensure the safety of seafood products in global trade. In this work, the previously developed SERS method for the determination of histamine was tested to determine the influence of matrix effect on the performance of the method and to investigate the ability of different chemometric tools to overcome matrix effect issues. Experiments were performed on bluefin tuna (Thunnus thynnus) and bonito (Sarda sarda) samples exposed to varying levels of microbial activity. Spectral analysis confirmed the significant effect of sample matrix, related to different fish species, as well as the extent of microbial activity on the predictive ability of PLSR models with R² of best model ranging from 0.722–0.945. Models obtained by ANN processing of factors derived by PCA from the raw spectra of the samples showed excellent prediction of histamine, regardless of fish species and extent of microbial activity (R² of validation > 0.99).

Advances on the rapid and multiplex detection methods of food allergens

With the gradually increasing prevalence of food allergy in recent years, food allergy has become a major public health problem worldwide. The clinical symptoms caused by food allergy seriously affect people's quality of life; there are unknown allergen components in novel food and hidden allergens caused by cross contamination in food processing, which pose a serious risk to allergy sufferers. Thus, rapid and multiplex detection methods are required to achieve on-site detection or examination of allergic components, so as to identify the risk of allergy in time. This paper reviews the progress of high-efficiency detection of food allergens, including enhanced traditional detection techniques and emerging detection techniques with the ability high-throughput detection or screening potential food allergen, such as xMAP, biosensors, biochips, etc. focusing on their sensitivity, applicability of each method in food, along with their pretreatment, advantages, limitation in the application of food analysis. This paper also introduces the challenges faced by these high-efficiency detection technologies, as well as the potential of customized allergen screening methods and rapid on-site detection technology as future research directions.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Nanostructured Biosilica of Diatoms: From Water World to Biomedical Applications

Diatoms—unicellular photosynthetic algae—are promising natural sources of nanostructured silica. These microorganisms produce in their membrane approximately a highly ordered porous cell wall called a frustule as protection from environmental stress. Diatom frustules consist of hydrated silica that show peculiar properties including biocompatibility, tailorable surface chemistry, chemical inertness, and thermal stability. Frustules harvested from aquatic ecosystems or diatomaceous fossil sediments represent an excellent cost-effective source of biosilica for a broad range of biomedical applications. The porous ultrastructure of the frustules displays a large surface area available for coating with various biomolecules through different functionalization methods. In this review article, we highlight the main features of diatom biosilica and present some of the most advantageous properties that support the employment of frustules in the field of drug delivery, biosensing, and regenerative medicine. In particular, it is offered an insight into the most common functionalization strategies through which diatom physicochemical properties can be modified and tailored according to the described field of application.

Facile Histamine Detection by Surface-Enhanced Raman Scattering using SiO2@Au@Ag Alloy Nanoparticles

Histamine intoxication associated with seafood consumption represents a global health problem. The consumption of high concentrations of histamine can cause illnesses ranging from light symptoms, such as a prickling sensation, to death. In this study, gold-silver alloy-embedded silica (SiO2@Au@Ag) nanoparticles were created to detect histamine using surface-enhanced Raman scattering (SERS). The optimal histamine SERS signal was measured following incubation with 125 μg/mL of SiO2@Au@Ag for 2 h, with a material-to-histamine solution volume ratio of 1:5 and a phosphate-buffered saline-Tween 20 (PBS-T) solvent at pH 7. The SERS intensity of the histamine increased proportionally with the increase in histamine concentration in the range 0.1-0.8 mM, with a limit of detection of 3.698 ppm. Our findings demonstrate the applicability of SERS using nanomaterials for histamine detection. In addition, this study demonstrates that nanoalloys could have a broad application in the future.

Optical sensors for determination of biogenic amines in food

This review presents the state-of-the-art of optical sensors for determination of biogenic amines (BAs) in food by publications covering about the last 10 years. Interest in the development of rapid and preferably on-site methods for quantification of BAs is based on their important role in implementation and regulation of various physiological processes. At the same time, BAs can develop in different kinds of food by fermentation processes or microbial activity or arise due to contamination, which induces toxicological risks and food poisoning and causes serious health issues. Therefore, various optical chemosensor systems have been devised that are easy to assemble and fast responding and low-cost analytical tools. If amenable to on-site analysis, they are an attractive alternative to existing instrumental analytical methods used for BA determination in food. Hence, also portable sensor systems or dipstick sensors are described based on various probes that typically enable signal readouts such as photometry, reflectometry, luminescence, surface-enhanced Raman spectroscopy, or ellipsometry. The quantification of BAs in real food samples and the design of the sensors are highlighted and the analytical figures of merit are compared. Future instrumental trends for BA sensing point to the use of cell phone-based fully automated optical evaluation and devices that could even comprise microfluidic micro total analysis systems.

Dielectric Nanoparticles Coated upon Silver Hollow Nanosphere as an Integrated Design to Reinforce SERS Detection of Trace Ampicillin in Milk Solution

Surface-enhanced Raman scattering (SERS) technique is competent to trace detection of target species, down to the single molecule level. The detection sensitivity is presumably degraded by the presence of non-specific binding molecules that occupy a SERS-active site (or hot spot) on the substrate surface. In this study, a silver hollow nano-sphere (Ag HNS) with cavity has been particularly designed, followed by depositing dielectric nanoparticles (Di NPs) upon Ag HNS. In the integrated nanostructures, Di NPs/Ag HNS were furthermore confirmed by cutting through the cross sections using the Focused Ion Beam (FIB) technique, which provides the Scanning Electron Microscope (SEM) with Energy-dispersive Spectroscope (EDS) mode for identifying the distribution of Di NPs upon Ag HNS. The results have indicated that Di NPs/Ag HNS exhibits small diameter of cavity, and among Di NPs in this study, Al2O3 with lower dielectric constant provides a much higher SERS enhancement factor (e.g., ~6.2 × 107). In this study, to detect trace amounts (e.g., 0.01 ppm) of Ampicillin in water or milk solution, Al2O3 NPs/Ag HNS was found to be more efficient and less influenced by non-specific binding molecules in milk. A substrate with integrated plasmonic and dielectric components was designed to increase the adsorption of target species and to repulse non-specific binding molecules from SERS-active sites.

Tetrahydrocannabinol Sensing in Complex Biofluid with Portable Raman Spectrometer Using Diatomaceous SERS Substrates

Using thin-layer chromatography in tandem with surface-enhanced Raman spectroscopy (TLC-SERS) and tetrahydrocannabinol (THC) sensing in complex biological fluids is successfully conducted with a portable Raman spectrometer. Both THC and THC metabolites are detected from the biofluid of marijuana-users as biomarkers for identifying cannabis exposure. In this article, ultra-sensitive SERS substrates based on diatomaceous earth integrated with gold nanoparticles (Au NPs) were employed to detect trace levels of cannabis biomarkers in saliva. Strong characteristic THC and THC metabolite SERS peaks at 1601 and 1681 cm-1 were obtained despite the moderate interference of biological molecules native to saliva. Urine samples were also analyzed, but they required TLC separation of THC from the urine sample to eliminate the strong influence of urea and other organic molecules. TLC separation of THC from the urine was performed by porous microfluidic channel devices using diatomaceous earth as the stationary phase. The experimental results showed clear separation between urea and THC, and strong THC SERS characteristic peaks. Principal component analysis (PCA) was used to analyze the SERS spectra collected from various THC samples. The spectra in the principal component space were well clustered for each sample type and share very similar scores in the main principal component (PC1), which can serve as the benchmark for THC sensing from complex SERS spectra. Therefore, we proved that portable Raman spectrometers can enable an on-site sensing capability using diatomaceous SERS substrates to detect THC in real biological solutions. This portable THC sensing technology will play pivotal roles in forensic analysis, medical diagnosis, and public health.