Investigation of nonlinear relationship of surface enhanced Raman scattering signal for robust prediction of thiabendazole in apple

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.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

Quantitative prediction of minced chicken gel strength under ultrasonic treatment by NIR spectroscopy coupled with nonlinear chemometric tools evaluated using APaRPs

Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0-50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp2 = 0.8772) with the ELM model, demonstrating potential for rapid, non-destructive prediction of minced chicken gel strength quality.

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions

Background

Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outcomes.

Methods

This paper proposed a label-free and highly sensitive classification method for serum of patients with different degrees of gastric lesions by combining surface-enhanced Raman scattering (SERS) and machine learning analysis. Specifically, we prepared Au lotus-shaped (AuLS) nanoarrays substrates using seed-mediated and liquid-liquid interface self-assembly method for measuring SERS spectra of serum, and then the collected spectra were processed by principal component analysis (PCA) - multi-local means based nearest neighbor (MLMNN) model to achieve differentiation.

Results

By employing this pattern analysis, AuLS nanoarray substrates can achieve fast, sensitive, and label-free serum spectral detection. The classification accuracy can reach 97.5%, the sensitivity is higher than 96.7%, and the specificity is higher than 95.0%. Moreover, by analyzing the PCs loading plots, the most critical spectral features distinguishing different degrees of gastric lesions were successfully captured.

Conclusion

This discovery lays the foundation for combining SERS with machine learning for real-time diagnosis and recognition of gastric lesions.

Quantitative and rapid detection of nanoplastics labeled by luminescent metal phenolic networks using surface-enhanced Raman scattering

The escalating prevalence of nanoplastics contamination in environmental ecosystems has emerged as a significant health hazard. Conventional analytical methods are suboptimal, hindered by their inefficiency in analyzing nanoplastics at low concentrations and their time-intensive processes. In this context, we have developed an innovative approach that employs luminescent metal-phenolic networks (L-MPNs) coupled with surface-enhanced Raman spectroscopy (SERS) to separate and label nanoplastics, enabling rapid, sensitive and quantitative detection. Our strategy utilizes L-MPNs composed of zirconium ions, tannic acid, and rhodamine B to uniformly label nanoplastics across a spectrum of sizes (50-500 nm) and types (e.g., polystyrene, polymethyl methacrylate, polylactic acid). Rhodamine B (RhB) functions as a Raman reporter within these L-MPNs-based SERS tags, providing the requisite sensitivity for trace measurement of nanoplastics. Moreover, the labeling with L-MPNs aids in the efficient separation of nanoplastics from liquid media. Utilizing a portable Raman instrument, our methodology offers cost-effective, swift, and field-deployable detection capabilities, with excellent sensitivity in nanoplastic analysis and a detection threshold as low as 0.1 μg/mL. Overall, this study proposes a highly promising strategy for the robust and sensitive analysis of a broad spectrum of particle analytes, underscored by the effective labeling performance of L-MPNs when coupled with SERS techniques.

SERS mapping combined with chemometrics, for accurate quantification of methotrexate from patient samples

Despite the technological development in Raman instrumentation that has democratized access to 2D sample scanning capabilities, most quantitative surface-enhanced Raman scattering (SERS) analyses are still performed by only acquiring a single or a few spectra per sample and performing univariate data analysis on those. This strategy can however reach its limit when analytes need to be detected and quantified in complex matrices. In that case, surface fouling and competition between the target analyte and interfering compounds can impair univariate SERS data analysis, underlining the need for a more in-depth data analysis strategy based on exploiting of full-spectrum information. In this paper, a multivariate data analysis strategy was developed, for analyzing SERS maps of methotrexate (MTX) from patient samples, including all steps from baseline correction, selection of wavelength, and the relevant pixels in the map (image threshold segmentation), as well as quantitative model construction based on partial-least squares regression. Among the different baseline correction methods evaluated, standard normal variable transformation and Savitzky-Golay smoothing proved to be more suitable, while the genetic algorithm wavelength screening method was able to screen out MTX-related SERS spectral regions more efficiently. Importantly, with the here-developed process, it was sufficient to use MTX-spiked commercial serum when building quantitative models, removing the need to work with MTX-spiked patient samples, and consequently enabling time- and resource-saving quantitative analyses. Besides, the developed multivariate data analysis approach showed superior performances compared with univariate analysis, with 30 % improved sensitivity (detection limit of 5.7 µM), 25 % higher reproducibility (average relative standard variation of 15.6 %), and 110 % better accuracy (average prediction error of -10.5 %).

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

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.

Recent advances in rare earth ion-doped upconversion nanomaterials: From design to their applications in food safety analysis

The misuse of chemicals in agricultural systems and food production leads to an increase in contaminants in food, which ultimately has adverse effects on human health. This situation has prompted a demand for sophisticated detection technologies with rapid and sensitive features, as concerns over food safety and quality have grown around the globe. The rare earth ion-doped upconversion nanoparticle (UCNP)-based sensor has emerged as an innovative and promising approach for detecting and analyzing food contaminants due to its superior photophysical properties, including low autofluorescence background, deep penetration of light, low toxicity, and minimal photodamage to the biological samples. The aim of this review was to discuss an outline of the applications of UCNPs to detect contaminants in food matrices, with particular attention on the determination of heavy metals, pesticides, pathogenic bacteria, mycotoxins, and antibiotics. The review briefly discusses the mechanism of upconversion (UC) luminescence, the synthesis, modification, functionality of UCNPs, as well as the detection principles for the design of UC biosensors. Furthermore, because current UCNP research on food safety detection is still at an early stage, this review identifies several bottlenecks that must be overcome in UCNPs and discusses the future prospects for its application in the field of food analysis.

Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

The misuse of chloramphenicol (CAP) has led to the development of drug-resistant strains that pose significant threats to public health. Here, we propose a universal flexible surface-enhanced Raman spectroscopy (SERS) sensor utilizing gold nanotriangles (AuNTs) and polydimethylsiloxane (PDMS) film for rapid detection of CAP in food samples. Initially, AuNTs@PDMS with unique optical and plasmonic properties were used to collect spectra of CAP. Afterward, four chemometric algorithms were executed and compared. Accordingly, random frog-partial least squares (RF-PLS) exhibited optimum results with correlation coefficient of prediction (Rp = 0.9802) and the lowest root-mean-square error of prediction (RMSEP = 0.348 µg/mL). Furthermore, the sensor's efficacy to detect CAP in milk samples was confirmed, and the findings were compatible with the conventional HPLC approach (P > 0.05). Therefore, the proposed flexible SERS sensor could effectively be used to monitor milk quality and safety.

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.

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.

Optimization of near-infrared reflectance models in determining flavonoid composition of okra (Abelmoschus esculentus L.) pods

Okra pods have been utilized as a functional food due to their rich active ingredient composition, especially the high content of flavonoid compounds. This study conducted near-infrared spectroscopy (NIRS) modeling optimization and external validation based on the flavonoid components of 219 pod samples. Spectral correlation analyses identified two types of spectral response patterns classified as quercetin-3-O-xylose (1-2) glucoside (QOXG) and total flavonoid content (TFC), consisting of six different spectral regions. Different modeling effects were observed for QOXG and TFC with various spectral region combination analyses, where the lower wave-number region contributed more to both flavonoids calibration models. The combination of standard normal variate / "1, 9, 3" / partial least squares was found to be the most effective for developing calibration models for both flavonoids. The resulting models had small root mean square errors of prediction for external validation and high determination coefficients, indicating their usefulness for rapid prediction of flavonoid composition in okra pods.

Au/SiNCA-based SERS analysis coupled with machine learning for the early-stage diagnosis of cisplatin-induced liver injury

Cisplatin has been widely applied in the clinical treatment of various cancers, whereas liver injury induced by its hepatotoxicity is still a severe issue. Reliable identification of early-stage cisplatin-induced liver injury (CILI) can improve clinical care and help to streamline drug development. Traditional methods, however, cannot achieve enough information at the subcellular level due to the requirement of the labeling process and low sensitivity. To overcome these, we designed an Au-coated Si nanocone array (Au/SiNCA) to fabricate the microporous chip as the surface-enhanced Raman scattering (SERS) analysis platform for the early diagnosis of CILI. A CILI rat model was established, and the exosome spectra were obtained. The principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) classification algorithm was proposed as the multivariate analysis method to build the diagnosis and staging model. The PCA-RCKNCN model has been validated to achieve a satisfactory result, with accuracy and AUC of over 97.5%, and sensitivity and specificity of over 95%, indicating that SERS combined with the PCA-RCKNCN analysis platform can be a promising tool for clinical applications.

A Novel Colorimetric Sensor Array Coupled Multivariate Calibration Analysis for Predicting Freshness in Chicken Meat: A Comparison of Linear and Nonlinear Regression Algorithms

As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat.

Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (R_p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (R_p = 0.937, RPD = 3.38) and CP (R_p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Simultaneous determination of benzimidazole fungicides in food using signal optimized label-free HAu/Ag NS-SERS sensor

Extensively employed pesticide in agriculture causes residue in food products that would threaten public health safety. The surface-enhanced Raman scattering (SERS) signal reliant on double sensing of carbendazim and thiabendazole in a single step is achieved without the aid of any bio-recognition element. A label-free anisotropic bimetallic hollow Au/Ag nanostars (HAu/Ag NS) SERS substrate was synthesized with numerous hot spots for Raman molecule through a galvanic displacement-free deposition. The individual and mixed analyte calibration results were compared based on the identified peak at 1224 (carbendazim) and 778 (thiabendazole) cm^-1 and exhibited insignificant differences. The sensor could detect carbendazim and thiabendazole up to 4.28 × 10^-4 and 6.04 × 10^-4 µg·g^-1 or µg·mL^-1 in both individual and mixture of their extract. The recovery for accuracy and precision analysis was 91.54-98.26 % in rice and water. Finally, validation results were achieved satisfactorily (p > 0.05) with HPLC.

Recent advances in the application of Raman spectroscopy for fish quality and safety analysis

Fish is one of the highly demanded aquatic products, and its quality and safety play a pivotal role in daily diet. However, the possible hazardous substance in perishable fish both in pre- and postharvest periods may decrease their values and pose a threat to public health. Laborious and expensive traditional methods drive the need of developing effective tools for detecting fish quality and safety properties in a rapid, nondestructive, and effective manner. Recent advances in Raman spectroscopy (RS) and surface-enhanced Raman scattering (SERS) have shown enormous potential in various aspects, which largely boost their applications in fish quality and safety evaluation. They have incomparable merits such as providing molecule fingerprint information and allowing for rapid, sensitive, and noninvasive detection with simple sample preparation. This review provides a comprehensive overview focusing on the applications of RS and SERS for fish quality assessment and safety inspection, highlighting the hazardous substance and illegal behavior both in preharvest (veterinary drug residues and environmental pollutants) and postharvest (freshness and illegal behavior) particularly. Moreover, challenges and prospects are also proposed to facilitate the vigorous development of RS and SERS. This review is aimed to emphasize potential opportunities for applying RS and SERS as promising techniques for routine food quality and safety detection. PRACTICAL APPLICATION: With these applications, it can be clearly indicated that RS and SERS are promising and powerful in fish quality and safety surveillance, thereby reducing the occurrence of commercial fraud and food safety issues. More efforts still should be concentrated on exploiting the high-performance Raman instruments, establishing a universal Raman database, developing reproducible SERS substrates and combing RS with other versatile spectral techniques to promote these technologies from laboratory to practice. It is hoped that this review should arouse more research interests in RS and SERS technologies for fish quality and safety surveillance, as well as provide more insights to make a breakthrough.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

Identification of characteristic volatiles and metabolomic pathway during pork storage using HS-SPME-GC/MS coupled with multivariate analysis

Previous researches have been conducted evaluating the volatile compounds of pork. However, data regarding the changes in volatiles and metabolic pathways during pork storage were inadequately investigated. Herein, a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) coupled multivariate analysis was proposed for characterizing the profiles of volatile compounds and metabolic pathways during pork storage. A total of 37 metabolites, including aldehydes, ketones, alcohols etc. were successfully identified. Multivariate statistical analysis revealed a substantial variation in metabolite phenotype among samples over the pork storage period, with 12 characteristic metabolites and 5 potential characteristic metabolites screened as biomarkers. Moreover, three metabolomic pathways analysis and transformation between each other (thermal reactions, lipid metabolism and amino acid metabolism) reveals the underlying mechanisms of metabolites change of pork. Therefore, the present study may provide insight into future understanding of the variation in the pork metabolite profiles.

Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.

Rapid determination of process parameters during simultaneous saccharification and fermentation (SSF) of cassava based on molecular spectral fusion (MSF) features

Simultaneous saccharification and fermentation (SSF) of cassava is one of the key steps in the production of fuel ethanol. In order to improve the monitoring efficiency of the ethanol production process and the product yield, this study puts forward a new idea for monitoring of the cassava SSF process based on the molecular spectroscopy fusion (MSF) technique. Savisky-Golay (SG) combined with standard normal variable (SNV) was used to preprocess the obtained Raman spectra and near-infrared (NIR) spectra. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic wavelengths of the preprocessed Raman spectra and the NIR spectra, and the optimized features were fused in the feature layer. The support vector machine (SVM) model of the process parameters during the cassava SSF based on the MSF features was established. The experimental results showed that compared with the best CARS-SVM model based on the single-molecule spectral features, the performance of the best CARS-SVM model based on fusion features has been significantly improved. For detection of the glucose content, the RMSEP, R_P² and RPD of the best CARS-SVM model were 5.398, 0.957 and 4.922, respectively. For detection of the ethanol content, the RMSEP, R_P² and RPD of the best CARS-SVM model were 4.394, 0.977 and 6.758, respectively. The obtained results reveal that the combination of MSF technique and appropriate chemometric methods can achieve high-precision quantitative detection of the process parameters during the cassava SSF. This study can provide technical basis and experimental reference for the development of portable spectrometer equipment for process monitoring of the cassava SSF.

Rapid detection and prediction of chloramphenicol in food employing label-free HAu/Ag NFs-SERS sensor coupled multivariate calibration

Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001-1000 µg/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 µg/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra- and interday recovery for both samples were in the range of 92.62-96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision.

Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm

Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.

2D leaf-like ZIF-L decorated with multi-walled carbon nanotubes as electrochemical sensing platform for sensitively detecting thiabendazole pesticide residues in fruit samples

Excessive use of pesticides in modern agriculture results in large amounts of pesticide residues in agricultural production, greatly threatening human health. Herein, novel two-dimensional leaf-like zeolitic imidazolate framework-L decorated with multi-walled carbon nanotubes (MWCNTs/ZIF-L) was prepared by a facile solvent way and exploited as electrode material for sensitive electrochemical sensing of thiabendazole (TBZ). Two-dimensional ZIF-L presents high surface area, large pore volume, and abundant active sites, which exhibits high enrichment ability towards TBZ molecules, while the MWCNTs interspersed on ZIF-L can prominently enhance the electron transport capability and improve the electrocatalytic activity for TBZ oxidation. Due to the intriguing synergy between the components, the MWCNTs/ZIF-L-based electrochemical sensor reveals a limit of detection (LOD) of 6.0 nmol·L^-1, which is lower than that reported in most literatures. Additionally, satisfactory reproducibility and repeatability, long-term stability, and excellent selectivity are acquired. The proposed method was also applied for the detection of TBZ in apple and orange samples with acceptable recoveries.

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.

Logarithmic Data Processing Can Be Used Justifiably in the Plotting of a Calibration Curve

The article is a response to a recent opinion piece that log concentration values should not be applied in analytical chemistry. An essential aim in the development of analytical chemistry methods is to obtain more sensitive and accurate detection values. For the application of chemical analysis methods, the obtained experiment data need to fit with the mathematical functions in the first place. As influenced by different detection principles and analytical methods, data can be displayed in a coordinate system with two linear axes for linear function fitting, or the data can first be taken through a logarithmic transformation and then for function fitting. Using raw data or data after logarithmic transformation primarily depends on analytical principles, without special rules of data formats. For example, ultraviolet-visible spectrophotometric data are more suitable for direct linear fitting. However, enzyme-catalyzed reaction or electrochemical data in logarithmic form are more appropriate for function fitting. This transformation of data form will not affect the soundness of fit statistics; rather, it simplifies the complexity of function analysis and calculation, which are the essence of analytical chemistry. In this brief article, we provide justification and legitimacy of the application of logarithmic processing in various fields of quantitative analytical chemistry.

Standardization of Raman spectra using variable penalty dynamic time warping

Raman spectroscopy can provide structural fingerprints to identify molecules by means of spectral library searching. However, it is difficult to share the spectral library between different Raman spectrometers because of the nonlinear displacement in Raman shift. In this study, we propose a Raman spectra Standardization method using Variable Penalty dynamic time warping (RS-VPdtw), which can synchronize the nonlinear displacement between spectra acquired with different spectrometers. We have compared the standardization performance of RS-VPdtw and MWFFT on the spectra of 13 real samples acquired with 6 different spectrometers. The mean spectral similarity of RS-VPdtw and MWFFT increased from 0.79 to 0.97 and 0.91 respectively. Results show that RS-VPdtw is significantly better than MWFFT in Raman spectra standardization. The Raman spectra acquired with different spectrometers can be standardized by RS-VPdtw to search the same spectral library, which can avoid the time-consuming and labor-intensive reestablishment of spectral libraries for different spectrometers. This means that RS-VPdtw is a promising and valuable method to solve the spectra standardization problem in large-scale applications of Raman spectroscopy.

Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R_P = 0.9616, RPD = 3.807 for TPC, R_P = 0.9657, RPD = 3.367 for TFC, R_P = 0.9683, RPD = 2.728 for ABTS assay, and R_P = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.

Rapid detection of mercury in food via rhodamine 6G signal using surface-enhanced Raman scattering coupled multivariate calibration

Considering food safety and limitations of biorecognition elements, this study focused on the development of a novel method for predicting mercury (Hg²⁺) in fish and water samples using surface-enhanced Raman scattering (SERS) coupled wavenumber selection chemometric method. Herein, core-shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as SERS substrate, and rhodamine 6G (R6G) was used as signaling probe for Hg²⁺. In the presence of Hg²⁺, citrate ion of Au@Ag NPs induced complexation and become amalgam causes desorption of R6G occurred, resulted in decreased SERS signal intensity. Compared to surface Plasmon resonance method, SERS coupled genetic algorithm-partial least squares realized good correlation coefficient (0.9745 and 0.9773) in their prediction over the concentration ranges 1.0 × 10² to 1.0 × 10^-3 µg/g. The recovery (88.45 - 94.73%) and precision (coefficient of variations, 3.28 - 5.76%) exhibiting satisfactory results suggested that the proposed method could be employed to predict Hg²⁺ in fish and water samples towards quality and safety monitoring.

Determination of perchlorate in tea using SERS with a superhydrophobically treated cysteine modified silver film/polydimethylsiloxane substrate

Perchlorate is a new type of persistent pollutant, which interferes with the synthesis and secretion of thyroxine and affects human health. The EU's limit for perchlorate in tea is 750 μg kg^-1. The surface-enhanced Raman scattering (SERS) technique has the characteristics of a simple pretreatment method, rapid detection, high sensitivity, high specificity and great stability in the detection of perchlorate. This study proposed a novel superhydrophobic SERS substrate, which can be used to detect perchlorate in tea. Firstly, a chemical deposition method was used to deposit a silver film on the surface of a thin layer of polydimethylsiloxane. After drying, the substrate was immersed in 1H,1H,2H,2H-perfluorodecyltriethoxysilane aqueous solution for 15 hours to make the surface of the substrate superhydrophobic. Then cysteine molecules were deposited on the surface of the silver film/polydimethylsiloxane by incubation. The superhydrophobic surface has a unique enrichment effect on the highly diluted solution, and perchlorate has a strong affinity for the amino group of cysteine. We collected the Raman spectra of 9 gradient concentrations (1-100 μmol L^-1) of perchlorate-spiked tea samples on the hydrophobic substrate, and a linear model of the relationship between the SERS spectral intensity and the concentrations of perchlorate in tea was established. This method reached a good limit of detection of 0.0067 μmol L^-1 (0.82 μg kg^-1) in tea, which showed that the developed sensor has high sensitivity and could be used as a fast and simple technique for quantitative detection of perchlorate based on SERS technology.

Fabricating a nano-bionic sensor for rapid detection of H2S during pork spoilage using Ru NPs modulated catalytic hydrogenation conversion

Rapid, sensitive and on-site monitoring of meat spoilage is highly essential for food safety. Hydrogen sulfide (H₂S) a typical volatile, produced during enzymatic hydrolysis is considered as a reliable marker for evaluating meat freshness. Herein, a novel nano-bionic sensor based on the superior catalytic activity of ruthenium nanoparticles (Ru NPs) has been fabricated for H₂S quantification. The activity sites of Ru NPs were poisoned in the presence of H₂S, thereby affecting its catalytic efficiency via reducing the degradation of azo dye. The developed nano-bionic sensor achieved a selective response toward H₂S, with capability for on-site surveillance of the pork freshness in the linear range (0-1800 nM). A higher correlation was obtained between the H₂S content and the total viable count during the 9-period pork spoilage process (R² = 0.9633 and 0.9769). Moreover, the proposed method exhibits high selectivity in the presence of other characteristic volatiles encountered during the pork storage process.

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10⁸ was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10^-4 to 1.0 × 10³ µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10^-4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).