Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection

Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.

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 of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.

Detection of Chlorpyrifos Using Bio-Inspired Silver Nanograss

Chlorpyrifos (CPF) is widely used as an organophosphorus insecticide; however, owing to developmental neurotoxicity, genotoxicity, and other adverse effects, it is harmful not only to livestock but also to humans. Therefore, the use of CPF was recently regulated, and its sensitive detection is crucial, as it causes serious toxicity, even in the case of residual pesticides. Because it is hard to detect the chlorpyrifos directly using spectroscopy (especially in SERS) without chemical reagents, we aimed to develop a SERS platform that could detect the chlorpyrifos directly in the water. In this study, we utilized the intrinsic properties of natural lawns that grow randomly and intertwine with each other to have a large surface area to promote photosynthesis. To detect CPF sensitively, we facilitated the rapid fabrication of biomimetic Ag nanograss (Ag-NG) as a surface-enhanced Raman spectroscopy (SERS) substrate using the electrochemical over-deposition method. The efficiency of the SERS method was confirmed through experiments and finite element method (FEM)-based electromagnetic simulations. In addition, the sensitive detection of CPF was enhanced by pretreatment optimization of the application of the SERS technique (limit of detection: 500 nM). The Ag-NG has potential as a SERS platform that could precisely detect organic compounds, as well as various toxic substances.

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.

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

Quality control of plant-based medicine and supplements must be carried out to ensure uniformity in quality and safety in their use, resulting in the need for effective and accurate analytical methods. Infrared spectroscopy is a method of qualitative and quantitative analysis that is fast, time-saving, cost-effective,accurate, and nondestructive. This method has been applied for quantitative analysis of compounds in complex matrices such as plant-based medicine and supplements supported by chemometrics techniques. The success of infrared spectroscopy applications for quantitative analysis of phytochemicals and adulterants content in plant-based medicine and supplement can happen by several factors. This article highlights the effect of spectral preprocessing and variable selection on quantitative analysis of phytochemical and adulterant in plant-based medicine and supplements using infrared spectroscopy. Literature search was conducted with PubMed, Google Scholar, and Science Direct by selecting quantitative analysis research on plant-based medicines and supplements that utilize spectral preprocessing techniques and variable selection in processing data analysis. The preprocessing spectra and variables selection can affect the accuracy and precision of infrared spectroscopy methods. The variable selection can be done using the wavenumber point technique, the wavenumber interval, or a combination thereof. Variable selection is more commonly used for near-infrared data than for IR data. The optimization of the preprocessing spectra and variables selection technique will be useful in increasing the ability of infrared spectroscopy in predicting compound levels.

Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (R_P = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.

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

Thiabendazole (TBZ) is extensively used in agriculture to control molds; residue of TBZ may pose a threat to humans. Herein, surface-enhanced Raman spectroscopy (SERS) coupled variable selected regression methods have been proposed as simple and rapid TBZ quantification technique. The nonlinear correlation between the TBZ and SERS data was first diagnosed by augmented partial residual plots method and calculated by runs test. Au@Ag NPs with strong enhancement factor (EF = 4.07 × 10⁶) of Raman signal was used as SERS active material to collect spectra from TBZ. Subsequently, three nonlinear regression models were comparatively investigated and the competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) achieved a higher correlation coefficient (Rp² = 0.9406) and the lower root-mean-square-error of prediction (RMSEP = 0.5233 mg/L). Finally, recoveries of TBZ in apple samples were 83.02-93.54% with relative standard deviation (RSD) value < 10%. Therefore, SERS coupled CARS-ELM could be employed as a rapid and sensitive approach for TBZ detection in Fuji apples.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms

BACKGROUND

Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy.

RESULTS

Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (R_p ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin.

CONCLUSION

The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.