High-performance SERS detection of pesticides using BiOCl-BiOBr@Pt/Au hybrid nanostructures on styrofoams as 3D functional substrate

Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400-1700 cm-1 spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications.

Retention time-independent strategy for screening pesticide residues in herbs based on a fingerprint database and all ion fragmentation acquisition with LC-QTOF MS

A retention time (RT)-independent strategy for nontargeted screening of pesticide residues in herbs was exploited using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF MS). The core of this strategy is a fingerprint database coupled with a data-independent acquisition (DIA) scan mode of all ion fragmentation (AIF). In the fingerprint database, a total of 150 pesticides with quasimolecular ions and fragment ions at five-level collision energies were collected as qualified ions for screening. During the data acquisition, the AIF scan was performed via real unbiased full-spectrum MS/MS acquisition. Six herb matrices spiked with 30 banned pesticides were used to evaluate the applicability of the strategy in real samples. The use of the narrow ion mass extraction window (10 mDa) and the narrow RT window (0.1 min) enabled the effective extraction of spectra from noisy backgrounds and the discovery of suspected pesticides via similarity matching of filtered qualified ions. On average, more than 11/30 of pesticides at 1 ng mL-1 and more than 23/30 of pesticides at 10 ng mL-1 or lower could be screened out in each matrix using at least two qualified ions. In addition, the AIF mode exhibited superior anti-interference capability compared to data-dependent acquisition (DDA) and sequential window acquisition of all theoretical mass spectra (SWATH), as determined by comparing the limits of screening (LOSs) of 30 banned pesticides spiked into Isatidis Folium. Finally, the developed strategy was applied to screen pesticide residues in extracts of Ganoderma and Foeniculi Fructus. Phorate-sulfone and phorate-sulfoxide were found in Ganoderma, as well as terbufos-sulfone and terbufos-sulfoxide were found in Foeniculi Fructus. In conclusion, the developed RT-independent strategy based on a fingerprint database and AIF acquisition with LC-QTOF MS seems to be one of the most efficient tools for the analysis of nontargeted pesticide residues in complicated matrices.

Photoinduced Electron-Transfer-Mediated Differential Recognition of Proteins on Plasmonic Surfaces

Photoinduced enhanced Raman spectroscopy (PIERS) has emerged as an efficient technique for enhancing the vibrational modes of analyte molecules adsorbed on a plasmonic nanoparticle-semiconductor hybrid material through chemical enhancement governed by electron transfer from the semiconductor to the plasmonic nanoparticles under an additional ultraviolet (UV) preirradiation step. The increase in chemical enhancement is imperative in analyzing and detecting pharmaceutically important moieties, such as amino acids and proteins, with a low Raman scattering cross section, even in complex biological environments. Herein, we demonstrate that UV preirradiation induced the creation of additional oxygen vacancies by introducing a low concentration (≈1%) of Ni as a dopant in the 2D platelike morphology of the BiOCl semiconductor; i.e., defect states in the semiconductor can induce charge transfer from the semiconductor to the plasmonic nanoparticles. This phenomenon facilitates electron transfer to the adsorbed analyte on the plasmonic surface. Additionally, we have shown the usefulness of this method in protein immobilization on the substrate surface, followed by the identification of a specific protein in the mixture of proteins. Proteins containing cysteine residues capture these electrons to form a surface-bound thiol group via a transient disulfide electron adduct radical. This allows differential binding of the protein molecules to the semiconductor plasmonic hybrid depending on the concentration of surface cysteine residues in proteins. Through PIERS and principal component analysis, we demonstrate the possibility of probing and distinguishing biomolecules based on their surface composition and secondary structure components even in their mixtures, thus paving the way for efficient analysis of complex biological systems.

Fabricating BiOI nanostructures armed catalytic strips for selective electrochemical and SERS detection of pesticide in polluted water

We have constructed a dual mode catalytic strip equipped with 2D BiOI nanostructures and deployed for dual mode detection sensing of hazardous trichlorophenol (TCP). Synthesized BiOI nanostructures are investigated for its crystal architecture, morphology and chemical composition. The BiOI are loaded onto the catalytic strips with the assistance of gravity offered drying process. The BiOI nanostructures offers a very less charge transfer resistance indicating its superior catalytic properties upon the electrochemical impedance studies. It reflected on providing an excellent limit of detection (LOD) and linear sensing range for TCP in electrochemical mode. For SERS, a thin plasmonic Au layer is sputter coated on BiOI equipped catalytic strips (Au@BiOI) for the TCP detection. An impressive enhancement factor of 10⁷ is obtained for SERS detection of TCP with good LOD of 10^-10 M. Fabricated dual mode BiOI based strips are thoroughly examined for operational stability and performance in real time conditions. The fabricated high performance dual mode platform for the detection of hazardous pesticides appears to be a promising prospect for the on-the-spot investigation.

Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.

Determination and dietary intake risk assessment of 14 pesticide residues in apples of China

The presence of pesticide residues in apples raises serious health concerns. In this study, a novel, sensitive, high-performance method was developed to simultaneously analyze the residues of 14 pesticides in apples using modified QuEChERS sample pretreatment coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The optimized purification procedure demonstrated satisfactory recovery and precision for all the tested pesticides. The limits of detection (LOD) and quantification (LOQ) values of 14 pesticides in the apple matrix ranged from 0.03 μg/kg to 0.3 μg/kg and 0.1 μg/kg to 1.0 μg/kg, respectively. The proposed method detected six pesticides in the apple samples collected from 20 counties in China's major apple-producing regions. Furthermore, the risk quotient (RQ, %) of the detected pesticides was evaluated by the national estimated acceptable daily intake. The RQs of six pesticides in Chinese people of different age groups were less than 100%.