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The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200-2000 cm-1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury-argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury-argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.
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http://dx.doi.org/10.3390/s24041217 | DOI Listing |
Spectrochim Acta A Mol Biomol Spectrosc
August 2025
Institute of Biomedical Precision Testing and Instrumentation, College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China. Electronic address:
Excessive pesticide residues have posed a severe risk to human health and ecological environment, making stringent testing of them a crucial step in food safety supervision. Herein, a flexible paper-based SERS platform composed of AgNPs and MoS nanoflowers was developed for the rapid and ultra-sensitive detection of thiram in juice. Among it, the hydrophobic MoS-modified paper was first fabricated, followed by co-depositing small-sized AgNPs and thiram molecules onto its surface for SERS analysis.
View Article and Find Full Text PDFFood Chem
August 2025
Institute of Endotypes in Oncology, Metabolism and Immunology "G. Salvatore" (IEOMI), National Research Council of Italy, Naples, Italy. Electronic address:
Hydrogel-based flexible plasmonic devices represent a cutting-edge technology for real-time monitoring of food safety, particularly for pesticide detection. This study presents a cost-effective, portable, and sensitive method to detect dimethoate (DMT), a hazardous organophosphorus pesticide, at concentrations below the maximum residue limit (MRL) of 0.01 ppm on olives.
View Article and Find Full Text PDFNanomaterials (Basel)
August 2025
School of Physics, Changchun Normal University, Changchun 130032, China.
A pyrochlore-type crystal structure photocatalytic nanomaterial, HoFeSbO, was successfully synthesized using a hydrothermal method. Additionally, a fluorite-structured BiYbO was prepared via rare earth Yb doping. Finally, a novel HoFeSbO/BiYbO heterojunction photocatalyst (HBHP) was fabricated using a solvothermal method.
View Article and Find Full Text PDFSci Rep
August 2025
Society for Medicines Security Research, 4F Venture Business Laboratory, Kanazawa University Kakuma-machi, Kanazawa, Ishikawa, 920-1192, Japan.
Substandard and falsified medicines threaten global health and require reliable data and screening technologies to combat their spread. This study examined the quality of 241 samples containing azithromycin, cefixime, esomeprazole and losartan collected from licenced private vendors in the Saptari (121 samples; convenience sampling) and Kathmandu (120 samples; randomised sampling) districts of Nepal. Nearly 10% (24 samples; 95% CI 6.
View Article and Find Full Text PDFAdv Food Nutr Res
August 2025
Departament d'Enginyeria Química, Universitat Rovira i Virgili, Tarragona, Spain.
The integration of machine learning (ML) with vibrational spectroscopy has revolutionized the food industry, advancing the ways food quality, authenticity, and safety are analyzed. ML methods, including traditional approaches such as support vector machines (SVMs) and partial least squares regression (PLSR), and advanced deep learning techniques like neural networks (NNs), enable the efficient and precise processing of complex multivariate datasets. Vibrational spectroscopy methods-near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy- are non-destructive, versatile, and provide detailed molecular insights.
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