Dataset Information

Surface Enhanced Raman Spectroscopy for Quantitative Analysis: Results of a Large-Scale European Multi-Instrument Interlaboratory Study.

ABSTRACT: Surface-enhanced Raman scattering (SERS) is a powerful and sensitive technique for the detection of fingerprint signals of molecules and for the investigation of a series of surface chemical reactions. Many studies introduced quantitative applications of SERS in various fields, and several SERS methods have been implemented for each specific application, ranging in performance characteristics, analytes used, instruments, and analytical matrices. In general, very few methods have been validated according to international guidelines. As a consequence, the application of SERS in highly regulated environments is still considered risky, and the perception of a poorly reproducible and insufficiently robust analytical technique has persistently retarded its routine implementation. Collaborative trials are a type of interlaboratory study (ILS) frequently performed to ascertain the quality of a single analytical method. The idea of an ILS of quantification with SERS arose within the framework of Working Group 1 (WG1) of the EU COST Action BM1401 Raman4Clinics in an effort to overcome the problematic perception of quantitative SERS methods. Here, we report the first interlaboratory SERS study ever conducted, involving 15 laboratories and 44 researchers. In this study, we tried to define a methodology to assess the reproducibility and trueness of a quantitative SERS method and to compare different methods. In our opinion, this is a first important step toward a "standardization" process of SERS protocols, not proposed by a single laboratory but by a larger community.

SUBMITTER: Fornasaro S

PROVIDER: S-EPMC7997108 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Surface Enhanced Raman Spectroscopy for Quantitative Analysis: Results of a Large-Scale European Multi-Instrument Interlaboratory Study.

Fornasaro Stefano S Alsamad Fatima F Baia Monica M Batista de Carvalho Luís A E LAE Beleites Claudia C Byrne Hugh J HJ Chiadò Alessandro A Chis Mihaela M Chisanga Malama M Daniel Amuthachelvi A Dybas Jakub J Eppe Gauthier G Falgayrac Guillaume G Faulds Karen K Gebavi Hrvoje H Giorgis Fabrizio F Goodacre Royston R Graham Duncan D La Manna Pietro P Laing Stacey S Litti Lucio L Lyng Fiona M FM Malek Kamilla K Malherbe Cedric C Marques Maria P M MPM Meneghetti Moreno M Mitri Elisa E Mohaček-Grošev Vlasta V Morasso Carlo C Muhamadali Howbeer H Musto Pellegrino P Novara Chiara C Pannico Marianna M Penel Guillaume G Piot Olivier O Rindzevicius Tomas T Rusu Elena A EA Schmidt Michael S MS Sergo Valter V Sockalingum Ganesh D GD Untereiner Valérie V Vanna Renzo R Wiercigroch Ewelina E Bonifacio Alois A

Analytical chemistry 20200221 5

Surface-enhanced Raman scattering (SERS) is a powerful and sensitive technique for the detection of fingerprint signals of molecules and for the investigation of a series of surface chemical reactions. Many studies introduced quantitative applications of SERS in various fields, and several SERS methods have been implemented for each specific application, ranging in performance characteristics, analytes used, instruments, and analytical matrices. In general, very few methods have been validated a ...[more]

PMID: 32045217

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Project description:BackgroundEarly detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established cancer screening technologies are limited for use, especially for multi-cancer early detection. In this study, we described a serum-based platform integrating surface-enhanced Raman spectroscopy (SERS) technology with resampling strategy, feature dimensionality enhancement, deep learning and interpretability analysis methods for sensitive and accurate pan-cancer screening.MethodsTotally, 1655 early-stage patients with breast cancer (BC, n = 569), lung cancer (LC, n = 513), thyroid cancer (TC, n = 220), colorectal cancer (CC, n = 215), gastric cancer (GC, n = 100), esophageal cancer (EC, n = 38), and 1896 healthy controls (HC) were enrolled. The serum SERS spectra were obtained from each participant. Data dimension enhancement was conducted by heatmap transformation and continuous wavelet transform (CWT). The dimensionalization SERS spectral data were subsequently analyzed by residual neural network (ResNet) as convolutional neural network (CNN) algorithm. Class activation mapping (CAM) method was performed to elucidate the potential biological significance of spectral data classification.ResultsAll participants were divided into a training set and a test set with a ratio of 7:3. The BorderlineSMOTE method was selected as the most appropriate resampling strategy and the deep neural network (DNN) model achieved desirable performance among all groups (accuracy rate: 93.15%, precision rate: 88:46%, recall rate: 85.68%, and F1-score: 86.98%), with the generated AUC values of 0.991 for HC, 0.995 for BC, 0.979 for LC, 0.996 for TC, 0.994 for CC, 0.982 for GC, and 0.941 for EC, respectively. Furthermore, the combination use of SERS spectra data and ResNet (form of heatmap) were also capable of effectively distinguishing different categories and making accurate predictions (accuracy rate: 94.75%, precision rate: 89.02, recall rate: 86.97, and F1-score: 87.88), with the AUC values of 0.996 for HC, 0.995 for BC, 0.988 for LC, 0.999 for TC, 0.993 for CC, 0.985 for GC, and 0.940 for EC, respectively. Additionally, strong wave number range of the spectral data was observed in the CAM analysis.ConclusionsOur study has offered a highly effective serum SERS-based approach for multi-cancer early detection, which might shed new light on cancer screening in clinical practice.

Dataset Information

Surface Enhanced Raman Spectroscopy for Quantitative Analysis: Results of a Large-Scale European Multi-Instrument Interlaboratory Study.

Publications

Surface Enhanced Raman Spectroscopy for Quantitative Analysis: Results of a Large-Scale European Multi-Instrument Interlaboratory Study.

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