Single-model multi-tasks deep learning network for recognition and quantitation of surface-enhanced Raman spectroscopy

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

Single-model multi-tasks deep learning network for recognition and quantitation of surface-enhanced Raman spectroscopy

Surface-enhanced Raman scattering (SERS) spectroscopy analysis has long been the central task of nanoscience and nanotechnology to realize the ultrasensitive recognition/quantitation applications. Recently, the blooming of artificial intelligence algorithms provides an edge tool to revolutionize the spectroscopy analysis, especially for multiple substances analysis and large-scale data handling. In this study, a single-model multi-tasks deep learning network is proposed to simultaneously achieve the qualitative recognition and quantitative analysis of SERS spectroscopy. The SERS spectra of two kinds of hypoglycemic drugs (PHE, ROS) and the corresponding mixtures are collected, respectively, with the concentration grade from 10^-4 M to 10^-8 M. Based on the SERS spectroscopy dataset, the loss functions and hyperparameters of the multi-tasks classifications model are optimized, and the recognition accuracies are tested by simulation experiments. It is demonstrated that the accuracy rates of qualitative and quantitative analysis can reach up to 99.0% and 98.4%, respectively. Moreover, the practical feasibility of this multi-tasks model is demonstrated by using it to achieve qualitative and quantitative analysis of PHE and ROS in complex serum matrix. Overall, this single-model multi-tasks deep learning network shows significant potential for the recognition and quantitation of SERS spectroscopy, which provides the algorithmic and experimental basis for large-scale and multiple substances SERS spectra analysis.