Classification of pathogens by Raman spectroscopy combined with generative adversarial networks

Yu, Shixiang; Li, Hanfei; Li, Xin; Fu, Yu; Liu, Fanghua

doi:10.1016/j.scitotenv.2020.138477

Cited by 37 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, since the recorded spectra is subject to stray light in the instrument, ambient temperature and the non-linear response of the detector, these will lead to poor classification of the linear model [ 38 , 39 ]. Experiments show that the feature fusion method can not only effectively overcome the influence of these factors, but also improve the classification accuracy, recall rate and F1 score of the linear model.…”

Section: Discussionmentioning

confidence: 99%

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Zhang

Yan

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Zhang

Yan

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In addition, a single-layer multiple-kernel-based convolutional neural network (SLMK-CNN) containing one convolutional layer with five different kernels, one flatten layer, and two fully-connected layers was created for Raman spectra obtained from porcine skin samples [51]. Notably, for pathogen classification, Yu et al, even combined Raman spectroscopy with GAN to achieve high accuracy when the training dataset size is limited [52]. In their GAN model, the generator G (a multilayer perceptron) worked for data augmentation and the discriminator D (a multilayer deep neural network) acted as a classifier.…”

Section: Spectral Data Highlightingmentioning

confidence: 99%

“…Examples of typical deep learning applications for Raman spectroscopy. Dong et al[37], Lee et al[38], Kirchberger-Tolstik et al[39], Maruthamuthu et al[40], Cheng et al[41], Fan et al[42], Fu et al[43], Houston et al[44], Ho et al[45], Ding et al[46], Chen et al[47], Saifuzzaman et al[48], Pan et al[49,50], Sohn et al[51], Yu et al[52], Thrift and Ragan[53], and Zhang et al[54] …”

mentioning

confidence: 99%

Deep Learning for Raman Spectroscopy: A Review

2022

View full text Add to dashboard Cite

Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.

show abstract

“…Further increase in the spectral data volume and application of advanced machine learning algorithms may be required to differentiate the three strains. [22,43]…”

Section: Differentiation Of Bacteria With Bare Ag Npsmentioning

confidence: 99%

“…[17,18] However, due to the extremely high similarity of bacterial SERS spectra, large amounts of data and artificial intelligence are required to improve the discrimination efficiency. For example, artificial intelligence methods based on multiclass support vector machines (MC-SVM), [19] convolutional neural networks (CNN), [20,21] and generative adversarial networks (GANs) [22] can identify pathogens with similar spectra successfully. Despite the achievements based on this combination, acquiring massive spectral data will inevitably increase labour intensity and technical difficulty.…”

Section: Introductionmentioning

confidence: 99%

Boosting bacteria differentiation efficiency with multidimensional surface‐enhanced Raman scattering: the example of Bacillus cereus

Zhu

Wang

et al. 2022

Luminescence

View full text Add to dashboard Cite

Surface-enhanced Raman scattering (SERS) is a powerful tool for constructing biomolecular fingerprints, which play a vital role in differentiation of bacteria. Due to the rather subtle differences in the SERS spectra among different bacteria, artificial intelligence is usually adopted and enormous amounts of spectral data are required to improve the differentiation efficiency. However, in many cases, large volume data acquisition on bacteria is not only technical difficult but labour intensive. It is known

show abstract

Classification of pathogens by Raman spectroscopy combined with generative adversarial networks

Cited by 37 publications

References 33 publications

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Deep Learning for Raman Spectroscopy: A Review

Boosting bacteria differentiation efficiency with multidimensional surface‐enhanced Raman scattering: the example of Bacillus cereus

Contact Info

Product

Resources

About