Recognition of Food-Borne Pathogenic Bacteria by Raman Spectroscopy Based on Random Forest Algorithm

Qi, 王其 Wang; Wandan, 曾万聃 Zeng; Zhiping, 夏志平 Xia; Zhiping, 李志萍 Li; Han, 曲晗 Qu

doi:10.3788/cjl202148.0311002

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…proposed the classification of mineral species based on Siamese network combined with Raman spectroscopy data. Wang Qi [7] proposed a new fast method for identifying foodborne pathogenic bacteria by studying Raman spectral data of various foodborne pathogenic bacteria samples and proposed a classification model combining data dimensionality reduction by principal component analysis, random forest algorithm and Raman spectroscopy. Ying Zhao [8] et al…”

mentioning

confidence: 99%

Raman spectrum classification and identification of COVID-19 based on RFE-RF

Yang

Zeng

Wang

2023

Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023)

View full text Add to dashboard Cite

The Raman spectral data feature is generally the Raman wavelength of the sample, and there is a correlation between the feature attributes. Too many features will lead to weak generalization ability of the model, so a Recursive Feature Elimination (RFE) dimensionality reduction method combined with BP neural network is proposed to classify the Raman spectrum of the COVID-19. Firstly, the collected serum Raman spectral data of the population were processed, the maximum and minimum standard scaling method (Min-Max), the Savitzky-Golay smoothing filter method, and then the recursive feature elimination (RFE-RF) based on the random forest base model and Two different dimensionality reduction methods of PCA reduce the dimensionality of Raman spectral data and classify them through the BP neural network algorithm model. The experimental results show that the RFE-RF dimensionality reduction method can improve the accuracy of the classification algorithm, providing a new idea for the detection of the COVID-19, with high accuracy, and the classification accuracy of the model is 92.47%.

show abstract

mentioning

confidence: 99%

Raman spectrum classification and identification of COVID-19 based on RFE-RF

Yang

Zeng

Wang

2023

Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023)

View full text Add to dashboard Cite

show abstract

Real-time LIBS monitoring of laser-based layered controlled paint removal from aircraft skin based on random forest

et al. 2023

View full text Add to dashboard Cite

Laser-induced breakdown spectroscopy (LIBS) is expected to be used for real-time monitoring and closed-loop control of laser-based layered controlled paint removal (LLCPR) from aircraft skin. However, the LIBS spectrum must be rapidly and accurately analyzed, and the monitoring criteria should be established based on machine learning algorithms. Hence, this study develops a self-built LIBS monitoring platform for the paint removal process utilizing a high-frequency (kilohertz-level) nanosecond infrared pulsed laser and collects the LIBS spectrum during the laser removal process of the top coating (TC), primer (PR), and aluminum substrate (AS). After subtracting the spectrum’s continuous background and screening the key features, we construct a classification model of three types of spectra (TC, PR, and AS) based on a random forest algorithm, and the real-time monitoring criterion based on the classification model and multiple LIBS spectra was established and verified experimentally. The results show that the classification accuracy is 98.89%, the time-consuming classification is about 0.03 ms per spectrum, and the monitoring results of the paint removal process are consistent with the macroscopic observation and microscopic profile analysis results of the samples. Overall, this research provides core technical support for the real-time monitoring and closed-loop control of LLCPR from aircraft skin.

show abstract

Benchmarking the use of Dimensional Reduction Techniques: A Case Study of Oesophageal Cancer Image Analysis

Nath,

Panda,

Kar

2024

Preprint

View full text Add to dashboard Cite

The dimensionality reduction method is one of the most popular approaches for handling complex data characterised by numerous features and variables. In this work, we benchmarked the application of different techniques to interpret cancer-based in vivo microscopic images. We focus on several dimensionality reduction methods, including PCA, LDA, t-SNE, and UMAP, to evaluate the performance of the image dataset analysis (5043 images). The benchmarking study establishes the efficacy of traditional machine learning algorithms for biomedical image analysis. Model descriptions based on logistic regression, support vector, K-means clustering, K-nearest neighbour, random forest, gradient boosting, and adaboost classifiers were employed. The study also evaluates the importance of visualisation techniques relevant for identifying hidden patterns, anomalies, and trends that are not readily discernible in high-dimensional data. The benchmarking study uses approaches like random splits and K-fold cross-validation. Further evaluation metrics such as accuracy, sensitivity, specificity, and ROC-AUC score are employed to assess the performance of the employed dimensionality reduction methods. Their relevance for data visualisation as well as predictive modelling is demonstrated. Overall, the study is useful for understanding the relevance of effective data classification and visualisation problems, thereby enhancing the interpretability and analysis of biomedical images.

show abstract

Recognition of Food-Borne Pathogenic Bacteria by Raman Spectroscopy Based on Random Forest Algorithm

Cited by 5 publications

References 0 publications

Raman spectrum classification and identification of COVID-19 based on RFE-RF

Raman spectrum classification and identification of COVID-19 based on RFE-RF

Real-time LIBS monitoring of laser-based layered controlled paint removal from aircraft skin based on random forest

Benchmarking the use of Dimensional Reduction Techniques: A Case Study of Oesophageal Cancer Image Analysis

Contact Info

Product

Resources

About