Due to the growing societal demand for public safety and environmental protection, there is an increasing focus on the technology of rapid on-site bacterial detection. The present study aims to establish an analytical method using microscopic confocal Raman spectroscopy combined with chemometrics to accurately identify different bacteria, thus enabling nondestructive testing. This paper examines the Raman spectra of Gram-negative bacteria Escherichia coli, Gram-positive bacteria Staphylococcus aureus, Bacillus cereus, and others. The spectra were obtained using a microscopic confocal Raman spectrometer and analyzed for their characteristics. The Matlab-based PCA method reduces the dimensionality of the data. Six pattern recognition algorithms in chemometrics, namely linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), naive Bayesian (NB) model, classification decision tree (CT) and support vector machine (SVM), were used to classify the Raman spectral data. The results show that linear discriminant analysis and the k-nearest neighbor model achieved a recognition accuracy of 97.62%. The method has several advantages, including no damage to bacterial samples, simple operation, and ideal identification effect. It provides guidance and a basis for accurate identification of Gram-negative and Gram-positive bacteria.
Photoacoustic spectroscopy (PAS) is a highly sensitive sensor for trace gases and is widely used. It is a research hotspot for detecting chemical poisons and toxic gases. A spectrum measurement platform based on quantum cascade laser (QCL) and a temperature-controlled photoacoustic cell were designed and constructed. Based on this, the photoacoustic spectroscopy method was established to test objects with distinct physical and chemical properties, using dimethyl methyl phosphate (DMMP) and dichloromethane (CH2Cl2) as research subjects. Photoacoustic spectroscopy has been successfully used to detect chemical agents in a wide concentration range of DMMP. The concentration range of 40 mg/m3 ~ 787.05 mg/m3 exhibits strong absorption and spectral characteristics. The test results confirm that photoacoustic spectroscopy detection technology is not dependent on detection wavelength, unlike traditional spectroscopy detection technology. This demonstrates the extensive application of this detection technology and provides a technical way for rapid on-site screening.
Fast and accurate identification of unknown hazardous solid are of pivotal interest in public security and safety. In this research, the Raman spectra of ten dangerous were measured: four biotoxins (including aconitine, tetrodotoxin, α -conotoxin GI and ricin), six explosives (including Octogen, Hexanitrohexaazaisowurtzitane, Hexogen, Trinitrotoluene, Triacetone triperoxide and Black powder). The micro confocal Raman spectroscopy was used to obtain the spectrum data. Structural assignments to Raman bands observed in the spectrum were also proposed. On this basis, The principal component analysis (PCA) method is used to reduce the dimension of spectral data, and the linear discriminant analysis (LDA) pattern is developed based on Python language to establish recognition algorithm. The recognition algorithm based on the linear discriminant analysis could achieve a high recognition accuracy of 98.61%. Meanwhile, all the testing process could be completed within a few minutes without loss of samples. It suggested from this study that the combination of Raman spectroscopy of fingerprint characteristics and pattern recognition algorithm can be used for rapid screening of unknown compounds. Moreover, this method provides solutions for timely deletion of unknown compounds.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.