Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Naseri, Ahmad; Khalilzadeh, Javad; Reza Darbani, Seyyed Mohammad; Akbari, Mohammad Reza; Eslamimajd, Abdollah

doi:10.1039/d3ja00337j

J. Anal. At. Spectrom.

2024

DOI: 10.1039/d3ja00337j

|View full text |Cite

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Ahmad Naseri,

Javad Khalilzadeh,

Seyyed Mohammad Reza Darbani

et al.

Abstract: In this work, laser-induced breakdown spectroscopy is used to investigate the elemental structure and discrimination of bacteria in the growth process. Two bacterial specimens including gram-negative E. coli and gram-positive...

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 32 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Bimodal data fusion of LIBS spectroscopy and plasma acoustic emission signals: improving the accuracy of machining process identification for low roughness samples

Xiong,

Cui,

Yang

et al. 2024

J. Anal. At. Spectrom.

View full text Add to dashboard Cite

show abstract

Bimodal data fusion of LIBS spectroscopy and plasma acoustic emission signals: improving the accuracy of machining process identification for low roughness samples

Xiong,

Cui,

Yang

et al. 2024

J. Anal. At. Spectrom.

View full text Add to dashboard Cite

show abstract

Laser-Induced Breakdown Spectroscopy–Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma

Chen,

Zhang,

Huang

et al. 2024

Foods

View full text Add to dashboard Cite

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model’s robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Abstract: In this work, laser-induced breakdown spectroscopy is used to investigate the elemental structure and discrimination of bacteria in the growth process. Two bacterial specimens including gram-negative E. coli and gram-positive...

Cited by 2 publications

References 32 publications

Bimodal data fusion of LIBS spectroscopy and plasma acoustic emission signals: improving the accuracy of machining process identification for low roughness samples

Bimodal data fusion of LIBS spectroscopy and plasma acoustic emission signals: improving the accuracy of machining process identification for low roughness samples

Laser-Induced Breakdown Spectroscopy–Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma

Contact Info

Product

Resources

About