Machine learning-assisted optical nano-sensor arrays in microorganism analysis

Advances in the fabrication of potential nanomaterials for diagnosis and effective treatment of tuberculosis

“…For example, nanomaterials have been extensively used for constructing electrochemical sensors for detecting pesticides, 54,55 dyes, 56,57 and biological molecules 58 as well as optical sensors to detect analytes, such as glucose, 59 polyphenols, 60 pesticides, 61 metal ions, 62 crude oil, 63 and microorganisms. 64,65 Nanomaterials have been widely utilized in the fields of biomedicine and bioengineering. For instance, they have been…”

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

“…On the basis of whether the training data is labeled, machine learning algorithms can be categorized into unsupervised learning algorithms and supervised learning algorithms. 11,66 Unsupervised Learning Algorithm. Unsupervised learning algorithms can learn from unlabeled data sets and visually represent them.…”

“…Understanding the principles underlying these algorithms enhances interpretability of mathematical models and provides a theoretical foundation for selecting, analyzing, and interpreting disease diagnosis models. On the basis of whether the training data is labeled, machine learning algorithms can be categorized into unsupervised learning algorithms and supervised learning algorithms. , …”

Novel Digital SERS-Microfluidic Chip for Rapid and Accurate Quantification of Microorganisms

“…Another technique involves the application of machine learning algorithms to analyze SERS spectra and establish predictive models for microbial quantification. 20,21 This involves training the algorithms using various SERS spectra of known microbial concentrations to learn the patterns and correlations between spectral features and sample concentrations, thus, enhancing the accuracy and reliability of quantitative testing. Furthermore, the progress in nanotechnology has facilitated the development of SERS active nanoprobes that can specifically bind to target microorganisms utilizing recognition elements like antibodies or aptamers.…”

“…One immediate approach is the development of a more stable and consistent SERS substrate, which, despite advancements in nanotechnology, remains challenging to achieve perfectly uniform and reproducible SERS substrates. Another technique involves the application of machine learning algorithms to analyze SERS spectra and establish predictive models for microbial quantification. , This involves training the algorithms using various SERS spectra of known microbial concentrations to learn the patterns and correlations between spectral features and sample concentrations, thus, enhancing the accuracy and reliability of quantitative testing. Furthermore, the progress in nanotechnology has facilitated the development of SERS active nanoprobes that can specifically bind to target microorganisms utilizing recognition elements like antibodies or aptamers. ,− These nanoprobes can generate SERS signals proportional to the concentration of microorganisms.…”