A thin aluminum film substrate and microarrays thereof including a substrate and a thin film of aluminum deposited on the substrate for surface plasmon resonance analysis. Methods of forming the thin aluminum film substrate and microarrays including providing a substrate, using electron-beam physical vapor deposition (EBPVD) to deposit a thin film of Al on a surface of the substrate. Also disclosed are methods of detecting an analyte, wherein a functionalized surface of the thin aluminum film includes a biomolecule and the methods include applying a sample including the analyte to the thin aluminum film substrate, and using surface plasmon resonance (SPR) spectroscopy to detect molecular interactions between the biomolecule and the analyte at a surface of the thin aluminum film substrate. In some examples, an unmodified Al film with an Al.sub.2O.sub.3 layer is effective in enriching phosphorylated peptides. In some examples, a coating of an ionic polymer is used to analyze charged-based interactions of biomolecules.

Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

A method for extracting Raman characteristic peaks employing improved principal component analysis comprising: using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples; and performing preprocessing on the Raman spectroscopic data, performing principal component analysis, establishing a principal component loading scatter plot, extracting dot characteristics from the principal component loading scatter plot, analyzing same, and performing filtering on the dot characteristics to obtain Raman characteristic peaks. The method for extracting Raman characteristic peaks employing improved principal component analysis is used to extract Raman characteristic peaks from pork and beef samples, and then the Raman characteristic peaks are inputted into a classifier to undergo classification, thereby achieving high accuracy and quick classification.

A method for extracting Raman characteristic peaks employing improved principal component analysis comprising: using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples; and performing preprocessing on the Raman spectroscopic data, performing principal component analysis, establishing a principal component loading scatter plot, extracting dot characteristics from the principal component loading scatter plot, analyzing same, and performing filtering on the dot characteristics to obtain Raman characteristic peaks. The method for extracting Raman characteristic peaks employing improved principal component analysis is used to extract Raman characteristic peaks from pork and beef samples, and then the Raman characteristic peaks are inputted into a classifier to undergo classification, thereby achieving high accuracy and quick classification.

Configurable handheld biological analyzers and related biological analytics methods are described for identification of biological products based on Raman spectroscopy. A biological classification model configuration is loaded into a computer memory of a configurable handheld biological analyzer having a processor and a scanner. The biological classification model configuration includes a biological classification model configured to receive a Raman-based spectra dataset defining a biological product sample as scanned by the scanner. A spectral preprocessing algorithm is executed to reduce a spectral variance of the Raman-based spectra dataset. The biological classification model identifies a biological product type based on the Raman-based spectra dataset and further based on a classification component selected to reduce at least one of (1) a Q-residual error or (2) a summary-of-fit value of the biological classification model. The biological classification model configuration is transferrable to and loadable on other configurable handheld biological analyzers.

Configurable handheld biological analyzers and related biological analytics methods are described for identification of biological products based on Raman spectroscopy. A biological classification model configuration is loaded into a computer memory of a configurable handheld biological analyzer having a processor and a scanner. The biological classification model configuration includes a biological classification model configured to receive a Raman-based spectra dataset defining a biological product sample as scanned by the scanner. A spectral preprocessing algorithm is executed to reduce a spectral variance of the Raman-based spectra dataset. The biological classification model identifies a biological product type based on the Raman-based spectra dataset and further based on a classification component selected to reduce at least one of (1) a Q-residual error or (2) a summary-of-fit value of the biological classification model. The biological classification model configuration is transferrable to and loadable on other configurable handheld biological analyzers.

Methods to detect contaminants in a solution and applications thereof are described. Generally, solutions are printed onto a substrate and then imaged via Raman spectroscopy, which can be utilized to detect signals derived from contaminants.

A laser analysis device includes a laser analysis unit that a sample is irradiated with laser light, a cover that covers a periphery of the laser analysis unit, so as to prevent the laser light from being emitted to outside, and has a slit in at least a part of the cover, a fastener configured to open and close the slit, and an interlock mechanism including a key provided on the fastener and a detector that detects a state in which the fastener is fully closed, in which in a state where the detector has detected that the fastener is fully closed, laser light having a predetermined intensity or more is introduced into the laser analysis unit.

A laser analysis device includes a laser analysis unit that a sample is irradiated with laser light, a cover that covers a periphery of the laser analysis unit, so as to prevent the laser light from being emitted to outside, and has a slit in at least a part of the cover, a fastener configured to open and close the slit, and an interlock mechanism including a key provided on the fastener and a detector that detects a state in which the fastener is fully closed, in which in a state where the detector has detected that the fastener is fully closed, laser light having a predetermined intensity or more is introduced into the laser analysis unit.