A Raman spectrometer 1 comprising a laser 1001 for illuminating a sample S under investigation, an auto-focusing system for focusing the laser 1001 on the sample S under investigation, and a detector 1010 for detecting Raman spectra emitted in response to illumination by the laser 1001. The auto-focusing system further comprises at least one adjustable focusing element for adjusting the location of the focus of the laser, a determination unit 1012 for determining a selected location for the focus of the laser 1001, and a control unit for adjusting the adjustable focusing element to focus the laser at said selected location determined by the determination unit 1012. The auto-focusing system is arranged under the control of software to enable determination of the selected location for the focus of the laser 1001.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (no limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC), Internet of Thing (IoT), Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

A measurement method includes: (a) measuring an emission intensity for each wavelength of light detected from a plasma generated in a plasma processing apparatus at each different exposure time by a light receiving element; (b) specifying, with respect to each of a plurality of different individual wavelength ranges that constitutes a predetermined wavelength range, a distribution of the emission intensity in the individual wavelength range measured at an exposure time at which an emission intensity of a predetermined wavelength included in the individual wavelength range becomes an emission intensity within a predetermined range; (c) selecting a distribution of the emission intensity in the individual wavelength range from the distribution of the emission intensity specified in (b); and (d) outputting the distribution of the emission intensity selected for each individual wavelength range.

A data processing apparatus comprises processing circuitry configured to generate normalized data by normalizing time series data of multiple wavelengths, measured from a substrate, by using predetermined reference data; divide the normalized data into a plurality of regions for each predetermined time range and predetermined wavelength range; for each respective region of the plurality of regions, extract an outlier value in the respective region as a representative value; and convert the representative values from the plurality of regions into color data to generate image data from the color data.

In some implementations, a device may identify, based on spectroscopic data, a pseudo steady state end point indicating an end of a pseudo steady state associated with the blending process. The device may identify a reference block and a test block from the spectroscopic data based on the pseudo steady state end point. The device may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate a statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block. The device may determine whether the blending process has reached a steady state based on the statistical detection signal.

Manufacturing a device may include inspecting a surface of an inspection target device. The inspecting may include forming a metal layer on a surface of the inspection target device on which a minute pattern is formed, directing a beam of light to be incident and normal to the surface of the inspection target device, determining a spectrum of light reflected from the surface of the inspection target device, and generating, via the spectrum, information associated with a structural characteristic of the minute pattern formed on the inspection target device. The inspection target device may be selectively incorporated into the manufactured device based on the generated information.

A system includes a discharge tool positioned within a wellbore and configured to generate an electrical discharge that interacts with a rock formation proximate to the discharge tool, wherein the interaction of the electrical discharge with the rock formation vaporizes a portion of the rock formation to generate a discharge plasma. The system further includes an optical emission spectroscopy (OES) sub-system configured to determine an elemental composition of the portion of the rock formation based on optical emission generated by the discharge plasma, wherein at least a portion of the OES sub-system is positioned within the wellbore.

A system and method for detecting pathogenetic analytes including exciting a large target input area with radiation to produce scattered light to form an input beam, reformatting, with an optical slicer system, the input beam to produce an output beam, dispersing the output beam to produce an output area, capturing excitation data from the output area; and determining, with a processor, a presence of a particular analyte in the input area based on the excitation data. The input area can be greater than 100 micron squared and less than one million microns squared. The optical slicer system can be a high throughput virtual slit system. SERS analysis detects analytes of interest with both high resolution and sensitivity simultaneously, and is applicable for detection of the presence of viruses.

A spectrometer system comprising a housing configured as a handheld device with a screen; a source of narrow band illumination; a sensor that detects Raman scattering signals; a source of wide band illumination; an optical element that detect Fourier transform infrared (FTIR) signals; a memory device comprising a library of information with Raman scattering reference information and FTIR reference information; and a processor configured to execute software instructions, wherein the software instructions are configured to: direct the narrow band illumination to the sample; detect the Raman scattering signals; direct the wide band illumination to the sample; detect the FTIR signals; determine a composition of the sample from a similarity between the Raman scattering spectral information and the Raman scattering reference information, and from a similarity between the FTIR spectral information and the FTIR reference information; and display the composition of the sample on the screen.