In one aspect, a hyperspectral image measurement device is provided to include: a main body; an illumination module disposed in the main body and including LEDs having different peak wavelengths to irradiate light to a subject; a camera disposed on the main body and receiving light reflected from the subject to acquire an image of the subject; a barrel having a contact surface contacting the subject, the contact surface located to be spaced apart from the illumination module and the camera module by a predetermined distance; and a reference cover located on the contact surface and including a standard reflection layer for reflecting light irradiated from the illumination module toward the camera module.

The present invention is directed to a computer-implemented method of automatically identifying the presence of one or more elements in a sample via optical emission spectroscopy. The method includes the steps of obtaining sample spectrum data from the sample, obtaining a list of one or more predetermined emission wavelengths for each element in the periodic table quantifiable by optical emission spectroscopy, each predetermined emission wavelength being associated with a list of one or more potential interference emission wavelengths, determining a list of one or more analyte wavelengths corresponding to spectral peaks in the sample spectrum data based on the list of emission wavelengths, for each analyte wavelength, determining whether the corresponding spectral peak has a likelihood of being affected by an interference emission wavelength causing spectral interference based on the list of one or more potential interference emission wavelengths corresponding to the analyte wavelength, determining a revised list of one or more analyte wavelengths by removing from the list of analyte wavelengths, analyte wavelengths corresponding to spectral peaks having a likelihood of being affected by an interference emission wavelength, and determining a level of confidence that one or more elements are present in the sample based on a set of criteria applied to the revised list of analyte wavelengths.

According to one implementation, an inflammable spark estimation system includes: a photodetector for measuring intensity of discharge light arising from a spark arising from a structural object made of a plurality of materials; and a data processing system configured to determine whether the spark has inflammability, based on the intensity of the discharge light measured by the photodetector, with referring to determination information. The determination information has been determined based on features of waveforms of wavelength spectra of possible discharge light arising from possible inflammable sparks respectively arising from possible materials of which the structural object may be made. The data processing system is configured to further determine which of the plurality of the materials the spark has arisen from, based on the intensity of the discharge light measured by the photodetector, with referring to the determination information, when the spark has been determined to have the inflammability.

A method of determining a peak intensity in an optical spectrum is described. The method includes producing a two-dimensional array of spectrum values by imaging the optical spectrum onto a detector array. An offset using an actual location and an expected location of a peak of an interpolated subarray is used to adjust an expected location of another peak that is within another two-dimensional subarray. Interpolated spectrum values are then used to produce a peak intensity value of the second peak.

Disclosed herein are spectrometer support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a spectrometer support apparatus may: receive, for each of a plurality of calibration samples of an analyte at different known concentrations, an array of spectrometer output intensities of the calibration sample, wherein different ones of the spectrometer output intensities in the array are associated with data representative of different deflection amounts; train a machine-learning computational model, using the plurality of known concentrations of the analyte in the calibration samples and the associated plurality of arrays of spectrometer output intensities, to output a concentration of the analyte in a target sample based on an input array of spectrometer output intensities of the target sample; and use the trained machine-learning computational model as a calibration model for the analyte for subsequent spectrometer operation.

The invention is a method for processing a calibration spectrum acquired by a spectrometric detector of X or gamma photons. The method comprises a taking into account of a parametric form of a calibration function, the calibration function linking the rank of an energy channel to an energy value. The method comprises a confrontation between the channels of the peaks of the calibration spectrum and emission energies of calibration isotopes. The confrontation makes it possible to define the values of the parameters of the calibration function.

A mode of a spectrometer according to the present invention includes a spectrum measurement unit (11, 12) configured to repeatedly measure a spectrum over a predetermined wavelength range for measurement target light that is laser light; a peak counting unit (21, 22) configured to, every time a spectrum is obtained by the spectrum measurement unit, detect a peak from the spectrum and count the number of detected peaks; and a display processing unit (24) configured to display a numerical value of a peak counting result by the peak counting unit on a screen of a display unit in real time. With the spectrometer of the above mode, adjustment and the like of the multimode laser oscillator can be efficiently and accurately performed.

A system for determining a spectrum includes an interface and a processor. The interface is configured to receive a sample set of intensity data for an array of spatial locations and a set of spectral configurations. The processor is configured to determine a region of interest using the sample set of intensity data and determine a spectral peak for the region of interest.

According to an example, a plurality of pixels of a modulator upon which an input light beam impinges may be modulated to apply a first asymmetrical attenuation pattern on the input light beam and to direct a first attenuated light beam from the modulator and a first power level of the first attenuated light beam may be measured. The plurality of pixels may be modulated to apply a second asymmetrical attenuation pattern on the input light beam and to direct a second attenuated light beam from the modulator, and a second power level of the second attenuated light beam may be measured. A difference between the first power level and the second power level may be calculated and a modified peak frequency for an attenuated light beam from the calculated difference may be calculated.

According to one implementation, an explosive spark estimation system includes an explosive spark estimation system includes a measuring system and a processing system. The measuring system is adapted to measure intensity of light, included in a spark occurred from an object to be tested. The light is within at least one specific wavelength band. The processing system is adapted to determine whether the spark is explosiveness based on the intensity of the light. Further, according to one implementation, an explosive spark estimation method includes: measuring intensity of light, included in a spark occurred from an object to be tested, within at least one specific wavelength band; and determining whether the spark is explosive, based on the intensity of the light.