A hand-held microfluidic testing device is provided that includes a housing having a cartridge receiving port, a cartridge for input to the cartridge receiving port having a sample input and a channel, where the channel includes a mixture of Raman-scattering nanoparticles and a calibration solution, where the calibration solution includes chemical compounds capable of interacting with a sample under test input to the cartridge and the Raman-scattering nanoparticles, and an optical detection system in the housing, where the optical detection system is capable of providing an illuminated electric field, where the illuminating electric field is capable of being used for Raman spectroscopy with the Raman-scattering nanoparticles and the calibration solution to analyze the sample under test input to the cartridge.

A system includes a tunable Fabry-Perot etalon, a detector, and a processor. The tunable Fabry-Perot etalon has a settable gap. The detector measures light intensity transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine the calibrated spectral measurement, wherein the calibrated spectral measurement is based at least in part on a measurement set of detected light intensities for a selected set of settable gaps and a reconstruction matrix. The reconstruction matrix is based at least in part on calibration measurements using one or more field material targets, prior stored full calibrations for each of the one or more field material targets, and the selected set of settable gaps.

Aspects relate to a spectral modeling system for building chemometrics (calibration) models for spectral devices targeting ultra-wide-scale deployment. The spectral modeling system includes a spectral converter for generating a plurality of artificial spectra using spectral data of a plurality of samples measured by a subset of a plurality of spectral devices and spectral device characteristics representing spectral variations in the plurality of spectral devices. The spectral modeling system further includes a chemometrics engine for generating a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (not 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.

The present invention relates to a computer-implemented method for identifying an incorrectly or non-calibrated infrared spectrometer, comprising the steps of a) recording an infrared spectrum of a sample with a first infrared spectrometer to provide a sample infrared spectrum, b) recording an infrared spectrum of the same sample as in step a) with a second infrared spectrometer to provide a reference infrared spectrum, wherein said second spectrometer is a correctly calibrated infrared spectrometer, or b′) providing a reference spectrum of the same sample as in step a), wherein said reference spectrum was recorded on a second infrared spectrometer, which is a correctly calibrated spectrometer, c) determining a difference between the wavelength of each extreme point in the sample of step a) and the wavelength of each extreme point in the reference spectrum of step b) or b′), and d) indicating the infrared spectrometer of step a) as incorrectly calibrated or non-calibrated, when at least one difference was determined in step c).

A calibration method includes placing an LED light source having a given wavelength range inside a reference apparatus; acquiring first data as an emission intensity of light at a wavelength, a light amount of the light being adjusted in stages by changing the light amount output from the LED light source; storing the first data in a memory; placing the LED light source in a calibration target apparatus; acquiring second data as an emission intensity of light at a wavelength, a light amount of the light being adjusted in the stages by changing the light amount output from the LED light source; and calculating a calibration formula based on the first data stored in the memory and the second data.

An optical wavelength calibrator is configured to be used on premises with instrumentation such as an optical spectrum analyzer. The on-premises calibrator includes both a fixed wavelength source and a tunable wavelength source, with a variable optical attenuator controlling the power level of a calibration beam provided as an output. A controller within the on-premises calibrator is used to generate the control signals for the various components in response to received external commands, typically via from an external GUI of the user's computer system. The controller is used in combination with the tunable wavelength source to provide a series of output calibration signals at different wavelengths, providing the ability to performance calibration across a desired spectral region and not just a single wavelength. The on-premises calibrator maintains real-time wavelength stability of the instrument to minimize down time when compared off-site extensive re-calibration services.

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.

An example system includes a first light source, a second light source, a photodetector, and an electronic control device. The electronic control device is operable to cause the first light source to emit first light within a range of wavelengths towards a subject, and measure, using the photodetector, the first light reflected from the subject. The electronic control device is also operable to cause the second light source to emit second light including a plurality of emission peaks within the range of wavelengths, and measure, using the photodetector, the second light. The electronic control device is also operable to determine spectral information regarding the subject based on the measured first light and the measured second light.

A spectroscopic analysis device (1) according to the present disclosure includes a controller (40) that acquires refractive index information on a sample (S) based on information on a first spectroscopic spectrum in a first wavelength band in which only a resonance spectrum of surface plasmon occurs within a spectroscopic spectrum, determines, based on the acquired refractive index information, an incidence angle of irradiation light (L1) irradiated by an irradiator (10) with respect to a membrane (M) such that the peak wavelength of the resonance spectrum and the peak wavelength of an absorption spectrum of the sample (S) match in a second spectroscopic spectrum in a second wavelength band in which the resonance spectrum and the absorption spectrum occur within the spectroscopic spectrum, and analyzes the state of the sample (S) from information on the second spectroscopic spectrum obtained based on the determined incidence angle.