According to one embodiment, an optical inspection method acquires, for an image point imaged through separating light from an object point into beams of light in at least two different and independent wavelength ranges, a hue vector having, as independent axes, intensities of the beams of light in the at least two different and independent wavelength ranges. The method then acquires, from the hue vector, information regarding a ray direction of the light from the object point.

The invention relates to a method for identifying one or more spectral features in a spectrum (4, 5) of a sample for a constituent analysis of the sample, comprising providing the spectrum (4, 5), predefining an approximation function (6), which is a continuously differentiable mathematical function, respectively forming an (n−1)-th order derivative (7, 8, 9) of the spectrum (4, 5) and of the approximation function (6), wherein the number n>1, generating a correlation matrix (10) from the two (n−1)-th order derivatives (7, 8, 9), and respectively identifying the spectral feature or one of the spectral features in each case as a function of a local extremum (i) of the correlation matrix (10) for at least one extremum (i) of the correlation matrix (10) in order to simplify the constituent analysis of the sample.

An emitter assembly and waveform sensor assembly for visualizing a target is disclosed. The emitter assembly is configured to emit electromagnetic radiation and includes a first emitter configured to emit at least one of visible light, infrared radiation, or a combination thereof and a second emitter configured to emit structured electromagnetic radiation. The waveform sensor assembly is configured to detect the electromagnetic radiation emitted by the emitter assembly and obtain three-dimensional images corresponding to the detected electromagnetic radiation.

The present disclosure relates to a spectrometer using multiple light sources. The spectrometer includes: a sample unit accommodating the sample; a multiple-light-sources unit irradiating light of different wavelengths to the sample unit; a sensor unit configured to measure absorbance generated at a wavelength of a light source irradiated to the sample unit; and a multiple scatterer configured to amplify the number of multiple scattering of the light source irradiated to the sample unit, wherein the sensor unit derives spectrum information by measuring absorbance at different wavelengths.

The present disclosure relates to a spectrometer using multiple light sources. The spectrometer includes: a sample unit accommodating the sample; a multiple-light-sources unit irradiating light of different wavelengths to the sample unit; a sensor unit configured to measure absorbance generated at a wavelength of a light source irradiated to the sample unit; and a multiple scatterer configured to amplify the number of multiple scattering of the light source irradiated to the sample unit, wherein the sensor unit derives spectrum information by measuring absorbance at different wavelengths.

Provided are a data processing apparatus, a data processing method, a data processing program, an optical element, an imaging optical system, and an imaging apparatus which can select two or more wavelengths suitable for discrimination of a desired subject among a plurality of subjects. In a data processing apparatus (10-1) including a processor, the processor performs data acquisition processing of acquiring first spectral data of a first subject and second spectral data of a second subject, and wavelength selection processing of selecting a plurality of specific wavelengths from wavelength ranges of the acquired first spectral data and second spectral data, and in the wavelength selection processing, the plurality of specific wavelengths are selected based on a difference in a feature amount between the first spectral data and the second spectral data.

The present invention relates generally to a slope spectroscopy standards and methods of making slope spectroscopy standards, specifically standards and methods of developing standards specifically for variable pathlength (slope) measurements.

Disclosed are methods of fabricating an integrated computational element for use in an optical computing device. One method includes providing a substrate that has a first surface and a second surface substantially opposite the first surface, depositing multiple optical thin films on the first and second surfaces of the substrate via a thin film deposition process, and thereby generating a multilayer film stack device, cleaving the substrate to produce at least two optical thin film stacks, and securing one or more of the at least two optical thin film stacks to a secondary optical element for use as an integrated computational element (ICE).

A blood sample collector can be used to collect a blood sample from a subject. The blood sample collector can be placed in a receptacle of a spectrometer to measure spectral data from the blood sample while the blood sample separates. The container may comprise a window to allow light such as infrared light to pass through the container, with the blood sample at least partially separating within the container between spectral measurements, which can provide improved accuracy of the measurements and additional information regarding the sample. The container may comprise an elongate axis and the container configured for placement in the spectrometer receptacle with the elongate axis extending toward a vertical direction in order to improve gravimetric separation of the blood sample. The spectrometer can be configured to measure the blood sample at a plurality of heights along the sample as the sample separates.

The invention relates to a method for identifying one or more spectral features in a spectrum (4, 5) of a sample for a constituent analysis of the sample, comprising providing the spectrum (4, 5), predefining an approximation function (6), which is a continuously differentiable mathematical function, respectively forming an (n−1)-th order derivative (7, 8, 9) of the spectrum (4, 5) and of the approximation function (6), wherein the number n>1, generating a correlation matrix (10) from the two (n−1)-th order derivatives (7, 8, 9), and respectively identifying the spectral feature or one of the spectral features in each case as a function of a local extremum (i) of the correlation matrix (10) for at least one extremum (i) of the correlation matrix (10) in order to simplify the constituent analysis of the sample.