An imaging system includes a light source, an imaging apparatus that captures an image of a subject illuminated by light from the light source to generate image data, which includes image information regarding each of four or more bands or information regarding a compressed image in which the image information regarding the four or more bands is compressed as a single image, and a processing apparatus. The processing apparatus determines whether or not pixel values of pixels in the image data satisfy a predetermined condition, and causes, in a case where the predetermined condition is not satisfied, a lighting condition caused by the light source to be changed under a condition where a spectral shape of light from the light source does not change at the subject's location.

A monochromator apparatus in a grating-based optical spectrum analyzer (OSA) includes a diffraction grating, a reflector element, and a tunable spectral filter. The tunable spectral filter may include a band-pass filter, a low-pass filter, a high-pass filter, or a linear variable filter, for example. A spectral window of the filter may move synchronously with the scanning spectral window of the OSA. A high-pass filter implementation may be introduced during a subsequent portion of the OSA window before the optical ghost signal starts appearing. The filter may either be linearly scanned across the optical beam, angularly tuned, or introduced in a subsequent portion of the OSA scanning window.

A non-contact system for the sensing of pH 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 a pH of a fluid.

A method of operating a spectrometer controller is provided. The method comprises obtaining an interfered peak using a detector of a spectrometer, wherein the interfered peak is produced by a plurality of spectral emissions of different wavelengths, each of the plurality of spectral emissions in the interfered peak incident on the detector at an associated detector location. For one or more of the spectral emissions of the interfered peak, an associated curve is generated using a neural network, wherein the neural network is trained to output data indicative of a shape of the associated curve based on data representative of the associated detector location. For one or more of the spectral emissions of the interfered peak, the associated curve is output.

A measuring device (1) includes a first signal generation section (3) and a first removal section (5). The first signal generation section (3) generates a first source signal (x1(t)) including a fundamental and a plurality of harmonics based on a first physical quantity (p1) and a second physical quantity (p2). The first removal section (5) removes some or all of the harmonics from the first source signal (x1(t)). The first source signal (x1(t)) is a periodic signal, and one period of the first source signal (x1(t)) includes a first signal (p1), a second signal (p2), and a reference signal (pr). The first signal (p1) has a first duration (w1) and indicates the first physical quantity (p1). The second signal (p2) has a second duration (w2) and indicates the second physical quantity (p2). The reference signal (pr) has a third duration (w3) and indicates the reference physical quantity (pr).

Various embodiments of a system for linear unmixing of spectral images using a dispersion model are disclosed herein.

A method includes, with a Raman spectrometer: determining a measured spectrum; determining a second derivative of a difference spectrum function corresponding to a difference between the measured spectrum and a product of a scaling coefficient and a reference spectrum of contributions of interfering influences included in the measured spectrum; determining a coefficient value of the scaling coefficient minimizing an error function including a term corresponding to an error of the difference spectrum function due to peaks included in the reference spectrum, the term including a sum of areas enclosed underneath the second derivative in all spectral regions in which the second derivative is positive, and/or a sum of areas enclosed underneath the second derivative in all spectral regions in which the second derivative is negative; and determining a Raman difference spectrum corresponding to the difference between the measured spectrum and a product of the coefficient value and the reference spectrum.

Technical improvements for performing optical NIRS measurements using an optical sensor are provided, including A) a specific measurement method, B) a specific cable design for such a sensor, C) a light shielding cover to be used with such a sensor, and D) a specific internal light shielding to be used inside the sensor. All of these aspects can be used for improving the accuracy and robustness of the optical measurements to be performed with the sensor.

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 monochromator apparatus in a grating-based optical spectrum analyzer (OSA) includes a diffraction grating, a reflector element, and a tunable spectral filter. The tunable spectral filter may include a band-pass filter, a low-pass filter, a high-pass filter, or a linear variable filter, for example. A spectral window of the filter may move synchronously with the scanning spectral window of the OSA. A high-pass filter implementation may be introduced during a subsequent portion of the OSA window before the optical ghost signal starts appearing. The filter may either be linearly scanned across the optical beam, angularly tuned, or introduced in a subsequent portion of the OSA scanning window.