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 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.

Provided are a method and a device, for processing spectrum data of an image sensor. The method includes obtaining spectrum response signals corresponding to channels of spectrum data of light, the spectrum data being obtained from an object by an image sensor; determining a set of bases corresponding to the obtained spectrum response signals; performing, based on the determined set of bases, a change of basis on at least one basis included in the determined set of bases; and generating, by using a pseudo inverse, reconstructed spectrum data from the spectrum response signals on which the change of basis has been performed.

Disclosed herein are systems and methods of estimating the autofluorescence (AF) signal and other non-target signals in each channel of a multi-channel image of a biological sample that is stained with one or more fluorescent labels. In some embodiments, the estimated autofluorescence signal can then be subtracted or masked from the multi-channel image. In some embodiments, the autofluorescence-removed multichannel image can then be use for further processing (e.g. image analysis, etc.).

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

Provided is a food monitoring apparatus including at least one light source configured to selectively radiate light of a first wavelength band and light of a second wavelength band that is different from the first wavelength band to food, at least one image sensor configured to obtain a visible image of the food and a hyperspectral image of the food based on light scattered, emitted, or reflected from the food, and at least one processor configured to obtain first information of the food based on the visible image, and to obtain second information of the food based on the first information and the hyperspectral image.

Provided is a food monitoring apparatus including at least one light source configured to selectively radiate light of a first wavelength band and light of a second wavelength band that is different from the first wavelength band to food, at least one image sensor configured to obtain a visible image of the food and a hyperspectral image of the food based on light scattered, emitted, or reflected from the food, and at least one processor configured to obtain first information of the food based on the visible image, and to obtain second information of the food based on the first information and the hyperspectral image.

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 temporal-spectral multiplexing sensor for simultaneous or near simultaneous spatial-temporal-spectral analysis of an incoming optical radiation field. A spectral encoder produces a time series of spectrally encoded optical images at a high sampling rate. A series of full panchromatic spectrally encoded optical images are collected at a rate similar to the sampling rate. A detector records at least one spatial region of the spectrally encoded optical image. A processor is configured to process two series of spectrally encoded optical images to produce an artifact-free spectral image. The processing includes using the panchromatic images to normalize the spectrally encoded images, and decoding the normalized encoded images to produce high fidelity spectral signatures, free of temporal artifacts due to fluctuations at frequencies slower than the sampling rate for polychromatic images.

A temporal-spectral multiplexing sensor for simultaneous or near simultaneous spatial-temporal-spectral analysis of an incoming optical radiation field. A spectral encoder produces a time series of spectrally encoded optical images at a high sampling rate. A series of full panchromatic spectrally encoded optical images are collected at a rate similar to the sampling rate. A detector records at least one spatial region of the spectrally encoded optical image. A processor is configured to process two series of spectrally encoded optical images to produce an artifact-free spectral image. The processing includes using the panchromatic images to normalize the spectrally encoded images, and decoding the normalized encoded images to produce high fidelity spectral signatures, free of temporal artifacts due to fluctuations at frequencies slower than the sampling rate for polychromatic images.