Abstract: One embodiment provides an optical spectrometer. The optical spectrometer can include a lens-and-filter system configured to collect light scattered from a sample, a spot converter configured to convert a substantially circular beam outputted from the lens-and-filter system into a substantially rectangular beam, and a slit comprising a rectangular aperture to allow a predetermined portion of the substantially rectangular beam to enter the rectangular aperture while blocking noise. The slit can further include at least one microelectromechanical systems (MEMS)-based movable structure configured to adjust a width of the rectangular aperture.

Abstract: One embodiment provides an optical spectrometer. The optical spectrometer can include a lens-and-filter system configured to collect light scattered from a sample, a spot converter configured to convert a substantially circular beam outputted from the lens-and-filter system into a substantially rectangular beam, and a slit comprising a rectangular aperture to allow a predetermined portion of the substantially rectangular beam to enter the rectangular aperture while blocking noise. The slit can further include at least one microelectromechanical systems (MEMS)-based movable structure configured to adjust a width of the rectangular aperture.

Abstract: One embodiment provides an apparatus for facilitating raster scanning of an optical spectrometer. The apparatus can include an enclosure, a lens holder situated within the enclosure, and an actuation mechanism coupled to the lens holder. The lens holder is configured to hold a lens that focuses excitation light onto a sample surface, and the actuation mechanism is configured to cause the lens holder to perform a motion according to a predetermined pattern, thereby causing the focused excitation light to raster scan the sample surface.

Abstract: Verification systems for testing food products or other samples may include a mobile analytical device, a mobile accessory device such as a smart phone, and a remote, e.g., cloud-based, computing system. The mobile analytical device is adapted to generate a sensor output that is indicative of a molecular composition of the sample. The mobile accessory device may be adapted to receive the sensor output from the mobile analytical device. The remote computing system may be adapted to analyze analytical data using artificial intelligence (AI) and/or machine learning (M-L) to make an authentication determination of the sensor output relative to a predefined product database. The mobile accessory device may be adapted to upload the sensor output to the remote computing system by a communication network, and the remote computing system may be adapted to download the authentication determination to the mobile accessory device by the same communication network.

Abstract: Verification systems for testing food products or other samples include a mobile analytical device, a mobile accessory device such as a smart phone, and a remote, e.g., cloud-based, computing system. The mobile analytical device is adapted to generate a sensor output that is characteristic of the sample. The mobile accessory device may be adapted to receive the sensor output from the mobile analytical device. The remote computing system may be adapted to analyze analytical data using artificial intelligence (AI) and/or machine learning (M-L) to make an authentication determination of the sensor output relative to a. predefined product database. The mobile accessory device may be adapted to upload the sensor output to the remote computing system by a communication network, and the remote computing system may be adapted to download the authentication determination to the mobile accessory device by the communication network.