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.

This disclosure relates to processing a spectral dataset, such as a hyperspectral image or a large collection of individual spectra taken with the same spectrometer, to increase the signal-to-noise ratio. The methods can also be used to process a stack of images that differ by acquisition time rather than wavelength. The methods remove most of the sensor background noise with minimal corruption of image texture, anomalous or rare spectra or waveforms, and spectral or time resolution.

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).

The present invention includes: a light supply part; an interfering light formation part; and a detection part, in which the interfering light formation part includes a fixed reflection part, a movable reflection part, and a moving part that moves and fixes the movable reflection part along a base plane, the fixed reflection part includes a first reflection surface that reflects supplied light supplied from the light supply part and a second reflection surface provided so as to be plane-symmetrical with the first reflection surface with respect to the base plane and to be orthogonal to the first reflection surface, and the movable reflection part includes a third reflection surface and a fourth reflection surface parallel to a first reflection surface and a second reflection surface of the fixed reflection part, respectively.

The invention relates to a characterization device (50) for characterizing a sample (S) comprising: a memory (MEM) storing a measured spectrum (A.sub.s+p) of said sample, performed through a translucent material, and a measured spectrum of the translucent material (A.sub.p), a processing unit (PU) configured to: determine a spectral energy (E.sub.s+p) of the measured spectrum (A.sub.s+p) of the sample through the translucent material (A.sub.s+p), estimate a coefficient ({circumflex over ()}) from said spectral energy (E.sub.s+p) and, determine a corrected spectrum (.sub.s) of the sample from the measured spectrum (A.sub.s+p) of the sample through the translucent material and from a corrected spectrum of the translucent material (.sub.p),
said corrected spectrum of the translucent material (.sub.p) being determined from the measured spectrum of the translucent material (A.sub.p) and from the estimated coefficient ({circumflex over ()}).

Disclosed herein are devices and methods for screening gemstones (e.g., diamonds). In particular, the disclosed method and system can efficiently and accurately identify and distinguish genuine earth-mined gemstones (e.g., diamond) from synthetic and treated gemstones or gemstone simulants.

A Raman spectrum detecting method and electronic device are disclosed. In one aspect, an example method includes detecting and obtaining a first Raman spectrum signal of a package. A second Raman spectrum signal of the object is detected and obtained with the package. The first Raman spectrum signal is successively subtracting from the second Raman spectrum signal to obtain a series of third Raman spectrum signals with package interference eliminated. Information entropies of the third and first Raman spectrum signals are calculated and compared with information entropy of the first Raman spectrum signal. Information entropies of third Raman spectrum signals greater than the first Raman spectrum signal are set into an information entropy sequence to be selected, and a minimum information entropy from the sequence is selected. The third Raman spectrum signal corresponding to the minimum information entropy is used as an optimized Raman spectrum signal with package interference eliminated.

The present invention is a method of removing the illumination and background spectral components thus isolating spectra from multi-spectral and hyper-spectral data cubes. The invention accomplishes this by first balancing a reference and sample data cubes for each spectra associated with each location, or pixel/voxel, in the spatial image. The set of residual spectra produced in the balancing step is used to obtain and correct a new set of reference spectra that is used to remove the illumination and background components in a sample data cube.

The present invention is directed to a computer-implemented method of automatically identifying the presence of one or more elements in a sample via optical emission spectroscopy. The method includes the steps of obtaining sample spectrum data from the sample, obtaining a list of one or more predetermined emission wavelengths for each element in the periodic table quantifiable by optical emission spectroscopy, each predetermined emission wavelength being associated with a list of one or more potential interference emission wavelengths, determining a list of one or more analyte wavelengths corresponding to spectral peaks in the sample spectrum data based on the list of emission wavelengths, for each analyte wavelength, determining whether the corresponding spectral peak has a likelihood of being affected by an interference emission wavelength causing spectral interference based on the list of one or more potential interference emission wavelengths corresponding to the analyte wavelength, determining a revised list of one or more analyte wavelengths by removing from the list of analyte wavelengths, analyte wavelengths corresponding to spectral peaks having a likelihood of being affected by an interference emission wavelength, and determining a level of confidence that one or more elements are present in the sample based on a set of criteria applied to the revised list of analyte wavelengths.

The present invention is a method of removing the illumination and background spectral components thus isolating spectra from multi-spectral and hyper-spectral data cubes. The invention accomplishes this by first balancing a reference and sample data cubes for each spectra associated with each location, or pixel/voxel, in the spatial image. The set of residual spectra produced in the balancing step is used to obtain and correct a new set of reference spectra that is used to remove the illumination and background components in a sample data cube.