In some implementations, a device may determine that an unknown sample is an outlier sample by using an aggregated classification model. The device may determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample. The device may cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.

A method for optimizing an output result of a spectrometer and an electronic device using the method are provided. The method includes the following. First spectral data and second spectral data are obtained. A plurality of pipelines including a first pipeline and a second pipeline are obtained. The first pipeline is selected from the plurality of pipelines as a selected pipeline. The output result corresponding to the second spectral data is generated according to the selected pipeline. A performance of the first pipeline is calculated according to the first spectral data, and a first instruction is generated according to the performance. The selected pipeline is changed into the second pipeline according to the first instruction to update the output result.

A multispectral sensor device may include a sensor array comprising a plurality of channels and one or more processors to determine that a time-sensitive measurement is to be performed, wherein the time-sensitive measurement is to be performed using data collected by one or more channels of the plurality of channels; cause the data to be collected by a proper subset of channels, of the plurality of channels, wherein the proper subset of channels includes the one or more channels; and determine the time-sensitive measurement based on the data.

A measurement method includes: (a) measuring an emission intensity for each wavelength of light detected from a plasma generated in a plasma processing apparatus at each different exposure time by a light receiving element; (b) specifying, with respect to each of a plurality of different individual wavelength ranges that constitutes a predetermined wavelength range, a distribution of the emission intensity in the individual wavelength range measured at an exposure time at which an emission intensity of a predetermined wavelength included in the individual wavelength range becomes an emission intensity within a predetermined range; (c) selecting a distribution of the emission intensity in the individual wavelength range from the distribution of the emission intensity specified in (b); and (d) outputting the distribution of the emission intensity selected for each individual wavelength range.

An identification apparatus 1000 includes a light collecting unit 20 configured to collect scattered light from a sample, spectroscopic elements 150l and 150h configured to disperse light from the light collecting unit 20, an imaging unit 170 that includes a plurality of light detection elements arrayed in a row direction 172r and a column direction 172c and to which optical spectra from the spectroscopic elements 150l and 150h are projected along the row direction 172r, and an acquisition unit 30 configured to acquire spectral information about the sample based on an output signal from the imaging unit 170. The optical spectra corresponding to the sample are projected to the imaging unit 170 discontinuously in at least one of the row direction 172r and the column direction 172c.

In some implementations, a device may receive spectroscopic data associated with a dynamic process. The device may generate a principal component analysis (PCA) model based on a first block of spectra from the spectroscopic data. The device may project a second block of spectra from the spectroscopic data to the PCA model generated based on the first block of spectra. The device may determine a value of a metric associated with the second block based on projecting the second block of spectra to the PCA model. The device may determine whether the dynamic process has reached an end point based on the value of the metric associated with the second block.

A method for true-to-sample measurement by a mobile ingredient analysis system having a housing with a window, an interface for an external reference unit, a display and operating unit, a light source, an optical spectrometer, a camera, an internal reference unit, and an electronic control unit. The method includes: selecting a calibration product suitable for a sample to be examined; performing a plausibility check of the calibration product, an incorrect selection being signaled and an alternative calibration product being selected; outputting measurement conditions comprising the measurement point to be selected and measurement duration for the selected calibration product; capturing measured values of the sample by the spectrometer under the measurement conditions and with simultaneous monitoring of the measurement conditions; processing the captured measured values by means of an electronic control unit, each measured value captured while the measurement conditions were met being declared valid; outputting the measured values deemed valid.

A system for remote-controlling a spectrometer, which includes: at least one spectrometry device including a spectrometer and auxiliary modules, the spectrometry device being configured to measure spectrometry data on an object and/or a process; a control device configured to control the spectrometry device, the control device including an element for controlling the spectrometry device, an element for acquiring and processing the spectrometry data, and an element for remote communication; and at least one interface modules configured to communicate with the control device remotely. The remote-control device is configured to communicate with the interface module via Internet, and the spectrometry device is interchangeable. Also, a device for remote-controlling a spectrometry system that is configured to be used in a system for remote-controlling the spectrometer.

DCS analyzer including a memory to store autocorrelation values, model parameters, fitting parameters, and simulated correlation values from a DCS model; a mean square error (MSE) module to compute MSE between theoretical autocorrelation values computed from the model parameters and measured autocorrelation values; a sorting module to sort three latest MSE values obtained from the MSE module and generate indexes of largest, medium, and smallest MSE values; a convergence checking module to determine whether convergence is reached in solving an autocorrelation equation; a search module to calculate αD.sub.B and β values at reflection, extension, contraction, and shrink locations; a comparison module to compare two latest MSE values and find new αD.sub.B and β values to replace values associated with a largest MSE; a state controller coupled with the memory and the modules to control an operation thereof; and an output buffer to present a fitted solution of the autocorrelation equation.

Systems and methods here may be used for automated capturing and analyzing spectrometer data of multiple sample gemstones on a stage, including mapping digital camera image data of samples, applying a Raman Probe to a first sample gemstone under evaluation on the stage, receiving spectrometer data of the sample gemstone from the probe, automatically moving the stage to a second sample, using the image data, and analyzing the other samples.