The present invention relates to a computer-implemented method for identifying an incorrectly or non-calibrated infrared spectrometer, comprising the steps of a) recording an infrared spectrum of a sample with a first infrared spectrometer to provide a sample infrared spectrum, b) recording an infrared spectrum of the same sample as in step a) with a second infrared spectrometer to provide a reference infrared spectrum, wherein said second spectrometer is a correctly calibrated infrared spectrometer, or b) providing a reference spectrum of the same sample as in step a), wherein said reference spectrum was recorded on a second infrared spectrometer, which is a correctly calibrated spectrometer, c) determining a difference between the wavelength of each extreme point in the sample of step a) and the wavelength of each extreme point in the reference spectrum of step b) or b), and d) indicating the infrared spectrometer of step a) as incorrectly calibrated or non-calibrated, when at least one difference was determined in step c).

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.

In accordance with particular implementations of the invention described herein, a sample for analysis is illuminated under each of one or more narrow-band light sources. The light incident upon this sample is received by a sensor that generates measurement data in response thereto. One or more processors are configured to receive the measurement data and derive an excitation response curve and a fluorescent response curve from the measurement data. The processor is further configured to generate a fluorescent profile value using measurements from the fluorescent response curve for each of the captured narrow band measurement data and an excitation profile value corresponding to the area under the fluorescence curve divided by the area under the excitation curve. The generated fluorescent profile and excitation profile are both output as a dataset providing improved measurement values over similar approaches in the art.

[Problem to be Solved] To select a marker peak which characterizes a difference between groups, even when the number of samples belonging to each group is small. [Solution] A peak matrix is created based on the peaks detected from mass spectra of a plurality of samples belonging to a plurality of groups (S1-S3). Each row of the peak matrix represents a peak-intensity distribution for a large number of samples at one mass-to-charge-ratio value. If there is no difference between the groups at a certain mass-to-charge-ratio value, the peak-intensity distribution at that mass-to-charge-ratio value should be a lognormal distribution (or normal distribution). Accordingly, a hypothesis test for the conformity of the peak-intensity distribution to the lognormal distribution is performed for each mass-to-charge-ratio value (S5). A mass-to-charge-ratio value at which a significant difference has been found is selected as a candidate of the marker peak (S6).

A method for the non-destructive testing of the heating of a part made from polymer material, the method comprising the following steps: a) carrying out a measurement by infrared spectroscopy on a part to be tested and extracting therefrom at least one of absorbance values and transmittance values according to a spatial frequency; and b) from the measurement of at least one of absorbance and transmittance, determining the period of time during which said region of the part to be tested has been subjected to a given heating temperature and determining said heating temperature, using a reference database comprising at least one of absorbance measurements and transmittance measurements, the measurements established over a plurality of reference samples made from polymer material that have been subjected to a given temperature during a given period of time.

A system and method for finding the peak wavelength of the spectrum sensed by an LSPR spectrometer is described herein. The method comprises reading an image representing the reflected/absorbed spectrum, using a mathematical model of the LSPR spectrometer system to estimate a parametric curve representing the absorbance/reflectance spectrum, and adjusting or optimizing the parameters of the parametric curve so as to increase the likelihood of the parametric curve representing the sensed spectrum. Also described herein is a novel method to achieve LSPR peak wavelength signal noise reduction using an adaptive regularization algorithm.

A terahertz wave spectrometry system that is capable of identifying analyzing target molecules contained in an analyte even if the analyte contains water, by activating a water remover to remove water according a comparison of absorption spectrums so that water in the analyte is easily removed without causing the analyzing target molecules to disappear due to decomposition or denaturation.

There is provided a detector, and a method of calibrating or correcting a detection value in a wavelength range within an evaluable range by using a detection value in a wavelength range other than the evaluable range by using the detector. The detector includes an active layer containing a quantum well or quantum dots, and that is capable of sweeping a detection peak wavelength of a detection spectrum in a wavelength range within an evaluable range and a wavelength range other than the evaluable range, and is configured to correct or calibrate a detection value in the wavelength range within the evaluable range using a detection value in the wavelength range other than the evaluable range.

Problem to be Solved

To select a marker peak which characterizes a difference between groups, even when the number of samples belonging to each group is small.

Solution

A peak matrix is created based on the peaks detected from mass spectra of a plurality of samples belonging to a plurality of groups (S1-S3). Each row of the peak matrix represents a peak-intensity distribution for a large number of samples at one mass-to-charge-ratio value. If there is no difference between the groups at a certain mass-to-charge-ratio value, the peak-intensity distribution at that mass-to-charge-ratio value should be a lognormal distribution (or normal distribution). Accordingly, a hypothesis test for the conformity of the peak-intensity distribution to the lognormal distribution is performed for each mass-to-charge-ratio value (S5). A mass-to-charge-ratio value at which a significant difference has been found is selected as a candidate of the marker peak (S6).

Infrared detection systems according to embodiments include an infrared detector, a storage, and a correction calculator. The infrared detector is configured to detect infrared light by absorbing and photoelectrically converting infrared light of a specific wavelength range. The infrared detector is capable of sweeping an absorption peak wavelength of an absorption spectrum of infrared light. The storage is configured to store a plurality of correction coefficients for correcting a detection value from the infrared detector in accordance with the absorption peak wavelength of the infrared detector with respect to a wavelength range of an atmospheric window. The correction calculator corrects the detection value from the infrared detector for each absorption peak wavelength using corresponding correction coefficients stored in the storage.