A system for generating hyperspectral imaging data for measuring biochemical compositions is disclosed which includes a spectral imaging device adapted to acquire one or more hyperspectral linescan images, an optical imaging device with a red-green-blue (RGB) sensor adapted to acquire an RGB dataset, a processor adapted to co-locate a plurality of pixels in the RGB dataset vs. a corresponding plurality of pixels of the one or more hyperspectral linescan datasets, establish a transformation matrix utilizing the plurality of co-located pixels, apply the transformation matrix to the RGB dataset to thereby generate the hyperspectral dataset, and analyze the generated hyperspectral image dataset to determine the biochemical compositions.

The present invention relates to vitro method for analysing a liquid sample as to the presence, identity and properties of microbes comprising: a) isolating microbes from the liquid sample; b) analysing said microbes spectroscopically by means of spontaneous Raman spectroscopy; and c) determining antibiotic susceptibility of said microbes spectroscopically by means of spontaneous Raman spectroscopy. The present invention also refers to device for analysing a liquid sample as to the presence, identity and properties of microbes, wherein the device comprises as a first unit (i) a chip comprising a filtering unit and an antibiotics exposure unit capable of determining the susceptibility of microbes to an antibiotic; as a second unit (ii) a Raman spectroscopy system; and as a third unit (iii) an evaluation module which is coupled to the Raman spectroscopy system.

The use of Raman spectroscopy for the monitoring and assessment of viral titre is disclosed. A method of quantifying viral titre in a sample using Raman spectroscopy, comprises the steps of: (a) providing a sample and irradiating the sample with a light source; (b) measuring the total intensity of Raman scattered light within each one of a plurality of wavenumber ranges to obtain a wavenumber intensity data set for the sample, wherein the plurality of wavenumber ranges are pre-selected and are characteristic of the vims in the sample; (c) performing mathematical data processing steps on the wavenumber intensity data; and (d) quantifying the viral titre based upon the output of the mathematical data processing steps.

A fine ratio measuring device that measures a ratio of fines adhering to the surface of a material in the form of lumps, the fine ratio measuring device includes: an illumination unit that illuminates the material in the form of lumps; a spectrometer that performs spectral analysis on light reflected from the material in the form of lumps to measure spectral reflectance; and an arithmetic device that extracts at least one feature quantity from the spectral reflectance measured by the spectrometer and computes the fine ratio from the extracted at least one feature quantity.

A method including generating integrated computational element (ICE) models and determining a sensor response as the projection of a convolved spectrum associated with a sample library with a plurality of transmission profiles determined from the ICE models. The method includes determining a regression vector based on a multilinear regression that targets a sample characteristic with the sensor response and the sample library and determine a plurality of regression coefficients in a linear combination of ICE transmission vectors that results in the regression vector. The method further includes determining a difference between the regression vector and an optimal regression vector. The method may also include modifying the ICE models when the difference is greater than a tolerance, and fabricating ICEs based on the ICE models when the difference is within the tolerance. A device and a system for optical analysis including multiple ICEs fabricated as above, are also provided.

According to an example aspect of the present invention, there is provided a method for analysing a chemical composition of a target, the method comprising placing an electrically tunable Fabry-Perot interferometer in a path of radiation emitted by a radiation source, and detecting the radiation, which has passed the Fabry-Perot interferometer and which has passed or was reflected by the target, by means of a detector, and wherein detection is made such that multiple pass bands are allowed to be detected simultaneously.

A method for extracting Raman characteristic peaks employing improved principal component analysis comprising: using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples; and performing preprocessing on the Raman spectroscopic data, performing principal component analysis, establishing a principal component loading scatter plot, extracting dot characteristics from the principal component loading scatter plot, analyzing same, and performing filtering on the dot characteristics to obtain Raman characteristic peaks. The method for extracting Raman characteristic peaks employing improved principal component analysis is used to extract Raman characteristic peaks from pork and beef samples, and then the Raman characteristic peaks are inputted into a classifier to undergo classification, thereby achieving high accuracy and quick classification.

Embodiments of this invention relate generally to a miniature multi-spectral system to detection pathogen, biomarkers, or any compound from a sample. In one example, a miniature multi-spectral system comprises a first miniature spectrometer to generate a first spectral output based on a sample, a second miniature spectrometer to generate a second spectral output based on the sample, and a processor coupled to the first and the second miniature spectrometers. The processor is configured to execute instructions to perform data fusion of the first and second spectral outputs to generate fused data, and to apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample.

The present invention relates to a quick quantitative analysis method and analyzer for mixture concentration information. A quick quantitative analysis method for a mixture based on spectral information includes: collecting a sample of a mixture solution, and injecting into a DAD to obtain DAD data; then, calculating a spectral library by a quick qualitative analysis method based on a vector error algorithm to obtain a spectral error vector; determining a spectral curve of a component included in the data corresponding to the sample, according to a value of the error vector; and calculating a content flag value of each component in the sample and concentration information of each substance.

A method for improving an identification accuracy of mixture components by using a known mixture Raman spectrum is disclosed. After calculating a first similarity between a to-be-tested Raman spectrum characteristic vector group and a pure substance Raman spectrum characteristic vector group of an nth kind of pure substance in a Raman spectrum standard library, the method uses a known mixture library to calculate to obtain a second similarity between a to-be-identified substance Raman spectrum characteristic vector group and a spectral peak characteristic vector group with offset information corresponding to a pure substance in a known mixture, and determines a similarity between a to-be-tested mixture and the nth kind of pure substance according to the first similarity and all second similarities to thus obtain a component identification result. The present application uses the known mixture library to assist the Raman spectrum standard library in searching.