A chemical pattern recognition method for evaluating the quality of a traditional Chinese medicine based on medicine effect information, comprising: collecting chemical information of a traditional Chinese medicine sample, obtaining medicine effect information reflecting a clinical therapeutic effect thereof, performing spectrum-effect relationship analysis on the chemical information and the medicine effect information, and obtaining an index significantly related to the medicine effect as a feature chemical index; dividing the traditional Chinese medicine sample into a training set and a test set; using a pattern recognition method to extract a feature variable from samples of the training set by taking the feature chemical index as an input variable; building a pattern recognition model using the feature variable; and substituting feature variable values of samples of the test set into the model, and completing chemical pattern recognition evaluation of the quality of the traditional Chinese medicine. According to the method, chemical reference substances are not needed, the chemical pattern recognition model is built on the basis of the feature chemical index reflecting the medicine effect, the one-sidedness and the subjectivity of the existing standards are overcome, and a traditional Chinese medicine quality evaluation system capable of reflecting both the clinical therapeutic effect and overall chemical composition information is finally formed.

Techniques for acquiring and processing data in combination with a photonic sensor system-on-a-chip (SoC) (1) to provide real-time calibrated concentration levels of an analyte (e.g., a constituent molecule within a biological substance) are described. A raw signal (1300) to be analyzed is collected by the sensor chip (1) via diffuse reflectance or transmittance. Determination of the analyte concentration is based on, in part, Beer-Lambert principles and facilitated by applying (2240) scattering correction to the raw signal (1300) prior to decomposition and analysis thereof.

Aspects relate to a compact and low-cost gas analyzer that can be used for different types of gas analysis, such as air quality analysis. The gas analyzer can include a light source, a gas cell configured to receive a sample (e.g., a gas under test), a spectral sensor including a spectrometer and a detector, and an artificial intelligence (AI) engine. Light can enter the gas cell and interact with the sample to produce output light that may be measured by the spectral sensor. The resulting spectrum produced by the spectral sensor may be analyzed by the AI engine to produce a result. The gas analyzer further includes a self-calibration component configured to enable calibration of the sample spectrum to compensate for spectral drift of the spectral sensor.

A method for analyzing 2D material thin film and a system for analyzing 2D material thin film are disclosed. The detection method includes the following steps: capturing sample images of 2D material thin films; measuring the 2D material thin films by a Raman spectrometer; performing a visible light hyperspectral algorithm on the sample images by a processor to generate a plurality of visible light hyperspectral images; performing a training and validation procedure, performing an image feature algorithm on the visible light hyperspectral images, and establishing a thin film prediction model based on a validation; and capturing a thin-film image to be measured by the optical microscope, performing the visible light hyperspectral algorithm, and then generating a distribution result of the thin-film image to be measured according to an analysis of the thin film prediction model.

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.

In the method for optical monitoring and/or determination of properties on samples, monochromatic electromagnetic radiation with a predetermined wavelength is sequentially directed from several radiation sources onto a sample influenced by an electronic evaluation unit. The respective intensity specific to the wavelength of the electromagnetic radiation scattered and/or reflected by the sample is detected by at least one detector and fed to the electronic evaluation unit for spectrally resolved evaluation in order to use it to monitor and/or determine properties of the respective sample.

Disclosed is an operating method of a metal sorting system using laser induced breakdown spectroscopy (LIBS), which may include: analyzing a metal component distribution for various metals using LIBS library information; setting multiple clusters according to the metal component distribution; performing first regression component analysis with respect to spectral data of a metal sample; calculating a probability that the spectral data will belong to each of the set multiple clusters using the first regress component analysis result; performing second regression component analysis with respect to the spectral data which belong to each cluster; and discriminating a type of metal sample by a weighted sum of the calculated probability and the second regression component analysis result.

Aspects relate to a spectroscopic analyzer device that can be used for biological sample detection, and specifically for virus infection detection. The spectroscopic analyzer device includes a spectrometer, such as a micro-electro-mechanical systems (MEMS) based infrared spectrometer, and an artificial intelligence (AI) for screening of viral samples. In addition, the spectroscopic analyzer device includes a light source and a disposable optical component configured to receive a sample and to facilitate light interaction with the sample.

Disclosed are methods and systems for accurate modeling of the soil-water retention curve (SWRC) for any soil texture class and with varying amounts of soil organic matter. The disclosed method leverages near-visible infrared spectroscopy (vis-NIRS) to obtain rapid measurements at low soil-water potential that are used to model soil-water retention functions.

Aspects relate to mechanisms for mass screening of samples. A portable laboratory device based on spectroscopic analysis of samples containing analytes under test can facilitate the mass screening. The portable laboratory device can include a sample head including a structure configured to facilitate application of the sample to the sample head and an optical measurement device including one or more light sources and a spectrometer. Light from the light source(s) incident on the sample may be directed to the spectrometer to obtain a spectrum of the sample. The optical measurement device can further include a data transfer device configured to provide the spectrum obtained by the spectrometer to a spectrum analyzer to produce a result from the spectrum.