The present invention provides a method for spectroscopic imaging by illuminating a sample with a short infrared pulse, directing one or more probe beams to the sample, and measuring light that is reflected, transmitted, or re-emitted inelastically, where a spectrum is collocated by varying the wavelength of the infrared pulse and an image is collected by moving the sample and probe beam relative to each other as the steps are repeated multiple times to create a multidimensional spectroscopic image of the sample. Also disclosed are the related methods for analyzing the data and the related system for spectroscopic imaging.

The present invention provides a method for spectroscopic imaging by illuminating a sample with a short infrared pulse, directing one or more probe beams to the sample, and measuring light that is reflected, transmitted, or re-emitted inelastically, where a spectrum is collocated by varying the wavelength of the infrared pulse and an image is collected by moving the sample and probe beam relative to each other as the steps are repeated multiple times to create a multidimensional spectroscopic image of the sample. Also disclosed are the related methods for analyzing the data and the related system for spectroscopic imaging.

An integrated rapid non-destructive detection system for multi-index of meat quality comprises a spectrometer for obtaining near-infrared spectra of a sample; an industrial tablet computer, comprising: a model embedding module for storing multiple prediction models; a model determining module connected with model embedding module and configured to call prediction model; an index prediction module connected with spectrometer and model determining module for receiving near-infrared spectra and predicting index data of the sample combining with called prediction model; wherein the number of detector elements of spectrometer is determined by an ultimate minimal resolution, and a resolution of the sample is controlled to the ultimate minimal resolution during an acquisition process; which realizes automatic black-white calibration, non-destructive detection of multi-index, improves building efficiency of model, while maintaining building stability, and optimizes detection instrument volume.

An integrated rapid non-destructive detection system for multi-index of meat quality comprises a spectrometer for obtaining near-infrared spectra of a sample; an industrial tablet computer, comprising: a model embedding module for storing multiple prediction models; a model determining module connected with model embedding module and configured to call prediction model; an index prediction module connected with spectrometer and model determining module for receiving near-infrared spectra and predicting index data of the sample combining with called prediction model; wherein the number of detector elements of spectrometer is determined by an ultimate minimal resolution, and a resolution of the sample is controlled to the ultimate minimal resolution during an acquisition process; which realizes automatic black-white calibration, non-destructive detection of multi-index, improves building efficiency of model, while maintaining building stability, and optimizes detection instrument volume.

An apparatus for detecting matter including: a light source arrangement adapted to emit a first and a second set of light beams towards a first detection zone through which the matter is provided. A spectroscopy system adapted to receive and analyse light which is reflected and/or scattered by matter in the first detection zone. A laser triangulation system including, a laser arrangement adapted to emit a line of laser light towards a second detection zone. A camera-based sensor arrangement configured to receive and analyse light which is reflected and/or scattered by matter in the second detection zone. The received light of the spectroscopy system completely or partially intersects the received light of the camera-based sensor arrangement and/or the line of laser light.

There is provided a quantitative method for determining a level of a sanding surface preparation of a carbon fiber composite surface, prior to the carbon fiber composite surface undergoing a post-processing operation. The quantitative method includes fabricating a ladder panel of levels of sanding correlating to an amount of sanding of sanding surface preparation standards for a reference carbon fiber composite surface of reference carbon fiber composite structure(s); using surface analysis tools to create target values for quantifying the levels of sanding; measuring, with the surface analysis tools, sanding surface preparation location(s) on the carbon fiber composite surface of a test carbon fiber composite structure, to obtain test result measurement(s); comparing the test result measurement(s) to the levels, to obtain test result level(s); determining if the test result level(s) meet the target values; and determining whether the carbon fiber composite surface is acceptable to proceed with the post-processing operation.

There is provided a quantitative method for determining a level of a sanding surface preparation of a carbon fiber composite surface, prior to the carbon fiber composite surface undergoing a post-processing operation. The quantitative method includes fabricating a ladder panel of levels of sanding correlating to an amount of sanding of sanding surface preparation standards for a reference carbon fiber composite surface of reference carbon fiber composite structure(s); using surface analysis tools to create target values for quantifying the levels of sanding; measuring, with the surface analysis tools, sanding surface preparation location(s) on the carbon fiber composite surface of a test carbon fiber composite structure, to obtain test result measurement(s); comparing the test result measurement(s) to the levels, to obtain test result level(s); determining if the test result level(s) meet the target values; and determining whether the carbon fiber composite surface is acceptable to proceed with the post-processing operation.

A method of harvesting plant product from a plant in a single pass using a combine harvester is disclosed. In the method, the plant has a protein content gradient that varies along a height of the plant. The method includes identifying, along a longitudinally-extending stalk of the plant, an upper protein gradient of the plant including high protein plant product and a lower protein gradient of the plant including lower protein plant product, wherein the high protein plant product from the upper protein gradient of the plant meets a threshold protein content that is higher than that of the lower protein plant product. The method also includes separately and substantially simultaneously harvesting the high protein plant product from the upper protein gradient and the lower protein plant product from the lower protein gradient in the single pass, and isolating the high protein plant product from the lower protein plant product.

A method of harvesting plant product from a plant in a single pass using a combine harvester is disclosed. In the method, the plant has a protein content gradient that varies along a height of the plant. The method includes identifying, along a longitudinally-extending stalk of the plant, an upper protein gradient of the plant including high protein plant product and a lower protein gradient of the plant including lower protein plant product, wherein the high protein plant product from the upper protein gradient of the plant meets a threshold protein content that is higher than that of the lower protein plant product. The method also includes separately and substantially simultaneously harvesting the high protein plant product from the upper protein gradient and the lower protein plant product from the lower protein gradient in the single pass, and isolating the high protein plant product from the lower protein plant product.

A cancer cell detection device includes a computer with a database and a display and a microscope coupled to the computer. The microscope has a base upon which a biopsy sample can be placed. The device further includes a camera coupled to the microscope and computer. The camera is configured to capture images of the biopsy sample. The device also has a filter configured to attach to the microscope and a connection feature for connecting the computer to the camera and the filter. The computer further includes a processor that processes the images captured by the camera and classifies the images according to known variables stored in the database.