A system and method for detecting pathogenetic analytes including exciting a large target input area with radiation to produce scattered light to form an input beam, reformatting, with an optical slicer system, the input beam to produce an output beam, dispersing the output beam to produce an output area, capturing excitation data from the output area; and determining, with a processor, a presence of a particular analyte in the input area based on the excitation data. The input area can be greater than 100 micron squared and less than one million microns squared. The optical slicer system can be a high throughput virtual slit system. SERS analysis detects analytes of interest with both high resolution and sensitivity simultaneously, and is applicable for detection of the presence of viruses.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.

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.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (not limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC), Internet of Thing (IoT), Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (no limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC) Internet of Thing (IoT) Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

A method of generating object surface texture in thermal infrared images is disclosed which includes receiving heat radiation from a scene by a spectropolarimetric imaging system, generating a plurality of spectral frames associated with the scene, each frame having a plurality of pixels, for each pixel from the generated plurality of spectral frames, extracting spectral information associated with the scene, including pixel-specific temperature representing an objects temperature, and thermal texture factor representing the objects texture, for each of a plurality of materials having a specific emissivity in a library, generating reference spectral information as a function of temperature and thermal texture, matching the extracted spectral information for each pixel from the generated plurality of spectral frames to the generated reference spectral information using a statistical method to minimize the associated variation, and extracting spectral metadata from the matched reference spectral information for the associated material based on the match.

A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

The conformal spectral library training method (CSLTM) of the disclosure allows sets of voltages for an optical filter to be calculated by way of a direct calculation without processing large amounts of spectral information, which significantly increases the speed of processing spectral information.