A spatial gradient-based fluorometer featuring a signal processor or processing module configured to: receive signaling containing information about light reflected off fluorophores in a liquid and sensed by a linear sensor array having a length and rows and columns of optical elements; and determine corresponding signaling containing information about a fluorophore concentration of the liquid a fluorophore concentration of the liquid that depends on a spatial gradient of the light reflected and sensed along the length of the linear sensor array, based upon the signaling received.

A state detection system includes a sensor (10) that includes an electromagnetic wave reflecting material (13) and a resonator (11) disposed adjacent to or integrally with the electromagnetic wave reflecting material (13), and that detects a state change of a surrounding object or surrounding environment as a change in its own electromagnetic wave reflection characteristic, a reader (20) that transmits an electromagnetic wave to the sensor (10), that receives a reflected wave of the electromagnetic wave, and that acquires reflected wave spectrum information of the sensor (10), and an analysis device (30) that estimates a current state of a detection target of the sensor (10) by applying information regarding reflected wave intensities at a plurality of frequency positions of the reflected wave spectrum to a learning model (30D) generated in advance on a basis of training data of a reflected wave spectrum for each state of the sensor (10).

A method for identifying carpet materials includes receiving, by a controller coupled to an NIR spectrometer, a predetermined number of NIR measurements of a sample of the carpet material conducted over a bandwidth of a subset of a full NIR spectrum. The NIR spectrometer performs the NIR measurements under control of the controller. Also, sending the NIR measurements to a remote identification server including a spectra library of known carpet materials. The method also includes receiving a matching result from the remote identification server, sending the matching result to a remote appraisal server, and receiving an appraised value of the carpet material.

A method of training a machine learning model for determining the composition of a mixture includes obtaining, using Fourier-transform infrared (FTIR) spectroscopy, a spectrum for each of a plurality of mixtures its constituent components. A concentration of each constituent component is known for each of the plurality of mixtures. A plurality of features is extracted from each of the obtained spectra. A machine learning model is trained using the plurality of features. An apparatus for determining formation of a product includes a reactor for containing a reaction mixture and an FTIR spectrometer for producing a spectrum of a sample of the reaction mixture. A processor extracts features from the spectrum; provides the features to an ML model trained using a plurality of mixtures of the constituent components to obtain a concentration of one or more of the constituent components; and determines the formation of the product based on the concentration.

A method, computer program product, and apparatus are provided for controlling components of a detection device. The device may detect turbidity of liquid with sensors such as a density sensor and/or nephelometric sensor. A light modulation pattern may reduce or eliminate interference in sensor readings. Readings may be performed during off cycles of an illumination light to reduce interference but to provide improved visibility of a tube. Dark and light sensor readings may be performed with an emitter respectively off or on to account for ambient light in subsequent readings. Readings from the density sensor and/or nephelometric sensor may be used to calculate McFarland values. The device may be zeroed based on an emitter level that results in a sensor reading satisfying a predetermined criterion.

A spectrophotometer includes: a sample-chamber lid capable of opening and closing an opening portion of a sample chamber for setting a sample and a reference sample; and sample-chamber lid opening-closing detecting means for detecting an opening-closing state of the sample-chamber lid, and the spectrophotometer is capable of controlling a measurement of a xenon flash tube as a light source, a spectroscope, a detector, an amplifier, an AD converter, a processor, a storage device, and a data display part. In the spectrophotometer, the light source is turned on after a state of the lid changing from an opening state to a closing state is detected in a sample-setting instruction state by the sample-chamber lid opening-closing detecting means; absorbancy, transmissivity, reflectivity, a sample-side energy value, or a reference-side energy value is measured; and a measurement result is displayed on the data display part.

Naphthalene, benzene, toluene, xylene, and other volatile organic compounds VOCs have been identified as serious health hazards. Embodiments of the invention are directed to methods and apparatus for near-real-time in-situ detection and accumulated dose measurement of exposure to naphthalene vapor and other hazardous gaseous VOCs. The methods and apparatus employ excitation of fluorophors native or endogenous to compounds of interest using light sources emitting in the ultraviolet below 300 nm and measurement of native fluorescence emissions in distinct wavebands above the excitation wavelength. The apparatus of some embodiments are cell-phone-sized sensor/dosimeter “badges” to be worn by personnel potentially exposed to hazardous VOCs. The badge sensor of some embodiments provides both real time detection and data logging of exposure to naphthalene or other VOCs of interest from which both instantaneous and accumulated dose can be determined.

A method for determining the concentration of a fluorescent and/or fluorescence-labeled analyte, and to a calibration method for preparing such determination, for use in the field of biological and environmental analysis in order to improve the accuracy of concentration determination, comprising the following steps: performing fluorescence measurements for calibration samples that have predetermined concentrations of a plurality of fluorescent and/or fluorescence-labeled reference analytes R.sub.j that differ from each other by values m of a diffusion measure characterizing the diffusion of the reference analyte, in order to determine the values i of a concentration-dependent parameter I; establishing functions F.sub.j(c)=i which describe the dependence of the parameter I on the concentration; determining the values of a slope parameter a for the respective reference analyte as a derivative of the respective function at c=0; determining the values m.sub.j of the diffusion measure for the reference analytes; establishing the dependence of the slope parameter a on the diffusion measure by a function E(m)=a; determining the value a.sub.sample specific to the analyte using the value m.sub.sample of the diffusion measure and the function E(m)=a; establishing an analyte-specific function F.sub.sample(c)=i; performing fluorescence measurements for the analyte and determining the concentration of the analyte using the value i of the concentration-dependent parameter I and the inverse function F.sup.−1.sub.sample(c).

A system senses analytes through one or more sensors that detect or measure a physical characteristic. The one or more sensor generate a spectroscopic-data signal corresponding to the detection. An edge device communicatively couples the one or more sensors that communicatively couples a wide-area network coupling a cloud service. The edge device includes a data acquisition device that receives spectroscopic data signals from the one or more sensor and a processor that processes the spectroscopic-data signals to identify an analyte. The edge device also includes a transceiver that transmits data identifying the analytes to the cloud service.

Defects from a hot scan can be saved, such as on persistent storage, random access memory, or a split database. The persistent storage can be patch-based virtual inspector virtual analyzer (VIVA) or local storage. Repeater defect detection jobs can determined and the wafer can be inspected based on the repeater defect detection jobs. Repeater defects can be analyzed and corresponding defect records to the repeater defects can be read from the persistent storage. These results may be returned to the high level defect detection controller.