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.

Instruments, systems, and methods for measuring optical density of microbiological samples are provided. In particular, optical density instruments providing improved safety, efficiency, comfort, and convenience are provided. Such optical density instruments include a handheld portion and a base station. The optical density instruments may be used in systems and methods for measuring optical density of biological samples.

Semi-automated quantitative phenotype analysis of organisms, such as plant organisms, bacterial organisms, and the like. An imaging system, image capture specifications, a semi-automated quantitative image analysis process, and automated batch processing of acquired images that enables completely quantitative experimental readouts that produces data rapidly and objectively.

A turbidity sensor featuring a signal processor or processing module configured to: receive signaling containing information about light reflected off suspended matter in a liquid and sensed by a linear sensor array having rows and columns of optical elements; and determine corresponding signaling containing information about a concentration of turbidity of the liquid, based upon the signaling received

A swept frequency fluorometer having a signal processor or processing module configured to:

receive signaling containing information about reflected light off one or more fluorescence species-of-interest in a liquid sample that is swept with light having a variable frequency range, the information including a characteristic optical frequency corresponding to a fluorescence species-of-interest in the liquid, and a characteristic/lifetime optical frequency corresponding to a distinct fluorescence lifetime in which the fluorescence species-of-interest remains in an excited state; and

provide corresponding signaling containing information about an identity of the fluorescence species-of-interest detected and distinguished from overlapping fluorescence species in the liquid using the characteristic/lifetime optical frequency, based upon the signaling received

A substance detection method and apparatus, and a detection device are provided. The method includes: obtaining spectral information and image information of a substance to be detected; performing image recognition according to the image information to obtain appearance state information of the substance to be detected; determining a sub-database matching the appearance state information in a preset spectral database according to the appearance state information, wherein the spectral database includes a plurality of sub-databases classified according to the appearance state of the substance; and matching the spectral information with the obtained spectral information in the sub-database to obtain a detection result of the substance to be detected.

A fluorescence-lifetime imaging microscopy method with time-correlated single-photon counting includes using excitation light pulses separated in each case by a measurement interval to excite a sample to emit fluorescence photons. A detector signal that represents the captured fluorescence photons is generated. Detection times are determined based on the detector signal. Imaging is performed based on the detection times. The detection times of all captured fluorescence photons are compiled in a first data memory, common to a plurality of image pixels. The detection times of only those fluorescence photons which were captured in a predetermined number within the respective measurement intervals are compiled in a second data memory, common to the same plurality of image pixels. The detection times compiled in the data memories are combined within a calculation step. The results of the calculation step are stored in a third data memory.

A turbidity measurement method includes irradiating a first irradiation light L1 having a first spectrum E1, detecting a first measured light ML1 based on the first irradiation light L1, irradiating a second irradiation light L2 having a second spectrum E2 different from the first spectrum E1, detecting a second measured light ML2 based on the second irradiation light L2, calculating turbidity of a liquid to be measured, and correcting at least one of a first parameter related to turbidity calculation associated with the first irradiation light L1 and a second parameter related to turbidity calculation associated with the second irradiation light L2 so that the calculated turbidity of the liquid to be measured corresponds to the turbidity of the liquid to be measured as measured using another light source serving as a standard of comparison.

A method and a device for testing an electrode sheet, the method may comprise: acquiring M pieces of test data of a tab, wherein the tab protrudes from the end face of the electrode sheet along the first direction, the M pieces of test data are the test data of the tab at M consecutive positions along the second direction, the M pieces of test data are the test data of the height of the tab in the first direction, the second direction is perpendicular to the first direction, and M is a positive integer greater than 1; and determining whether the shape of the tab is normal based on the M pieces of test data.

Disclosed are a material classification apparatus and method based on a multi-spectral NIR band. A material classification apparatus based on a multi-spectral NIR band includes: an input unit configured to acquire a multi-band NIR image of a target; an attention module configured to generate a spatio-spectral correlation map considering spatial information on the multi-band NIR image and a correlation between each band; and a classification model unit configured to analyze the spatio-spectral correlation map and output a material classification label for the target.