The type of blood collection tube (1) inserted into this system is automatically recognized, a lifting operation for checking the state and volume of analyte is switched on the basis of the automatic recognition result, a mechanical operation parameter based on the blood collection tube type is determined, and the blood collection tube (1) is grasped and lifted. Then, an image processing threshold is changed on the basis of the automatic recognition result, and image processing is performed using a threshold parameter based on the blood collection tube type to check the state and volume of the analyte. This enables precise automatic checking of the state and/or volume of the analyte for various types of blood collection tubes.

A sample rack transport apparatus for transporting a sample rack to a sample analyser, comprising: a bidirectional transmission track for bidirectionally transmitting a sample rack without passing through the sample analyser; a feed channel in parallel with the bidirectional transmission track, wherein the sample rack may be delivered from the bidirectional transmission track to the feed channel and to the sample analyser; an unloading cache region located between the bidirectional transmission track and the feed channel, the unloading cache region being used for storing the sample tack; and an unloading mechanism for delivering the sample rack in the feed channel to the unloading cache region for storage, or delivering the sample rack stored in the unloading cache region to the bidirectional transmission track. Also provided are a sample analysis device and a sample analysis system using the sample rack transport apparatus.

The specimen-container loading or storing unit includes: a plurality of types of specimen trays having a different size of a specimen tray that is provided with a plurality of specimen container installation parts for holding a specimen container; a specimen container transferring mechanism for transferring the specimen container between a conveyance line and the specimen tray; and a specimen tray installation part capable of installing the plurality of types of specimen trays.

The sample rack conveyance device includes a pusher member, a driving unit, a control unit, a front rack detection sensor, and a pusher movement amount storage unit. The front rack detection sensor moves together with the pusher member, detects an additional sample rack arranged more frontward in the conveyance direction than the sample rack conveyed by the pusher member. The pusher movement amount storage unit stores the movement amount at the time of movement from a sample rack conveyance start position on the pusher member. Furthermore, the control unit controls driving of the driving unit on the basis of the movement amount stored in the pusher movement amount storage unit.

The sample rack conveyance device includes a conveyance mechanism, a re-inspection waiting tray, a recovery tray, a conveyance lane for re-inspection, a control unit, and a recovery start signal output unit. At reception of the recovery start signal from the recovery start signal output unit, the control unit moves a pusher member of the conveyance mechanism to bring the pusher member of the conveyance mechanism into contact with the last sample rack accommodated on the upstream side in the conveyance direction on the re-inspection waiting tray or the recovery tray.

A computer-implemented method for spectroscopic analysis of biological material is provided that includes analyzing samples of biological material from a plurality of sources, and delivering samples of biological material to at least one flow cell for spectroscopy, and determining whether the spectroscopic analysis for each sample of the plurality of samples is or is predicted to be ambiguous in that it is affected by at least two non-discriminable factors. If such a determination is made, a disambiguating step can be performed.

Systems and apparatus with circuits that are configured to detect fallen containers upstream or proximate an intake zone suitable for automated evaluation apparatus using different sensors, including at least one lower sensor and at least one upper sensor which is positioned to project an optical signal at a height corresponding to a top portion of an upright container to thereby allow an increased reliability in detection of different orientations and positions of fallen containers. An optional second lower sensor may be used which is longitudinally spaced apart from the first lower sensor and the lower sensors can transmit optical signals across the container travel path that do not intersect.

A method and automated apparatus for rapid non-invasive detection of a microbial agent in a test sample is described herein. The apparatus may include one or more means for automated loading, automated transfer and/or automated unloading of a specimen container. The apparatus also includes a detection system for receiving a detection container, e.g., container or vial, containing a biological sample and culture media. The detection system may also include one or more heated sources, holding structures or racks, and/or a detection unit for monitoring and/or interrogating the specimen container to detect whether the container is positive for the presence of a microbial agent therein. In other embodiment, the automated instrument may include one or more, bar code readers, scanners, cameras, and/or weighing stations to aid in scanning, reading, imaging and weighing of specimen containers within the system.

According to one embodiment, an inspection system includes a first measurement unit for measuring a target, a first determination unit for making a first determination on whether the target includes a predetermined object using a first machine learning model based on a measurement result by the first measurement unit, a second measurement unit for measuring the target, a second determination unit for making a second determination on whether the target includes the predetermined object based on a measurement result by the second measurement unit, and a processing unit for generating first update data of the first machine learning model based on the second determination.

Embodiments disclosed herein relate to methods and systems for performing automated assays, and particularly to performing sequential assays on a sample on an automated instrument.