Automated Optical Traceability for Biological Sample Cassettes
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Solution Overview
Problem
Manual verification of embedding cassettes during the dehydration phase of biological samples is tedious and time-consuming, leading to potential errors and loss of material, especially with the increasing number of samples due to growing disease incidence rates.
Innovation Solution
An automated equipment and method that uses image capture and computer processing to determine an optimal work zone for detecting and reading encoded data on embedding cassettes, compatible with various basket models, allowing for rapid and accurate traceability by identifying structural features specific to each basket model and activating the detection means only in the optimal zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification of embedding cassettes is performed, then traceability can be ensured, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical verification with an automated optical reading system. Image capture means (camera) and encoded data detection means (barcode/QR code reader) automatically scan and verify cassette identification codes, eliminating the need for manual visual inspection while maintaining traceability reliability
Solution Approach 2:
The system enables self-verification through automated detection. The reading apparatus autonomously scans encoded data on cassettes, compares it with the basket manifest, and identifies discrepancies without human intervention, making the verification process self-sufficient and highly efficient
2Quantity of substance
If the number of cassettes increases due to growing disease incidence, then more samples can be processed, but manual verification becomes even more time-consuming
Solution Approach 1:
The automated reading system processes multiple cassettes simultaneously or in rapid sequence, using optical scanning to read encoded data much faster than manual verification. This maintains verification efficiency even as the total number of samples and cassettes increases
Solution Approach 2:
The reading apparatus is designed to work with multiple basket models and various encoded data formats (barcodes, QR codes). The system can handle different cassette sizes and arrangements, making it universally applicable regardless of the volume or type of samples being processed
3Reliability
If automated detection means are activated across the entire basket area, then all cassettes can be read, but the device complexity increases
Solution Approach 1:
The patent implements a selective reading strategy where the system determines the optimal work zone within the basket and activates detection means only in that specific area. This localized approach maintains detection reliability for all cassettes while reducing the active detection area, thereby simplifying the device requirements
Solution Approach 2:
The system dynamically adjusts the reading zone based on basket model identification and cassette arrangement. The optimal work zone is determined through image processing and can be adjusted for different basket configurations, making the detection system adaptable rather than static and complex
Data Source
AI summary
The equipment for traceability of biological samples placed in embedding cassettes stored in baskets includes an apparatus equipped with a device for detecting and reading encoded data and a controller of the device for detecting and reading encoded data, and a computer processor configured to automatically determine an optimal work zone, based on the model of basket housing cassettes, generate a signal representative of the zone, and transmit the corresponding information to the controller for the device for detecting and reading the encoded data. There is also an image capture device configured to capture and transmit at least one image of at least a portion of the basket. The computer processor is configured to search for a structural feature specific to a basket and to identify from the captured image of the portion of the basket, the model of the basket and determine the corresponding optimal work zone.