Dynamic Portion Cutting via Fat Content Sensing
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Solution Overview
Problem
Existing methods for dividing food products into portions prior to cooking fail to ensure consistent weight after cooking, leading to under-weight or over-weight issues due to varying fat and lean content, resulting in product loss and inconsistent cooking quality.
Innovation Solution
A processing apparatus comprising a cutting machine, controller, and sensing arrangement that adjusts portion thickness based on the cross-sectional dimension and cook-out parameter related to the proportion of constituents like fat, lean, and bone, ensuring portions achieve a predetermined weight after cooking, while improving cooking uniformity and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the cutting thickness is set to ensure high-fat portions meet minimum weight after cooking, then high-fat portions achieve target weight, but high-lean portions exceed the minimum threshold resulting in substantial product give-away
Solution Approach 1:
The cutting machine dynamically adjusts slice thickness in real-time based on sensed fat content and cross-sectional area of each portion. The controller modifies cutting parameters on-the-fly rather than using fixed thickness settings, enabling adaptation to varying product composition along the log.
Solution Approach 2:
The sensing arrangement provides feedback signals about fat content and cross-sectional dimensions to the controller, which then adjusts cutting thickness accordingly. This closed-loop control system uses real-time product analysis to optimize portion weight after cooking.
Solution Approach 3:
Each portion is cut with a thickness tailored to its specific fat content and cross-sectional area. High-fat portions receive thinner cuts while lean portions receive thicker cuts, ensuring local optimization rather than uniform treatment of all portions.
2Productivity
If fixed cutting thickness is used for all portions, then cutting operation is simple and fast, but cooked portions have inconsistent weight and cooking quality
Solution Approach 1:
The sensing arrangement analyzes the cross-sectional area and fat content of each portion before the cutting operation. This preliminary measurement allows the controller to pre-calculate the optimal cutting thickness for each portion, ensuring precision without slowing down the cutting process.
Solution Approach 2:
The system replaces manual or fixed mechanical cutting thickness control with an automated optical/sensing-based control system. The sensing arrangement and controller substitute for traditional mechanical thickness adjustment mechanisms, enabling dynamic precision control.
Data Source
AI summary
A new method for dividing a food product log into separate portions prior to cooking of the portions uses a machine that cuts portions from an end of the product log, a controller that controls the operation of the cutting machine, and a sensing arrangement that generates a signal dependent on a cross-sectional dimension of the product at the end. The controller determines the thickness of the next portion to be cut from the end by the cutting machine using the signal from the sensing arrangement and the value of a cook-out parameter related to the proportion of the end of the product that is formed by at least one constituent of the product. The thickness is calculated with a view to the portion achieving a predetermined target weight after it has been cooked.