Device and method for cooking rice
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Solution Overview
Problem
Conventional rice cookers lack the ability to adapt cooking programs to the specific nature of rice, leading to inconsistent organoleptic and nutritional properties, and existing spectral detection systems are complex, costly, and unreliable.
Innovation Solution
A rice cooking system that includes a near-infrared spectrometer and a predictive model to analyze rice spectra and determine optimal cooking instructions, using a dosing glass with wireless communication and a power source for efficient data exchange and processing, allowing for accurate adjustment of cooking parameters like soaking time and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user interface is added to allow users to declare the nature of rice, then the cooking program can be adapted to specific rice types, but the user interface complexity increases
Solution Approach 1:
The system automatically detects rice nature using a near-infrared spectrometer and predictive model without requiring user input. The rice cooker performs self-analysis by acquiring spectral data from the rice in the cooking chamber and comparing it against reference spectra to identify rice type and adjust cooking parameters automatically.
Solution Approach 2:
The manual user interface input method is replaced with an optical detection system. Instead of requiring users to select rice types through buttons or displays, the system uses near-infrared spectral analysis to automatically identify rice nature and determine optimal cooking parameters.
2Measurement precision
If a spectral detection device is positioned in the cooking tank, then rice analysis can be performed, but the device complexity and manufacturing cost increase
Solution Approach 1:
The near-infrared spectrometer serves multiple functions: it acts as both a communication device for data exchange and a spectral analysis tool for rice identification. By integrating these functions into a single device, the system reduces overall complexity compared to having separate communication and detection systems.
Solution Approach 2:
The system uses an intermediary predictive model that compares acquired spectral data against reference spectra stored in a database. This model acts as a mediator between the raw spectral data and the final rice identification, simplifying the decision-making process and reducing the complexity of direct analysis algorithms.
3Measurement precision
If a spectral detection device is placed in the cooking tank, then rice analysis is possible, but the device reliability decreases due to thermal stress
Solution Approach 1:
The system acquires spectral data before the cooking process begins, when the cooking chamber is still at ambient temperature. This preliminary measurement allows rice analysis to be performed without exposing the spectrometer to high cooking temperatures, thereby maintaining device reliability while still enabling accurate rice identification.
Solution Approach 2:
The measurement process is separated from the cooking process. Spectral acquisition occurs as a distinct preliminary step before heating begins, allowing the detection device to remain outside the high-temperature cooking environment while still providing the necessary analytical data for cooking parameter optimization.
4Measurement precision
If statistical analysis is performed on rice spectra, then more accurate cooking instructions can be determined, but more computing resources are required
Solution Approach 1:
The system performs spectral analysis at a limited set of key wavelengths relevant to rice identification rather than analyzing the entire spectrum. By focusing on specific spectral regions that are most indicative of rice type and properties, the system achieves accurate identification with reduced computational requirements.
Solution Approach 2:
Reference spectral data and cooking parameter mappings are pre-calculated and stored in a database during system setup. During actual operation, the system only needs to acquire the current rice spectrum and perform a comparison query against the pre-established database, rather than performing full statistical analysis in real-time, significantly reducing energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system reliably identifies rice nature and optimizes cooking for better taste and nutrition by determining specific cooking sequences based on spectral analysis, reducing user complexity and manufacturing costs while ensuring accurate results.
Implementation Method 1
a near-infrared spectrometer capable of acquiring at least one analysis spectrum of the rice disposed in the container
Data Source
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AI summary
The present invention relates to a system (1) for cooking rice comprising: - a rice cooker (10) comprising processing means (12) suitable for executing a sequence of instructions for controlling steps of preparing and cooking rice or simply steps for cooking rice received in the vessel (11), - a measuring cup (20) comprising a near infrared spectrometer (22) suitable for acquiring at least one analysis spectrum (SA) of the rice placed in a container (21), - a predetermined predictive model (40) created using a database (30) comprising reference instruction sequences (SIR) associated with reference analysis spectra (SAR) relating to different types of rice, the processing means (12) being designed to determine a particular instruction sequence (SIP) according to the predetermined predictive model (40) and the at least one analysis spectrum (SA) of the rice produced using the near infrared spectrometer (22).