APPARATUS FOR ANALYSING EMULSION QUALITY

Abstract

An instrument 101 for analyzing emulsion quality, creaming, and sedimentation processes comprises a transparent vessel 102, multiple light sources 103, and detectors 104 arranged vertically. A motorized stirrer 105 ensures uniform mixing. A temperature control system 106 maintains the desired conditions. A microprocessor 107 controls operations and processes data. The instrument uses light transmission analysis 108 to determine particle size distribution and concentration changes over time. Machine learning algorithms 109 enhance data interpretation. A user interface 110 displays results and allows parameter adjustments. The instrument provides real-time, non-destructive analysis of emulsion stability and behavior, enabling optimization of formulations in pharmaceutical and other industries.

Specification

Description:The instrument for analyzing emulsion quality, creaming, and sedimentation processes comprises several key components and features, each designed to contribute to its overall functionality and effectiveness.

1. Transparent Vessel (102):
The instrument is a transparent vessel 102 designed to hold the emulsion sample. This vessel is typically made of high-quality borosilicate glass or similar transparent material that is chemically inert and resistant to temperature changes. The vessel's transparency allows for the passage of light through the sample, which is crucial for the analysis process.

Key features of the transparent vessel include:
- Capacity: The vessel can accommodate sample volumes ranging from 10 mL to 500 mL, with different sizes available for various applications.
- Shape: The vessel has a cylindrical shape with a flat bottom to ensure uniform light transmission and even distribution of the sample.
- Markings: Graduated markings on the side of the vessel allow for accurate volume measurements.
- Lid: A removable, airtight lid prevents evaporation and contamination during long-term studies.

2. Light Sources (103):
Multiple light sources 103 are arranged vertically along the height of the transparent vessel. These light sources are designed to emit monochromatic light at specific wavelengths optimal for emulsion analysis.

Key features of the light sources include:
- Type: LED light sources are used for their stability, long life, and low heat generation.
- Wavelengths: The instrument includes light sources emitting at multiple wavelengths (e.g., 470 nm, 530 nm, 630 nm, and 850 nm) to analyse different aspects of the emulsion.
- Arrangement: The light sources are positioned at regular intervals (typically 5-10 mm apart) along the height of the vessel.
- Control: Each light source can be individually controlled and modulated by the microprocessor for precise measurements.

3. Detectors (104):
Corresponding to each light source, there are multiple detectors 104 arranged on the opposite side of the transparent vessel. These detectors measure the intensity of light transmitted through the emulsion sample.

Key features of the detectors include:
- Type: High-sensitivity photodiodes are used for accurate light intensity measurements.
- Spectral Response: The detectors are chosen to match the wavelengths of the light sources.
- Arrangement: Detectors are aligned with their corresponding light sources for direct transmission measurements.
- Data Collection: Each detector continuously records light intensity data, which is then processed by the microprocessor.

4. Motorized Stirrer (105):
A motorized stirrer 105 is integrated into the instrument to ensure uniform mixing of the emulsion sample before and during analysis.

Key features of the motorized stirrer include:
- Design: The stirrer consists of a vertical shaft with adjustable impeller blades.
- Speed Control: The stirring speed can be precisely controlled from 50 to 1000 RPM.
- Programmable Operation: The stirrer can be programmed to operate at specified intervals and durations.
- Material: The stirrer is made of chemically inert materials to prevent sample contamination.

5. Temperature Control System (106):
The temperature control system 106 maintains the desired temperature conditions throughout the analysis process.

Key features of the temperature control system include:
- Heating Element: A resistive heating element surrounds the transparent vessel for uniform heating.
- Cooling System: A Peltier-based cooling system allows for precise temperature reduction and control.
- Temperature Sensors: Multiple Pt100 resistance temperature detectors (RTDs) are placed at different locations to ensure uniform temperature distribution.
- Control Range: The system can maintain temperatures from 4°C to 60°C with an accuracy of ±0.1°C.
- Temperature Cycling: Programmable temperature profiles allow for stability studies under varying conditions.

6. Microprocessor (107):
A sophisticated microprocessor 107 serves as the brain of the instrument, controlling all operations and processing data.

Key features of the microprocessor include:
- Control Functions: The microprocessor manages light source activation, detector readings, stirrer operation, and temperature control.
- Data Processing: Raw data from the detectors is processed in real-time to extract information about emulsion characteristics.
- Interface Management: The microprocessor handles user interface operations and data display.
- Connectivity: USB and Ethernet ports allow for data export and remote operation.

7. Light Transmission Analysis (108):
The instrument utilizes light transmission analysis 108 as its primary analytical technique.

Key aspects of the light transmission analysis include:
- Multi-Height Measurements: Light transmission is measured at multiple heights simultaneously, providing information about concentration gradients.
- Time-Resolved Data: Continuous measurements allow for tracking of dynamic processes in the emulsion.
- Wavelength-Dependent Analysis: By using multiple wavelengths, the instrument can differentiate between particle size changes and concentration changes.

8. Machine Learning Algorithms (109):
Advanced machine learning algorithms 109 are employed to enhance data interpretation and provide insights into emulsion behavior.

Key features of the machine learning algorithms include:
- Pattern Recognition: The algorithms identify patterns in light transmission data that correspond to specific emulsion behaviors.
- Predictive Modeling: Based on initial measurements, the system can predict long-term stability trends.
- Comparative Analysis: The algorithms can compare new data with a database of known emulsion behaviors to classify the sample's characteristics.

9. User Interface (110):
A user-friendly interface 110 allows operators to control the instrument and view results.

Key features of the user interface include:
- Touch Screen Display: A high-resolution touch screen provides easy access to all functions.
- Real-Time Data Visualization: Graphs and charts display emulsion characteristics in real-time.
- Parameter Adjustment: Users can easily modify analysis parameters, such as measurement intervals and temperature profiles.
- Data Export: Results can be exported in various formats for further analysis or reporting.
10. Sample Holders for Small Volume Analysis (111):
Specialized sample holders 111 are provided for analyzing small volumes of emulsions.

Key features of the small volume sample holders include:
- Capacity: These holders can accommodate sample volumes as low as 1 mL.
- Design: The holders are designed to fit within the main transparent vessel while maintaining temperature control and stirring capabilities.
- Material: Made of transparent, chemically inert materials compatible with various emulsion types.

11. Calibration System (112):
An integrated calibration system 112 ensures the accuracy and reliability of measurements.

Key features of the calibration system include:
- Standard Samples: A set of standard emulsion samples with known characteristics is provided for calibration.
- Automated Procedure: The instrument guides users through a step-by-step calibration process.
- Calibration Schedule: The system prompts users to perform calibrations at recommended intervals.

Operation and Workflow
1. Sample Preparation:
- The emulsion sample is prepared according to the formulation requirements.
- The appropriate sample volume is selected based on the analysis needs.

2. Instrument Setup:
- The user selects the analysis parameters through the user interface (110).
- Parameters include measurement duration, temperature profile, stirring intervals, and light source settings.

3. Sample Loading:
- The prepared emulsion is carefully loaded into the transparent vessel 102 or the small volume sample holder 111.
- The vessel is securely placed in the instrument, ensuring proper alignment with light sources and detectors.

4. Analysis Initiation:
- The user starts the analysis through the user interface 110.
- The microprocessor 107 initiates the predetermined sequence of operations.

5. Temperature Control:
- The temperature control system 106 brings the sample to the specified starting temperature.
- If a temperature profile is selected, the system follows the programmed changes over time.

6. Stirring:
- The motorized stirrer 105 operates according to the set parameters, ensuring uniform mixing of the sample.

7. Light Transmission Measurements:
- Light sources 103 are activated sequentially or simultaneously, as per the analysis protocol.
- Detectors 104 continuously measure the transmitted light intensity at different heights of the sample.

8. Data Processing:
- The microprocessor 107 collects and processes the raw data from the detectors.
- Light transmission analysis 108 algorithms calculate parameters such as particle size distribution and concentration gradients.

9. Machine Learning Analysis:
- Machine learning algorithms 109 analyse the processed data to identify patterns and predict emulsion behavior.

10. Real-Time Display:
- The user interface 110 displays real-time graphs and data, showing the evolution of emulsion characteristics over time.

11. Data Export and Reporting:
- Upon completion of the analysis, the user can export the data and generate reports through the user interface.

12. Cleaning and Maintenance:
- After analysis, the transparent vessel and stirrer are cleaned according to standard protocols.
- The instrument prompts for calibration (112) if required.

Method of Performing the Invention:
1. Instrument Preparation:
- Ensure the instrument is calibrated using the integrated calibration system 112.
- Clean the transparent vessel 102 and motorized stirrer 105 thoroughly with appropriate solvents and dry completely.
- Verify that all light sources 103 and detectors 104 are functioning correctly through a system diagnostic check.

2. Sample Preparation:
- Prepare the emulsion sample according to the formulation of interest.
- For small volume analysis, use 5 mL of the sample in the specialized small volume sample holder (111).
- For standard analysis,
2. Sample Preparation (continued):
- For standard analysis, use 100 mL of the sample in the main transparent vessel 102.
- Ensure the sample is at room temperature before analysis, unless a specific starting temperature is required.

3. Parameter Setting:
- Through the user interface 110, set the following parameters:
a. Analysis duration: Typically, 24 hours for long-term stability studies.
b. Temperature profile: e.g., constant 25°C or cycling between 4°C and 40°C.
c. Stirring protocol: e.g., initial 2 minutes at 500 RPM, then 10 seconds every hour at 200 RPM.
d. Light source activation: all wavelengths (470 nm, 530 nm, 630 nm, and 850 nm) at 5-minute intervals.
e. Data collection rate: every 30 seconds during the first hour, then every 5 minutes.

4. Sample Loading and Analysis Initiation:
- Gently pour the prepared emulsion into the vessel, avoiding air bubble formation.
- Secure the vessel in the instrument, ensuring proper alignment.
- Close the instrument's chamber to maintain temperature control.
- Initiate the analysis through the user interface 110.

5. Monitoring and Data Collection:
- The microprocessor 107 controls the entire analysis process automatically.
- Monitor the real-time data display on the user interface for any unexpected behavior.
- The instrument collects data on light transmission at different heights and wavelengths over time.

6. Data Processing and Interpretation:
- The light transmission analysis 108 algorithms process the raw data to determine:
a. Particle size distribution changes over time.
b. Creaming or sedimentation rates.
c. Phase separation onset and progression.
- Machine learning algorithms 109 analyse the processed data to:
a. Identify patterns indicative of emulsion instability.
b. Predict long-term stability based on short-term data.
c. Compare results with known stable and unstable emulsion profiles.

7. Result Analysis and Reporting:
- After the analysis is complete, review the graphical representations of emulsion behavior.
- Export the raw data and processed results for further analysis if needed.
- Generate a comprehensive report including:
a. Particle size distribution over time.
b. Creaming/sedimentation index.
c. Stability prediction.
d. Comparison with predefined stability standards.

8. Clean-up and Maintenance:
- Carefully remove and dispose of the sample according to laboratory protocols.
- Clean the vessel and stirrer with appropriate solvents and dry thoroughly.
- Run a brief system check to ensure all components are functioning correctly for the next analysis.

This method ensures optimal utilization of the instrument's capabilities, providing comprehensive and reliable data on emulsion quality, creaming, and sedimentation processes.
, Claims:1. An instrument for analysing emulsion quality, creaming, and sedimentation processes, comprising:
? transparent vessel 102 for holding an emulsion sample;
? multiple light sources 103 arranged vertically along the height of the transparent vessel;
? multiple detectors 104 arranged vertically along the height of the transparent vessel, opposite to the light sources;
? a motorized stirrer 105 for mixing the emulsion sample;
? a temperature control system 106 for maintaining desired temperature conditions;
? a microprocessor 107 for controlling instrument operations and processing data;
? light transmission analysis algorithms 108 for determining particle size distribution and concentration changes;
? machine learning algorithms 109 for enhancing data interpretation;
? a user interface 110 for displaying results and allowing parameter adjustments;
? specialized sample holders 111 for small volume analysis; and
? a calibration system 112 for ensuring measurement accuracy.
2. A method for analyzing emulsion quality, creaming, and sedimentation processes using the instrument of claim 1, comprising the steps of:
? preparing an emulsion sample;
? loading the sample into the transparent vessel or specialized sample holder;
? setting analysis parameters through the user interface;
? initiating the analysis process;
? controlling temperature and stirring conditions;
? measuring light transmission at multiple heights and wavelengths over time;
? processing the collected data using light transmission analysis algorithms;
? applying machine learning algorithms to interpret the processed data;
? displaying real-time results on the user interface; and
? generating a comprehensive report on emulsion stability and behavior.
3. The system as claimed in claim 1, wherein the light sources emit light at wavelengths of 470 nm, 530 nm, 630 nm, and 850 nm.
4. The system as claimed in claim 1, wherein the temperature control system maintains temperatures from 4°C to 60°C with an accuracy of ±0.1°C.
5. The system as claimed in claim 1, wherein the motorized stirrer operates at speeds ranging from 50 to 1000 RPM.
6. The system as claimed in claim 1, wherein the specialized sample holders accommodate sample volumes as low as 1 mL.
7. The method as claimed in claim 2, further comprising the step of predicting long-term emulsion stability based on short-term measurement data using the machine learning algorithms.
8. The method as claimed in claim 2, wherein the light transmission measurements are taken at intervals ranging from 30 seconds to 5 minutes.
9. The method as claimed in claim 2, wherein the analysis duration ranges from 1 hour to 30 days.
10. The system as claimed in claim 1, further comprising a remote access capability for controlling the instrument and accessing data through a network connection.

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