1Rice Appearance Quality TesterOperation specification: Standardized process ensures detection accuracy

Equipment inspection and calibration

Hardware inspection: Before starting up, confirm that all components of the instrument (such as the camera, light source, and sample tray) are not damaged and connected tightly and reliably. Check if the power cord is grounded to avoid electromagnetic interference.

Environmental adaptation: Place the instrument on a horizontal, dry, and ventilated table, away from direct sunlight and vibration sources. The laboratory needs to control temperature and humidity (such as temperature 20-25 ℃, humidity ≤ 60%), and the processing plant needs to avoid dust interference.

Calibration process:

Use standard samples (such as reference rice with known grain size and color) for calibration, adjust the brightness of the light source to evenly cover the detection area.

Adjust the camera focal length to ensure clear and blurred images. Some high-end models support automatic calibration function, which can shorten preparation time.

After daily startup or before changing the testing batch, recalibration is required to eliminate the impact of environmental changes.

Sample preparation and loading

Sample selection: Randomly select 100-200g of rice from the batch to be tested, ensuring that the sample is representative. Avoid selecting samples that are damp, moldy, or have too many impurities.

Preprocessing:

Laboratory: Clean the samples to remove dust, air dry until constant weight (to avoid moisture affecting color detection).

Processing plant: If testing raw materials, impurities such as stones and rice husks need to be removed; If testing the finished product, it can be used directly.

Uniform spreading: Spread the sample evenly on the sample tray, covering the entire detection area with a thickness not exceeding 5mm. Avoid overlapping or stacking to prevent image analysis errors.

Parameter setting and detection startup

Parameter configuration:

Select the mode according to the testing requirements (such as "particle analysis", "color grading", "defect detection").

Set the light source type (such as LED white light, near-infrared light) and exposure time (usually 10-50ms).

Processing plants can preset commonly used parameter templates (such as "precision rice detection" and "broken rice rate analysis") to reduce repetitive operations.

Start detection: Close the protective cover and start the program. The device automatically completes image acquisition, preprocessing (denoising, binarization), feature extraction (such as aspect ratio, chalkiness), and data analysis.

Result processing and equipment maintenance

Result output: After the detection is completed, the device generates a report (including data on particle distribution, broken rice rate, number of discolored particles, etc.) and an image comparison chart. The laboratory needs to save the original data for traceability, and the processing plant can directly print labels and attach them to the product packaging.

Cleaning and maintenance:

After each test, use a soft bristled brush to remove any residue from the sample disk to avoid scratching the surface.

Wipe the camera and light source with a dust-free cloth every week to prevent dirt from affecting the imaging quality.

Regular maintenance:

Check sensor sensitivity every quarter and replace aging components (such as light sources with a lifespan of approximately 5000 hours).

Contact manufacturers annually for in-depth calibration to ensure that the equipment meets national standards (such as GB/T 1354-2018).

2、 Efficiency improvement strategy: from process optimization to technology upgrade

Process optimization: reduce non detection time

Batch processing: Select models that support continuous testing of multiple sample disks (such as accommodating 4 sample disks) to achieve a "one disk testing, one disk preparation" assembly line operation.

Quick sample change: Design a detachable sample tray, and the processing factory can pre install the next batch of samples during the testing gap, reducing the sample change time to within 1 minute.

Software interaction: Choose software that supports shortcut key operations and batch data export to reduce manual input errors. For example, transferring data directly to the ERP system via USB or LAN.

Technological Upgrade: Introducing Intelligent Features

AI assisted analysis: Some high-end models (such as the X series of a certain brand) are equipped with deep learning algorithms that can automatically identify complex defects (such as insect damage and cracks), reducing the need for manual review.

Multi sensor fusion: Combining spectral sensors to detect the internal components of rice (such as moisture and protein), achieving synchronous evaluation of "appearance+interior" quality and improving the comprehensiveness of detection.

Cloud platform integration: Connect devices to the cloud through IoT technology to achieve remote monitoring, data sharing, and fault warning. For example, the headquarters of the processing plant can view the testing results of each production line in real time.

Personnel training and standardized management

Graded training:

Basic operations: Train operators to master equipment switching, calibration, and simple troubleshooting.

Advanced analysis: Train quality inspectors to interpret data reports and develop improvement measures (such as adjusting grinding processes based on broken rice rates).

SOP development: Develop an illustrated manual that specifies the time requirements and quality standards for each step. For example, it is stipulated that 'sample plating must be completed within 30 seconds, with a thickness error of ≤ 1mm'.

Performance evaluation: Incorporate testing efficiency (such as single batch testing time) and accuracy (such as deviation from manual review results) into KPIs to motivate employees to optimize operations.

IIIRice Appearance Quality TesterSuggestions for differentiated application scenarios

Laboratory scene:

Focusing on scientific research needs, select models that support high-precision measurement (such as particle size error ≤ 0.01mm) and customized analysis.

Equipped with microscope accessories for studying the influence of rice microstructure (such as starch particle distribution) on appearance.

Processing plant scenario:

Prioritize durable equipment (such as IP65 protection level) to adapt to dusty and humid environments.

Integrate into the production line to achieve closed-loop control of "online detection automatic sorting data feedback". For example, adjust the grinding pressure in real-time based on the broken rice rate.