Industry status
Since ancient times, China has upheld the principle that "food is the paramount necessity of the people." Consequently, food safety and quality inspection have become a major public concern and a societal priority. Food quality inspection revolves around three core objectives—safety, compliance, and quality consistency—covering the entire supply chain from raw materials to finished products.
The scope of food quality inspection is extensive, including:
Safety indicators (fundamental requirements): Such as whether pollutant limits exceed standards.
Sensory quality (direct experience): Perceptual attributes like appearance, odor, and taste, which serve as intuitive criteria for food grading.
Physicochemical and nutritional indicators (intrinsic quality): Core metrics reflecting composition, nutritional value, and processing precision.
Processing and traceability (quality assurance): Higher-grade foods often impose stricter requirements on production processes and traceability.
Through food grading, consumers can easily discern differences in food quality, while producers can enhance product competitiveness by improving grade levels and implementing differentiated pricing strategies.
Common Categories of Food Inspection
Foreign Object Detection in Food
Food Quality Inspection: Freshness & Appearance Defects
Food Composition Analysis
Food Origin Tracing
Food Ingredient Adulteration
Technical Background
Hyperspectral imaging technology, a novel remote sensing technique developed in the early 1980s, integrates morphological imaging with spectroscopic analysis into a unified framework, representing the future trajectory of advanced detection methodologies.
Product solution
Industrial Hyperspectral Camera
High frame rate
Wide FOV (Field of View)
Suited for industrial-grade applications, featuring multiple trigger control modes for coordinated operation with other devices
Data Processing Unit
X86 platform, Linux platform
Gigabit/10-Gigabit high-speed acquisition interface
Control interfaces such as RS232
Data Modeling and Processing Software
Supports real-time hyperspectral data/spectral data preprocessing
Supports real-time dark background compensation, illumination uniformity correction, full-pixel reflectance calculation, etc.
Supports traditional spectral algorithms for classification and identification
Supports machine learning algorithms, including both modeling and real-time processing modules
Industrial-Grade Dedicated Hyperspectral Camera
The HX Series of industrial-grade hyperspectral cameras are based on the prism-grating dispersion spectroscopy imaging technology independently developed by Hangzhou Gaopu Imaging. These cameras offer numerous advantages, including high spectral resolution, exceptional efficiency, excellent consistency, and user-friendly operation. Moreover, industrial applications demand equipment with high speed, robust performance, and reliability. The HX Series meets these requirements with high frame rates and wide field-of-view characteristics, addressing the efficiency needs of industrial users while providing outstanding cost-effectiveness for enhanced investment returns.
Featuring flexible pixel binning and customizable band selection capabilities, the HX Series significantly reduces the complexity of real-time data transmission and processing for users. This makes the cameras suitable for a wide range of applications, including industrial sorting, precision agriculture, food inspection, medical and pharmaceutical research, and environmental monitoring.
Application Cases
Foreign Object Detection (FOD)
The system demonstrates effective identification of agglomerated foreign matter in rice.
The system demonstrates effective identification of impurities such as wood chips and withered leaves in coffee beans.
Foreign Object Detection (FOD)
The system demonstrates effective differentiation and recognition of mushrooms from surface contaminants such as plastic film and dark spots.
The system achieves satisfactory results in identifying walnut shell fragments within walnut kernels.
Meat Composition and Safety Analysis
Applied to the non-destructive testing of chicken quality, it identifies and filters out chicken meat with a high degree of woodiness, thereby ensuring food quality and consumer experience.
Using a deep learning algorithm to establish a model and employing color rendering tools for mapping, woody chicken meat and normal chicken meat exhibit distinct differences. This functionality can be scaled for application in production lines to detect whether chicken breast exhibits woodiness.
Freshness Evaluation / Spoilage Degree Detection
The algorithm can rapidly identify withered areas on the surface of cabbage, thereby achieving effective differentiation of cabbages with varying levels of freshness.
Moisture Monitoring / Moisture Detection
Hyperspectral imaging enables rapid and non-destructive qualification testing of date moisture content.
Green indicates normal dates with qualified moisture content, while red signifies abnormal dates with unqualified moisture levels.
As illustrated, the combination of a hyperspectral camera and dedicated algorithms allows for highly distinct identification of whether the moisture content in dates meets the required standards.
Capsaicinoid Concentration Measurement
Hyperspectral imaging combined with algorithmic processing enables relatively accurate identification of hot pot base materials across six distinct spiciness levels (12°, 36°, 45°, 52°, 65°, and 75°).
Automated Doneness Inspection
Hyperspectral cameras, combined with dedicated algorithms, can efficiently and accurately distinguish between raw and roasted sesame seeds.
Meat Composition Analysis
With socio-economic development, consumers now have higher requirements regarding the composition of meat products. Meat with varying content of fat, lean meat, bone, and skin can be sold at different price points.
By utilizing hyperspectral cameras to collect data from pork samples, combined with dedicated algorithms, it is possible to identify and visually display the content of different components in the tested meat.
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