In an age where global regulations are tightened and consumer expectations are rising, the F&B industry is becoming increasingly more sophisticated visual testing technology. From finding defects to ensuring compliance, these automated inspection tools rebuild quality control, increased efficiency, reduced waste and improved safety. FoodBev's Siân Yates explores how cutting-edge technology is reshaping the industry of products that have been fully inspected at once.
In the food and beverage industry, traditional methods of quality inspection have always relied on human observation. This is an inherently inconsistent and flawed process. Automatic visual acuity testing systems offer a transformative alternative. By detecting foreign bodies, assessing product uniformity, and ensuring that only items that meet strict quality standards reach consumers, these systems significantly improve operational efficiency and minimize errors.
“As the food industry moves towards more automation, applications are becoming more and more complicated, primarily due to food variation,” said Anthony Romeo, product manager at US-based Vision Solutions Company Company. This complexity stems from the need for automated systems to adapt to a wide range of textures, sizes and ingredients in food, making accurate automation a key issue.
Stephan Pottel, Strategy Director at Zebra Technologies, highlighted the growing demand for intelligent automation. “The growing need for machine vision and 3D solutions has been enhanced, with vision guidance robotics such as inspection, conveyor belt picking and sorting workflows being enhanced to enhance deep learning and address more complex food and packaging use cases.
Key features of visual inspection
1. Defect detection
Vision inspection systems are excellent at identifying defects that may not be noticed by human inspectors. These systems utilize high-resolution cameras and advanced algorithms to detect foreign objects, surface defects, and size and shape inconsistencies. For example, in the fruit packaging industry, the vision system identifies bruised or rotten fruits, ensuring that only high-quality products are packaged and shipped.
2. Label verification
These technologies are increasingly being used for label verification, ensuring compliance with regulatory standards. The system can check correct placement, readability and labeling requirements, including allergen information and expiration dates. Vision is usually deployed for label verification rather than food surface defects, increasing compliance and reducing the risk of costly recalls.
3. Product uniformity assessment
In the food and beverage sector, maintaining product uniformity is important. Visual inspection systems can assess visual aspects such as size, shape, and color. For example, snack makers use vision tests to ensure that the tips are evenly shaped and colored, meeting consumer expectations for quality and appearance.
4. Adaptive manufacturing
Advanced vision systems, particularly those that incorporate AI and 3D technologies, allow for adaptive manufacturing processes. These systems allow real-time adjustment of production parameters based on visual data collected. For example, in a bakery, a vision system can monitor the size and shape of produced pastries, allowing you to adjust the baking time or temperature to ensure consistent quality.
The advancement of AI
Recent advances in AI, automation and 3D technology have significantly improved machine vision systems, improved accuracy, and provided realistic visual sensing capabilities. 3D imaging technology is used to evaluate product shape and size, ensuring that it meets packaging specifications. For example, in the seafood industry, 3D scanners evaluate the dimensions of fish fillets and ensure that they are cut to the correct size before packaging. This not only reduces waste, but also ensures consistency in product delivery.
Additionally, 3D profile sensors improve depth perception, improve quality control, and make them an essential tool for industrial automation. Oxipital AI's Romeo highlighted the possibilities of these technologies. “Removing defects before reaching customers is an important first step in which vision inspection technology plays a role, but we need to leverage more data.” By preventing defects from the start, manufacturers can increase yields and reduce waste.
An AI-powered visual inspection system can also facilitate real-time monitoring of production lines and identify potential issues before they escalate. This feature allows manufacturers to implement predictive maintenance, reduce downtime and improve overall efficiency.
AI and food safety
Consumer safety is a top priority in the food and beverage industry. AI plays a key role in real-time in process monitoring and analysis, helping manufacturers navigate the complexities of legal requirements and certification pressures from key retailers.
Pottel from Zebra Technologies explains: “AI is ideal for food and beverage products where classification, segmentation, object and anomaly detection is essential. It is also important to increase asset and inventory visibility, predict pollution risk, and maintain high safety standards across the supply chain.”
“Vision Technology helps you check food presentations… providing a fast, repeatable and reliable way to assess visual aspects of food, such as size, shape, and color.”
“This type of AI deployment supports rule-based machine learning and provides context for improving human decision-making. It also provides tools to extract and interpret as much data as possible from products, and facilitates the evolution and improvement of production processes through continuous exposure to vast data sets.”
Ai-Enhanced Vision Systems guides robots to handle foods that are particularly delicate or irregularly shaped. “AI has proven to be an excellent way to tackle applications with high frequency of naturally occurring natural organic variability, such as food,” explained Oxipital AI's Romeo, adding that this adaptability is particularly important when classifying fresh produce and packaging baked products.
Fortress technology uses AI to reduce the risk of contamination and identify defects. Jodie Curry, the company's commercial manager, told FoodBev: “The rationalization process reduces the risk of contamination and ensures consistent quality. The implementation of automated technology and digital tools helps identify inefficiencies and increase responsiveness.”
The role of the combined testing system
Integrating multiple testing technologies into a single system is another important trend in this area. These systems integrate a variety of testing techniques, including x-rays, checkways, and vision testing, to provide a comprehensive assessment of food. Combining these technologies allows manufacturers to ensure better quality control, better detection of defects and more efficient production lines. This trend allows for more accurate and reliable monitoring, reducing waste, improving safety standards and improving overall product quality.
For that part, the fortress offers a comprehensive, multi-layered system that allows for comprehensive inspections. The company has already contacted its proprietary data software package, 4.0, leveraging metal detection, x-ray and check-waing technology. Contact 4.0 allows the processor to view and collect data and safely monitor and oversee the performance of multiple fortress metal detectors, check wires, or combination inspection machines connected to the same network.
Deep learning and quality control
Deep learning is revolutionizing visual inspection by allowing machines to learn from the data and recognize variations of previously invisible defects, as explained by Zebra Technology.
This technology is essential to automate inspections and ensure quality. Deep Learning Optical Characteristics Recognition (OCR) also improves packaging inspections by ensuring label quality, regulatory compliance and brand protection. It can verify the presence of labels, check the accuracy of allergens, and prevent misunderstandings.
“The goal is to enhance quality control by capturing images and processing them against the quality control parameters of the set,” noted Gruettner of Mettler-Toledo.
Visual systems are increasingly deployed for label verification, ensuring compliance with legislative food labeling requirements. The Mettler-Toledo Label Inspection Portfolio features smart camera systems (V11, V13, V15) for basic label inspections such as barcodes, alphanumeric text, and label quality. For more advanced applications, PC-based V31 and V33 systems offer greater field of view, faster throughput, and enhanced inspection capabilities.
Oxipital AI uses 3D product scanning and synthetic data generation to eliminate the need for hand-label images. “All training is done through Oxipital AI, allowing food and beverage customers to deploy AI without the need for a team of experts,” Romeo said. “Our solutions are designed for immediate impact and do not require coding, DIY, or machine learning expertise to implement and maintain.”
Real-world applications and future prospects
According to Zebra's Global Manufacturing Vision Study, 66% of respondents plan to implement machine vision within the next five years, with 54% expecting AI to drive growth by 2029.
These figures, coupled with the expanding market for visual acuity testing systems, suggest
The majority of manufacturers prioritize the integration of these advanced technologies and see them as key tools for both immediate improvement and long-term growth.
This shift is partially driven by stricter government regulations and requires more accurate labeling and packaging. Many companies are already using AI to enhance operations, especially in the labeling process.
Despite its clear advantages, AI ingestion has slowed down. The main barrier appears to be cost. Initial integrations can be expensive, but AI has demonstrated significant long-term cost savings, making it a valuable investment over time.
Zebra's research shows that pressure to maintain quality while managing fewer resources is enhanced for manufacturers. As a result, cost remains an important consideration when implementing AI solutions.
Fortress recommends consolidating AI systems into a single interface. This will help you reduce costs in the long term. Curry told FoodBev: “The future of the food supply chain relies on sophisticated inspection systems that increase food safety, reduce product waste and require minimal factory floor space.”
She continued. “A combination system offers the advantages of space efficiency as all sales, services, parts and technical support are handled by one provider. A single interface promotes cost savings by simplifying training, improving operational safety, faster installation and reducing training times.”
As AI continues to evolve, its role in vision and inspection is set to expand. Advances in machine learning, sensor technology and robotics lead to even more sophisticated and efficient inspection systems, improving quality and safety standards for consumers around the world.
