Hyperspectral Imaging in Industrial Machine Vision
Companies in the food industry are facing a profound transformation: the pressure to produce continuously in compliance with legal requirements, fully traceable, and at the highest quality is steadily increasing. Conventional inspection methods such as manual visual checks, RGB cameras, or X-ray analyses reach their limits. Invisible foreign objects like soft plastics or organic contaminants, minimal differences in ripeness or texture, as well as defective batches, can remain undetected. Product recalls cause financial damages amounting to millions and severely damage consumer trust in brands and products. At the same time, demands on process reliability, speed, and automation continue to grow. In this challenging environment, hyperspectral imaging (HSI) provides a crucial solution: it detects what remains invisible to the human eye and conventional technology, securing quality and product safety at the molecular level automated, in real-time, and integrated into processes.
Many other industrial sectors also face the limitations of established machine vision systems. Whether in recycling plants, color measurement during production, surface coating inspection, art restoration, forensic trace analysis, or general quality assurance, decision-makers struggle with similar challenges. Small quality deviations or foreign materials often remain unnoticed with conventional methods. Misclassification of materials is common when different substances appear visually similar. The consequences range from inefficient processes and high waste to costly product recalls and damage to brand reputation. Against this background, hyperspectral imaging (HSI) is rapidly gaining importance. It promises to make the invisible visible and opens a new era in industrial machine vision where quality and safety can be elevated to unprecedented levels.
Limits of Conventional Machine Vision Systems
Traditional inspection methods such as RGB cameras, near-infrared (NIR) sensors, or X-ray scanners often reach physical limitations in complex inspection tasks. An RGB system captures only three broad color channels in visible light—fine spectral differences completely escape detection. NIR sensors or simple spectral filters provide some additional wavelength bands, but they still cannot truly analyze the chemical composition of an object. X-ray systems mainly detect differences in density; foreign objects with densities similar to the base material often remain invisible.
In the food industry, a particular challenge arises because quality assurance is under increasing regulatory and societal pressure. Foreign objects such as soft plastics, wood splinters, or insects are difficult to detect with RGB cameras or X-ray systems—especially if they are color-matched to the product or have similar density. Conventional systems frequently overlook critical contamination, leading to recalls that cause not only high costs but also lasting damage to brand reputation. This challenge is further intensified by the rising demand for unprocessed, “clean label” products: here, visual inspection is especially unreliable, as the human eye and RGB-based technologies quickly reach their limits.
In the recycling industry, not only economic efficiency but also environmental responsibility is increasingly in focus. Sorting plants are expected to separate plastics, textiles, wood, and metals by type to close resource loops and conserve materials. However, many materials are hardly distinguishable by color—for example, black polypropylene versus black polyethylene—or are contaminated with composite materials. The consequence is impurities in material streams that promote downcycling or render entire batches unusable. Faulty sorting here means not only economic loss but also hinders ecologically sustainable recycling pathways.
Hyperspectral Imaging Makes the Invisible Visible
This is where hyperspectral imaging (HSI) comes in, fundamentally changing the game. HSI combines established spectroscopy with modern camera technology: instead of measuring only three color points, an HSI camera captures the complete reflection spectrum of every single image pixel—often across hundreds of narrow spectral bands. This provides a unique spectral fingerprint for each point on an object, directly linked to its material composition. Differences hidden in the visible image become clearly apparent in the spectral profile. Different materials absorb and reflect light in characteristic ways. HSI exploits this principle to reveal chemical and physical differences—even when two objects appear identical to the human eye.
Functional Benefits: More Than Just Seeing
The unique capabilities of hyperspectral imaging (HSI) translate into tangible functional advantages across diverse applications.
Firstly, HSI enables real-time material classification. Every substance—whether plastic, metal, organic material, pigment, or coating—has a characteristic spectral pattern. Modern HSI systems rapidly recognize these patterns using AI algorithms, allowing for precise material sorting or unambiguous object identification.
In food production, HSI is not only a driver of efficiency but also a core element of process safety. By detecting invisible contaminations, differences in ripeness, or fermentation before packaging, production risks can be eliminated early. The technology also allows continuous monitoring of product consistency—such as fat content, water percentage, or sugar profile—so that quality is ensured inline and continuously, rather than through spot checks. For manufacturers, this means fewer complaints, more stable processes, and significantly improved compliance with regulatory requirements—all while reducing the workload on employees through automated controls.
Secondly, HSI brings true spectral analysis directly into the production process. Spectroscopy was previously mostly limited to lab instruments; now, it is available inline. Another major advantage is the detection of foreign materials and defects with unmatched precision. Hyperspectral systems detect contaminants down to fractions of a millimeter because each particle stands out due to its unique chemical composition. Finally, HSI supports broad automation in industrial image processing, enabling smarter, faster, and more reliable quality assurance.
Economic Value: Quality Pays Off
Beyond its technical capabilities, hyperspectral imaging delivers significant economic benefits. Foremost is the reduction of waste and defective production. By detecting even the slightest deviations early, defective parts are sorted out or processes corrected immediately—before an entire batch becomes unusable. Every avoided defective production saves material costs, energy, and disposal expenses.
Especially in the recycling sector, hyperspectral classification is key to ecological efficiency. Only through precise, spectrally based differentiation can complex material streams—such as mixed textiles, waste plastics, or types of reclaimed wood—be separated cleanly and thus directed to material recycling. Where conventional methods separate merely by appearance, HSI enables material-based decisions. The result: fewer sorting errors, higher quality recycles, and lower CO₂ footprints. Companies deploying this technology increase recovery rates and strengthen their ESG positioning—since the ability to transform waste into new raw materials becomes a measurable sustainability promise to customers, regulators, and investors.
Emotional Benefits for Decision-Makers: Confidence and Innovation Leadership
Hyperspectral imaging has evolved from a once exotic lab technique into a practical game changer for industrial image processing. In sectors ranging from recycling and manufacturing to art and forensics, HSI sets new benchmarks by solving problems traditional systems left unresolved. This enables companies not only to fundamentally improve quality assurance and reduce costs but also to strengthen their market position. Decision-makers investing in HSI today address urgent pain points of their industry and turn them into competitive advantages. The message of this strategic paper is clear: Those who make the invisible visible create value that goes far beyond what the eye can see.
About the Author
Dr. Matthias Locherer has been the Sales Director at Cubert GmbH since 2017. With a PhD in Earth Observation from Ludwig Maximilian University of Munich, he brings extensive expertise in remote sensing, spectral imaging, and data analysis. Matthias has contributed to numerous research projects and publications, particularly in the hyperspectral monitoring of biophysical and biochemical parameters using hyperspectral satellite missions. His deep knowledge of optical measurement techniques and physical modeling makes him a key driver in advancing innovative hyperspectral technologies at Cubert.
