Hinalea VSWIR Pro | Hyperspectral Camera 500-1700 nm

Food Safety 

The prevalence of pathogenic microorganisms, such as salmonella, listeria, and E. coli, in food products poses a significant threat to both consumer safety and the broader food industry. The failure to promptly detect these pathogens can lead to severe outbreaks of foodborne illnesses, resulting in an annual economic burden exceeding $100 billion and a global fatality toll exceeding four hundred thousand. Existing detection methods are expensive, involve intricate sample preparation, and take days to produce results, thereby amplifying the severity of outbreaks.

Hinalea Imaging has pioneered an innovative approach to rapid and reliable pathogen detection. Their self-contained system seamlessly integrates hyperspectral imaging, microscopy, and advanced deep machine learning, featuring automated sample loading and detection capabilities.

Through collaboration with the USDA, Hinalea Imaging has showcased the ability to rapidly identify pathogens in under 4 hours, significantly reducing the time required for diagnosis and response. This groundbreaking advancement paves the way for detection times of under an hour, transforming pathogen detection and response from a laborious and time-consuming lab process into a user-friendly point-of-care solution.

Food Quality

Conventional machine vision systems fall short in rigorously evaluating food and agricultural products. Commonly employed systems, using color video cameras with RGB filters to replicate human eye functionality, prove inadequate for many functions in modern processing environments.

Hyperspectral Imaging emerges as a next-generation technology that can either augment or replace existing machine vision systems. Offering high spectral and spatial resolution, hyperspectral imaging facilitates comprehensive and data-rich inspection of foods and other products. It extends into the UV or IR range, providing details on chemical and structural composition not discernible in the visible spectrum.

Hyperspectral imaging contributes additional spectral information, empowering manufacturers to enhance quality assurance in various food safety and quality domains. This includes detecting harmful foreign objects, identifying mechanical damage, and measuring food quality attributes such as ripeness, decay, freshness, and ingredient homogeneity.

Precision Agriculture 

With the global population projected to reach 9 billion, the agriculture industry faces heightened demand for food and water, necessitating improved crop yield and farm productivity. Precision agriculture emerges as a potential solution, combining new technology with traditional farming techniques to achieve more with less.

Hinalea Imaging offers intelligent hyperspectral imaging solutions to aid precision agriculture. These solutions, integrating cutting-edge imaging hardware with custom machine learning algorithms, can be mounted statically or deployed on UAVs or other vehicles.

Hyperspectral imaging provides additional spectral information for farmers, enabling applications such as plant health and hydration monitoring, weed mapping, disease detection, and optimizing harvesting processes.

Industrial & Defense 

In addition to optimizing and safeguarding food supply chains, Hinalea collaborates with customers to address diverse challenges in industrial and remote sensing applications. Projects vary, including aerial surveillance for threat detection, identification of chemical degradation of pipes, QA of electrical components and displays, inspection of packaging, and classification of raw materials.

Hinalea configures its imaging platform for each specific customer requirement, thanks to its flexible form factor.

Tailored solutions range from UAV-mounted SWIR imagers for real-time imaging and classification to imagers mounted on crawlers for inspecting pipes in environmentally hazardous areas. Additionally, Hinalea develops integrated in-line quality assurance systems for product grading and stand-alone end-of-line quality assurance systems for quality sampling.