In business and technology, an inflection point often signifies a crucial shift in the market, technology, or industry that has a profound impact on its future direction and outcomes.

Before we delve into the food testing industry let’s look at the inflection points in some other industries.

The inflection point in the photography industry is often associated with the shift from film to digital technology. It democratized photography by making it more accessible and affordable, led to new forms of image capture and manipulation, and ultimately reshaped the business models of traditional photography companies like Kodak.

The inflection point in the smartphone industry is often associated with the introduction of the iPhone by Apple in 2007. The iPhone revolutionized the market by combining a touch screen interface, a powerful operating system (iOS), and a wide range of functionalities into a single device. 

The inflection point in the electric vehicle industry is often associated with the introduction and success of Tesla's Model S. Released in 2012, the Model S demonstrated that electric vehicles could achieve long-range capabilities, high performance, and appeal to a broader consumer base. 

In each of the aforementioned instances, the companies that spearheaded the inflection point emerged as unequivocal victors, securing substantial market share and attaining unparalleled growth.

Now, at ImagoAI, we posit that AI-enabled Hyperspectral Imaging (AI-HSI) represents the inflection point for the food testing industry because of its ability to test for more complex parameters such as mycotoxins. 

The capabilities of AI-enabled HSI go beyond conventional test spectroscopy methods used to run tests for protein, fat, and moisture. It facilitates the handling of more intricate tests, such as mycotoxins, paving the way for the development of other rapid tests using AI-HSI.

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While traditional food testing methods such as spectroscopy have been around for decades, the shift is evident now. Normal NIR spectroscopy sufficed for protein, fat, and moisture tests, but mycotoxins posed a unique challenge. The key lies in the signal-to-noise ratio. 

For protein, fat, and moisture, where the signal-to-noise ratio is high, normal NIR instruments suffice. However, for mycotoxin tests with a low signal-to-noise ratio, a more powerful approach is essential. 

Unlike traditional spectroscopic instruments that could only capture data at a single point or a few points, HSI allows capturing at 100K+ pixels in a sample. 

This necessitates more potent AI algorithms to handle the intricacies of HSI data effectively.

Key advantages that are serving as catalysts for the adoption of AI-HSI tests include:

1. Real-time Results: Getting results in seconds enables swift decision-making.
2. Plug & Play Tests: No technical expertise is required, allowing anyone to be trained to conduct tests, addressing labor shortages in plants.
3. No Chemical Consumables: Eco-friendly testing becomes a game changer by eliminating chemical waste.
4. Result Consistency Across Plants: Traditional chemical based methods are prone to human error and show inconsistency in results.
5. Reducing Rework Incidents: Option to integrate real-time test results back into machines allows for proactive actions, reducing rework incidents.
6. Seamless Data Integration: Direct integration of results into ERP systems eliminates the need for manual data entry, simplifying the lives of stakeholders.

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The ongoing advancements in artificial intelligence and hyperspectral imaging technologies will result in even more sophisticated and versatile applications. We can anticipate further miniaturization of AI-HSI devices, potentially leading to their integration into everyday consumer devices, ensuring widespread accessibility and usability. 

From traditional methods relying on chemical consumables to the current frontier of AI-HSI, the industry has witnessed a remarkable evolution. The adoption of AI-HSI represents not just a technological leap but a paradigm shift towards more efficient, sustainable, and user-friendly testing methodologies. 

In our next blog, we will be discussing Carbon Footprint of test kits & AI-HSI tests, stay tuned.