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Item-Analyzing Spectrometers

Clean the Sky - Positive Eco Trends & Breakthroughs

The Stratio 'LinkSquare SDK' Analyzes for Color and Authenticity

— June 1, 2017 — Tech
Coming as a successor to the original 'LinkSquare' device, the Stratio 'LinkSquare SDK' is a handheld spectrometer that is capable of collecting various data points from the object around you. The device is capable of collecting the precise color data of a specific item, while also using near-infrared light in order to determine what the molecular makeup of a particular object is.

The Stratio 'LinkSquare SDK' is capable of distinguishing between items that are genuine and fraudulent such as medication and gemstones by measuring how objects respond to different kinds of light. The device comes with everything one needs to get started measuring and analyzing things around them along with a USB charging cable and computer software for downloading data.
Trend Themes
1. Handheld Spectrometry - The development of handheld spectrometry devices like Stratio's 'LinkSquare SDK' opens up opportunities for remote and in-the-field analysis of color and molecular makeup.
2. Object Authenticity Detection - Using spectrometry to evaluate the molecular makeup of specific items creates possibilities for identifying and eliminating the circulation of fraudulent products.
3. Data-collecting Tools - Spectrometry devices like the 'LinkSquare SDK', capable of collecting precise color and molecular data, offer innovative new tools for data collection and analysis.
Industry Implications
1. Pharmaceuticals - The 'LinkSquare SDK' and similar devices have the potential to revolutionize the pharmaceutical industry by detecting and preventing the distribution of counterfeit medication.
2. Gemstone Industry - The use of handheld spectrometry devices like the 'LinkSquare SDK' could transform gemstone industry through the improved detection of fraudulent stones.
3. Artificial Intelligence/machine Learning - The data collected by spectrometry devices presents opportunities for training AI and ML models for improved analysis of color and material composition.
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