April 27, 2015
Here are the most common terms and names used in the DevKit.
|SCiO Sensor||SCiO Sensor, also referred to as SCiO, is the world's first handheld molecular sensor that fits in the palm of your hand. SCiO Sensor comes with the SCiO Cover, used for protection and to calibrate your SCiO.|
|SCiO Accessories||Accessories available with SCiO include the SCiO Shade (for use in bright light or outdoor scanning) the Solid Sample Holder (for use with small, solid objects that don't fill the illumination field) and a Liquid Scanning Accessory (coming soon) for use when scanning liquids and non solids.|
|Sample or Sample Material||The physical matter or object to be scanned with your SCiO. Each sample must be unique. For example, one unique apple, pill or gemstone. Two different apples would count as two apple samples. The same apple may be used to scan the outside of the apple, the inside of the apple and a bruised section of the apple.|
|Scan||The act of using SCiO to measure the molecular fingerprint of a sample. A scan is a single measurement of a sample and contains the spectrum attached to metadata of that specific scan.|
|Attribute||Automatic (system generated) and sample (user generated) labels created for scans within SCiO Lab.|
|Data Collection||Collection of sample materials and their corresponding scans and metadata.|
|Reflectance||Reflectance spectrum is the spectrum of the light reflected off the sample. It is comprised of the raw spectrum of the sample normalized by the raw spectrum of the calibration apparatus. The raw spectrum is the direct reading of SCiO’s optical head.|
|Processed||One of the types of preprocessing methods available for scan data within SCiO Lab. "Processed" is useful for estimation (mainly) in which the absorption is supposed to be exponential (according to the Beer-Lambert model). "Processed" transforms it to a linear function of concentration.|
|Normalized||One of the types of preprocessing methods available for scan data within SCiO Lab. Due to the fact that SCIO is being used outside of controlled lab conditions by a wide array of users in different and uncontrolled physical settings, algorithmic compensation is available to account for variations in the way SCIO is used to scan. "Normalized" compensates for multiplicative factors, such as gain. Note: If used in conjunction with "Processed" optical length compensation is applied.|
|Model||Mathematical analysis used to transform spectra and meta data into a working algorithm that provides SCiO users with useful information/feedback on their scans. For example, a hard cheese model could allow SCiO users to scan a hard cheese and receive feedback on the fat, carbs and protein values of their sample. Those values would be generated from analysis of the scan using the hard cheese model, created on a hard cheese data collection in SCiO Lab.|
|Test Model||The ability to test the viability of a SCiO Model on additional samples before building a full mobile application. Once a model is created, selecting this option within a data collection in SCiO Lab Mobile enables developers to scan objects against their models and verify if the scan results match as expected.|
|Mobile API||Available in both Android and iOS versions, enables SCiO Developers to build their own mobile applications using SCiO Models.|
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