Table 2.

Main SK-UTALCA functionalities according to the program menu.

Main objective	Main commands	Secondary commands	Description
Input and output of information	Import X and Y data	Spectral data (X)	Import spectral data: first column or row (depends on the equipment) must include the assessed wavelengths.
		Samples per plot	Indicate samples per scan (definitions in Table 1).
		Transpose data	Software works only with wavelengths as columns; the user will be able to transpose their data.
		Response variable (Y)	Import response variables data (on columns) where the three first columns must be codes (free criteria).
	Export data	Average	It is possible to export the average of the samples per scan or each sample individually.
		Empty data	Data can be exported including or excluding cells deleted during the cleaning of the data matrix.
Cleaning data matrix	Noise analysis	Wavelength segments	Ten different segments to analyze in relation to the percentage change among a determined neighbor size.
			Noise elimination can be applied equally to all data (Group) or for each sample (Individual). Additionally, negative values can be also deleted.
	Scan analysis	Maximum variation coefficient	Criteria to select samples within a same scan where the variation coefficient, at any wavelength, is lower than the established threshold (Scans without problems) and those that exceeded it (Scans with problems).
		Samples to delete	If there are inconsistencies in one or more samples within the same scan, it is possible to select and delete them.
	Outlier analysis		Through a graphical analysis of the cloud of data points (response variable vs. SRI), it is possible to detect those out of range, identify the source of the problem and delete them in the case of clear evidence of a mistake.
Preliminary analysis	Collinearity analysis		For a given response variable, through linear or artificial neural network (ANN) analysis, it is possible to identify wavelengths without collinearity.
	Individual wavelength analysis		Through different regression models and statistical parameters, it is possible to identify wavelengths better associated with a given response variable.
	SRI analysis	Full report	Through different regression models and a coefficient of determination threshold, it is possible to identify SRIs that are better associated with a given response variable. The software will be launched with a database of 255 SRIs (Supplementary Table 1).
		Detailed index report	For subsequent graphical representation it is possible to export, for each genotype or measurement, individual values of SRIs and response variables.