In the main menu, users are required to load the spectral reflectance data [Spectral data (x)]. The first step in the use of SK-UTALCA software is to load the data set in Microsoft Excel format (xlsx format). To read the spectral data it is necessary to indicate the number of samples taken in each plot (Samples per plot). Depending on the equipment used, reflectance data is organized in columns or rows. In the software, the spectral bands need to be located in columns and the spectral data in rows (it is possible to use the “Transpose data” option to relocate the data set as needed). The first wavelength measured must be in the second column (the first column is for codification purposes and depends only on the user).

The second file needed (xlsx format) contains the values for the independent variables [Response variables (y)] for each plot. In this case, the spreadsheet must consider three codification columns and the first variable should be allocated to the fourth column.

The software has no limitation on the number of spectral data points (wavelengths or measurements) or response variables.

This first filter removes the spectral noise originated by the natural presence of certain elements in the atmosphere, such as water and carbon dioxide, which absorb specific wavelengths (Salisbury, 1998; Curtiss and Goetz, 2001; Psomas et al., 2005; Ma and Chen, 2006; Clevers et al., 2008). Researchers who screen hundreds or thousands of genotypes under field conditions usually consider at least three or four spectral samples per plot, generating a matrix of data that makes it difficult to objectively select the noise segment(s) for deletion. Furthermore, spreadsheet graphical options are usually restricted to a maximum number of data series per chart (e.g., ~255 in Excel for Windows or Mac), so there is no easy way to take a decision based on this tool. For this reason, for breeding purposes, conventional visual noise elimination is not a real alternative, restricting the criteria to the assumption of arbitrary limits, usually following thresholds from a third person or related articles.

With this module, it will be possible to analyze the spectral noise by considering up to ten independent segments. This will allow the user to set up different criteria in each segment, being more or less strict depending on the wavelengths analyzed, the data collected or previous knowledge. To apply the filter on each spectral signature, it is necessary to indicate the lower and upper limit for each segment (Wavelength segments), the maximum accepted percentage of variations (%) between two neighboring wavelengths, and the number of neighbors (N size) where the previous condition is found consecutively. The graphic window will show red crosses where the first criterion is satisfied and black ones where both have been met, this last condition determining where the software will perform the cleaning.

However, an objective selection is not the only important aspect of spectral noise. During the day there are environmental changes (e.g., relative humidity) that not only affect the magnitude of each problematic wavelength, but also the number of wavelengths involved. For instance, measurements performed under conditions of higher relative humidity (usually before midday) produce wider noise segments beyond 1000 nm; if the determination of the number of wavelengths to eliminate considers measurements across the whole day, the noise edges will be established by genotypes evaluated early in the day (broader noise segments), risking the loss of important spectral information from those assessed under lower relative humidity (usually after midday) and therefore possessing narrower noise segments.

Because of this, after the noise selection criteria (% and N Size) are established, the user has an opportunity to filter by considering all the measurements as one group (Group) or as individual scans (Individual). When the group filter is selected in a specific segment, the program analyzes each sample where the selection criteria are met, identifying the minor and major wavelengths that have problems in the spectral data file, and uses these two wavelengths to eliminate the noise from each sample uniformly. This is very similar to what is done visually, but with an objective approach. For the individual option, each sample will be filtered independently from the others, rescuing important information for modeling, or the use of SRIs.

In this module, the user will be able to analyze, identify and correct inconsistencies between spectral signatures from the same scan or plot, a problem that is often unnoticed. In general, for simplicity or to dilute any errors generated while collecting the data, there is a tendency to average samples within the same scan, which most of the time is done without any deeper analysis. As mentioned before, this should not be a complication when the data analysis considers a few measurements, but in breeding programs this search would be time consuming.

There are several aspects influencing the homogeneity between samples within the same scan, especially if the measurements were performed under field conditions. Unnoticed modification of the measurement angle during plot screening is probably the main source of variability. In practical terms it is difficult to maintain the exact angle of measurement, even for a few seconds (hand steadiness of the operator, distractions, or fatigue); each sample is derived from several integrations, usually more than 10, so the chance of making a mistake is not uncommon. When a plot is screened, it can be performed by keeping the fiber aimed at a single point (lower variability and representation) or across several plants (higher representation but greater variability); when the second option is taken, the chances of integrating other materials into a single scan or sample (e.g., soil, weeds, or air) are increased, and also enhanced by changes in measurement angles. Other considerations such as the effect of the wind speed or turbulence on the measured surface would be detected.

The user needs to set up the Maximum variation coefficient accepted for the samples belonging to the same scan. The software will find the scans where the limit is exceeded, at any wavelength, and this will be reported in the Scans with problems section. The samples that need to be checked can be individually analyzed on the graphical window, where it is possible to visualize all the samples in a single graph, identifying (zooming in and out) and deleting those spectral signatures with problems.

It is important to mention that the samples selected with problems within a same scan, do not necessarily need to be modified. This decision will depend on the magnitude of the differences between samples and the number of wavelengths involved. In cases where the user decides to intervene in a scan, it is possible to select and delete one or more samples from the Samples to delete section.

This third filter is designed for rapid identification of problems associated with inconsistencies within spectral data. When outlier data is found, it will be necessary to evaluate the permanence of these in the data matrix.

Because of the high number of genotypes and samples per scan, it is difficult to identify data points that do not follow the general trends. Field experience has proven that is common to find small clouds of data whose main source of error comes from the calibration process. For example, the sun's movement throughout the day requires calibrations to be performed every 10–15 min. Due to distractions or tiredness during long working hours, the calibration can be forgotten, generating differences in the sun's incidence angle and therefore variations in the reflectance readings. Another form of user error, although less common and related to specific devices, may occur if the user has left the mouse cursor on one of the calibration icons (optimization, dark current, or white reference), performing an unconscious and incomplete calibration with a random click and thus generating undetectable reading errors.

In this module, it is possible to integrate a visual analysis of the reflectance and the response variable data at the same time. The user has four graphs to explore outlier information, evaluating different SRIs, and traits. In this section, it is also possible to Edit each graph, selecting data that need to be removed from the data matrix.

For these actions, the software will average the samples per scan to generate each SRI. This is important because the user should check the Noise Analysis and Scan Analysis modules first.

Collinearity or multicollinearity is a problem in regression analysis where the predictor variables “X” are themselves highly correlated (Draper and Smith, 2003). With the use of high-resolution spectral reflectance data, the collinearity problem is inherent to the data collection method employed because several wavelengths are highly correlated. If the goal is to understand how several predictor variables impact on a specific response variable “Y,” the collinearity is a big issue. Therefore, depending on the modeler's interest, it may be necessary to implement a collinearity analysis before construction of complex models (e.g., multilinear regression model).

In this module, the user can identify wavelengths that deliver the same predictive information for a given response variable, keeping only those that best explain it. This analysis can be performed (collinearity test setting) by linear regression, indicating the threshold coefficient of determination (R square cutoff), or through Artificial Neural Networks (ANN), considering a training process by Levenberg-Marquardt (trainlm), and Mean Squared Error (MSE) as a performance indicator. Depending on the data matrix and computer performance, the non-linear approach (ANN) could take several minutes or hours.

For the construction of new SRIs and regression models, it would be desirable to know the degree of dependency between individual wavelengths and the response variable. In this module, the researcher can study the behavior of each wavelength relative to each variable under study, considering one, or more of the following models:

Polynomial 1: y = p1 · x + p2

For this analysis, the user can select different statistics to sort the results (adjusted and non-adjusted determination coefficient, root mean squared error, sum of squares due to errors, and degree of freedom). It is also necessary to set up a minimum or maximum value for the selected statistics in order to export just those results (Values above or below). The exported file will show, for each wavelength, the statistics values for the selected model(s) where those minimum or maximum values were met.

This module and the following one (SRI analysis) work with sample averages, forcing the user to perform a deep preliminary analysis, thus avoiding any error in the data matrix.

The implementation of concatenate formulas in spreadsheets is helpful for automating time-consuming procedures. However, due to the number of scans, samples per scans, measured wavelengths, evaluated response variables, and tested SRIs, the physical size of the resulting spreadsheets (several MB) implies the need for high performance computers.

By evaluating the same regression models reviewed with the previous function, the user will be able to identify the SRIs (initially 255: Jordan, 1969; Rouse et al., 1973; Rouse, 1974; Tucker, 1979; Hardisky et al., 1983; Guyot and Baret, 1988; Guyot et al., 1988; Huete, 1988; Baret et al., 1989; Clevers, 1989; Curran, 1989; Hunt and Rock, 1989; Major et al., 1990; Barnes et al., 1992, 2000; Chappelle et al., 1992; Gamon et al., 1992; Peñuelas et al., 1993a,b, 1994, 1995, 1997; Vogelmann et al., 1993; Carter, 1994; Gitelson and Merzlyak, 1994, 1997; McMurtrey et al., 1994; Qi et al., 1994; Roujean and Breon, 1995; Smith et al., 1995; Chen, 1996; Chen and Cihlar, 1996; Filella et al., 1996; Fourty et al., 1996; Gao, 1996; Ma et al., 1996; Rondeaux et al., 1996; Huete et al., 1997; van Deventer et al., 1997; Blackburn, 1998, 1999; Datt, 1998, 1999; Merton, 1998; Peñuelas and Filella, 1998; Gamon and Surfus, 1999; Gitelson et al., 1999, 2001, 2003, 2005, 2006; Merzlyak et al., 1999; Peñuelas and Inoue, 1999; Daughtry et al., 2000; Marshak et al., 2000; Thenkabail et al., 2000; Broge and Leblanc, 2001; Raun et al., 2001; Zarco-Tejada et al., 2001, 2003a,b, 2005; Broge and Mortensen, 2002; Haboudane et al., 2002, 2004; Read et al., 2002; Serrano et al., 2002; Sims and Gamon, 2002; Gupta et al., 2003; Hansen and Schjoerring, 2003; Steddom et al., 2003; Viña, 2003; Dash and Curran, 2004; Gandia et al., 2004; Le Maire et al., 2004, 2008; Schlemmer et al., 2005; Zhao et al., 2005; Vincini et al., 2006; Babar et al., 2006a,b; Mirik et al., 2006a,b; Inoue et al., 2007, 2008; Prasad et al., 2007; Rodríguez-Pérez et al., 2007; Zhu et al., 2007; Rama Rao et al., 2008; White et al., 2008; Wu et al., 2008a,b; Richter et al., 2009; Serbin et al., 2009; Stroppiana et al., 2009; Yañez et al., 2009; Dzikiti et al., 2010; Herrmann et al., 2010; Mistele and Schmidhalter, 2010; Yao et al., 2010, 2011; Garrity et al., 2011; Hernández-Clemente et al., 2011; Main et al., 2011; Pimstein et al., 2011; Tian et al., 2011, 2014; Winterhalter et al., 2011; Wang et al., 2011a,b) having the higher adjusted coefficients of determination (Adj. RSquare values above) in relation to a response variable. Internally, the software will select all combinations (regression model, SRI, and response variable) where the adjusted coefficient of determination was reached. The Export data option will generate a report that includes all the statistics analyzed in the previous function for the best-evaluated regression model and for each one (in the case that more than two were tested).

For publication purposes this module also includes an exportable Detailed index report, where it is possible to select specific SRIs and response variables. The report will include the SRI and variable values for each of the measurements, allowing the user to create XY graphs.

During the 2011/12 growing season, 386 genotypes of wheat (Triticum spp. L.) from different breeding programs (INIA-Chile, INIA-Uruguay and CIMMYT) were assessed under three water regimens (fully irrigated, mild water deficit and severe water deficit). This trial was established at Santa Rosa Experimental Station (36° 32′ S, 71° 55′ W; 217 m.a.s.l.), Regional Research Center INIA Quilamapu (Chillán, VIII Region, Chile), considering an alpha-lattice design (386 genotypes + 2 cvs. replicated seven times to assess field variability) and two replications.

Reflectance measurements were performed using a portable spectroradiometer (FieldSpec® 3 Jr, ASD Inc., Boulder, CO, USA) (350–2500 nm), between 12:00 and 16:00 h, on clear days (solar radiation higher than 800 Wm⁻²). Prior to the first measurement and every 15 min, the equipment was calibrated using a field reference panel (Spectralon, ASD Inc., Boulder, CO, USA). The equipment was configured to read three samples per scan. Each plot (genotype) was scanned once.

A detailed methodology can be found in Lobos et al. (2014). For purposes of this article, only one environment (fully irrigated), one phenological stage (grain filling) and one replicate will be considered.

In this section, we highlight some of the key results of the analysis performed using the SK-UTALCA software.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.