## 2 Materials and Methods
### NIRS Measurements, Data Pretreatment and Analysis  
Flour samples from the above and underground organs of both crops studied were inserted into a spectrometer cell (small ring cup, 50 mm diameter) and tightly compressed with the cell disposable backs to remove the air between the flour particles. The NIR spectra were recorded using a NIRSystems 5000-M (FOSS, Silver Spring, MD, USA), through the WinISI II software version 1.5e (FOSS, USA). The absorbance values were recorded between 1100 and 2498.2 nm in reflectance mode, as log 1/R (where R is the reflectance of the sample), at every 2 nm. All samples' spectra were recorded in triplicate, with 25 scans on average per sample.  
Partial least squares (PLS) multivariate data analysis was performed between NIR spectra and reference values to develop prediction models for each trait in the study. Chemical quality analysis data, randomly selected from 75% of the sample flours from the 1 st trial, were used as references to construct the calibration models. A spectral pretreatment, based on standard normal variate (SNV) and  
Figure 1: Taro and sweet potato crops were submitted to drought stress for two agronomic trials in distinct experimental sites, but they share the same steps from harvest until analysis. A—harvest of the plants of the caro drought assays performed in an open greenhouse in 2015 and 2017, B—harvest of the plants of the sweet potato drought trials implemented in a split-plot field design in 2017 and 2018, C—oven-drying the underground and aboveground plant organs at 65 \({}^{\circ}\)C for 48 h, D—milling the dried samples and placing them into vacuum-sealed bags stored at -35 \({}^{\circ}\)C, E—flours from the 1 st drought assay were chemically evaluated for quality constituents, F—NIR prediction of the quality constituents of the sample flours obtained from the 2 nd drought assay.  
detrend corrections, was performed with the PLS regression model using the 1 st spectrum derivative. PCA was also used to define the spectral boundaries of both crop sample spectra, through their average structure and distribution, with spectral outlier removal based on Mahalanobis distance (H) \(>\) 3.  
Full cross-validation was performed as leave-one-out for all calibration models to determine the number of factors for model calibration. One sample prediction was repeated until all the calibration samples were predicted, measuring the difference between actual and predicted values calculated throughout whole cross-validation calibrations. The calibration models were evaluated by the standard error of calibration (SEC), standard error of cross-validation (SECV), determination coefficient of cross-validation (r\({}^{2}\)cv), and ratio performance to deviation (RPD = SD/SECV).  
The calibration models were then validated with an independent set of samples. The performance for each calibration model by predicting the remaining 25% of the sample flows from the 1 st trial was evaluated through the standard error of prediction (SEP) and the determination coefficient of prediction (r\({}^{2}\)pred). This indicates whether the reference values and NIR prediction performance is reliable enough to be used with good accuracy to predict the quality constituents of the sample flows obtained from the 2 nd trial. A robust fitting prediction model indicates higher \(r^{2}\)pred and RPD, associated with the lower SEC, SECV, and SEP values [10,18,50].