RMSECV was plotted against LVs to set the optimal number of LVs. In order to identify anomalous samples (outliers) the leverage criterion and the Student residuals were used. The leverage criterion represents the influence of each sample Fasudil manufacturer in the regression model, with a threshold equal to 3 LV/n where n is the number of samples. The student residual indicates if the sample is within a normal distribution,

with a confidence level of 95%, assuming a threshold value of ±2.5. Afterwards, the models were tested to predict SSC and TA with validation set. The best calibration models were selected based on the highest correlation coefficient of validation (R  2) along with the lowest RMSECV and the lowest root mean square error of prediction (RMSEP). RMSEP was then expressed as RMSEP% corresponding to the percentage of error of prediction calculated with RMSEP divided by the mean values of measured quality parameters in fruits from the validation set ( Duarte, Barros, Delgadillo, Almeida, & Gil, 2002). equation(1) RMSEP=∑i=1nyi-yˆi2nwhere: yiyi = known

value; yˆi = calculated or predicted value and n = number of samples in the validation set. This value represents the average error that can be expected for the prediction of future samples, with a confidence interval of 95%. The general shapes of the spectra for the three fruit types were quite similar, though the spectra for the passion fruit showed weak absorption intensity and a slight displacement, possibly due to the thickness of the

skin (Fig. 1). The main absorption peaks coincided for all three fruits. selleck chemicals The peak at 1190 nm corresponds to the second and third C–H overtone regions, associated with sugar (Osborne, Fearn, & Hindle, 1993). The peak at 1500 nm overlaps with the first O–H overtone region related to organic acids (Roberts et al., 2004). In general, the absorbance patterns seen here can be loosely related to the functional groups associated with water and sugars. Indeed, most fruits contain 80–90% of water and show a rising sugar content throughout ripening. The spectra obtained here for apricot and tomato can be compared to other studies, apricot (Bureau et al., 2009) and tomato (Sirisomboon, 4��8C Tanaka, Kojima, & Williams, 2012). To the best of our knowledge, no study was published for passion fruit. The samples showed a large variability of SSC and TA for fruits of the three species used in this trial. These results confirm that selected fruits were in different ripening stages. Statistical analysis for the calibration and validation sample sets, i.e., data ranges, means, standard deviations (SD) and number of samples for SSC and TA are shown on Table 1. For fruits from the three different plant species used in this trial, different calibration models were calculated. The spectra pre-processing and the number of factors were both taken into consideration to determine the best models.