## 3 Results and Discussion
### Partial Least Squares (PLS) Prediction Models Based on Near Infrared (NIR) Measurements
The ascorbic and citric acid also showed good prediction performance with the r\({}_{\text{cv}}\) of 0.87 and 0.82 and SECV of 0.06 and 0.82 for citric and ascorbic acids, respectively (Table 2). The individual acid models (citric and ascorbic) demonstrated the applicability of a portable spectrometer (Neospectra) to make accurate predictions in a few seconds without any sample preparation, with low errors of 0.06 g 100 g\({}^{-1}\) and 3.78 mg 100 g\({}^{-1}\) for citric and ascorbic acid, respectively (Table 2). Our organic acids results were comparable to those obtained in other studies that used mid-IR spectroscopy [29; 44]. Correlation plots, besides statistical PLSR model results, are helpful to visualize the relationship between measured and NIR predicted quality parameters levels. Figures 3 and 4 show the correlation plots between reference values and predicted values obtained by using NIR spectra for all quality parameters in both steps (calibration and prediction), and Figures 5 and 6 show the regression vectors of PLSR models. The correlation plots show a high degree of linearity that indicates that the models are able to accurately predict the tomato quality attributes based on the NIR spectral data. The regression vectors information enables identifying the most important absorption bands related to each quality parameter, showing the bands more responsible for the prediction of quality parameter. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & **Type of Tomatoes** & **Preprocessing** & **N cal** & **F** & **SECV** & **r\({}_{\text{cv}}\)** & **N pred** & **SEP** & **r\({}_{\text{pre}}\)** \\ \hline SSC (\({}^{\circ}\)Brix) & Roma-Round & Mean center + Smooth & 152 & 5 & 0.23 & 0.88 & 38 & 0.22 & 0.87 \\  & Cherry-Grape & (15) + 2 nd deriv (15) & 166 & 6 & 0.64 & 0.89 & 42 & 0.52 & 0.88 \\ \hline Glucose (g. 100 g\({}^{-1}\)) & All types & Norm + 2 nd deriv (15) & 250 & 4 & 3.01 & 0.87 & 58 & 2.91 & 0.83 \\ \hline Fructose (g. 100 g\({}^{-1}\)) & All types & Norm + 2 nd deriv (15) & 250 & 5 & 3.48 & 0.87 & 58 & 2.83 & 0.87 \\ \hline TA (g citric acid. 100 g\({}^{-1}\)) & Roma-Round & Mean center + Smooth & 98 & 6 & 0.03 & 0.87 & 25 & 0.02 & 0.89 \\  & Cherry-Grape & (21) + 2 nd deriv (21) & 39 & 7 & 0.05 & 0.92 & 10 & 0.04 & 0.94 \\ \hline Ascorbic acid (mg. 100 g\({}^{-1}\)) & All types & Norm + 2 ndderiv (15) & 100 & 6 & 3.78 & 0.82 & 46 & 4.09 & 0.81 \\ \hline Citric acid (g. 100 g\({}^{-1}\)) & All types & Norm + 2 nd deriv (25) & 100 & 6 & 0.06 & 0.87 & 46 & 0.07 & 0.86 \\ \hline SSC: soluble solid contents; TA: total acidity; N cal: number of samples in the calibration set; N pred: number of samples in the prediction set; F: factors; SECV: standard error of cross validation; r\({}_{\text{val}}\): correlation coefficient of cross validation; SEP: standard error of prediction; r\({}_{\text{pre}}\): correlation coefficient of prediction; Norm: normalization; Deriv: derivative; OSC: orthogonal signal correction. & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical performance of the partial least squares regression (PLSR) models developed. The main responsible bands found were absorption bands around 2000 (O-H absorption region), 1800 (3 rd C-H overtone), 1490 (O-H stretch), and 1370 nm (C-H combinations) are tightly related to absorption of molecules present in quality attributes in tomatoes (carbohydrates, organic acids, and water) [7]. Figure 3: Scatter plot of PLSR models predicted and actual value for fructose (**A**), glucose (**B**), ascorbic (**C**) and citric acid (**D**). White and black squares represent calibration and validation set samples, respectively. Black regression line: calibration set; red regression line: external validation set. Figure 4: Scatter plot of PLSR models predicted and actual value for SSC, round and roma tomatoes (**A**) and grape and cherry (**B**); total acidity for round and roma tomatoes (**C**) and grape and cherry (**D**). White and black squares represent calibration and validation set samples, respectively.