## 3 Results and Discussion
### Partial Least Squares (PLS) Prediction Models Based on Near Infrared (NIR) Measurements
The weak band close to 1800 nm has been assigned to the third C-H overtone and related to sugar content. In Figure 2, it is also possible to see the spectra pattern variation among the four different tomato types. Two separated groups were clustered, tomatoes with less sugar and more water content (Roma and round) and fruits with high sugar content and less water (grape and cherry). Those groups have distinct spectrum patterns, mainly in the absorption bands related to O-H overtone (around 2000 nm), C-H third overtone and combinations bands (2500-2272 and around 1800 nm) [43]. Those are characteristic regions for water and sugar compounds absorption [7,41]. The absorption bands in the NIR region are complex, with broad and overlapped bands. This overlap can difficult the interpretation and requires multivariate analysis to understand and explain the set of spectra and identify its correlation with parameters of interest [44,42]. Using appropriate chemometric tools, preprocessing, and transformation, these broad bands are especially useful for quantitative analysis. Table 2 shows the statistical performance of the PLS models developed and each transformation used to develop these models. For the development of the PLS prediction models of fructose, glucose, citric, and ascorbic acid, the full spectra (1295-2611 nm) were used. For soluble solids and total acidity prediction models, reduced spectral ranges were used: SSC cherry-grape: 1356-2231 nm; SSC Roma-round: 1374-1930 nm; TA cherry-grape: 1576-2505 nm; TA Roma-round: 1674-2611 nm. Figure 2: Normalized spectra of different types of tomato (Roma, round, grape, and cherry) collected using a portable NIR infrared (Neospectra-Module). To improve the correlation performances of the SSC and the TA models, separate calibration models were generated for Roma and round (large varieties) and grape and cherry (small varieties) tomatoes. Good prediction performances were obtained for the SSC with correlation coefficients (r\({}_{\text{cv}}\)) of 0.88 and 0.89 and standard error of cross-validation (SECV) of 0.23 and 0.64 for the large (Roma-round) and small (grape-cherry) varieties, respectively (Table 2). The SECV for both SSC models were low when considering the wide range of concentrations (3-10 \({}^{\circ}\)Brix for the Roma-round varieties and 4.0-11.2 \({}^{\circ}\)Brix for the grape-cherry varieties). The model performance for SSC was superior to that reported in evaluating the feasibility of a miniaturized NIR spectrometer to determine the quality attributes of San Marzano (large varieties) tomatoes [21], reporting r\({}_{\text{pre}}\) = 0.8 and standard error of prediction values of 0.43 \({}^{\circ}\)Brix. [PERSON] et al. [40] used a portable NIR spectrometer (950 to 1650 nm) equipped with a fiberoptic probe to evaluate Cherry tomato quality with a narrow soluble solid content ranging from 5.5-7.40 \({}^{\circ}\)Brix, with model performances that gave a standard error of prediction of 0.20 \({}^{\circ}\)Brix and R\({}^{2}\)\({}_{\text{pre}}\) = 0.86. Good statistical performances were also obtained for the TA models, the r\({}_{\text{cv}}\) values were 0.87 and 0.92, while the SECV values were 0.03 and 0.05 for the large and small varieties, respectively (Table 2). Again, our models performed better than those reported by [PERSON] et al. [21] that reported standard error of prediction of 0.48% and R\({}^{2}\)\({}_{\text{pre}}\) = 0.79 and showed similar errors to those reported by [PERSON] et al. [45]. The rest of the models, including glucose, fructose, citric, and ascorbic acid, were generated by integrating all the samples (both small and large varieties) into the models. The fructose and glucose models showed similar performances (in terms of r\({}_{\text{cv}}\) and SECV), the r\({}_{\text{cv}}\) values were 0.87 and 0.87; also, the SECV values were 3.01 and 3.48 for glucose and fructose models, respectively (Table 2).