## 3 Results and Discussion
### Partial Least Squares (PLS) Prediction Models Based on Near Infrared (NIR) Measurements  
Handheld units are valuable tools for field applications to provide real-time information on the phenotypic traits expressed by fruits for breeding selection. Remote sensing and high-performance computing are paving the way for affordable, automated and non-invasive sensors [35]. Most studies employing handheld devices in tomatoes have employed low-cost silicon-based CCD (charge-coupled-device) detectors with operational wavelength ranges limited to the short wave NIR (SWNIR, 780-1400 nm) range. However, the SWNIR does not typically coincide with unique spectral signatures that can be used for adequate quantification of food quality traits, especially for commodities targeting sugars and acids components because their unique absorption bands occur in the longer wavelengths (>1400 nm) [36]. Thus, prediction of the soluble solids, sugars, and acids of the fresh tomatoes by handheld NIR devices operating in the SWNIR (shortwave near infrared) have shown limited success [4, 37, 38, 39]. Our study is the first study to report on the use of a on-chip Fourier transform (FT) NIR spectrometers equipped with a MEMS interferometer (1350-2560 nm) and InGaAs detector arrays enabling access to the longer-wave spectral range where critical spectroscopic signatures related to the quality of tomato products can be obtained. Figure 2 shows the NIR spectra of all four tomato types (Roma, round, grape, and cherry) that consist of overtones and combination bands of fundamental vibrations [7, 40, 41]. Two strong NIR \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Tomato Type** & **N** & **Average** & **SD** & **Range** & **CV (\%)** \\ \hline Soluble solids (\({}^{\circ}\) Brix) & all & 319 & 5.72 & 1.68 & 2.92–11.22 & 29.47 \\  & Roma & 106 & 4.45 & 0.82 & 2.92–9.93 & 11.52 \\  & round & 47 & 4.59 & 0.53 & 3.49–5.95 & 11.54 \\  & grape & 93 & 6.81 & 1.24 & 4.29–9.36 & 18.25 \\  & cherry & 73 & 6.91 & 1.8 & 3.96–11.22 & 26.13 \\ Glucose (g. L\({}^{-1}\)) & all & 319 & 16.61 & 7.37 & 4.68–39.12 & 44.44 \\  & Roma & 106 & 12.92 & 4.29 & 7.57–32.87 & 33.18 \\  & round & 47 & 12.06 & 3.31 & 4.68–21.74 & 27.46 \\  & grape & 93 & 20.66 & 7.14 & 7.94–35.79 & 34.58 \\  & cherry & 73 & 20.26 & 8.34 & 8.65–39.12 & 40.47 \\ Fructose (g. L\({}^{-1}\)) & all & 319 & 22.25 & 8.71 & 5.33–47.01 & 39.17 \\  & Roma & 106 & 16.18 & 4.34 & 10.57–36.04 & 26.84 \\  & round & 47 & 17.10 & 5.48 & 5.33–42.67 & 32.05 \\  & grape & 93 & 26.39 & 8.13 & 11.44–45.18 & 30.83 \\  & cherry & 73 & 28.41 & 8.40 & 15.31–47.01 & 29.56 \\ TA (g citric acid. 100 g\({}^{-1}\)) & all & 204 & 0.41 & 0.2 & 0.12–1.40 & 49.31 \\  & Roma & 84 & 0.40 & 0.21 & 0.13–1.40 & 51.96 \\  & round & 45 & 0.38 & 0.2 & 0.15–1.29 & 53.59 \\  & grape & 43 & 0.49 & 0.22 & 0.15–1.09 & 45.68 \\  & cherry & 32 & 0.40 & 0.14 & 0.12–1.77 & 36.91 \\ Ascorbic acid (mg. 100 g\({}^{-1}\)) & all & 149 & 29.32 & 14.71 & 3.77–77.91 & 44.84 \\  & Roma & 63 & 27.58 & 11.42 & 12.09–56.91 & 41.39 \\  & round & 35 & 24.6 & 9.13 & 11.30–41.65 & 37.12 \\  & grape & 27 & 33.85 & 22.59 & 3.78–77.91 & 52.10 \\  & cherry & 24 & 38.83 & 12.32 & 14.70–62.29 & 31.73 \\ Citric acid (g. 100 g\({}^{-1}\)) & all & 149 & 0.54 & 0.21 & 0.11–1.10 & 39.28 \\  & Roma & 63 & 0.54 & 0.22 & 0.11–0.97 & 41.99 \\  & round & 35 & 0.42 & 0.18 & 0.18–0.99 & 43.52 \\  & grape & 27 & 0.56 & 0.19 & 0.15–1.10 & 34.49 \\  & cherry & 24 & 0.64 & 0.14 & 0.42–1.04 & 21.99 \\ \hline \hline \end{tabular} N: number of samples; SD: Standard Deviation; CV: Coefficient of variation; TA: total acidity.  
\end{table} 
Table 1: Values of the quality parameters determined in tomato samples, from 2018 and 2019 growing seasons, using reference methods. bands assigned to O-H absorbance are shown between 1923-1960 (first overtone) and 1515-1400 nm (O-H stretching) [42]. The bands from 1350-1428 and 2300-2480 nm can be assigned to combination bands of C-H, typically from carbohydrates [7].