## 3 Results and Discussion
### Quantification of Individual and Total Sugars by Regression Analysis  
PLSR analysis was performed to generate prediction models for both ground and intact samples by combining the spectral data collected using the handheld FT-NIR spectral sensor with the reference analysis results for individual and total sugars from the HPLC. The robustness of the ground and intact cereal models was evaluated by using an external validation set. The generated PLSR models' performance statistics for total sugar and the individual sugars (sucrose, glucose, fructose) based on the handheld FT-NIR spectral sensor measurements are provided in Table 2. Our analysis used constrained spectral ranges associated with specific signatures of the investigated compounds (sugars) instead of using the whole spectral range during PLSR model development to increase the prediction ability of the models [43]. In most cases, the prediction model performance improved with a higher number of orthogonal latent variables or factors, since each factor explains variance in the model. However, including a redundant number of factors into a model may integrate the random noise or irrelevant components besides the relevant variance and reduce the model performance, which is called overfitting the model. Likewise, employing fewer factors than the optimal number, incorporating less variance than needed, is called underfitting [35]. The optimum number of factors that explain the required variance and give the minimum SECV ranged from 5 to 6. Correlation coefficient (R) of the model measures the strength of the relationship between measured and predicted sugar contents, and +1 indicates total positive linear correlation. SECV provides the possible error between measured and predicted values. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Parameter**} & \multicolumn{6}{c}{**Calibration Model**} & \multicolumn{6}{c}{**External Validation Model**} \\ \cline{3-10}  & & **Range \({}^{\text{a}}\)** & **N \({}^{\text{b}}\)** & **Factor** & **SECV \({}^{\text{c}}\)** & **RCV \({}^{\text{d}}\)** & **Range** & **N \({}^{\text{e}}\)** & **SEP \({}^{\text{f}}\)** & **RPE \({}^{\text{g}}\)** \\ \hline \multirow{4}{*}{Ground} & Sucrose & 0.5–43.2 & 127 & 5 & 1.93 & 0.98 & 0.5–43.2 & 32 & 2.18 & 0.97 \\  & Glucose & 1.2–3.9 & 125 & 6 & 0.14 & 0.94 & 1.2–3.8 & 31 & 0.14 & 0.94 \\  & Fructose & 1.1–5.1 & 125 & 6 & 0.25 & 0.95 & 1.1–5.1 & 31 & 0.26 & 0.95 \\  & Total & 3.7–51.7 & 127 & 6 & 1.99 & 0.98 & 3.7–51.7 & 32 & 1.95 & 0.98 \\  & Sucrose & 0.5–39.2 & 45 & 5 & 2.42 & 0.97 & 0.5–34.9 & 11 & 2.38 & 0.96 \\  & Glucose & 1.2–3.8 & 46 & 5 & 0.20 & 0.94 & 1.2–3.8 & 11 & 0.20 & 0.96 \\  & Fructose & 2.5–4.8 & 42 & 6 & 0.21 & 0.92 & 2.5–4.7 & 11 & 0.20 & 0.93 \\  & Total & 5.4–47.1 & 45 & 5 & 2.48 & 0.96 & 5.7–42.8 & 11 & 2.39 & 0.97 \\ \hline \hline \end{tabular}  
* The unit of the range is g/100 g. \({}^{\text{b}}\) Number of samples used in calibration models. \({}^{\text{c}}\) Standard error of cross-validation.
* Correlation coefficient of cross-validation. \({}^{\text{e}}\) Number of samples used in external validation models. \({}^{\text{f}}\) Standard error of prediction. \({}^{\text{g}}\) Correlation coefficient of prediction for external validation. The unit of the SECV and SEP values is g/100 g.  
\end{table} 
Table 2: Statistical performance of the prediction models developed using a handheld FT-NIR spectral sensor for predicting individual (sucrose, glucose, and fructose) and total sugar content in ground and intact breakfast cereals. Figure 2: Raw FT-NIR spectra of ground and intact cereals (high and low in sugar content). All generated models had good performances in terms of high R and low SECV values (Table 2). Furthermore, the PLSR plots (Figure 3a-d) generated for the sucrose and total sugar content in ground and intact breakfast cereals display a good correlation between the measured reference values and predicted sugar content by FT-NIR sensor.