# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 3 Results and Discussion
### IR Spectral Analysis of the Samples
Using an independent data set to externally validate the model is the most robust method to ensure the model accuracy, as the data set in external validation is not part of the model development process [50]. A comparison of statistical performances between calibration and external validation models, including correlation coefficients (RCV and RPV) and errors (SECV and SEP), revealed similar results (Table 2), confirming the robustness of the calibration model. This suggests that, when the models encounter new samples within the tested range, they will predict with comparable performance. Figure 2a-h depict the correlation plots between reference values and predicted values for various parameters, including soluble solids, sucrose, glucose, fructose, total sugars, TA, citric acid, and ascorbic acid, obtained using FT-IR spectra. The figures also illustrate the distribution of external validation set samples within the range of the calibration set. The correlation plots revealed a pronounced linearity, signifying the models' capability to precisely predict FJ quality attributes using the FT-IR spectral data. Our models demonstrated similar or better performances in estimating \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Calibration Model**} & \multicolumn{3}{c}{**External Validation Model**} & \multirow{2}{*}{**RCP**} & \multirow{2}{*}{**RCV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} & \multirow{2}{*}{**RPV**} \\  & **Range** & **N**  & **RCV**  & **SEP**  & **RPV**  \\ \hline Soluble solid (\({}^{\circ}\)Brix) & 3.78–15.99 & 53 & 5 & 0.23 & 1 & 7.56–16.03 & 14 & 0.26 & 0.99 & 8.84 & 32.38 \\ Glucose (g/100 g) & 13.90–57.34 & 51 & 5 & 3.01 & 0.93 & 13.69–33.43 & 13 & 3.49 & 0.93 & 3.49 & 5.66 \\ Fructose (g/100 g) & 7.52–73.08 & 53 & 2 & 3.52 & 0.98 & 6.65–70.81 & 13 & 3.06 & 0.99 & 5.91 & 20.94 \\ Sucrose (g/100 g) & 11.93–123.78 & 46 & 6 & 5.75 & 0.98 & 18.33–125.36 & 12 & 6.03 & 0.99 & 6.11 & 17.74 \\ Total sugar (g/100 g) & 60.84–151.87 & 53 & 5 & 6.32 & 0.95 & 67.61–153.50 & 14 & 8.96 & 0.95 & 3.10 & 9.59 \\ Titralable  acidity (mg/100 g) & 129.15–1139.12 & 53 & 5 & 59.42 & 0.97 & 180.09–891.04 & 14 & 54.22 & 0.97 & 4.43 & 13.11 \\ Citric acid (mg/100 g) & 18.6–1114.0 & 53 & 5 & 55.35 & 0.98 & 178.1–1056.9 & 14 & 66.04 & 0.96 & 4.31 & 13.31 \\ Ascorbic  acid (mg/100 g) & 50 & 5 & 4.37 & 0.95 & 0.70–54.10 & 12 & 4.53 & 0.97 & 3.17 & 11.79 \\ \hline \hline \end{tabular}  
* Number of samples used in calibration models; \({}^{\circ}\) standard error of cross-validation; \({}^{\circ}\) correlation coefficient of cross-validation; \({}^{\ddagger}\) number of samples used in external validation models; \({}^{\circ}\) standard error of prediction; \({}^{\ddagger}\) correlation coefficient of prediction for external validation. \({}^{\lx@sectionsign}\) Residual predictive deviation. \({}^{\lx@sectionsign}\) Range error ratio. SECV and SEP are in units of the predicted parameters.  
\end{table} 
Table 2: Statistical performances of the fruit juice PLSR models developed during the calibration and external validation steps. the quality parameters of fruit juices, including a lower R\({}_{\text{Pre}}\) and SEP, compared to those reported by other researchers for fruit juices using a benchtop FT-IR sensor [51; 52; 53]. Model accuracy was further evaluated using RPD and RER values, with RPDs ranging from 3.49 to 8.84 and RERs ranging from 5.66 to 32.38 (Table 2). According to the RPD and RER results, all generated models, except for the glucose predic  
Figure 2: Partial least squares regression (PLSR) calibration and external validation plots for soluble solids (**a**), glucose (**b**), fructose (**c**), sucrose (**d**), total sugar (**e**), titralude acidity (**f**), citric acid (**g**), and ascorbic acid (**h**) contents in fruit juice samples using the portable FT-IR sensor.