# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Stages** & **Samples** & \begin{tabular}{c} **Observations** \\ **(Samples \(\times\) Targets)** \\ \end{tabular} & **R** & **Slope** & \
\begin{tabular}{c} **Performance** \\ **(MSE)** \\ \end{tabular} \\ \hline \multicolumn{8}{c}{Model 6 (Inputs: raw rice e-nose; targets: raw rice aroma)} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} LM \\ 10 neurons \\ \end{tabular} } & Training & 356 & 11,748 & 0.95 & 0.90 & \(3.37\times 10^{9}\) \\  & Validation & 77 & 2541 & 0.95 & 0.92 & \(3.47\times 10^{9}\) \\  & Testing & 77 & 2541 & 0.93 & 0.87 & \(5.15\times 10^{9}\) \\  & Overall & 510 & 16,830 & 0.95 & 0.90 & \(-\) \\ \hline \multicolumn{8}{c}{Model 7 (Inputs: raw rice e-nose; targets: cooked rice aroma)} \\ \hline BR & Training & 357 & 3570 & 0.98 & 0.97 & \(5.77\times 10^{8}\) \\  & Testing & 153 & 1530 & 0.92 & 0.95 & \(3.43\times 10^{9}\) \\  & Overall & 510 & 5100 & 0.96 & 0.96 & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._while other machine learning methods are single-target [85; 86]. Since NIR spectra can be used to predict multiple quality traits, the ANN model may provide a greater advantage to obtain simultaneous predictions of rice quality traits. The fact that 17 types of rice were included in this study helps generate a more general machine learning model that can be applied to classify various types of rice available in the market. This includes the types of rice from similar cultivars, subspecies (e.g., _japonica_ and _india_), and origin. Although previous studies had contributed to applying chemical and aroma fingerprinting in assessing rice quality traits, this study presents the feasibility of using low-cost sensors to assess rice quality traits based on qualitative and quantitative techniques. These findings suggested that the chemical fingerprinting of rice obtained using the handheld NIR spectrometer may be used to classify different types of rice and predict the aroma and physicochemical parameters of the rice. Moreover, the aroma fingerprints obtained from the e-nose sensors have great possibilities to be utilized as a reliable, low-cost alternative for rice aroma profiling and quality monitoring since the sensor can be miniaturized and installed in rice processing facilities. The method proposed in the study has some advantages in avoiding tedious sample preparation and saving time for routine inspection to assess cooked rice quality traits under similar cooking procedures. Furthermore, the proposed method may also reduce the requirement to run multiple analytical tests to assess various rice quality traits at every routine inspection (e.g., texture analyzer to determine the cooked rice texture, GC-MS analysis to determine the relative abundance of rice aroma,  
Figure 5: The overall correlation of the regression ANN models developed to predict rice aroma and physicochemical quality for **(a)** Model 3; inputs: first derivative of NIR absorbance; targets: raw rice aroma, **(b)** Model 4; inputs: first derivative of NIR absorbance; targets: cooked rice aroma, **(c)** Model 5; inputs: first derivative of NIR absorbance; targets: cooked rice physicochemical quality, **(d)** Model 6; inputs: e-nose outputs; targets: raw rice aroma, and **(e)** Model 7; inputs: e-nose outputs; targets: cooked rice aroma. and other physicochemical tests). In further studies, the use of chemical fingerprinting obtained from low-cost and portable tools could be a means of a high-throughput and non-invasive approach for on-site quality inspection. This will also provide a promising solution to prevent destructive testing while obtaining the sample data. Further research should include other quality traits such as biochemical (starch, amylose, fat, and protein) data and sensory quality to assess the association between rice quality traits and consumer preferences.