# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 3 Results and Discussion
Model 6 showed high overall accuracy (R = 0.95) and showed no overfitting signs, having a lower MSE value for training (3.37 \(\times\) 10\({}^{9}\)) compared to validation (MSE = 3.47 \(\times\) 10\({}^{9}\)) and testing (MSE = 5.15 \(\times\) 10\({}^{9}\)). Additionally, Model 7 had high overall accuracy (R = 0.96) with training and testing MSE values of 5.77 \(\times\) 10\({}^{8}\) and 3.43 \(\times\) 10\({}^{9}\), respectively, showing that the MSE value for training was lower than the testing stage. Figure 5 shows the overall regression models used to predict the aroma (Models 3 and 4; Models 6 and 7) and color, texture, and pH (Model 5) of the rice. The accuracy for the regression models developed using the first derivatives of NIR absorbance values (Model 3-5) and e-nose readings (Models 6 and 7) as inputs were high, with the R values of the models ranging between 0.95 and 0.98. These results are in agreement with previous studies, which obtained high accuracy predictions using ANN algorithms in food and beverages research areas, such as in coffee aroma assessment [25], mold growth prediction [76], and mulberry fruit grading [77]. It should be noted that Model 4, Model 5, and Model 7 are valid to predict the cooked rice quality traits using the standard method specified in Section 2. It is because different cooking procedures may affect the cooked rice quality, as suggested in previous studies by [PERSON] et al. [6], [PERSON] et al. [78], and [PERSON] et al. [79]. Following the present results, previous studies have shown that the chemometrics of rice obtained from low-cost sensors have been successfully applied to monitor rice quality. For example, [PERSON] et al. [80] used the e-nose developed by the research team from the Agricultural Engineering Laboratory of Chiang Mai University to detect rancidity and insect infestation in brown rice during storage. The authors used the e-nose readings coupled with partial least square-discriminant analysis (PLS-DA) to classify the brown rice samples according to the normal, rancid, and infested rice groups. However, the study would have been more complete if it had included different levels of rancidity and severity of insect infestation over time to assess the sensitivity of the e-nose system. Furthermore, findings from this study supported previous studies that applied chemical and aroma fingerprinting for food authentication to detect food fraud [81; 82; 83]. The application of NIR absorbance values obtained in this study showed superior accuracy in predicting the physicochemical quality of the rice using the ANN modeling than those reported by [PERSON] and [PERSON] [74]. The authors used the absorbance values measured using a Fourier transform NIR (FT-NIR) spectrometer coupled with the PSLR model to predict the hardness (coefficient of determination, R\({}^{2}\) = 0.70) and toughness (R\({}^{2}\) = 0.66) of cooked parboiled rice. However, the moderate accuracy of PLSR models indicated a weak predictive ability to estimate the rice quality adequately [84]. In another study, [PERSON] et al. [60] compared the ability of multiple linear regression (MLP) and ANN to predict the biochemical composition and pasting parameters of rice. The authors found that the ANN (R\({}^{2}\) = 0.97-0.99) presented a higher determination coefficient than the MLP (R\({}^{2}\) = 0.27-0.96), showing the ability of ANN to produce a higher accuracy model. Moreover, one of the advantages of using ANN is that the model can perform multi-target prediction,