## 4 Examples of Combining ML Tools to Interpret the RVA Profile  
A summary of the studies that have reported the development of applications by combining ML tools with RVA is provided in Table 2. Some of these applications will be discussed in more detail in the following sections. [PERSON] et al. (2018) [25] reported the utilization of RVA to evaluate waxy and non-waxy rice grain samples stored in paddy form at an ambient room temperature (28-32 \({}^{\circ}\)C) for 1 year. In this study, the RVA profiles of the rice samples were recorded every month, and the data were analyzed using both partial least squares (PLS) regression and supervised self-organizing maps (supervised SOMs) to predict the time of storage of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample, Crop, or Food** & **Machine Learning Tool** & **See References for the Specific Application** \\ \hline Maize, Rice, Barley, Cricket, & & \\ Flaxseed, Chickpea, Wheat, & & \\ Cassava, Tapioca, Proso & & [16, 17, 25, 32, 36, 39, 40] \\ Millet, Starch & & \\ \hline Barley, Rice, Starch, Cricket, & & \\ Flaxseed, Chickpea & & [16, 17, 25, 33, 34, 35, 40] \\ \hline Rice & SVM & [31, 41] \\ \hline Rice & ANN & [29, 34] \\ \hline Rice & KNN & [29, 41] \\ \hline \hline \end{tabular} ANN: artificial neural network; PCA: principal component analysis; PLS: partial least squares; PLS-DA: partial least squares discriminant analysis; SVM: support vector machines.  
\end{table} 
Table 2: Summary of applications of the Rapid Visco Analyser (RVA) combined with machine learning tools as reported in the scientific literature. sample. The PLS regression models developed using the RVA profiles resulted in a root mean square error of cross-validation (RMSECV) of 1.2 and a coefficient of determination in cross-validation (R\({}^{2}\)) of 0.90, with a ratio of prediction to deviation (RPD) of 3.2. The PLS variable influence on projection (PLS-VIP) indicated that PV and FV were the parameters with a strong influence on the PLS models that were used to predict the time of storage [25]. The achieved R\({}^{2}\) value of 0.90 indicates a strong correlation between the predicted and actual values, suggesting that the model effectively captures the underlying patterns in RVA pasting properties in relation to storage. Given the inherent variability in starch gelatinization and pasting behavior due to differences in botanical origin, processing conditions, and moisture content, an R\({}^{2}\) of 0.90 is considered highly acceptable for food-based regression models. Additionally, the RMSECV value of 1.2 demonstrates the model's robustness in minimizing prediction errors across different storage conditions. While direct comparisons with other studies may vary due to differences in experimental design, sample composition, and preprocessing techniques, the performance achieved here aligns with the general expectations for PLS models when applied in food science. It is important to note that variations in the data distribution, feature selection, and noise levels can all influence the model accuracy. In particular, the use of derivative preprocessing and outlier removal likely contributed to enhanced model reliability, which is discussed in the next section of this review. To assure the safety and origin of the ingredients used, the malting, beer, and whisky industries are required to implement traceability systems [16; 17; 26]. Considering that starch is the main component in barley grain, any change in its biophysical or biochemical properties will influence the functional and malting properties of the sample (e.g., endosperm breakdown, protein content, cell walls, AMY and AMP contents and ratios, starch granule structure, lipids, and particle size) [16; 17; 26]. The ability of RVA combined with principal component analysis (PCA) and PLS-DA was evaluated to identify the origin of commercial barley varieties gown in different regions of South Australia. The PLS-DA models correctly classified 96.3 and 97.8% of the samples according to harvest and locality.