## 3 Machine Learning Tools  
Enhancing the information generated by the RVA beyond the interpretation of a single parameter (e.g., PV or FV) requires the utilization of data analytics tools, including techniques and methods that are available in the fields of chemometrics and ML [22; 23; 24]. The utilization of ML tools to analyze an RVA profile has allowed for the determination of the geographical origin of a sample (e.g., region, country), to identify the different varieties or species (e.g., cereals), or to monitor the process that a given sample had undergone (e.g., heating). Specifically, the utilization of methods such as PCA or cluster analysis (CA) applied to the RVA profile has been reported by different authors [16; 18]. PCA is an unsupervised machine learning technique used to reduce the dimensionality of data while preserving as much variance as possible. It transforms a large set of correlated variables into a smaller set of uncorrelated variables called principal components (PCs), which retain the most significant patterns in the dataset. In RVA analysis, PCA is widely used to classify starch sources based on their pasting properties, detect variability among starch samples, and visualize trends in large datasets [16; 18]. The primary advantage of PCA is its ability to highlight dominant trends in RVA profiles without requiring prior knowledge about sample classification. However, it does not establish causal relationships between pasting properties and starch composition, making it more suitable for exploratory data analysis rather than predictive modeling. Additionally, PCA requires proper data preprocessing (e.g., centering and scaling) to ensure meaningful component extraction, and the interpretation of PCs may sometimes be challenging when multiple factors influence the RVA viscosity profiles simultaneously. Other machine learning tools have been used to develop quantitative models of the relationship between the RVA profile and other chemical properties, where the incorporation of algorithms such as PLS regression are the most utilized [16; 17; 18]. PLS is a supervised machine learning technique designed to establish a predictive relationship between input \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Abvs.** & **Definition** & **Significance** \\ \hline
**Breakdown** & BD, BVis, BKD & The difference between the peak viscosity (PV) and Trough/Minimum Viscosity (TV). It represents the reduction in viscosity due to the breakdown of starch granules under heat and shear. & BD, or BKD, indicates the stability of starch under heat and shear stress. A higher breakdown means that the starch is less stable and more prone to degradation, which can affect the texture and viscosity in food products. \\ \hline
**Trough/Minimum Viscosity** & TV, Tmin, Trough, TH & The lowest viscosity after peak viscosity during the holding period, representing the point at which the starch granules are most disrupted. & TV, or TH, gives insight into the extent of granule breakdown and the ability of the starch to maintain viscosity during processing. \\ \hline
**Setback** & SB, STB & The difference between final viscosity (FV) and Trough Viscosity (TV). It measures the increase in viscosity during the cooling phase as the starch granules start to reassociate and form a gel. & SB, or STB, is important for understanding starch \\ \hline
**Final Viscosity** & FV & The viscosity at the end of the RVA test during the cooling phase. It reflects the viscosity of the starch gel after it has been heated and then cooled. & FV is a key indicator of the final texture and stability of starch-based products, such as sauces, gels, and baked goods. It also provides insight into starch retrogradation. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._variables (e.g., RVA pasting parameters) and response variables (e.g., amylose content, starch composition).