## 6 Advantages and Limitations of ML Combined with RVA  
The RVA has been widely used in both research and the food industry, where the interpretation of RVA data is generally straightforward and simple by calculating single parameters (e.g., PV, FV) [10]. However, the information provided by the RVA instrument can also be considered as generic and limited, where several factors, such as the particle size and moisture content, can affect the calculated RVA parameters [8; 9]. Some of these issues are known to contribute to the variability and reproducibility of the RVA instrument compared to other techniques [8; 9; 10]. Consequently, the definition of protocols and methodsthat incorporate these issues are of importance to compare the results from different laboratories or applications [8; 9; 10]. The application of ML tools to instrumental techniques such as the RVA is considered by some researchers as a mere exercise conducted by data scientists or modeling experts, where the development of the so-called complex algorithms and their application to simple instruments such as the RVA do not contribute with a better understanding of the pasting properties of the starch and starchy foods. However, the analysis of the RVA profile by incorporating ML tools (e.g., PCA, PLS, ANN) into the interpretation of the results has provided us with the means to classify samples according to species, variety, and processing or even to detect contamination [31; 35; 39; 41]. PCA has been particularly valuable in RVA data analysis by identifying dominant patterns and clustering samples with similar pasting properties. Its advantages lie in its ability to simplify high-dimensional data, provide insights into sample variability, and serve as an effective exploratory tool for detecting quality variations in starch. However, a key limitation of PCA is that it does not directly model relationships between RVA parameters and specific starch properties. It only reduces the data complexity, making it less suitable for tasks requiring predictive modeling or a detailed mechanistic understanding of starch's behavior. PLS, on the other hand, is a powerful tool for predictive modeling using RVA data. It allows researchers to establish robust relationships between pasting properties and the compositional or functional characteristics of starch-based ingredients. PLS is particularly useful in food formulation, enabling ingredient optimization based on functional properties derived from RVA analysis. Furthermore, PLS models are highly dependent on the dataset quality and require careful validation to prevent overfitting. Unlike PCA, which provides easy-to-interpret visual patterns, PLS involves a more complex interpretation of latent variables and requires appropriate sample selection to ensure model generalizability. Beyond the utilization of a given algorithm to analyze the data, the preprocessing of the RVA profile using derivatives has provided a new dimension for the interpretation of the RVA profile [37; 38]. The first derivative contributes to eliminating a constant offset, while the second derivative removes both the offset and the linear term (e.g., linear fitting applied to the spectrum) [37; 38]. In some cases, the second-order derivative can accentuate sharp features in the profile and contributes to resolving overlapping profiles [37; 38]. However, limitations in the use of derivatives are associated with the fact that high-order derivatives (e.g., second and third derivatives) can be more sensitive to instrumental noise or unwanted artifacts during the analysis compared with lower-order ones (first derivatives) [37; 38; 42]. Overall, the use of derivatives has provided researchers and analysts with a tool to better understand the pasting properties of starch, as well as to better interpret complex interactions between starch and other components during the analysis beyond the interpretation of a single parameter that is provided by the RVA instrument (e.g., FV).