## 1 Introduction  
Starch is found in a wide range of seeds (e.g., cereal grains, pulses) and tubers from different agricultural crops (e.g., maize, wheat, rice, barley, sweet potato, and cassava), and it serves as the main source of energy in the human diet [1]. Moreover, this polysaccharide is also a key component in the formulation of animal diets (mainly chicken and pigs), as it provides a source of highly digestible energy [2]. Starch plays a significant role in the texture, stability, functional properties, and digestibility of starchy food ingredients and food products [3, 4]. As a result, its influence extends beyond nutrition, contributing to food and feed functionality, making it a critical nutrient in food and nutrition [3, 4]. The biochemical characteristics and rheological properties of starch are associated with the proportion, chain length, and structure of both amylose (AMY) and amylopectin (AMP) [3, 5, 6, 7, 8]. Both AMY and AMP influence the viscosity or gelatinization properties of the starch, changing the functional properties of the food ingredient or product, as well as its utilization (e.g., bread making, malt and beer properties, sauces, or in biopolymer formulations) [3, 5, 6, 7, 8]. The retrogradation of starch is a process that is associated with the disaggregation of the AMY and AMP chains that are present in the gelatinized starch paste or sample, as well as their reassociation to form a more ordered structure [8, 9, 10, 11]. Other starch properties that influence gelatinization and retrogradation are associated with the characteristics of the starch granule (e.g., type of granule, particle size) such as swelling, and breakdown, contributing to the texture and stability of starchy food ingredients and products [12]. The Rapid Visco Analyser (RVA) is a widely used instrument for measuring the rheological properties of starch and starchy ingredients under controlled heating and cooling conditions. By providing viscosity profiles, the RVA helps researchers and food scientists evaluate starch's behavior in food and industrial applications [10]. However, while the RVA is efficient in capturing pasting properties, its interpretation is often limited to conventional parameters such as the pasting temperature (PT), peak viscosity (PV), breakdown (BD), setback (SB), and final viscosity (FV) [10]. These standard parameters, although useful, do not fully explain the complexity of starch's structure-function relationships. Additionally, variability in starch properties due to differences in botanical origin, processing methods, and environmental factors can make RVA data interpretation challenging [8]. A key limitation of RVA-based starch analysis is its reliance on traditional statistical approaches that often fail to capture hidden patterns, nonlinear relationships, and multivariate interactions in viscosity profiles. Conventional analysis typically considers only a few isolated parameters, potentially overlooking critical insights into starch's gelatinization and retrogradation mechanisms. Furthermore, inconsistencies in RVA testing protocols across different laboratories can lead to variability in reported results, impacting the reproducibility and comparability of the data [9; 10]. To overcome these limitations, the integration of machine learning (ML) techniques, such as principal component analysis (PCA) and partial least squares (PLS) regression, provides a more comprehensive approach to RVA data analysis [10]. These ML tools enable the identification of dominant trends, classification of starch types based on their pasting behavior, and predictive modeling of starch's functionality. PCA is particularly useful for reducing data dimensionality and visualizing trends, while PLS enhances the predictive capabilities of RVA by correlating pasting properties with compositional attributes. By leveraging ML, RVA analysis can transition from a purely empirical approach to a more data-driven methodology, offering deeper insights into starch's behavior in food formulations.