## 8 Final Considerations  
Different researchers have shown that starch's pasting properties can be measured using empirical methods such as the RVA. The analysis of RVA profiles combined with ML tools has shown that is possible to use not only a single parameter (e.g., PV or FV) but also to interpret changes in the profile associated with the gelatinization of the starch, as well as their effect on the properties (e.g., functional properties, interactions between chemical components including lipids and water) of the analyzed food ingredient and food product.  
Most of the applications that are discussed in the scientific literature on the analysis of cereals and starchy foods using RVA have reported and interpreted single parametersderived from the RVA profile (e.g., PV, PT, and FV). Although this approach is considered the routine application of the RVA to gather information about starch's gelatinization or the pasting properties of a sample, other information about the starch's pasting characteristics is lost during the analysis, weakening the ability of the RVA to be a valuable analytical tool. Consequently, the utilization and incorporation of ML tools (e.g., algorithms and preprocessing) has been shown to enhance the information that is generated by this type of instrument.  
Therefore, the routine application of the RVA instrument to evaluate the gelatinization and retrogradation of starch can be enhanced by the incorporation of ML tools. This approach will enable a greater understanding of the rheological or pasting properties of starch by analyzing and interpreting the whole RVA viscosity profile. Finally, the utilization of pasting properties and empirical methods such as the RVA need further development, where research and development will play a key role to better understand the gelatinization and retrogradation of starch and starchy foods.  
The advantage of machine learning (ML) is not solely focused on developing a model to predict a single parameter (e.g., PV). Rather, the overall benefits of these tools lie in their ability to mine data from the RVA trace and use it as a fingerprint. Furthermore, the RVA fingerprint, when combined with ML tools, can identify trends or patterns in the dataset or group of samples. This can be used to classify or identify samples based on AMY or AMP content, distinguish between waxy and non-waxy rice samples, predict storage duration in starchy foods, determine the geographical origin of barley and rice samples, identify the type of starch in different flour types, and detect the addition of insects to cereal flour, among other applications. Without the integration of the RVA profile with multivariate analysis (MVA) and ML tools, these applications would not be possible through the sole interpretation of a single RVA value.  
Conceptualization, D.C. and J.R.N.; methodology, D.C.; software, S.A., D.C. and J.R.N.; validation, S.A., D.C. and J.R.N.; formal analysis, D.C. and J.R.N.; investigation, J.R.N. and D.C.; resources, S.A., D.C. and J.R.N.; data curation, D.C. and J.R.N.; writing--original draft preparation, D.C. and J.R.N.; writing--review and editing, S.A., D.C. and J.R.N.; visualization, J.R.N. and D.C.; supervision, D.C.; project administration, D.C. All authors have read and agreed to the published version of the manuscript.  
The support of the University of Queensland and Queensland Alliance for Agriculture and Food Innovation is acknowledged. [PERSON] acknowledges the support of the Australian Research Council: 'A Deadly Solution: Towards an Indigenous-led bush food industry', grant ID: GA141113.  
Not applicable.  
The datasets used and analyzed within the study may be obtained from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.