## 7 Commercial Considerations Regarding ML in the Food Industry  
The integration of ML tools into RVA analysis has significant implications for commercial applications, particularly in the areas of quality control (QC) and online automation in food manufacturing. Many industries, such as the snack industry, rely on rapid and accurate starch characterization for ensuring product consistency and functional performance [43]. ML-powered data analysis can enhance decision-making by providing real-time insights into ingredient functionality and formulation optimization.  
Advancements in cloud computing and Internet of Things technologies have facilitated the adoption of ML-driven platforms in food production environments [44]. Several cloud-based platforms allow for real-time monitoring of RVA profiles, automatic anomaly detection, and predictive quality assurance [45]. Examples include AI-driven software that integrates with laboratory instruments to streamline data acquisition and processing, providing manufacturers with automated alerts when deviations from the expected starch behavior occur.  
The implementation of ML-enhanced RVA analysis in commercial food production will drive greater efficiency, reduce operational risks, and improve product consistency. Future developments may include the application of deep learning models and automated machine learning frameworks to further refine predictive analytics for food texture and ingredient stability [46]. These advancements highlight the transformative potential of ML in modernizing starch quality assessments in both research and industry applications. Recently, Perten (Perking-Elmer) has included in the Prediction Pack an optional feature that is accessible to all users of the Thermocline for Windows (TCW) software for the RVA, the doughLAB for Windows (DLW) software for the doughLAB and micro-doughLAB, and the TexCalc software for the TVT [47]. This feature allows The Prediction Pack to import PCA or PLS models created in The Unscrambler software to predict a desired parameter [47].  
Despite its potential, the integration of ML with RVA analysis presents several challenges that need to be addressed for successful implementation in both research and industrial settings. One of the key challenges is the availability of large, high-quality datasets for ML training and validation, as many food industry datasets remain proprietary, limiting broader applications. Additionally, the computational requirements for advanced ML models, such as deep learning, can be a barrier for small to medium-sized enterprises that lack infrastructure for large-scale data processing. While cloud-based ML solutions provide an alternative, concerns regarding cost and data security persist. Another critical issue is the lack of standardized ML methods for RVA data interpretation. Variability in preprocessing techniques, feature selection, and model validation methods can result in inconsistent results across different studies and applications. Addressing these challenges requires the development of standardized ML pipelines, greater access to diverse datasets, and optimization of computational efficiency to enable real-time quality control in food manufacturing.