Low moisture foods (LMF) do not support microbial growth; however, cells can persist in these foods for extended periods of time. Microbial contamination can occur at any stage during the production of LMF, and the lack of water in LMF systems reduces the efficacy of conventional thermal inactivation due to enhanced microbial thermal resistance and low thermal conductivity. Over a decade of outbreaks have highlighted the food safety risks associated with LMF and anchored the need for novel food safety technologies for these systems. This research utilized a multifaceted approach including predictive modeling and bench-scale studies to address this need.The first objective of this study is to assess the kinetics of thermal pathogen inactivation under moderate temperature (<60°C) dehydration of high-water activity foods to LMF. Heat-assisted dehydration is a dynamic process that increases the complexity of validating microbial inactivation outcomes. Accurate assessments of in-process microbial inactivation must account for these dynamic processing variables such as air currents, temperature, and humidity within the dehydration unit that affect product temperature and water activity (aw), critical factors for pathogen inactivation. Results indicated that the relative impact of aw was product dependent and appeared to have a non-linear impact on D-values. While the isothermal results suggested significant microbial inactivation might be achieved, the dehydrator studies showed that the combination of extended come-up time and decreasing aw in dynamic systems provided minimal inactivation. In the second objective, large historical data sets are leveraged using a meta-analysis approach to drive a more global understanding of key experimental features that determine microbial thermal inactivation in LMF systems. Previous studies have variably identified a range of experimental variables (microbial target, temperature, aw, inoculation and recovery method, and the physicochemical properties of the food matrix) that can impact inactivation kinetics. However, a quantitative and generalizable understanding is lacking. Capitalizing on the extensive published literature using predictive modeling can facilitate a more comprehensive understanding of thermal inactivation kinetics, as an alternative to exclusively relying on extensive empirical experiments. In total, 27 studies containing 782 data points were identified and analyzed. Results indicated that inoculation and recovery methods, aw, temperature, as well as matrix composition had a significant effect on microbial inactivation. Additionally, when modeling large data sets, data complexity was found to negatively affect model performance. In objectives three and four, high temperature (125°C to >300°C), non-polluting, non-chemical superheated steam (SHS) was introduced as a novel tool for sanitization in LMF processing environments. Sanitation in dry food processing environments is challenging due to the exclusion of water. However, SHS, with greater thermal energy and the ability to effectively penetrate cavities and crevices could be effectively used in food processing environments where high temperature and short exposure times are anticipated for the elimination of target microorganisms. The effect of surface characteristics, food matrix, and thermal gradient during SHS exposure to various microbial targets was assessed. Surface thickness, distance from the steam nozzle, distance from the steam point of impingement on the coupon, and ambient temperature had a great effect on the surface temperature profile, thus affecting microbial inactivation. The findings from this study suggest that short exposures provide high microbial kill and temperature gradient necessary for an effective sanitation process in a food production facility. The outcomes from this work have significant practical relevance as the results not only provide a better understanding of how various factors affect thermal inactivation kinetics and sanitation in LMF, but they can also help with prospective study design in thermal process validations and ensure LMF safety.