The objective of this study is to apply natural language processing to identifying innovative technology trends related to food waste treatment, biogas, and anaerobic digestion. The methodology used involved analyzing large volumes of text data mined from 3186 patents related to these three fields. Latent Dirichlet Allocation and the perplexity method were used to identify the main topics which the patent corpora were comprised of and which technological concepts were most associated with each topic. In addition, term frequency-inverse document frequency (TF-IDF) was used to gauge the "emergingness" of certain technical concepts across the patent corpora in various years. The key results were as follows: (1) perplexity computations showed that a 20 topic models were feasible for these patent corpora; (2) topics were identified, providing an accurate picture of the patenting landscape in the analyzed fields; (3) TF-IDF analysis on unigrams, bigrams, and trigrams, supplemented with network graph analysis, revealed emerging technology trends in each year. This study has important implications for governments who need to decide where to invest resources in anaerobic food waste treatment.