Research on the use of microbial biosensors for monitoring wastewater contaminants is a topic that covers few publications compared to their applicability in other fields, such as biomedical research. For this reason, a systematic analysis of the topic was carried out, for which research-type articles were reviewed during the period 2012 to September 2022. For this, different search platforms were used, including PubMed, ScienceDirect, Springer Link, and Scopus, and through the use of search equations a relevant bibliography was located. After that, the research articles were selected based on exclusion criteria. As a result, it was found that, of the 126 articles, only 16 articles were strictly related to the topic, since there was a duplication of articles among the different databases. It was possible to demonstrate the usefulness of microorganisms as components of biosensors to monitor BOD, heavy metals, and inorganic contaminants in wastewater that also had a high sensitivity. Additionally, recombinant DNA techniques were shown to improve the performance of this type of biosensor and can finally be coupled to other emerging technologies, such as microbial fuel cells (MFCs). In conclusion, it was established that microbial biosensors have high acceptability and monitoring characteristics that make them a useful tool to detect low concentrations of pollutants in wastewater that can also provide results in real-time, thus generating forms of ecological safety and social responsibility in companies where wastewater is generated.

Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm⁻¹ associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined.