Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

Cereals are among the most vulnerable substrates to mycotoxin contamination. One of the major fungal species colonising the ecosystem of stored grain is Penicillium verrucosum because of its ability to produce ochratoxin A (OTA). Knowledge of favourable environmental conditions promoting fungal growth and mycotoxin biosynthesis seems to be a crucial step towards preventing mycotoxin formation in stored grain. The aim of the study was to examine the effect of temperature and different cereal substrates (wheat, triticale, rye, barley, maize, rice) on fungal growth and OTA and citrinin (CIT) production by a mycotoxin-producing P. verrucosum strain at one moisture content. The correlation between fungal growth and mycotoxin biosynthesis was also investigated. For this purpose, cereal substrates moistened, autoclaved and inoculated with P. verrucosum spores were stored at 10°C, 20°C and 30°C for 40 days. After the storage period, the ergosterol (ERG) content as a fungal growth biomarker and OTA and CIT levels were determined using HPLC. Fungal growth was observed on all cereals at each temperature. Maximum ERG levels were found at 30°C and the values ranged from 401 to 3280 mg kg(-1) of grain. Among tested cereals, the highest ergosterol content was found on rice at all applied temperatures. Accumulation of OTA and CIT was detected in samples stored at 20°C and 30°C, while optimal conditions for the production of both mycotoxins were observed at 20°C. The maximum concentration of OTA (31.9 ± 4.3 (SD) μg kg(-1)) and CIT (74.8 ± 5.7 μg kg(-1)) were found on rice. The ability to biosynthesise OTA and CIT at 10°C was not detected. Analysis of variance showed that single factors, temperature and cereal matrices and their interactions had significant effects on ERG, OTA and CIT levels. Optimal conditions for fungus growth did not coincide with the ones for mycotoxin formation.

Investigations were performed to examine the possibility of using an electronic nose to monitor development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device manufactured by Sensigent was used to analyse volatile organic compounds. Each sample of infected material was divided into three parts and the degree of spoilage was measured in three ways: analysis of colony forming units, determination of ergosterol content, and measurement of volatile organic compounds with the e-nose. Principal component analysis was performed on the generated patterns of signals and six groups of different spoilage levels were isolated. An analysis of sensorgrams for a few sensors with a strong signal for each group of rapeseed spoilage was performed. The ratio of the association time to the steady state was calculated. This ratio was different for the low level and the highest level of ergosterol and colony forming units. The results have shown that the e-nose can be a useful tool for quick estimation of the degree of rapeseed spoilage.