Methods for Early Detection of Microbiological Infestation of Buildings Based on Gas Sensor Technologies

Abstract

In this review, the problem of microbiological infestation of buildings was discussed. The techniques of detection were described as well, with special attention drawn to the rapid-early detection methods based on gas sensor arrays. The physical and chemical conditions of the building environment conducive to the development of microorganisms and the technical conditions influencing the problem of microbiological infestation were investigated. Additionally, the harmful effects on human health caused by the microbiological contamination were discussed, with a short review of particular groups of microorganisms causing sick building syndrome. Among the detection techniques, the traditional microbiological techniques as well as the molecular and chemical methods were presented. Different designs of the gas sensor arrays together with the various techniques of analyzing the received multidimensional signal were described, analyzed, and compared in detail.

Detection of Chaetomium globosum, Ch. cochliodes and Ch. rectangulare during the Diversity Tracking of Mycotoxin-Producing Chaetomium-like Isolates Obtained in Buildings in Finland

“…In mammalian cells, they may interfere with cellular structures (e.g., biomembranes) and with important cellular processes (e.g., protein, RNA and DNA syntheses; ion homeostasis; energy metabolism; and mitochondrial functions) [11][12][13][14][15][16][17][18]. Most mycotoxins exert immunosuppressive effects, and many of them are cytotoxic/cytostatic and thus could potentially damage the skin and lungs and could affect the gut microbiota [19][20][21][22]. In addition, they may affect the physical defense mechanisms of the respiratory tract, and this phenomenon is described as the ciliostatic effect of the metabolites produced by Chaetomium spp.…”

“…In addition, they may affect the physical defense mechanisms of the respiratory tract, and this phenomenon is described as the ciliostatic effect of the metabolites produced by Chaetomium spp. Ciliostatic effects may reduce the ability of the respiratory tract to purify particulate pollutants (including bacteria or viruses) [20,[23][24][25]. Moreover, mycotoxins could damage the macrophages of alveoli, thereby preventing the elimination of pollutants from the deeper lungs, resulting in increased susceptibility to infectious diseases and in reduced defense against other contaminants [19,[23][24][25][26][27][28].…”

Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

“…IOMS has gained a great deal of popularity and applicability over the last few years in the field of air quality and, in particular, for the monitoring of odours due to the annoyance and impact induced by the growing number of emissions in the environment by industrial activities [ 5 , 6 ]. Furthermore, IOMS possessed numerous advantages over sensorial (e.g., dynamic olfactometer) and analytical instrument (e.g., Gas chromatography–mass spectrometry, colorimetric method, catalytic, infrared and electrochemical sensors, photoionization detector, differential optical absorption spectroscopy) because it is applicable for in-situ and in real time measurements [ 5 , 6 , 7 , 8 ]. Meanwhile, other techniques are combined with IOMS, such as pre-concentrator (i.e., silicon-micro), to improve the recognizing capability [ 9 ], Bluetooth to a smartphone for remote-controlling applications [ 4 ], and GC-MS to identify the responsible gas compounds in odour emission [ 10 , 11 , 12 ].…”

Opto-Electronic Nose Coupled to a Silicon Micro Pre-Concentrator Device for Selective Sensing of Flavored Waters

“…A deeper multivariate data analysis of the odor pattern datasets generated from these two measurement methods were performed using principal component analysis (PCA). PCA is a well-known analytical tool in the domain of sensor arrays, as it offers the advantages of visualizing in two dimensions multidimensional data and highlighting the relations between the analyzed samples [45]. However, despite those major advantages, the reading and interpretation of a PCA plot could be difficult to handle for a non-initiated user.…”