Decay detection of constructional softwoods using machine olfaction

Fungal decomposition of wood severely affects the soundness of timber constructions. The diagnosis of wood decay requires direct observations or sampling by skilled experts. Wood decay often occurs in obscure spaces, including the enclosed inner spaces of walls or under the floor. In this study, we examined the ability of machine olfaction to detect odors of fungi grown on common construction softwoods to provide a novel diagnostic method for wood construction soundness. The combination of a simple device equipped with semiconductor gas sensors (gas sensor array) and multivariate analysis discriminated a fungi-related odor from control odor without instrumental analysis (e.g., gas chromatography). This method is often referred to as machine olfaction or electronic nose. We measured the odor of wood test pieces that were infected with Fomitopsis palustris or Trametes versicolor and sound test pieces using a gas sensor array. The sensor responses of the specimens showed different patterns between the inoculated and control samples. Each specimen class formed independent groups in a principal component score plot, almost regardless of wood species, fungal species, or cultivation period. This method provides a new decay diagnosis method that is cost-effective and easy to operate.

Development of Portable E-Nose System for Fast Diagnosis of Whitefly Infestation in Tomato Plant in Greenhouse

An electronic nose (E-nose) system equipped with a gas sensor array and real-time control panel was developed for a fast diagnosis of whitefly infestation in tomato plants. Profile changes of volatile organic compounds (VOCs) released from tomato plants under different treatments (i.e., whitefly infestation, mechanical damage, and no treatment) were successfully determined by the developed E-nose system. A rapid sensor response with high sensitivity towards whitefly-infested tomato plants was observed in the E-nose system. Results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) indicated that the E-nose system was able to provide accurate distinguishment between whitefly-infested plants and healthy plants, with the first three principal components (PCs) accounting for 87.4% of the classification. To reveal the mechanism of whitefly infestation in tomato plants, VOC profiles of whitefly-infested plants and mechanically damaged plants were investigated by using the E-nose system and GC-MS. VOCs of 2-nonanol, oxime-, methoxy-phenyl, and n-hexadecanoic acid were only detected in whitefly-infested plants, while compounds of dodecane and 4,6-dimethyl were only found in mechanically damaged plant samples. Those unique VOC profiles of different tomato plant groups could be considered as bio-markers for diagnosing different damages. Moreover, the E-nose system was demonstrated to have the capability to differentiate whitefly-infested plants and mechanically damaged plants. The relationship between sensor performance and VOC profiles confirmed that the developed E-nose system could be used as a fast and smart device to detect whitefly infestation in greenhouse cultivation.

Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient’s exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

Urine LOX-1 and Volatilome as Promising Tools towards the Early Detection of Renal Cancer

Renal cell carcinoma (RCC) represents around 3% of all cancers, within which clear cell RCC (ccRCC) are the most common type (70–75%). The RCC disease regularly progresses asymptomatically and upon presentation is recurrently metastatic, therefore, an early method of detection is necessary. The identification of one or more specific biomarkers measurable in biofluids (i.e., urine) by combined approaches could surely be appropriate for this kind of cancer, especially due to easy obtainability by noninvasive method. OLR1 is a metabolic gene that encodes for the Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), implicated in inflammation, atherosclerosis, ROS, and metabolic disorder-associated carcinogenesis. Specifically, LOX-1 is clearly involved in tumor insurgence and progression of different human cancers. This work reports for the first time the presence of LOX-1 protein in ccRCC urine and its peculiar distribution in tumoral tissues. The urine samples headspace has also been analyzed for the presence of the volatile compounds (VOCs) by SPME-GC/MS and gas sensor array. In particular, it was found by GC/MS analysis that 2-Cyclohexen-1-one,3-methyl-6-(1-methylethyl)- correlates with LOX-1 concentration in urine. The combined approach of VOCs analysis and protein quantification could lead to promising results in terms of diagnostic and prognostic potential for ccRCC tumors.

Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

The leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40–50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag–Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag–Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L 0 has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag–Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag–Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.