Lung cancer, chronic obstructive pulmonary disease (COPD) and asthma are inflammatory diseases that have risen worldwide, posing a major public health issue, encompassing not only physical and psychological morbidity and mortality, but also incurring significant societal costs. The leading cause of death worldwide by cancer is that of the lung, which, in large part, is a result of the disease often not being detected until a late stage. Although COPD and asthma are conditions with considerably lower mortality, they are extremely distressful to people and involve high healthcare overheads. Moreover, for these diseases, diagnostic methods are not only costly but are also invasive, thereby adding to people’s stress. It has been appreciated for many decades that the analysis of trace volatile organic compounds (VOCs) in exhaled breath could potentially provide cheaper, rapid, and non-invasive screening procedures to diagnose and monitor the above diseases of the lung. However, after decades of research associated with breath biomarker discovery, no breath VOC tests are clinically available. Reasons for this include the little consensus as to which breath volatiles (or pattern of volatiles) can be used to discriminate people with lung diseases, and our limited understanding of the biological origin of the identified VOCs. Lung disease diagnosis using breath VOCs is challenging. Nevertheless, the numerous studies of breath volatiles and lung disease provide guidance as to what volatiles need further investigation for use in differential diagnosis, highlight the urgent need for non-invasive clinical breath tests, illustrate the way forward for future studies, and provide significant guidance to achieve the goal of developing non-invasive diagnostic tests for lung disease. This review provides an overview of these issues from evaluating key studies that have been undertaken in the years 2010–2019, in order to present objective and comprehensive updated information that presents the progress that has been made in this field. The potential of this approach is highlighted, while strengths, weaknesses, opportunities, and threats are discussed. This review will be of interest to chemists, biologists, medical doctors and researchers involved in the development of analytical instruments for breath diagnosis.

Honey is a natural sweetener, with an osmotic effect on microorganisms due to the increased sugar content and low amount of water. Cyclitols are minor constituents of honey. They play a defensive role in plants against unfavorable environmental conditions. Honey’s physicochemical properties can vary, resulting in a wide range of colors, flavors, scents, antioxidant activity, dissimilar values of pH, acidity, electrical conductivity, etc. Some literature regarding correlation between honey types is already available, but a comprehensive study displaying an ample evaluation of multifarious aspects is still needed. This study focuses on the correlation between 18 honey types, originating from 10 countries, collected during four years, summarizing a total of 38 samples. A total of 6 physicochemical properties and 18 target components (sugars and cyclitols) were considered as variables. A correlation analysis is presented between the investigated parameters and between honey types, together with the statistical analysis which allowed for observation of the clusters’ distribution according with the investigated variables.

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs’ latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.

This experiment observed the evolution of metabolite plumes from a human trapped in a simulation of a collapsed building. Ten participants took it in turns over five days to lie in a simulation of a collapsed building and eight of them completed the 6 h protocol while their breath, sweat and skin metabolites were passed through a simulation of a collapsed glass-clad reinforced-concrete building. Safety, welfare and environmental parameters were monitored continuously, and active adsorbent sampling for thermal desorption GC-MS, on-line and embedded CO, CO(2) and O(2) monitoring, aspirating ion mobility spectrometry with integrated semiconductor gas sensors, direct injection GC-ion mobility spectrometry, active sampling thermal desorption GC-differential mobility spectrometry and a prototype remote early detection system for survivor location were used to monitor the evolution of the metabolite plumes that were generated. Oxygen levels within the void simulator were allowed to fall no lower than 19.1% (v). Concurrent levels of carbon dioxide built up to an average level of 1.6% (v) in the breathing zone of the participants. Temperature, humidity, carbon dioxide levels and the physiological measurements were consistent with a reproducible methodology that enabled the metabolite plumes to be sampled and characterized from the different parts of the experiment. Welfare and safety data were satisfactory with pulse rates, blood pressures and oxygenation, all within levels consistent with healthy adults. Up to 12 in-test welfare assessments per participant and a six-week follow-up Stanford Acute Stress Response Questionnaire indicated that the researchers and participants did not experience any adverse effects from their involvement in the study. Preliminary observations confirmed that CO(2), NH(3) and acetone were effective markers for trapped humans, although interactions with water absorbed in building debris needed further study. An unexpected observation from the NH(3) channel was the suppression of NH(3) during those periods when the participants slept, and this will be the subject of further study, as will be the detailed analysis of the casualty detection data obtained from the seven instruments used.