Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the 'NOS.E' was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system's full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments.

Correction to "Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical "-omics" Data with Multiple Explanatory Variables".

[This corrects the article DOI: 10.1021/acsomega.3c01613.].

A forensically validated genetic toolkit for the species and lineage identification of the highly trafficked shingleback lizard (Tiliqua rugosa).

Shingleback lizards (Tiliqua rugosa) are among the most trafficked native fauna from Australia in the illegal pet trade. There are four morphologically recognised subspecies of shinglebacks, all with differing overseas market values. Shinglebacks from different geographic locales are often trafficked and housed together, which may complicate identifying the State jurisdiction where the poaching event occurred. Additionally, shinglebacks can be housed and trafficked with other species within the same genus, which may complicate DNA analysis, especially in scenarios where indirect evidence (e.g. swabs, faeces) is taken for analysis. In this study, a forensic genetic toolkit was designed and validated to target shingleback DNA for species identification and geographic origin. To do this, field sampling across Australia was conducted to expand the phylogeographic sampling of shinglebacks across their species range and include populations suspected to be poaching hotspots. A commonly used universal reptile primer set (ND4/LEU) was then validated for use in forensic casework related to the genus Tiliqua. Two additional ND4 primer sets were designed and validated. The first primer set was designed and demonstrated to preferentially amplify an ∼510 bp region of the genus Tiliqua over other reptiles and builds on existing data to expand the available phylogeographic database. The second primer set was designed and demonstrated to solely amplify an ∼220 bp region of T. rugosa ND4 over any other reptile species. Through the validation process, all primers were demonstrated to amplify T. rugosa DNA from a variety of sample types (e.g. degraded, low quality and mixed). Two of the primer sets were able to distinguish the genetic lineage of T. rugosa from the phylogeographic database. This work provides the first forensically validated toolkit and phylogeographic genetic database for Squatmate lizards.

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical "-omics" Data with Multiple Explanatory Variables.

Biological volatilome analysis is inherently complex due to the considerable number of compounds (i.e., dimensions) and differences in peak areas by orders of magnitude, between and within compounds found within datasets. Traditional volatilome analysis relies on dimensionality reduction techniques which aid in the selection of compounds that are considered relevant to respective research questions prior to further analysis. Currently, compounds of interest are identified using either supervised or unsupervised statistical methods which assume the data residuals are normally distributed and exhibit linearity. However, biological data often violate the statistical assumptions of these models related to normality and the presence of multiple explanatory variables which are innate to biological samples. In an attempt to address deviations from normality, volatilome data can be log transformed. However, whether the effects of each assessed variable are additive or multiplicative should be considered prior to transformation, as this will impact the effect of each variable on the data. If assumptions of normality and variable effects are not investigated prior to dimensionality reduction, ineffective or erroneous compound dimensionality reduction can impact downstream analyses. It is the aim of this manuscript to assess the impact of single and multivariable statistical models with and without the log transformation to volatilome dimensionality reduction prior to any supervised or unsupervised classification analysis. As a proof of concept, Shingleback lizard (Tiliqua rugosa) volatilomes were collected across their species distribution and from captivity and were assessed. Shingleback volatilomes are suspected to be influenced by multiple explanatory variables related to habitat (Bioregion), sex, parasite presence, total body volume, and captive status. This work determined that the exclusion of relevant multiple explanatory variables from analysis overestimates the effect of Bioregion and the identification of significant compounds. The log transformation increased the number of compounds that were identified as significant, as did analyses that assumed that residuals were normally distributed. Among the methods considered in this work, the most conservative form of dimensionality reduction was achieved through analyzing untransformed data using Monte Carlo tests with multiple explanatory variables.