A novel use of infra-red spectroscopy (NIRS and ATR-FTIR) coupled with variable selection algorithms for the identification of insect species (Diptera: Sarcophagidae) of medico-legal relevance

A methodological approach to age estimation of the intra-puparial period of the forensically relevant blow fly Calliphora vicina via Fourier transform infrared spectroscopy

Estimating the age of immature blow flies is of great importance for forensic entomology. However, no gold-standard technique for an accurate determination of the intra-puparial age has yet been established. Fourier transform infrared (FTIR) spectroscopy is a method to (bio-)chemically characterise material based on the absorbance of electromagnetic energy by functional groups of molecules. In recent years, it also has become a powerful tool in forensic and life sciences, as it is a fast and cost-effective way to characterise all kinds of material and biological traces. This study is the first to collect developmental reference data on the changes in absorption spectra during the intra-puparial period of the forensically important blow fly Calliphora vicina Robineau-Desvoidy (Diptera: Calliphoridae). Calliphora vicina was reared at constant 20°C and 25°C and specimens were killed every other day throughout their intra-puparial development. In order to investigate which part yields the highest detectable differences in absorption spectra throughout the intra-puparial development, each specimen was divided into two different subsamples: the pupal body and the former cuticle of the third instar, that is, the puparium. Absorption spectra were collected with a FTIR spectrometer coupled to an attenuated total reflection (ATR) unit. Classification accuracies of different wavenumber regions with two machine learning models, i.e., random forests (RF) and support vector machines (SVMs), were tested. The best age predictions for both temperature settings and machine learning models were obtained by using the full spectral range from 3700 to 600 cm-1. While SVMs resulted in better accuracies for C. vicina reared at 20°C, RFs performed almost as good as SVMs for data obtained from 25°C. In terms of sample type, the pupal body gave smoother spectra and usually better classification accuracies than the puparia. This study shows that FTIR spectroscopy is a promising technique in forensic entomology to support the estimation of the minimum post-mortem interval (PMImin), by estimating the age of a given insect specimen.

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae)

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Pupal Age Estimation of Sarcophaga peregrina (Diptera: Sarcophagidae) at Different Constant Temperatures Utilizing ATR-FTIR Spectroscopy and Cuticular Hydrocarbons

Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) is a forensically important flesh fly that has potential value in estimating the PMImin. The precise pupal age estimation has great implications for PMImin estimation. During larval development, the age determination is straightforward by the morphological changes and variation of length and weight, however, the pupal age estimation is more difficult due to anatomical and morphological changes not being visible. Thus, it is necessary to find new techniques and methods that can be implemented by standard experiments for accurate pupal age estimation. In this study, we first investigated the potential of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and cuticular hydrocarbons (CHCs) for the age estimations of S. peregrina pupae at different constant temperatures (20 °C, 25 °C, and 30 °C). The orthogonal projections latent structure discrimination analysis (OPLS-DA) classification model was used to distinguish the pupae samples of different developmental ages. Then, a multivariate statistical regression model, partial least squares (PLS), was established with the spectroscopic and hydrocarbon data for pupal age estimations. We identified 37 CHCs with a carbon chain length between 11 and 35 in the pupae of S. peregrina. The results of the OPLS-DA model show a significant separation between different developmental ages of pupae (R2X > 0.928, R2Y > 0.899, Q2 > 0.863). The PLS model had a satisfactory prediction with a good fit between the actual and predicted ages of the pupae (R2 > 0.927, RMSECV < 1.268). The results demonstrate that the variation tendencies of spectroscopy and hydrocarbons were time-dependent, and ATR-FTIR and CHCs may be optimal for the age estimations of pupae of forensically important flies with implications for PMImin estimation in forensic practice.

Near infrared spectroscopy (NIRS) coupled with chemometric methods to identify and estimate taxonomic relationships of flies with forensic potential (Diptera: Calliphoridae and Sarcophagidae)

Infrared spectroscopy has been gaining prominence in entomology, such as for solving taxonomic problems, sexing adult specimens, determining the age of immature specimens, detecting drugs of abuse in fly larvae, and can be an important technique in Forensic Entomology. In order to help identify the species of Calliphoridae and Sarcophagidae families, the present study aimed to evaluate the use of near infrared spectroscopy (NIRS) coupled with chemometric methods for separating fly specimens into taxonomic categories and understanding the taxonomic relationship between them. Spectra collected from nine species of flies were subjected to unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA), in which we sought to visualize the relationship between the samples (segregation of genera and families) with subsequent identification. In PCA, the best model was achieved using five principal components (PCs), which explained 99.16% of total variance of the original data set. The first principal component (PC1) and the fourth principal component (PC4) provided the best segregation, the latter being more important in the segregation of the species Chrysomya albiceps, Lucilia eximia, and Ravinia belforti from the others. In the HCA dendrogram, there was a clear separation between the specimens by family (Calliphoridae and Sarcophagidae) and genera (Chrysomya, Lucilia, Oxysarcodexia, Peckia and Ravinia). This study shows that NIRS is efficient to identify flies' taxonomic properties, such as family and genera, providing quick evidence for the tested species identity.

Highly Efficient Use of Infrared Spectroscopy (ATR-FTIR) to Identify Aphid Species

Aphids are commonly considered to be serious pests for trees, herbaceous and cultivated plants. Recognition and identification of individual species is very difficult and is based mainly on morphological features. The aims of the study were to suggest the possibility of identifying aphids through the use of Fourier-transform infrared (FTIR) spectroscopy, and to determine which absorption peaks are the most useful to separate aphid species. Using FTIR spectroscopy, based on the chemical composition of the body, we were able to distinguish 12 species of aphid. We have shown that using nine distinct peaks corresponding to the molecular vibrations from carbohydrates, lipids, amides I and II, it is possible to accurately identify aphid species with an efficiency of 98%.

Interpretability of selected variables and performance comparison of variable selection methods in a polyethylene and polypropylene NIR classification task

Near infrared (NIR) spectra are collected as a high amount of absorption values which usually greatly exceeds the sample size. Variable selection methods are employed in NIR spectroscopy to avoid "curse of dimensionality" related issues. In this paper, we examined the interpretability of selected variables, that is, how much selected wavelengths are related to the chemical structure of the materials studied, and if the relation is important for classification performance. Additionally, we examined classification performance in dependence on the number of selected variables. For this purpose, relative standard deviation (RSD), successive projection algorithm (SPA), stepwise decorrelation of variables (SELECT), genetic algorithm (GA), principal component analysis (PCA), and random (RANDOM) variable selection were applied in two-class classification modelling using linear discriminant analysis (LDA) or a support vector machine (SVM). Different pre-treatments and sample sizes were considered. Variable selection improved classification performance and variables selected by a majority of the methods considered were conveniently related to chemical structure. Interpretability and performance increase/decrease depend greatly on the number of selected variables, however. Since selected variables reveal great chemical interpretability, some variable selection methods could be employed to determine material characteristic absorption bands. SELECT and SPA displayed the best properties among the methods considered. To avoid faulty results, optimization of the number of selected variables should become the crucial stage in the variable selection process.

Infrared spectroscopy and forensic entomology: Can this union work? A literature review

For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in Forensic entomology (FE) field, with vibrational techniques such as Near-infrared spectroscopy (NIRS) and Medium-infrared spectroscopy (MIRS) underutilized in forensic sciences. Thus, this work describes the potential of NIRS, MIRS, and other spectroscopic methodologies, for entomological analysis in FE, as well as discusses its future uses for criminal or civil investigations. After a thorough research on scientific journals database, a total of 33 publications were found in scientific journals, with direct or indirect application to FE, including experimental applications of NIRS and MIRS in taxonomic discrimination of species, larval age prediction, detection of toxic substances in insects from environments or crime scenes, and detection of internal or external infestations by live or dead insects in stored products. Besides, NIRS and MIRS combined with multivariate analysis were efficient, inexpensive, fast, and non-destructive analytical tools. However, more than 51% of the spectroscopic publications are concentrated in the stored products field, and so we discuss the need for expansion and more direct application in other FE areas. We hope the number of articles continues to increase, and as NIRS and MIRS technology progress, they advance in forensic research and routine use.

A Non-Invasive Approach in the Assessment of Stress Phenomena and Impairment Values in Pea Seeds Caused by Pea Weevil

Pea (Pisum sativum L.) is an important leguminous plant worldwide, in which pests trigger significant damage every year. One of the most important pest is pea weevil (Bruchus pisorum, L) which causes covert damage in crops. In the present study, our aim was to obtain precise information pertaining to the extent and the nature of damage in pea caused by B. pisorum by means of non-invasive imaging methods. The infested pea samples were analysed by an infrared thermometer and a bioluminescence plant imaging system as well as a computer tomograph under laboratory conditions. The calculated weight of organic matter destroyed by the developing larvae was 36.46%. The changing of RGB (red, blue, green) codes obtained through thermal imaging and the CPS (counts per second) values originating from bioluminescence imaging in infested samples were statistically verifiable. According to our CT assay, the damage caused by B. pisorum changed the tissue density, volume and shape of the pea seeds by the end of the development of the pest. The results of thermal and bioluminescence imaging contribute to a better understanding of the internal chemical processes and the CT analysis helps to understand the alteration trends of the inner structure of seeds caused by this pest.

Intraspecific differentiation of sandflies specimens by optical spectroscopy and multivariate analysis

Lutzomyia longipalpis and Lutzomyia cruzi are the main sandflies species involved in the transmission of Leishmania infantum protozoan in Brazil. The morphological characteristics can be used for species identification of males specimens, while females are indistinguishable. Although, sandflies identification is essential to understand vectorial capacity, and susceptibility to infectious agents or insecticides, there is a lack of new strategies for specimen identification. In this study, Fourier transform infrared photoacoustic spectroscopy combined with multivariate analysis identified intraspecific differences between Lutzomyia populations. Successfully group clustering was achieved by principal component analysis. The main differences observed can be related to the protein content of the specimens. A classification with 100% accuracy was obtained using machine learning approach, allowing the identification of sandflies specimens.

Time Flies-Age Grading of Adult Flies for the Estimation of the Post-Mortem Interval

The estimation of the minimum time since death is one of the main applications of forensic entomology. This can be done by calculating the age of the immature stage of necrophagous flies developing on the corpse, which is confined to approximately 2–4 weeks, depending on temperature and species of the first colonizing wave of flies. Adding the age of the adult flies developed on the dead body could extend this time frame up to several weeks when the body is in a building or closed premise. However, the techniques for accurately estimating the age of adult flies are still in their beginning stages or not sufficiently validated. Here we review the current state of the art of analysing the aging of flies by evaluating the ovarian development, the amount of pteridine in the eyes, the degree of wing damage, the modification of their cuticular hydrocarbon patterns, and the increasing number of growth layers in the cuticula. New approaches, including the use of age specific molecular profiles based on the levels of gene and protein expression and the application of near infrared spectroscopy, are introduced, and the forensic relevance of these methods is discussed.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.