ATR-FTIR spectroscopy combined with data manipulation as a pre-screening method to assess DNA preservation in skeletal remains

Forensic Science International: Genetics 44 (2020) 102196

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ATR-FTIR spectroscopy combined with data manipulation as a pre-screening method to assess DNA preservation in skeletal remains

T

Tamara Leskovara, Irena Zupanič Pajničb,*, Živa Miriam Geršakc, Ivan Jermand, Matija Črešnare a

Department of Archaeology, Faculty of Arts, University of Ljubljana, Zavetiška 5, 1000 Ljubljana, Slovenia Institute of Forensic Medicine, Faculty of Medicine, Korytkova 2, 1000 Ljubljana, Slovenia c University Medical Centre Ljubljana, Zaloška 7, 1000 Ljubljana, Slovenia d National Institute of Chemistry, Hajdrihova 19, 1000, Ljubljana, Slovenia e Department of Archaeology, Faculty of Arts, University of Ljubljana, Zavetiška 5, 1000 Ljubljana, Slovenia b

ARTICLE INFO

ABSTRACT

Keywords: Human skeletal remains ATR-FTIR spectroscopy Spectra exploration DNA preservation Pre-screening method

Skeletal remains are commonly subjected to various analyses, including DNA. As the remains are exposed to taphonomic processes after the death of the organism, their physicochemical structure undergoes alterations. The success and integrity of a DNA analysis is thus conditioned by the preservation state of the sample. In this study, ATR-FTIR spectroscopy with further data exploration was employed to characterize the physicochemical structure of the samples and its correlation with the preservation state of the DNA. The aim was to test the hypothesis that ATR-FTIR–obtained spectra contain enough information to allow classification of the samples based on the preservation of the DNA in the remains. In the study, 138 human bones and teeth originating from the 16th century BC to the 21 st century AD were used. The samples were cleaned and powdered following the established methodological procedures for DNA extraction. DNA was extracted and quantified. The samples were separated into four categories based on the amount of quantified DNA. The remaining powder was analyzed with ATR-FTIR spectroscopy and the spectra obtained were explored to extract physicochemical information. Before the exploration of the acquired data, samples were divided into groups A (n = 107) and B (n = 31). Statistical analyses and machine learning were performed on the group A samples. The protocol was then validated on the group B samples, which served to make predictions on the preservation of the DNA in the remains. The best results were achieved using a random forest learning algorithm employing either normalized spectra, second-derivative spectra, or five highest-ranked ratios. Even though overlapping remained, these findings indicate that ATR-FTIR spectroscopy with further exploration of the data has good potential as a pre-screening method for evaluating DNA preservation in skeletal remains.

1. Introduction Skeletal remains from forensic and archaeological settings contain a wealth of information, from species affiliation, life habits, diet, and health issues to migration and individual identification [1]. In order to obtain this information, remains are commonly subjected to various macroscopic, microscopic, and molecular analyses, including DNA [2]. However, after death, remains are exposed to taphonomic processes that induce physical, chemical, and biological changes, altering or destroying the embedded information [3–7]. The success and integrity of

results obtained with DNA analysis thus depend on the quality and quantity of the preserved DNA, which is strongly correlated with the taphonomic history and preservation state of the remains [8]. Even though the pathways of bone and tooth degradation are well-researched [9], detailed understanding of DNA preservation is still elusive. Research has shown that it is associated with both collagen and mineral in the tissues [10–12], but the relationship is complex and the quality and quantity of extractable DNA is highly variable even among samples originating from the same individuals and environments [13–17]. DNA analysis is invasive, laborious, relatively expensive, and time-

Corresponding author. E-mail addresses: [email protected] (T. Leskovar), [email protected] (I. Zupanič Pajnič), [email protected] (Ž.M. Geršak), [email protected] (I. Jerman), [email protected] (M. Črešnar). ⁎

https://doi.org/10.1016/j.fsigen.2019.102196 Received 27 April 2019; Received in revised form 28 October 2019; Accepted 29 October 2019 Available online 03 November 2019 1872-4973/ © 2019 Elsevier B.V. All rights reserved.

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consuming; thus, an informative pre-screening method that could help in selecting the most suitable samples for analysis would be of significant value.1 In general, dense and macroscopically well-preserved remains show the best DNA preservation [8,18], although macrostructural preservation can be misleading [19]. Thus, numerous microscopic and molecular techniques such as histology, collagen preservation, crystallinity indexes, flash pyrolysis with gas chromatography and mass spectrometry, osteocalcin analyses, proteomic analyses, and amino acid racemization [11,13,14,18,20–22] have been tested as pre-screening methods for evaluating the DNA preservation in skeletal remains. Although some of these techniques do show potential, most of them are still invasive, costly, and/or timeconsuming, whereas their predictive power remains limited and questionable. In this study, the utility of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) with further data exploration was tested as a pre-screening method for evaluating DNA preservation in various skeletal elements. Because most organic and inorganic components in the environment have dipole moments and are thus active in infrared radiation, FTIR spectroscopy is a convenient and relatively inexpensive technique for material characterization and exploration of its molecular structure. The addition of ATR minimizes sample preparation, improves signal-to-noise ratios, and increases comparability among samples [23,24]. FTIR spectroscopy has already been applied in studies investigating DNA preservation in skeletal remains, although with only minimal manipulation of the spectra obtained and relatively limited success [13,25–27]. We explored this further, hypothesizing that the ATR-FTIR–obtained spectra of samples from different skeletal elements of different ages and with various preservation states differ enough to allow for a general prediction of whether DNA extraction would be successful or not. However, because skeletal remains have complex and heterogeneous physicochemical properties, FTIR spectroscopy–based characterization of their molecular structure can be elusive [28]. Consequently, straightforward investigation of spectral bands is usually replaced by calculations of the ratios between peak heights and/or areas [29]. This introduces an additional step into the data processing, which is time-consuming, can lead to errors, and limits not only the information that can be obtained from the spectra but also the broader comparability of the results. In order to avoid this step, we included whole spectral bands in the study and compared these findings with the ones obtained from the ratios.

2.2. Sample preparation First, an 8 to 10 cm fragment of the compact cortical diaphysis of each femur and tibia, the same size fragment of the rib, whole molars, metatarsal, metacarpal, and carpal bones, phalanges, and petrous portions were powdered. Specific precautions were followed and skeletal remains were treated under conditions designed to minimize contamination. To reduce contamination from the previous handling, bone samples were cleaned mechanically (physical removal of the surface using a rotary sanding tool; Dremel, Racine, WI, USA) and chemically (washing in 5% Alconox detergent; Sigma-Aldrich, St. Louis, MO, USA, sterile bi-distilled water; Millipore, Darmstadt, Germany, and 80% ethanol; Fisher Scientific, Loughborough, UK), whereas teeth were cleaned chemically (washing in detergent, water, and ethanol) and irradiated with UV light for 2 × 30 min with the tooth rotated 180° between each exposure. The exposed bone surface was removed by drilling and the bone material acquired directly from the inside of the specimen [30]. To prevent bone warming during drilling and cutting, a lower speed setting was used for abrasion and cutting, and the bones were frequently cooled in liquid nitrogen. The samples were left to dry overnight at room temperature prior to being ground into a powder. To test the potential effect of chemicals used during the cleaning and liquid nitrogen used in the pulverization process, two additional samples were taken from six different femurs included in the study. Chemical washing was omitted in cleaning the twelve collected samples. Six samples were then pulverized using liquid nitrogen in the tissue homogenizer and six without liquid nitrogen. The rest of the procedure remained the same. Cleaning and grinding took place in a room designed exclusively for processing old skeletal remains [31], using an MC 3 closed microbiological safety laminar flow cabinet (Iskra Pio, Šentjernej, Slovenia, EU) equipped with HEPA filters and UV lights. Drilling, cutting, and grinding were performed with the strict precautions recommended by Parson et al. [32] and Pääbo et al. [15]. A Bead Beater MillMix 20 (Tehtnica, Domel, Železniki, Slovenia) tissue homogenizer with 25 ml metal grinding vials and metal balls 20 mm in diameter was used to obtain a fine powder [33]. To avoid overheating during powdering, the metal vials and bone/tooth samples were cooled in liquid nitrogen. Bone and tooth samples were ground for 1–2 min at a frequency of 30 Hz, and up to 4 g of sample was pulverized. The powder obtained was placed in a sterile tube. To avoid cross-contamination between samples, the entire workspace and the reusable tools for processing the teeth and bones were cleaned after processing each sample. Cleaning was performed by washing with bleach, sterile bi-distilled water, and 80% ethanol, followed by UV irradiation for 72 h, as described by Rohland and Hofreiter [34]. Single-use 50 ml plastic falcon tubes were used. The grinding vials were also sterilized before the 72 -h UV irradiation and were also irradiated for 30 min before use.

2. Materials and methods 2.1. Samples The study sample included 138 human bones and teeth from 65 different individuals (see Supplementary material, Table 1). Most of the samples included were taken from femurs, followed by teeth, metacarpals and metatarsals, petrous portions of the temporal bone, and phalanges. In a single case, a rib, carpal bone, and tibia were included. Ten samples originated from the 21 st century AD, 49 from the 20th, 14 from the 18th, seven from the 17th, 21 from the 16th, 13 from the 6th, 21 from the 3rd century AD, and two from the 16th century BC. Samples were divided into two groups, A and B. The first included 107 samples and the second 31 samples, covering the variety in the sample origins (skeletal element, century) as well as possible. All the statistical analysis and machine learning were performed on samples from group A, whereas samples from group B served for validation of the procedures and to make predictions on the preservation state of the DNA.

2.3. ATR-FTIR spectroscopy The powdered samples were scanned with the Bruker Vertex 80/ 80v. The region between 400 cm−1 and 4000 cm−1 was scanned because it corresponds to most of the organic and inorganic compounds in the skeletal remains [35]. Spectra were collected as an average of 64 scans with a resolution of 4 cm−1. Data were analyzed using OriginPro 2017 software (OriginLab Corporation) focusing on the region between 480 cm−1 and 2000 cm−1. The baseline was subtracted, followed by the normalization to the highest peak (v3PO4 at ∼1010 cm−1). Based on the previous studies exploring skeletal remains with FTIR spectroscopy, 12 ratios (Table 1) were chosen and calculated from the spectrum of each sample. In the wavelengths between 480 cm−1 and 1800 cm−1, which covers v1,3,4PO4, Amide I–III, and v2,3CO3 peaks, the Savitzky–Golay (SG) second derivative with five points of window was additionally performed on the normalized spectra.

1 Ongoing European research on the taphonomy of DNA clearly shows the interest, potential, and significance of the subject: https://www.synthesys.info/ joint-research-activities/synthesys-2-jras/jra2.html.

2

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Table 1 Calculated ratios. RATIO

∼ PEAKS

CHARACTERISATION

REFERENCE

CI = SF C/P 30/20 60/75 AmI/P AmI/CO3 CO3/P API BPI C/C AmIII/P 60/30

(560 + 600) / 585 1415 / 1010 1030 / 1020 1060 / 1075 1650 / 1010 1650 / 1415 1415 / 1010 1540 / 600 1415 / 600 1450 / 1415 1280 / 1010 1660 / 1630

crystallinity, indicating crystal size and order in the matrix carbonate relative to phosphate crystallinity index (stoichiometric vs. non-stoichiometric apatites) crystallinity index (comparative to 30/20) amide I relative to phosphate amide I relative to carbonate relative proportion of carbonates relative proportion of type A carbonates relative proportion of type B carbonates Ratio of A + B type carbonates to B carbonates α helices in amide III relative to phosphate α helices relative to random coils in Amide I

[36,37] [38] [39] [39] [40]

2.4. DNA extraction

[38,39] [41] [41] [42] [43,44]

point serial dilutions include four DNA standards (50 ng/μl, 2 ng/μl, 0.08 ng/μl and 0.0032 ng/μl) that were amplified in duplicate reactions in the same plate as the unknown samples. The results were used to generate a 4-point standard curve for the autosomal and degradation targets [48]. All PowerQuant amplifications were carried out in duplicate according to the manufacturer’s recommendations [48]. Duplicate amplifications of the positive and negative template control reactions were performed on each plate. Analyses were performed using the 7500 Real-Time PCR System (Applied Biosystems, AB, Foster City, CA, USA). Instrument calibration, setup, and programming were carried out according to the technical manual [48]. The ABI 7500 Real-Time PCR System was used to obtain raw data and the PowerQuant Analysis Tool (www.promega.com/resources/tools/powerquantanalysis-tool) served to analyze the data. The imported data were used to evaluate the standard curves (r2, slope, y intercept and amplification efficiency) and quantify the Auto and Deg targets in a sample. Negative-template controls and extraction-negative controls were also analyzed with the PowerQuant System in order to verify the cleanliness of laboratory plastics and reagents.

Genomic DNA was obtained from 0.5 g of bone or tooth powder using the full demineralization protocol described by Zupanič Pajnič [33]. In brief, bone/tooth powder was incubated in 10 ml of 0.5 M ethylenediaminetetraacetic acid (EDTA; Promega, Madison, WI, USA) overnight at 37 °C. After decalcification, the precipitate was washed with sterile bi-distilled water (Millipore). G2 extraction buffer, proteinase K (both Qiagen, Hilden, Germany), and 1 M DTT (Sigma-Aldrich) were added to the precipitate and incubated for 2 h at 56 °C. The supernatant was purified in a Biorobot EZ1 device (Qiagen) using the EZ1 DNA Investigator Card and EZ1 DNA Investigator Kit (Qiagen). Following the manufacturer’s instructions [45] the “trace” protocol was selected for purification. Extraction negative controls were included in the extraction process to verify the purity of the extraction reagents and plastics. 2.5. DNA quantification Most forensic DNA laboratories utilize commercial real-time PCR (qPCR) kits for detecting and quantification of biological evidence. DNA content in the bone/tooth extracts was determined with a multiplex qPCR method using the new, highly informative multiplex qPCR assay PowerQuant System (Promega) that can simultaneously quantify total human nuclear DNA, the extent of DNA degradation and presence of PCR inhibitors. To report on commercial qPCR assay used, the MIQE guidelines [46] were followed. Sensitivity, specificity, accuracy, repeatability and reproducibility of PowerQuant System were tested by Ewing et al. [47]. Some information on targets can be found in PowerQuant technical manual [48]. Briefly, two targets were amplified at the same autosomal multicopy locus to quantify the total human DNA and to assess the degree of DNA degradation present in samples. The Auto target for quantifying the total amount of human DNA in a sample was 85 bp long and the Deg target was 294 bp long. To eliminate any potential for amplification interference of one target by the other, the autosomal Auto and Deg amplicons were separated by several kilobases [47]. Beside determination of DNA concentration and degradation, the PowerQuant also detects the internal positive control (IPC), a DNA template that is included in every amplification reaction. Amplification of the IPC is used to detect inhibitors in the sample. The IPC target is the longest target in the PowerQuant System (435bp), making the IPC more susceptible to inhibitors than the other targets in the multiplex kit [48]. Reaction conditions and thermocycling parameters were according to the manufacturer’s recommendations [48]: 1 cycle at 98 °C for 2 min and 39 cycles at 98 °C for 15 s and 62 °C for 35 s. Two μl of DNA extracts, positive and negative controls were used in 20 μl reaction volume and reaction set up was performed manually. The PowerQuant Male gDNA Standard (Promega) provided with the PowerQuant System was used to generate all standard curves. This DNA consists of pooled human male DNA supplied at a concentration of 50 ng/μl. The four-

2.6. Statistical analyses In searching for the parameters that would allow for differentiation between samples with high or low potential for successful DNA extraction, all the data obtained were statistically analyzed using SPPS Statistics 22 (IBM Corporation) and Orange [49]. First, a direct comparison of the normalized spectra and their second derivative was made to see how informative these were regarding the preservation state of the sample. The software (Orange) automatically recognized 811 of the normalized spectra and 692 peaks of second-derivative spectra and the served as features for the statistical analyses. In the third case, ratios were calculated from the spectra and utilized as features. In all three cases, the skeletal element, the century in which the individual died, and whether or not the remains had been buried were also acknowledged. Because the amount of DNA in the samples varied significantly, four DNA categories were created based on the DNA quantification results. In the developmental validation of the PowerQuant system, concentrations of human DNA as low as 0.5 pg/μl were consistently detected [47], so we used this as the lower end of the dynamic range of the PowerQuant system (Table 2). Spearman’s correlation was performed to determine whether there were correlations between any of the 12 individual ratios (Table 1) and the quantity of the DNA extracted from the sample. To identify significant differences among samples belonging to different DNA categories, calculated ratios were compared using the Kruskal–Wallis and Mann–Whitney tests (p ≤ 0.05). In the case of normalized and second-derivative spectra, software recognized 811 and 692 peaks respectively. To reduce this high dimensionality, normalized spectra and second-derivative spectra were 3

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below 0.30. Treshold for r2 was set at 0.99, slope upper limit at -3.1 and slope lower limit al -3.6 for Auto and Deg target, as recommended by the manufacturer [48]. Values for r2 were higher than 0.99 and slope in recommended range (−3.6 to −3.1) for Auto and Deg target for all plates processed. In the process of extraction and quantification of skeletal remains, we minimized the possibility of contamination during DNA analysis. Extraction-negative controls were implemented in every batch of extraction and qPCR-negative controls in every qPCR amplification reaction. To minimize cross-contamination, no more than 11 bone/tooth samples were processed in one extraction batch and one blank control was included for each batch. The results do not indicate the presence of contamination because all extraction negative controls and all qPCR negative-template controls yielded no detectable amplification for Auto and Deg targets and had an IPC shift greater than 0.3.

Table 2 DNA categories (concentration of DNA expressed in ng/ul). DNA category

Auto (85 base pairs)

Deg (294 base pairs)

1 2 3 4

< 0.0005 ng/ul > 0.0005 ng/ul > 0.0005 ng/ul > 0.005 ng/ul

< 0.0005 ng/ul < 0.0005 ng/ul > 0.0005 ng/ul > 0.005 ng/ul

subjected to principal component analysis (PCA; variance covered > 70%). However, to retain the information on which features of the spectra define specific principal component (PC), linear discriminant analysis (LDA) [50,51] was performed for each PC. Normalized spectra, second-derivative spectra, and six ratios with the highest correlations to Auto and Deg DNA (based on the results from Spearman’s correlation) were subjected to neural network (NN) and random forest (RF) learning algorithms in order to classify the samples and make predictions. A NN is based on simple processing units (neurons). Neurons in NNs function like neurons in the brain: they process the input signals and convert them into an output similar to a neuron’s axon. The NN thus has the ability to weigh the strength of the interunit connections by learning from a set of training patterns and then making predictions and classifications [52]. The most important parameter in NN design is the fixation of neurons in hidden layer because their number influences the error and accuracy of the results. Too few of them make the model weak whereas too many can cause overfitting. In the study, recommendations from Sheela [53] were followed and neurons in the hidden layer set

3.2. Correlations Even though Spearman’s correlation highlighted some significant correlations between Auto or Deg DNA (p ≤ 0.05) and some of the ratios (SF, C/P, AmI/P, API, BPI, AmIII/P, and 60/30), the correlations were actually weak. The strongest correlation was between Deg DNA and 60/30, with a correlation coefficient of 0.406, followed by C/P and API with correlation coefficients of 0.313 and 0.306, respectively (see Supplementary material, Table 2). 3.3. Comparisons between categories The Kruskal–Wallis and Mann–Whitney tests highlighted significant differences in the C/P, AmI/P, API, BPI, and 60/30 ratios among the four DNA classes. Significant differences were seen

4n2 + 3

using the equation 2 , where n represents the number of input n 8 features. RF allows for classifications and predictions based on a large number of individual decision trees. Each decision tree makes its own classification of an instance based on a randomly selected set of attributes (features) provided and casts a vote for the final decision [54,55]. The main parameter in the RF is the number of trees because it has to provide a balance between area under the curve (AUC), which measures the quality of the model’s prediction, processing time, and memory usage. In order to find this balance, the recommendation by Oshiro et al. [55] was used, and the number of trees was set to 100. The seeds for the random generator were fixed because using the same seeds facilitates replicability of the results.

- Between DNA categories 1 and 3 in the 30/20, CP, AmI/P, APO, BPI, and 60/30 ratios; - Between DNA categories 1 and 4 in the C/P, API, C/C, and 60/30 ratios; - Between DNA categories 2 and 3 in the C/P, AmI/P, BPI, and 60/30 ratios; and - Between DNA categories 2 and 4 in the API, C/C and 60/30 ratios; whereas no significant differences were found between DNA categories 1 and 2 or 3 and 4 (see Supplementary material, Tables 3–9).

3. Results

3.4. Effect of cleaning

3.1. DNA quantification

The Kruskal–Wallis test on the six ratios (with strongest correlation to Auto and Deg DNA that were used in the learning algorithms and prediction models) obtained from the six samples subjected to three different preparation procedures (whole procedure, omitting chemical cleaning, and omitting chemical cleaning and liquid nitrogen during pulverization) showed no significant differences in the sample spectra (see Supplementary material, Table 10).

Table 1 in the Supplementary material shows the results of DNA quantification determined with the PowerQuant System (Promega). The results are the averages of the PowerQuant duplicates of all bone and tooth samples analyzed. The concentration of autosomal and degradation targets was expressed in ng DNA per μl of extract. The PowerQuant System is, according to the manufacturer’s information, sensitive down to 3.2 pg/μl of DNA extract. The lowest quantification standard used to create the standard curve is 0.0032 ng/ μl, which gives an indication of the intended sensitivities by marking the low end of the dynamic range of the kit [48]. In developmental validation of the PowerQuant system concentrations of human DNA as low as 0.5 pg/μl were consistently detected [47], so we used this as the limit of detection of the PowerQuant system. The presence of PCR inhibitors in the bone samples analysed was monitored by the IPC shift (difference in the quantification cycle for the IPC in an unknown sample and the IPC in the closest DNA standard of the standard curve). The minimum IPC shift value used to monitor PCR inhibition was set at 0.30, as recommended by the manufacturer [48]. No inhibition was detected in any of the skeletal remains analysed, IPC shift values being

3.5. Pca Three PC were necessary to cover > 70% (74%) of the variation in the normalized spectra, with PC 3 and PC 1 covering most of the variance (60%). LDA further showed that PC 3 is best defined by the spectral region between ∼890 cm−1 and 930 cm−1, mainly correlating with carbonates (see Supplementary material Fig. 1); PC 1 by the spectral region between ∼1610 cm−1 and 1660 cm−1, mainly correlating with Amide I (see Supplementary material, Fig. 1); and PC 2 by the spectral regions between ∼580 cm−1 and 600 cm−1 and between ∼1120 cm−1 and 1130 cm−1, both mainly correlating with phosphates (see Supplementary material, Fig. 3). 4

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Table 3 Performance statistics for Random Forest and Neural Network.

Ratios Normalised spectra 2nd derivative spectra

Method

Area under ROC

Classification accuracy

Precision

Recall

Random Forest Neural Network Random Forest Neural Network Random Forest Neural Network

0.992 0.601 0.774 0.628 0.848 0.616

0.944 0.411 0.542 0.458 0.617 0.393

0.951 0.391 0.510 0.436 0.579 0.374

0.944 0.411 0.542 0.458 0.617 0.393

3.6. Predictions

theory that DNA molecules and their survivability in skeletal remains are bound to the organic and inorganic fractions. Furthermore, the findings indicate that the state of carbonates, Amide I with its secondary structure, and phosphates all have to be acknowledged in sample characterization when assessing the potential of the remains to provide endogenous DNA. This supports previous conclusions that diagenetic changes in both mineral and collagen affect the success of DNA extraction from skeletal remains [13,14,25]. Even though some overlapping remained, newly included ratios presented significant differences (Table 4). Samples with no or only Auto DNA (categories 1 and 2) have lower amounts of carbonates, especially type A, and Amide I, especially α-helices, compared to samples with the highest amounts of extracted DNA (category 3 and 4). This is also visible when comparing normalized and second-derivative spectra, most obviously when comparing samples from DNA categories 1 and 4 (Fig. 1). The classification accuracy of the predictions based on the FTIR characterization and the quantity of extracted DNA was 0.54 to 0.94 (Table 3). Visualization with LDA offered some noticeable trends in the classifications of the samples. These were even more evident when the skeletal element and the sample age were also taken into consideration (Figs. 2 and 3). Two categories based on sample age emerged. Samples from the 20th and 21st centuries were concentrated on one side and samples from the 18th, 16th, 6th, and 3rd centuries were on the other side. In all the prediction scenarios, overlapping remained and only 10 or 11 out of 31 samples were correctly classified (see Supplementary material, Table 11). Nevertheless, with the exception of four samples (#94; 20th-century femur, #124; 21st-century femur, #74; 3rd-century metacarpal and #104; 20th-century femur) misclassifications only occurred within the range of one category and as samples from DNA category 1 were never classified into DNA categories 3 or 4, and samples from categories 3 and 4 never into category 1.This indicates that a gross separation of samples into categories of no or high potential for DNA extraction is possible using ATR-FTIR spectroscopy combined with further data exploration. It should be stressed that all the samples, regardless of their various origins, came from the same set. In other words, the samples in group B used to validate the methodology originated from the same set as the samples in group A used for the classifiers (NN and RF) to learn. Also, a comparison with samples with known amounts of extractable endogenous DNA is necessary for the learning process. Furthermore, even though gross assessment can be achieved directly through the comparison of the calculated ratios, it is only appropriate with the adoption of the same methodological procedures for the preparation and analysis of the samples.

Predictions made on samples from group A achieved higher accuracy with RF when compared to NN algorithms. The best results were produced using ratios, second-derivative spectra, and normalized spectra, respectively (Table 3). 4. Discussion DNA analysis can be highly informative and it therefore represents a common practice in archaeological and forensic science. However, after the death of an organism, DNA is no longer protected by the body’s repair mechanisms and is subject to molecular diagenesis, which leads to microsatellite artefacts or total DNA loss. Because the speed and extent of this diagenesis depend on various intrinsic and extrinsic factors, it is highly complicated. Furthermore, DNA structure can already be affected before death; for example, due to various drugs damaging the molecule [56]. Even though some gross implications based on the age of the remains, environmental conditions, and different skeletal elements with their macro- and microscopic structural differences do exist, the intercorrelation and complexity of all the factors involved make prediction of DNA preservation elusive [2,8,57]. The DNA molecule forms complexes with collagen [58] and is adsorbed on hydroxyapatite [59], and so both collagen and mineral should have an impact on its stabilization in skeletal remains. The correlation between the state of phosphates and carbonates, their dissolution/recrystallization processes, and the survivability of the DNA molecule through adsorption onto the surfaces of the mineral crystals is relatively obvious [6,13,14,20,27], whereas the correlation between the state of the organics and DNA preservation is still unclear. Some studies have proven the importance of the relationship [6,13,14,25], but others demonstrate a lack of it [11,27]. From the perspective of FTIR spectroscopy, the SF, C/P, and AmI/P ratios were previously proposed as the most promising indicators of DNA preservation in the skeletal remains [13,25–27], thus covering phosphates, carbonates, and collagen in the bone. Mainly, they found that the quantity of DNA in the sample is positively correlated to increasing AmI/P and C/P ratios and decreasing SF ratios. However, those studies involved a small number of samples with limited contextual, anatomical, and chronological origin. With the introduction of greater variety in the origin of the samples, here presented research found only weak (< 0.3) correlation between the aforementioned combination of ratios and quantity of the extracted DNA. This lack of correlation is most likely a consequence of the aforementioned high number of intercorrelated factors influencing the diagenesis of skeletal remains and the inclusion of samples with various origins in the study. However, additional exploration of the ratios and spectra using PCA and LDA exposed the spectral regions2 ∼1610–1660 cm−1, ∼890–930 cm−1, ∼580–600 cm−1, and ∼1120–1130 cm−1, and also the SF, C/P, AmI/ P, API, C/C, and 60/30 ratios as the most informative findings for assessing the DNA preservation in the remains. The results obtained from the spectra and extended set of ratios are consistent and agree with the 2

5. Conclusions Our exploration of the ATR-FTIR spectra demonstrates that diagenetic changes and the consequent state of the collagen, phosphates, and carbonates in skeletal remains all need to be taken into account in order to assess the preservation state of the endogenous DNA in the sample. Even though overlapping remained and classification of the samples into the correct DNA category was not without mistakes, using ratios

For spectral characterization of bone and teeth, see [24]. 5

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and normalized and second-derivative spectra all allowed for a relatively reliable separation between samples with no or high amounts of extractable endogenous DNA. Furthermore, the age of the sample greatly affected the classification because samples from the 21st and 20th centuries were clearly separated from the older ones. However, samples from group A and B all originated from the same set. Thus, the proposed methodology should next be validated using samples of a completely different origin. A drawback of the classification procedure based on spectra is the necessity for samples with known amounts of extractable endogenous DNA. With some caution, the ratios proposed here and in prior research

Table 4 Calculated ratios characterising DNA categories 1, 2, 3 and 4. RATIO

SF API AmI/P 60/30 C/P C/C

DNA CATEGORY 1

2

3

4

3.9 ± 0.33 0.18 ± 0.14 0.17 ± 0.09 0.51 ± 0.32 0.26 ± 0.05 0.77 ± 0.15

3.7 ± 22 0.28 ± 0.22 0.22 ± 0.11 0.67 ± 0.42 0.27 ± 0.05 0.77 ± 0.15

3.8 ± 23 0.40 ± 0.29 0.27 ± 0.13 0.77 ± 0.11 0.29 ± 0.04 0.79 ± 0.08

3.7 ± 0.34 0.64 ± 0.05 0.34 ± 0.04 0.91 ± 0.46 0.31 ± 0.01 0.87 ± 001

Fig. 1. Averaged normalised spectra of samples from DNA group 1 (black) and 4 (grey) in the region 480 cm−1 – 1750 cm-1 with isolated second-derivative spectra of most informative regions. Fig. 2. Separation of the group B samples based on ratios. Colour = DNA category: 1 – no DNA – 4 best-preserved DNA; shape = skeletal element: ● – femur, x – petrous portion, ▼ – bones of hand and feet, + - tooth; number = century in which the individual died.

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Fig. 3. Separation of the group B samples based on second-derivative spectra (left) and based on normalised spectra (right). Colour = DNA category: 1 – no DNA – 4 bestpreserved DNA; shape = skeletal element: ● – femur, x – petrous portion, ▼ – bones of hand and feet, + - tooth; number = century in which the individual died.

can serve as proxies for the gross assessment of DNA preservation, but only when adopting the same sample preparation and analysis procedure. On this point, we strongly agree with Kontopolous et al. [60] that a standard procedure for analyzing skeletal remains with FTIR spectroscopy should be established. Because the costs related to DNA analysis are relatively high, a reliable but affordable pre-screening method capable of identifying samples with potential for successful analyses is of high importance. FTIR spectroscopy, especially with the addition of ATR, provides good insight into the molecular structure of the sample but demands minimal sample preparation and is fast and relatively inexpensive. The process presented here indicates that this technique, accompanied by some relatively simple data exploration, can serve as a gross pre-screening step prior to DNA analysis and can help differentiate between samples with no or high potential for successful endogenous DNA extraction. Even though the end result of the process presented here is not perfect and FTIR spectroscopy, especially as a standalone technique, might only indicate gross preservation of DNA, we believe that this study contributes to the development of a reliable, fast, and affordable DNA pre-screening methodology.

development of operational procedures in heritage protection. Our thanks also go to Maja Janežič, Monika Arh, Marija Lubšina Tušek, Miha Murko, Evgen Lazar (all IPCHS CPA), and Matjaž Novšak (Arhej d.o.o.) for all the information about the archaeological remains from the archives, as well as Timotej Knific (National Museum of Slovenia) and Vesna Koprivnik (Regional Museum of Maribor) for contributing archaeological human remains from their museums to our study. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.fsigen.2019.102196. References [1] M. Prinz, A. Carracedo, W.R. Mayr, N. Morling, T.J. Parsons, A. Sajantila, R. Scheithauer, H. Schmitter, P.M. Schneider, DNA Commission of the International Society for Forensic Genetics (ISFG): recommendations regarding the role of forensic genetics for disaster victim identification (DVI), Forensic Sci. Int. Genet. 1 (2007) 3–12, https://doi.org/10.1016/j.fsigen.2006.10.003. [2] S. Mays, J. Elders, L. Humphrey, W. White, P. Marshall, Science and the Dead: a Guideline for the Destructive Sampling of Archaeological Human Remains for Scientific Analysis, English Heritage Publishing with the Advisory Panel on the Archaology of Burial in England, London, 2013. [3] M.J. Collins, C.M. Nielsen-Marsh, J. Hiller, C.I. Smith, J.P. Roberts, R.V. Prigodich, T.J. Wess, J. Csapo, A.R. Millard, G. Turner-Walker, The survival of organic matter in bone: a review, Archaeometry 44 (2002) 383–394, https://doi.org/10.1111/ 1475-4754.t01-1-00071. [4] R.E.M. Hedges, Bone diagenesis: an overview of processes, Archaeometry 44 (2002) 319–328, https://doi.org/10.1111/1475-4754.00064. [5] R. Alaeddini, S.J. Walsh, A. Abbas, Forensic implications of genetic analyses from degraded DNA—A review, Forensic Sci. Int. Genet. 4 (2010) 148–157, https://doi. org/10.1016/j.fsigen.2009.09.007. [6] A. Götherström, M.J. Collins, A. Angerbjörn, K. Lidén, Bone preservation and DNA amplification, Archaeometry 44 (2002) 395–404, https://doi.org/10.1111/14754754.00072. [7] K.E. Latham, M.E. Madonna, DNA survivability in skeletal remains, in: J.T. Pokines, S.A. Symes (Eds.), Man. Forensic Taphon, CRC Press, Boca Raton, 2013, pp. 403–426, , https://doi.org/10.1201/b15424-16 10.1201/b15424-16. [8] K.E. Latham, J.J. Miller, DNA recovery and analysis from skeletal material in modern forensic contexts, Forensic Sci. Res. 4 (2019) 51–59, https://doi.org/10. 1080/20961790.2018.1515594. [9] C. Kendall, A.M.H. Eriksen, I. Kontopoulos, M.J. Collins, G. Turner-Walker, Diagenesis of archaeological bone and tooth, Palaeogeogr. Palaeoclimatol. Palaeoecol. 491 (2018) 21–37, https://doi.org/10.1016/J.PALAEO.2017.11.041. [10] M. Salamon, N. Tuross, B. Arensburg, S. Weiner, Relatively well preserved DNA is present in the crystal aggregates of fossil bones, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 13783–13788. [11] C. Wadsworth, N. Procopio, C. Anderung, J.-M. Carretero, E. Iriarte, C. Valdiosera, R. Elburg, K. Penkman, M. Buckley, Comparing ancient DNA survival and proteome content in 69 archaeological cattle tooth and bone samples from multiple European sites, J. Proteomics 158 (2017) 1–8, https://doi.org/10.1016/j.jprot.2017.01.004. [12] C. Ottoni, B. Bekaert, R. Decorte, DNA degradation: current knowledge and progress in DNA analysis, Taphon. Hum. Remain. Forensic Anal. Dead Depos. Environ. (2017) 65–80, https://doi.org/10.1002/9781118953358.ch5.

Ethical standards The research project was approved by the Medical Ethics Committee of the Republic of Slovenia (0120-350/2018/6). Funding This study was partially financially supported by the Slovenian Research Agency (project “Determination of the most appropriate skeletal elements for molecular genetic identification of aged human remains” (J3-8214)). Declaration of Competing Interest The authors declare that they have no conflicts of interest. Acknowledgements We would like to thank Jože Grdadolnik from the National Institute of Chemistry for making the Bruker Vertex 80 spectrometer available to us, the Governmental Commission on Concealed Mass Graves of the Republic of Slovenia for their support in excavations of Second World War victims, and the head of the Center for Preventive Archaeology of the Institute for the Protection of Cultural Heritage of Slovenia (IPCHS CPA) Barbara Nadbath for recognizing this study as important for the 7

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[36] S. Weiner, O. Bar-Yosef, States of preservation of bones from prehistoric sites in the near East: a survey, J. Archaeol. Sci. 17 (1990) 187–196, https://doi.org/10.1016/ 0305-4403(90)90058-D. [37] T.A. Surovell, M.C. Stiner, Standardizing infra-red measures of bone mineral crystallinity: an experimental approach, J. Archaeol. Sci. 28 (2001) 633–642, https:// doi.org/10.1006/jasc.2000.0633. [38] L.E. Wright, H.P. Schwarcz, Infrared and isotopic evidence for diagenesis of bone apatite at Dos Pilas, Guatemala: palaeodietary implications, J. Archaeol. Sci. 23 (1996) 933–944, https://doi.org/10.1006/jasc.1996.0087. [39] M. Lebon, I. Reiche, J.J. Bahain, C. Chadefaux, A.M. Moigne, F. Fröhlich, F. Sémah, H.P. Schwarcz, C. Falguères, New parameters for the characterization of diagenetic alterations and heat-induced changes of fossil bone mineral using Fourier transform infrared spectrometry, J. Archaeol. Sci. 37 (2010) 2265–2276, https://doi.org/10. 1016/j.jas.2010.03.024. [40] C.N.G. Trueman, A.K. Behrensmeyer, N. Tuross, S. Weiner, Mineralogical and compositional changes in bones exposed on soil surfaces in Amboseli National Park, Kenya: diagenetic mechanisms and the role of sediment pore fluids, J. Archaeol. Sci. 31 (2004) 721–739, https://doi.org/10.1016/j.jas.2003.11.003. [41] C. Snoeck, J.A. Lee-Thorp, R.J. Schulting, From bone to ash: compositional and structural changes in burned modern and archaeological bone, Palaeogeogr. Palaeoclimatol. Palaeoecol. 416 (2014) 55–68, https://doi.org/10.1016/j.palaeo. 2014.08.002. [42] T.J.U. Thompson, M. Gauthier, M. Islam, The application of a new method of Fourier Transform Infrared Spectroscopy to the analysis of burned bone, J. Archaeol. Sci. 36 (2009) 910–914, https://doi.org/10.1016/j.jas.2008.11.013. [43] J. Habermehl, J. Skopinska, F. Boccafoschi, A. Sionkowska, H. Kaczmarek, G. Laroche, D. Mantovani, Preparation of ready-to-use, Stockable and reconstituted collagen, Macromol. Biosci. 5 (2005) 821–828, https://doi.org/10.1002/mabi. 200500102. [44] C. Chadefaux, A.-S. Le Hô, L. Bellot-Gurlet, I. Reiche, Curve-fitting Micro-ATR-FTIR studies of the amide I and II bands of type I collagen in archaeological bone materials, E-Preserv. Sci. 6 (2009) 129–137. [45] Qiagen Companies, EZ1 DNA Investigator Handbook, Qiagen Companies, Hilden, 2014. [46] S.A. Bustin, V. Benes, J.A. Garson, J. Hellemans, J. Huggett, M. Kubista, R. Mueller, T. Nolan, M.W. Pfaffl, G.L. Shipley, J. Vandesompele, C.T. Wittwer, The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments, Clin. Chem. 55 (2009) 611–622, https://doi.org/10.1373/clinchem. 2008.112797. [47] M.M. Ewing, J.M. Thompson, R.S. McLaren, V.M. Purpero, K.J. Thomas, P.A. Dobrowski, G.A. DeGroot, E.L. Romsos, D.R. Storts, Human DNA quantification and sample quality assessment: developmental validation of the PowerQuant system, Forensic Sci. Int. Genet. 23 (2016) 166–177, https://doi.org/10.1016/j. fsigen.2016.04.007. [48] Promega Corporation, PowerQuant System Technical Manual, Madison (2018). [49] J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, Orange: data mining toolbox in Python, J. Mach. Learn. Res. 14 (2013) 2349–2353. [50] A.M. Martinez, A.C. Kak, PCA versus LDA, IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001) 228–233, https://doi.org/10.1109/34.908974. [51] A.H. Sahoolizadeh, B.Z. Heidari, C.H. Dehghani, A new face recognition method using PCA, LDA and neural network, Int. J. Comput. Sci. Eng. 2 (2008) 218–223. [52] K. Gurney, An Introduction to Neural Networks, CRC press, 2014. [53] K.G. Sheela, S.N. Deepa, Review on methods to fix number of hidden neurons in neural networks, Math. Probl. Eng. 2013 (2013) 1–11. [54] L. Breiman, Random forests, Mach. Learn. 45 (2001) 5–32, https://doi.org/10. 1023/A:1010933404324. [55] T.M. Oshiro, P.S. Perez, J.A. Baranauskas, P. Perner (Ed.), How Many Trees in a Random Forest? BT - Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 154–168. [56] A.L.M. Batista de Carvalho, A.P. Mamede, A. Dopplapudi, V. Garcia Sakai, J. Doherty, M. Frogley, G. Cinque, P. Gardner, D. Gianolio, L.A.E. Batista de Carvalho, M.P.M. Marques, Anticancer drug impact on DNA – a study by neutron spectroscopy coupled with synchrotron-based FTIR and EXAFS, Phys. Chem. Chem. Phys. 21 (2019) 4162–4175, https://doi.org/10.1039/C8CP05881D. [57] Y.M. Coulson-Thomas, A.L. Norton, V.J. Coulson-Thomas, R. Florencio-Silva, N. Ali, S. Elmrghni, C.D. Gil, G.R.S. Sasso, R.A. Dixon, H.B. Nader, DNA and bone structure preservation in medieval human skeletons, Forensic Sci. Int. 251 (2015) 186–194, https://doi.org/10.1016/j.forsciint.2015.04.005. [58] G.M. Mrevlishvili, D.V. Svintradze, DNA as a matrix of collagen fibrils, Int. J. Biol. Macromol. 36 (2005) 324–326, https://doi.org/10.1016/j.ijbiomac.2005.07.006. [59] M. Okazaki, Y. Yoshida, S. Yamaguchi, M. Kaneno, J.C. Elliott, Affinity binding phenomena of DNA onto apatite crystals, Biomaterials 22 (2001) 2459–2464, https://doi.org/10.1016/S0142-9612(00)00433-6. [60] I. Kontopoulos, S. Presslee, K. Penkman, M.J. Collins, Preparation of bone powder for FTIR-ATR analysis: the particle size effect, Vib. Spectrosc. 99 (2018) 167–177, https://doi.org/10.1016/j.vibspec.2018.09.004.

[13] C. Schwarz, R. Debruyne, M. Kuch, E. McNally, H. Schwarcz, A.D. Aubrey, J. Bada, H. Poinar, New insights from old bones: DNA preservation and degradation in permafrost preserved mammoth remains, Nucleic Acids Res. 37 (2009) 3215–3229, https://doi.org/10.1093/nar/gkp159. [14] P.F. Campos, O.E. Craig, G. Turner-Walker, E. Peacock, E. Willerslev, M.T.P. Gilbert, DNA in ancient bone – Where is it located and how should we extract it? Ann. Anat. – Anat. Anzeiger 194 (2012) 7–16, https://doi.org/10.1016/j. aanat.2011.07.003. [15] S. Pääbo, H. Poinar, D. Serre, V. Jaenicke-Després, J. Hebler, N. Rohland, M. Kuch, J. Krause, L. Vigilant, M. Hofreiter, Genetic analyses from ancient DNA, Annu. Rev. Genet. 38 (2004) 645–679, https://doi.org/10.1146/annurev.genet.37.110801. 143214. [16] A. Miloš, A. Selmanović, L. Smajlović, R.L.M. Huel, C. Katzmarzyk, A. Rizvić, T.J. Parsons, Success rates of nuclear short tandem repeat typing from different skeletal elements, Croat. Med. J. 48 (2007) 486–493. [17] A.Z. Mundorff, E.J. Bartelink, E. Mar-Cash, DNA preservation in skeletal elements from the world trade center disaster: recommendations for mass fatality management, J. Forensic Sci. 54 (2009) 739–745, https://doi.org/10.1111/j.1556-4029. 2009.01045.x. [18] S. Haynes, J.B. Searle, A. Bretman, K.M. Dobney, Bone preservation and ancient DNA: the application of screening methods for predicting DNA Survival, J. Archaeol. Sci. 29 (2002) 585–592, https://doi.org/10.1006/jasc.2001.0731. [19] E. Hagelberg, J.B. Clegg, Isolation and characterization of DNA from archaeological bone, Proc. R. Soc. London B Biol. Sci. 244 (1991) 45–50. [20] C. Sosa, E. Vispe, C. Núñez, M. Baeta, Y. Casalod, M. Bolea, R.E.M. Hedges, B. Martinez-Jarreta, Association between ancient bone preservation and dna yield: a multidisciplinary approach, Am. J. Phys. Anthropol. 151 (2013) 102–109, https://doi.org/10.1002/ajpa.22262. [21] M.J. Collins, K.E.H. Penkman, N. Rohland, B. Shapiro, R.C. Dobberstein, S. RitzTimme, M. Hofreiter, Is amino acid racemization a useful tool for screening for ancient DNA in bone? Proc. R. Soc. London B Biol. Sci. (2009). [22] M. Buckley, C. Anderung, K. Penkman, B.J. Raney, A. Götherström, J. ThomasOates, M.J. Collins, Comparing the survival of osteocalcin and mtDNA in archaeological bone from four European sites, J. Archaeol. Sci. 35 (2008) 1756–1764, https://doi.org/10.1016/j.jas.2007.11.022. [23] M.M. Beasley, E.J. Bartelink, L. Taylor, R.M. Miller, Comparison of transmission FTIR, ATR, and DRIFT spectra: implications for assessment of bone bioapatite diagenesis, J. Archaeol. Sci. 46 (2014) 16–22, https://doi.org/10.1016/j.jas.2014.03. 008. [24] Cde C.A. Lopes, P.H.J.O. Limirio, V.R. Novais, P. Dechichi, Fourier transform infrared spectroscopy (FTIR) application chemical characterization of enamel, dentin and bone, Appl. Spectrosc. Rev. (2018) 1–23, https://doi.org/10.1080/05704928. 2018.1431923. [25] J.D. Fredericks, P. Bennett, A. Williams, K.D. Rogers, FTIR spectroscopy: a new diagnostic tool to aid DNA analysis from heated bone, Forensic Sci. Int. Genet. 6 (2012) 375–380, https://doi.org/10.1016/j.fsigen.2011.07.014. [26] V. Francigny, H. Hollund, A. de Vogt, E. Altena, P. de Knijff, C. Fallet, Limits of ancient DNA extraction from teeth: the case of sudanese Nubia, Nyame Akuma (2013) 13–29. [27] I. Kontopoulos, K. Penkman, G.D. McAllister, N. Lynnerup, P.B. Damgaard, H.B. Hansen, M.E. Allentoft, M.J. Collins, Petrous bone diagenesis: a multi-analytical approach, Palaeogeogr. Palaeoclimatol. Palaeoecol. 518 (2019) 143–154, https://doi.org/10.1016/j.palaeo.2019.01.005. [28] T.J.U. Thompson, M. Islam, M. Bonniere, A new statistical approach for determining the crystallinity of heat-altered bone mineral from FTIR spectra, J. Archaeol. Sci. 40 (2013) 416–422, https://doi.org/10.1016/j.jas.2012.07.008. [29] H. Ou‐Yang, E.P. Paschalis, W.E. Mayo, A.L. Boskey, R. Mendelsohn, Infrared microscopic imaging of bone: spatial distribution of CO3(2−), J. Bone Miner. Res. 16 (2001) 893–900, https://doi.org/10.1359/jbmr.2001.16.5.893. [30] S. Amory, R. Huel, A. Bilić, O. Loreille, T.J. Parsons, Automatable full demineralization DNA extraction procedure from degraded skeletal remains, Forensic Sci. Int. Genet. 6 (2012) 398–406, https://doi.org/10.1016/j.fsigen.2011.08.004. [31] M. Caputo, M. Irisarri, E. Alechine, D. Corach, A DNA extraction method of small quantities of bone for high-quality genotyping, Forensic Sci. Int. Genet. 7 (2013) 488–493, https://doi.org/10.1016/j.fsigen.2013.05.002. [32] W. Parson, L. Gusmão, D.R. Hares, J.A. Irwin, W.R. Mayr, N. Morling, E. Pokorak, M. Prinz, A. Salas, P.M. Schneider, T.J. Parsons, DNA commission of the international society for forensic genetics: revised and extended guidelines for mitochondrial DNA typing, Forensic Sci. Int. Genet. 13 (2014) 134–142, https://doi.org/10. 1016/j.fsigen.2014.07.010. [33] I.Z. Pajnič, Extraction of DNA from human skeletal material, Methods Mol. Biol. (2016), https://doi.org/10.1007/978-1-4939-3597-0_7. [34] N. Rohland, M. Hofreiter, Ancient DNA extraction from bones and teeth, Nat. Protoc. 2 (2007) 1756. [35] H.L. Buijs, L. Rochette, F. Chateauneuf, Evolution of FTIR technology as applied to chemical detection and quantification, Chem. Biol. Point Sensors Homel. Def. International Society for Optics and Photonics, 2004, pp. 132–143.

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