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Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures
Forensic Science International 304 (2019) 109956
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Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures Lina Möréna , Sebastian Jonssonb , Tobias Tengela , Anders Östina,* a b
The Swedish Defence Research Agency, FOI Cementvägen 20, 901 82 Umeå, Sweden Umeå University, 901 87 Umeå, Sweden
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 May 2019 Received in revised form 3 September 2019 Accepted 5 September 2019 Available online 20 September 2019
Riot control agents such as pepper sprays can be misused for antagonistic and criminal purposes. Several web-pages and YouTube videos are available describing how to make homemade pepper spray. In this study, we investigated whether it was possible to identify the origin of homemade pepper sprays based on chemical attribution signatures from thirteen different types of chili acquired from six different vendors analyzed by GC–MS. The results showed that it was possible to differentiate chili based on species, chili type and vendor using OPLS-DA. Application of an external test set of chilies acquired and extracted one year later than development of the models resulted in correct classification in all models. The models displayed high predictability, suggesting their use for prediction of the identity and origin of seized homemade pepper sprays. © 2019 Published by Elsevier B.V.
Keywords: Chemical forensics Homemade pepper spray Chemical attribution signatures Multivariate data analysis
1. Introduction Riot control agents (RCAs), e.g., teargases and pepper sprays, are defined as substances used for civilian peacekeeping that have an immediate effect on humans [1]. The purpose of using teargases and pepper sprays is to temporarily disable subjects [1]. Their effects have been described as non-lethal with a large margin of safety between amounts that cause intolerable effects and amounts that cause serious adverse responses [2]. Teargases and pepper sprays interact with sensory nerve receptors of mucosal surfaces and the skin at exposed sites, resulting in local discomfort and/or pain together with consequential reflexes [2]. Profuse lacrimation occurs when teargases and pepper sprays are applied to the eyes, causing temporary blindness that induces fear and disorientation of the exposed subject [1]. Use of RCAs by law enforcement agencies during riot control situations is allowed, but they are prohibited from use in warfare according to the chemical weapons convention [3]. The legality of personal possession of teargases and pepper sprays for self-defense varies between countries. They can also be misused for antagonistic or criminal acts. In 2017 in Turin, Italy, pepper spray was sprayed into a crowded city square in order to steal valuable belongings during the ensuing chaos [4]. The subsequent panic resulted in the injury to over 1500 people and the death of one person. A similar event
* Corresponding author. E-mail address: [email protected] (A. Östin). http://dx.doi.org/10.1016/j.forsciint.2019.109956 0379-0738/© 2019 Published by Elsevier B.V.
happened again in Italy in 2018, at a concert in Corinaldo, where a discharge of an irritant substance, believed to be pepper spray, caused panic that resulted in more than 50 people being injured and six people dying [5]. Attacks using homemade pepper spray have also been reported [6–8]. The active ingredient of pepper sprays is Oleoresin capsicum (OC) [1], which is a liquid extract of the dried fruits of chili peppers of the genus Capsicum [9]. More than 100 compounds have been identified in OC extracts [9]. However, capsaicinoids are known to be responsible for the pungent properties of chili peppers [10], as well as the disabling effect of pepper sprays [11]. Capsaicinoids interact with the vanilloid receptor subtype 1 (TRPV1), resulting in a burning sensation and pain [12]. The amounts of capsaicinoids, and consequent pungency, vary between different types of chili [9]. There are five domesticated species of Capsicum: C. annuum, C. baccatum, C. chinense, C. frutescens and C. pubescens [13]. The most pungent chili types belong to C. chinense [14]. Pepper sprays have been analyzed by applying either high performance liquid chromatography (HPLC) or gas chromatography (GC) for separation coupled with a suitable detection technique, such as mass spectrometry (MS) [11,15–18]. The pungency of chili as well as pepper sprays is often reported in Scoville Heat Units (SHU) [19], which are determined by quantifying the total amount of capsaicinoids present, usually using HPLC [20]. The OC mixture in pepper sprays varies depending on several factors, e.g., chili type, maturity of the fruit, growing environment, and conditions used during extraction [21]. Therefore, information on the appearance and formulation of pepper spray seized by the
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police could be of forensic significance in order to identify its origin. This especially applies to homemade pepper sprays, which, unlike commercially available pepper sprays, cannot be identified from their packaging. Chemical attribution signatures (CASs) are often used to extract forensic information regarding the production and origin of threat agents [22–25]. However, to the best of our knowledge, there are no reports of profiling using CASs of pepper sprays or chili pepper extracts with application of multivariate data analysis for prediction of origin. However, chemical profiling of extracts of chili peppers has been performed previously to study biodiversity and to identify unique flavor compounds in chili peppers [26,27]. In the present study, we investigated whether it was possible to extract CASs containing information about species, type of chili and origin/vendor of homemade pepper sprays to create a tool for classification/matching for the investigator. Chili extracts from thirteen different chili types and three different species purchased from different stores were analyzed using GC–MS and the data were evaluated using Orthogonal Projection to Latent StructuresDiscriminant Analysis (OPLS-DA). The resulting models based on extracted CASs according to species, chili type and vendor were validated using an external test set. Additionally, to examine whether it was possible to detect if a homemade pepper spray had been used antagonistically, chili extracts were added to a textile material and then extracted after a few hours, three days and six days. 2. Method 2.1. Samples Thirteen different chili types from three different species were included in the study (Table 1). Carolina Reaper, Bhut Jolokia,
Habanero (C. chinense) and Piri-Piri (C. frutescens) were chosen due to their pungency. Chili types from the species C. annuum were included due to their availability. Chilies of species C. chinense had to be purchased from three different webshops, (webshop 1, webshop 2 and webshop 3, were kryddlandet.se, extremefoods.se and chililovers.nu, respectively), except for Habanero, which was available at three local grocery stores (store 1, store 2 and store 3 is ICA, Willys and COOP, respectively). Five additional chili types from the species C. chinense were purchased as fresh chili from webshop 3. Chili types from species C. frutescens and C. annuum were purchased in dried and fresh forms, respectively, from webshops or local grocery stores. A detailed list of the different chili types and vendors is shown in Table 1. 2.2. Extraction of chili The extraction procedure followed the same methodology as a YouTube video [28] on how to extract capsaicinoids from peppers to replicate a plausible method for making homemade pepper sprays. Fresh chilies were cut into small pieces, placed on aluminum foil and dried in an oven at approximately 50 C for 72 h until dry. The dried chili was then ground into a powder using pestle and mortar. Chilies purchased as dried were also ground into a powder, whereas no preparation was necessary for chilies already purchased as a powder. Next, 0.1–3.0 g of each chili type was added to separate vials (4–40 mL Clear Vial, Screw Top, Solid cap with PFTE Liner, Supleco, Bellefonte, PA) together with 1–30 mL of LCgraded acetone, Merck KGaA (Darmstadt, Germany) to a concentration of 0.1 g dry chili/mL. Extractions were made in two rounds (round 1 and 2). All chili samples in batches 1–3 were extracted in both round 1 and round 2, except for batch 1 of Carolina Reaper and batch 1 of Piri-Piri, which were only extracted in round 1. All chili samples in batch 4 were extracted in round 2. All samples were
Table 1 Species, type, SHU and batch (form and vendor) of chilies used in the study. Batch 1
Batch 2
Batch 3
Batch 4
Species
Chili type
Form
Vendor
Form
Vendor
Form
Vendor
C. chinense
Carolina Reaper SHU 1 569 000 Carboruga SHU 1 500 000 Bhut Jolokia SHU >1 000 000 7 Pot Yellow SHU 800 000–1 000 000 7 pot Bubblegum Chocolate SHU 700 000–800 000 Habanero SHU 300 000 Fatalii Brown SHU 300 000 Big Peach Mojo
Dried
Webshop 21
Dried
Webshop 11,2
Powder
Webshop 11,2
C. frutescens
Piri-Piri SHU 100 000
Dried
Store 11
Dried
Webshop 11,2
Powder
Webshop 11,2
C. annuum
Jalapeño (green) SHU 4 000–8 000 Dutch Chili (red) SHU 2 500–5 000 Padrón (green) SHU 2500 Dutch Chili (green) SHU <2 500–5 000
Fresh
Store 11,2
Fresh
Webshop 21,2
Fresh
Store 31,2
Dried
Fresh
Webshop 11,2
Store 11,2
Dried
Dried
Webshop 21,2
Webshop 11,2
Powder
Powder
Form
Vendor
Fresh
Webshop 32
Fresh
Webshop 32
Fresh
Webshop32
Fresh
Store 22
Fresh
Webshop 32
Fresh
Webshop 32
Fresh
Store 22
Fresh
Store 12
Fresh
Store 22
Webshop11,2
Webshop 11,2
SHU denotes the approximate Scoville Heat Units provided by the vendors (or information obtained from chilepeppar.com and chilli-seedz.com if absent from the vendors). The SHUs for Big Peach Mojo and Carboruga were unknown. Form shows the state, i.e., fresh, dried or powder, of the chilies at purchase. Vendor indicates the store/webshop where the chilies were purchased. The superscripted numbers (1 and 2) denotes which round/rounds of extraction the chilies were included. Batches 1–3 were extracted in triplicate in both round 1 and 2, except for batch 1 Carolina Reaper and batch 1 Piri-Piri, which were only extracted in round 1. Batch 4 was extracted in round 2.
L. Mörén et al. / Forensic Science International 304 (2019) 109956
extracted in triplicate in both extraction rounds, except for batch 2, round 2 of Carolina Reaper and batch 1, round 2 of Bhut Jolokia (Table 1). Following extraction, the capped vials were inverted twice a day for seven days. 2.3. Sample preparation After seven days, the extracts were filtered into new vials and their volumes were reduced under nitrogen gas to a volume of 1 mL. Next, 5–150 mL (depending on the weight of chili used, 3.0 g– 0.1 g) of the extracts was added to GC vials together with GCgrade dichloromethane (Merck KGaA, Darmstadt, Germany) to a final volume of 200 mL. 2.4. GC–MS analysis The samples were analyzed in a randomized order using GC– MS. Two solvent blanks were analyzed between each sample to minimize carryover. A quality control (QC) sample containing a series of alkanes was also analyzed to determine retention indices (RIs). The instrument used was an Agilent 7890 gas chromatograph coupled to an Agilent 5975C MSD. A 30 m DB-5MS column (inner diameter 0.25 mm, film thickness 0.25 mm, from J&W Scientific) with a flow rate of 1 mL He/min was used to separate then analytes. The oven temperature was held at 40 C for 1 min, ramped at 10 C/ min to 300 C and held at 300 C for 5 min. Samples were injected (1 mL) using an autosampler (Agilent 7683B) with splitless injection (1 min splitless time) at a temperature of 200 C and ionized using electron ionization (EI). The MS was operated in full scan mode (m/z 29–550) with a transfer line temperature of 280 C and a source temperature of 230 C. 2.5. Data processing Chromatograms from the GC–MS analysis were examined using the Automatic Mass Spectral Deconvolution and Identification System (AMDIS) [29]. The analyzed QC sample was used to perform RI calibration. A library including all peaks from the chromatogram of all samples was constructed. The library consisted of both known and unidentified compounds. Detected molecular features were identified using the NIST Mass Spectral Library 2.3 (as of 2017). Thereafter, each spectrum was processed through the library, setting a minimum match factor of 70%. The acquired data were exported as a data matrix with rows corresponding to samples and columns to molecular features and area of the peaks. For each sample, the area of each peak was divided by the total area of all peaks for that sample to normalize the data and adjust for instrumental drift of sensitivity. The normalized data were imported into SIMCA (SIMCA 15.0; Sartorius Stedim Biotech Goettingen, Germany) for multivariate data analysis.
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2.6. Multivariate data analysis Prior to multivariate data analysis, the data were log transformed and UV-scaled. Initially, principal component analysis (PCA) was applied to obtain an overview of the variation of the data, observe groups and trends, and detect outliers. Thereafter, to differentiate the samples according to species, chili type, and origin (vendor), OPLS-DA was applied. Different OPLS-DA models were created, each built from one of the pre-set classes to obtain models that could be subsequently used to differentiate species, chili type and origin of the chili (vendor) (Fig. 1). Variables present only in two observations or less and variables displaying a lower significance to the class separation of interest, were excluded from the models. Variable selection was based on model weight values (w*), where variables with w* < w*average 1.5 standard deviation (SD) were excluded, or Variable Importance for the Projection (VIP) values, where variables with VIP < 1.0 were excluded. The models were validated using cross validation (CV), with replicate samples included in the same CV group to avoid overfitting. Cross-validated score scatter plots were generated for all models. All OPLS-DA models were evaluated by examining the number of components together with the measures R2X, R2Y and Q2. The latter three measures represent the fraction of the described variation in the data, the fraction of the between-class variation described, and the fraction of the between-class variation predicted estimated by cross-validation, respectively. Lastly, the most significant variables (molecular features) that differentiated the classes in each OPLSDA model were examined based on loadings and correlation coefficients. 2.7. Prediction of species, chili type and vendor/origin To validate the OPLS-DA models, we used an external test set consisting of chili extracts from the same species and chili types but purchased and extracted about 1 year after the chilies used to construct the model. Chili types included were Carolina Reaper (C. chinense), Bhut Jolokia (C. chinense), Habanero (C. chinense), PiriPiri (C. frutescens) and Jalapeño (C. annuum). The chilies were acquired from webshop 1 in dried form, except for Jalapeño, which was bought fresh from grocery store 1. The chilies were from different production batches than the ones previously analyzed, except for Habanero. The chilies were subjected to the same procedures of extraction and sample preparation as described above. In total, three replicates of Carolina Reaper, Bhut Jolokia and Habanero and 6 replicates (two different batches) of Piri-Piri were analyzed by GC–MS with the same parameters/settings as used when developing the model. The obtained data was processed as before – the spectra of all samples were processed through the same library and molecular identities in each sample with a match factor >70% were exported, normalized and imported to SIMCA, where the data set was predicted in the models. t-predicted score
Fig. 1. Route of differentiation of homemade pepper spray from unknown identity to species, type of chili and distributor of the chili.
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scatter plots and predicted Y values for all models were examined and evaluated. 2.8. Detection and identification of homemade pepper spray from a textile material Chili extracts from Carolina Reaper were added to 2 cm 3 cm pieces of a cotton textile material placed in a 7 mL vial. The extracts used were the triplicates prepared for model prediction (see above) diluted 10 in acetone before addition to the textile material. 100 mL of each triplicate was added to three pieces of textile material, giving nine samples in total. The vials with the
textile material were left open in a fume hood and samples were taken from one of each triplicate after 6 h, 3 days and 6 days by submerging the pieces of textile material in 3 mL of hypergrade acetonitrile (Merck, Darmstadt, Germany) added to the vials. For extraction of textile material, different solvents have been evaluated and based on the results acetonitrile is routinely used by our laboratory (data not shown). The vials were then capped and placed in an ultrasonic bath for 10 min. The extracts of the textile material were transferred to new vials, the volumes were reduced to 200 mL under N2 gas and analyzed by GC–MS. The same parameters/settings for GC–MS analysis were used as described above. The obtained data were processed as described and all
Fig. 2. Example chromatograms from (A) C. Chinense, (B) C. frutescens and (C) C. annuum. 8-methyl-6-nonenamide, an important molecular feature for the classification of C. chinense were detectable only in samples from C. chinense. Linoleic acid, an important molecular feature for the classification of C. frutescens were also detectable in both other species but in lower amounts. 2,4-decadienal an important molecular feature for C. annuum were only detectable in that species. The relative concentration of capsaicinoids are highest in C. Chinense and lowest in C. annuum, however, this difference were not important for the separation of species.
L. Mörén et al. / Forensic Science International 304 (2019) 109956
spectra were processed through the same library, where molecular identities with a match factor >70% were exported, normalized and imported to SIMCA. The imported data were predicted in the models and t-predicted score scatter plots and predicted Y values for the models were evaluated.
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Two models were created for types of chili in C. chinense and C. annuum, respectively. Since Piri-Piri was the only type of chili in C. frutescens included in this study, no model was created based on chili type for that species. Suppliers in Sweden use the name PiriPiri for several chili types of C. frutescens, and hence proper separation of different types was not possible.
3. Results and discussion 3.1. Data processing The data was processed through the constructed library and a total of 2043 features were detected. However, after excluding features only present in two observations or less, 1122 molecular features remained. Identification of the detected features was made using standard library searches, which gave presumed molecular identities for 173 features. Unidentified features were named according to their RI and retention time. 3.2. Differentiation between species To differentiate between the three species included in this study, i.e., C. chinense, C. frutescens and C. annuum, OPLS-DA was applied. After exclusion of variables with low significance to the class separation (w* < w*average 1.5 SD), 273 variables remained in the model that enabled complete separation between the three species (Fig. A1 A in appendix). The remaining variables constituted each species CAS. Features that distinguished the species were examined – the most prominent and significant ones are presented in Table A1 in the appendix. Some molecular features were unique for a certain species, i.e., detectable only in one species, whereas others were detected in all species but in varying amounts. Example chromatograms from each species are shown in Fig. 2. 8-Methyl-6-nonenamide, an important molecular feature for the classification of C. chinense were detectable only in samples from that species while linoleic acid, an important feature for the classification of C. frutescens were detectable in both other species but in lower amounts. 2,4-decadienal an important feature for C. annuum were only detectable in that species. Fig. 2 also show the great difference in capsaicinoid concentration and to ensure that the differentiation between species was not solely based on pungency, the capsaicinoids were excluded from the model. The resulting model contained the same number of components and values of R2X, R2Y and Q2 as the model with capsaicinoids included. This indicated that the amount of capsaicinoids (pungency) did not contribute to the observed differences between the species. 3.3. Differentiation between chili types within species To differentiate chili types within each species, OPLS-DA models were generated with type of chili as pre-defined classes.
3.3.1. C. chinense When differentiation between chili types was evaluated for C. chinense, the chili types Carolina Reaper, Bhut Jolokia and Habanero were included as they were hypothesized to be likely selected for preparation of homemade pepper sprays due to their pungency and availability. The other five chili types included in the species C. chinense were hard to acquire, and therefore only extracted once and in one batch, resulting in too few replicates. The cross validated score plot of the model is presented in Figure A1 B in the appendix. Complete separation was obtained between the three chili types. The final model consisted of 227 variables – the most prominent and significant ones are listed in Table A1 in the appendix. Some molecular features were unique to a specific chili type, whereas other molecular features were shared but differed in the amounts. For instance, benzyl benzoate, which can function as an insect repellant [30], was only identified in Carolina Reaper. Certain fatty acids and esters were also found to be unique for specific chili types. 3.3.2. C. annuum For the chili types in C. annuum (Fig. A1 C and D in appendix), a clear separation was obtained between the red and green chilies (the first predictive component), most likely due to molecular features present at different stages of ripening. In addition, all the three green chili types were completely separated (the second and third predictive component). The final OPLS-DA model consisted of 102 variables – the most prominent and significant ones are presented in Table A1 in the appendix. All chili types contained unique molecular features. Unique molecular features detected in the red Dutch chili included vitamin E and some fatty acids, together with molecular features with unknown identities. Padrón, Jalapeño and green Dutch chili also exhibited unique molecular features that were not identified. 3.4. Differentiation of chili type acquired from different vendors/origin To examine if the characteristics of a certain chili type differed depending on its origin, OPLS-DA models were generated with vendors (the different webshops and grocery stores) as predefined classes. Models were attempted for all chili types acquired from more than one vendor, i.e., Carolina Reaper, Bhut Jolokia, Habanero, Piri-Piri and Jalapeño. However, models could not be constructed for Bhut Jolokia and Jalapeño, i.e. between-vendorvariation could not be extracted. In contrast, models could be
Table 2 Summary of OPLS-DA models and their performance. Model based on
Including
Components
R2X(cum)
R2Y(cum)
Q2(cum)
Species
C. chinense C. frutescens C. annuum
2+2+0
0.48
0.94
0.84
Chili type
C. chinense* C. annuum
2+4+0 3+1+0
0.58 0.71
0.98 0.97
0.85 0.91
Vendor
Carolina Reaper Habanero Piri-Piri
1+1+0 2+1+0 1+2+0
0.71 0.69 0.76
0.99 0.99 0.99
0.92 0.97 0.98
*
Includes three chili types: Carolina Reaper, Bhut Jolokia and Habanero.
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L. Mörén et al. / Forensic Science International 304 (2019) 109956 Table 3 Confusion matrix for the test set prediction of species. Species
C. annuum
C. chinense
C. frutescens
C. annuum (Jalapeño) C. chinense (Bhut Jolokia, Carolina Reaper, Habanero) C. frutescens (Piri-Piri)
3 0
0 8
0 0
0
0
6
created for Piri-Piri, Carolina Reaper and Habanero (crossvalidated score plots of the models are presented in Figure A2 in the appendix), for which complete separation was possible depending on the vendor. The cross-validated score plots from the Piri-Piri model and the Carolina Reaper model showed a withinvendor-variation in the orthogonal component, which were lesser than the between-vendor-variation. The within-vendor-variation depended on round of analysis, as there was a small analytical variation between the two rounds of analysis. The final models included 134 variables for Piri-Piri, 185 for Carolina Reaper and 134 for Habanero – the most prominent and significant variables are presented in Table A1 in the appendix. An example of a molecular feature found in Piri-Piri from only one vendor was 8-methylnonanoic acid, which is a precursor in the biosynthesis of dihydrocapsaicin [31] and a possible degradation product of dihydrocapsaicin. Molecular features for Carolina Reaper that differed between vendors were mostly unidentifiable, with the exception of certain esters that were found to be specific for each vendor. Habanero from one distributor contained specific amides and fatty acids, whereas from another vendor contained specific esters and unidentified molecular features. Habanero acquired from a third vendor contained only one specific, unidentified molecular feature. The observed differences depending on vendor were likely due to different cultivars sold under the same tradename. For example, there are a large number of cultivars of Habanero available [32], and Piri-Piri and Birds Eye are sold under the same tradename PiriPiri in grocery stores. Furthermore, countries of origin, stages of ripening of the chili or drying temperatures during production might contribute to variation. A summary of the final OPLS-DA models is presented in Table 2. The results showed that extracts of the three species included in the study could be differentiated according to species, type and vendor. Several chili types within C. chinense and all analyzed chili types within C. annuum could be differentiated. In addition, three out of five chili types acquired from more than one vendor could be separated depending on their origin. 3.5. Prediction of species and chili type
Fig. 3. t-predicted score scatterplot of OPLS-DA model based on (A) species and type of chili in (B) C. chinense and (C and D) C. annuum. A) 18 replicates (white symbols with shapes according to their classification) predicted for the three species C. chinense (red circles), C. frutescens (yellow triangles) and C. annuum (green squares). B) Nine replicates (white symbols with shapes according to their classification) predicted for the three chili types Carolina Reaper (red triangles), Bhut Jolokia (orange circles) and Habanero (yellow squares). C and D) Three replicates (white circles) of Jalapeño predicted for the chili types red Dutch chili (red squares), Padrón (blue inverted triangles), green Dutch chili (green triangles) and
For further validation of the models, a prediction set was applied. Data for five chili types from a third round of analysis were used as a prediction set in all OPLS-DA models. The results of prediction using the models based on species and chili type are presented in Fig. 3. As shown in Table 3 and Table 4, the confusion matrices based on the predicted Y values, all samples were correctly classified based on the species and chili type. This indicates that it may be possible to extract specific CASs that could be used to predict the species and chili type of seized homemade pepper sprays. However, there is always a risk of obtaining false positive and false negative results, which could be limited by continuously extending the models with more reference material. Jalapeño (yellow circles). The three predictive components of the model were required to achieve complete separation of the four types of chili (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
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Table 4 Confusion matrix for the test set prediction of chili type. Chili type
Bhut Jolokia
Carolina Reaper
Habanero
Jalapeño
Dutch chili (green)
Padrón
Dutch chili (red)
Bhut Jolokia Carolina Reaper Habanero Jalapeño
3 0 0 –
0 3 0 –
0 0 3 –
– – – 3
– – – 0
– – – 0
– – – 0
Bhut Jolokia, Carolina Reaper and Habanero were predicted in the chili type model only including chilies from C. chinense while Jalapeño were predicted in the chili type model only including chilies from C. annuum. – denotes that the chili type was not included in the model.
The chosen method of modelling will reduce the risk of obtaining false positive results, as this method does not force the unknown observation into any of the classes.
3.6. Prediction of vendor The origins of Carolina Reaper, Habanero and Piri-Piri were all correctly predicted (Fig. 4, Table 5). Interestingly, as stated above, Carolina Reaper and Piri-Piri used in the prediction set were from different production batches than the ones used to develop the models, showing that the origin of these chili types could be classified correctly even when they originated from different production batches. The systematic variation between the vendors could be due to different cultivars sold under the same tradename, stages of ripening of the chili before harvesting as well as different growing environments, which could indicate different geographical locations. 3.7. Detection and identification of homemade pepper spray from a textile material Extracts from the textile material treated with Carolina Reaper (C. chinense, purchased from webshop 1) displayed, in addition to capsaicinoids, the characteristic chemical profile. This was the case for all replicates and time points analyzed. Furthermore, the species of chili was correctly predicted for all replicates and time points (Fig. 5, Table 6). However, prediction of the type of chili showed that the prediction performance declines with time. After Table 5 Confusion matrix for the test set predictions of vendor. Chili type
Vendor
Webshop 1
Store 1
Store 2
Webshop 2
Habanero Piri-Piri Carolina Reaper
Webshop 1 Webshop 1 Webshop 1
3 6 6
0 0 –
0 – –
– – 0
The test sets of Habanero, Carolina Reaper and Piri-Piri were predicted in three different models, one for each chili type.
Fig. 4. t-predicted score scatterplot of OPLS-DA model based on vendor for (A) PiriPiri, (B) Carolina Reaper and (C) Habanero. The prediction set is represented as white symbols with shapes indicating their classification. A) Six replicates of Piripiri predicted to two vendors webshop 1 (blue circles) and store 1 (red triangles). B) Three replicates of Carolina Reaper predicted to two vendors webshop 1 (blue circles) and webshop 2 (orange squares). C) Three replicates of Habanero predicted to three vendors webshop 1 (blue circles), store 1 (yellow squares) and store 2 (green triangles) (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 5. t-predicted score scatterplot of OPLS-DA model based on species. 9 replicates of Carolina Reaper extracted from textile material were predicted to three species C. chinense (red circles), C. frutescens (yellow triangles) and C. annuum (green squares). The prediction set (white circles) consisted of sampled material after 6 h, 3 days and 6 days (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
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Table 6 Confusion matrix for the textile material predictions of species, type and vendor. Species
Days
C. annuum
C. chinense
C. frutescens
Uncertain
C. chinense (Carolina Reaper)
0 3 6
0 0 0
3 3 3
0 0 0
0 0 0
Chili type Carolina Reaper
0 3 6
Vendor Webshop 1
0 3 6
Bhut Jolokia
Carolina Reaper
Habanero
Uncertain
0 0 0
2 1 0
0 0 0
1 2 3
Webshop 1
Webshop 2
Uncertain
3 3 3
0 0 0
0 0 0
An observation is considered uncertain if the Y predicted values are equally high for two or more classes.
Fig. 6. Sequential order of prediction of species, chili type and origin to track and match seized homemade pepper spray. The prediction set is represented as white symbols with shapes indicating their classification and the sequential order shows prediction of a chili pepper extract of Habanero chili from species, chili type within C. chinense, to vendor.
6 days, all three replicates show an uncertain prediction meaning that the predicted observations appears to belong to more than one class. Although chili type could not be conclusively predicted for all replicates, the vendor of the Carolina Reaper chilies was correctly predicted (Table 6). Even though information about chili type declines with time, there were still information in the sample that could be used as forensic intelligence. These results suggest that the species of chili used for a homemade pepper spray could be predicted from extracts of the textile material independently of time of application up to at least six days. This could be useful in cases where residues of suspected homemade pepper spray are present on the clothing of victims. 3.8. Forensic applicability The results demonstrate that OPLS-DA models could be used sequentially to predict first the species, then chili type and finally origin/vendor of chilies used in homemade pepper sprays. Data in Fig. 6 show that a new extract from chilies purchased approximately one year after the models were constructed was correctly classified as C. chinense in the species model and as Habanero in the model of chili type. Lastly, it was correctly classified as originating from the vendor webshop 1, in the model differentiating Habaneros from different vendors (Fig. 6). The results indicate that seized homemade pepper sprays could be analyzed by GC–MS, followed by data processing, and then classified based on the created models to acquire information on the species, chili type used and origin/vendor of the chili. Thus, chili pepper contains specific variation that could be of forensic interest as such information may be useful in investigations for law enforcement to track and match seized samples.
4. Conclusion The results of this study showed that it was possible to extract CASs that contained information about species, type and origin/ vendor of chilies that could be used to prepare homemade pepper sprays. Models differentiating species, type of chili and vendor were constructed that could be used for prediction of homemade pepper sprays. All the generated models gave correct classification of an external test set consisting of chili acquired one year after development of the models. Additionally, residues from homemade pepper spray on textile material were identified and the species of chili correctly classified. The high predictability of the models would be useful for prediction of the identity and origin of seized homemade pepper sprays and residues on clothing. Thus, modeling of the chili plant material chemical composition could aid the investigator in match and classify seizures and samples collected from victims. The models as presented here could provide useful investigative information and if extended provide source predictions to be made in a legal framework. CRediT authorship contribution statement Lina Mörén: Methodology, Software, Validation, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Sebastian Jonsson: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing original draft, Visualization. Tobias Tengel: Methodology, Investigation, Writing - review & editing. Anders Östin: Conceptualization, Writing - review & editing, Supervision, Project administration, Funding acquisition.
L. Mörén et al. / Forensic Science International 304 (2019) 109956
Declaration of Competing Interest None. Acknowledgement This work was financed by The Swedish Ministry of Defence.
[15]
[16] [17] [18]
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.forsciint.2019.109956.
[19] [20]
[21]
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Contents lists available at ScienceDirect
Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures Lina Möréna , Sebastian Jonssonb , Tobias Tengela , Anders Östina,* a b
The Swedish Defence Research Agency, FOI Cementvägen 20, 901 82 Umeå, Sweden Umeå University, 901 87 Umeå, Sweden
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 May 2019 Received in revised form 3 September 2019 Accepted 5 September 2019 Available online 20 September 2019
Riot control agents such as pepper sprays can be misused for antagonistic and criminal purposes. Several web-pages and YouTube videos are available describing how to make homemade pepper spray. In this study, we investigated whether it was possible to identify the origin of homemade pepper sprays based on chemical attribution signatures from thirteen different types of chili acquired from six different vendors analyzed by GC–MS. The results showed that it was possible to differentiate chili based on species, chili type and vendor using OPLS-DA. Application of an external test set of chilies acquired and extracted one year later than development of the models resulted in correct classification in all models. The models displayed high predictability, suggesting their use for prediction of the identity and origin of seized homemade pepper sprays. © 2019 Published by Elsevier B.V.
Keywords: Chemical forensics Homemade pepper spray Chemical attribution signatures Multivariate data analysis
1. Introduction Riot control agents (RCAs), e.g., teargases and pepper sprays, are defined as substances used for civilian peacekeeping that have an immediate effect on humans [1]. The purpose of using teargases and pepper sprays is to temporarily disable subjects [1]. Their effects have been described as non-lethal with a large margin of safety between amounts that cause intolerable effects and amounts that cause serious adverse responses [2]. Teargases and pepper sprays interact with sensory nerve receptors of mucosal surfaces and the skin at exposed sites, resulting in local discomfort and/or pain together with consequential reflexes [2]. Profuse lacrimation occurs when teargases and pepper sprays are applied to the eyes, causing temporary blindness that induces fear and disorientation of the exposed subject [1]. Use of RCAs by law enforcement agencies during riot control situations is allowed, but they are prohibited from use in warfare according to the chemical weapons convention [3]. The legality of personal possession of teargases and pepper sprays for self-defense varies between countries. They can also be misused for antagonistic or criminal acts. In 2017 in Turin, Italy, pepper spray was sprayed into a crowded city square in order to steal valuable belongings during the ensuing chaos [4]. The subsequent panic resulted in the injury to over 1500 people and the death of one person. A similar event
* Corresponding author. E-mail address: [email protected] (A. Östin). http://dx.doi.org/10.1016/j.forsciint.2019.109956 0379-0738/© 2019 Published by Elsevier B.V.
happened again in Italy in 2018, at a concert in Corinaldo, where a discharge of an irritant substance, believed to be pepper spray, caused panic that resulted in more than 50 people being injured and six people dying [5]. Attacks using homemade pepper spray have also been reported [6–8]. The active ingredient of pepper sprays is Oleoresin capsicum (OC) [1], which is a liquid extract of the dried fruits of chili peppers of the genus Capsicum [9]. More than 100 compounds have been identified in OC extracts [9]. However, capsaicinoids are known to be responsible for the pungent properties of chili peppers [10], as well as the disabling effect of pepper sprays [11]. Capsaicinoids interact with the vanilloid receptor subtype 1 (TRPV1), resulting in a burning sensation and pain [12]. The amounts of capsaicinoids, and consequent pungency, vary between different types of chili [9]. There are five domesticated species of Capsicum: C. annuum, C. baccatum, C. chinense, C. frutescens and C. pubescens [13]. The most pungent chili types belong to C. chinense [14]. Pepper sprays have been analyzed by applying either high performance liquid chromatography (HPLC) or gas chromatography (GC) for separation coupled with a suitable detection technique, such as mass spectrometry (MS) [11,15–18]. The pungency of chili as well as pepper sprays is often reported in Scoville Heat Units (SHU) [19], which are determined by quantifying the total amount of capsaicinoids present, usually using HPLC [20]. The OC mixture in pepper sprays varies depending on several factors, e.g., chili type, maturity of the fruit, growing environment, and conditions used during extraction [21]. Therefore, information on the appearance and formulation of pepper spray seized by the
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police could be of forensic significance in order to identify its origin. This especially applies to homemade pepper sprays, which, unlike commercially available pepper sprays, cannot be identified from their packaging. Chemical attribution signatures (CASs) are often used to extract forensic information regarding the production and origin of threat agents [22–25]. However, to the best of our knowledge, there are no reports of profiling using CASs of pepper sprays or chili pepper extracts with application of multivariate data analysis for prediction of origin. However, chemical profiling of extracts of chili peppers has been performed previously to study biodiversity and to identify unique flavor compounds in chili peppers [26,27]. In the present study, we investigated whether it was possible to extract CASs containing information about species, type of chili and origin/vendor of homemade pepper sprays to create a tool for classification/matching for the investigator. Chili extracts from thirteen different chili types and three different species purchased from different stores were analyzed using GC–MS and the data were evaluated using Orthogonal Projection to Latent StructuresDiscriminant Analysis (OPLS-DA). The resulting models based on extracted CASs according to species, chili type and vendor were validated using an external test set. Additionally, to examine whether it was possible to detect if a homemade pepper spray had been used antagonistically, chili extracts were added to a textile material and then extracted after a few hours, three days and six days. 2. Method 2.1. Samples Thirteen different chili types from three different species were included in the study (Table 1). Carolina Reaper, Bhut Jolokia,
Habanero (C. chinense) and Piri-Piri (C. frutescens) were chosen due to their pungency. Chili types from the species C. annuum were included due to their availability. Chilies of species C. chinense had to be purchased from three different webshops, (webshop 1, webshop 2 and webshop 3, were kryddlandet.se, extremefoods.se and chililovers.nu, respectively), except for Habanero, which was available at three local grocery stores (store 1, store 2 and store 3 is ICA, Willys and COOP, respectively). Five additional chili types from the species C. chinense were purchased as fresh chili from webshop 3. Chili types from species C. frutescens and C. annuum were purchased in dried and fresh forms, respectively, from webshops or local grocery stores. A detailed list of the different chili types and vendors is shown in Table 1. 2.2. Extraction of chili The extraction procedure followed the same methodology as a YouTube video [28] on how to extract capsaicinoids from peppers to replicate a plausible method for making homemade pepper sprays. Fresh chilies were cut into small pieces, placed on aluminum foil and dried in an oven at approximately 50 C for 72 h until dry. The dried chili was then ground into a powder using pestle and mortar. Chilies purchased as dried were also ground into a powder, whereas no preparation was necessary for chilies already purchased as a powder. Next, 0.1–3.0 g of each chili type was added to separate vials (4–40 mL Clear Vial, Screw Top, Solid cap with PFTE Liner, Supleco, Bellefonte, PA) together with 1–30 mL of LCgraded acetone, Merck KGaA (Darmstadt, Germany) to a concentration of 0.1 g dry chili/mL. Extractions were made in two rounds (round 1 and 2). All chili samples in batches 1–3 were extracted in both round 1 and round 2, except for batch 1 of Carolina Reaper and batch 1 of Piri-Piri, which were only extracted in round 1. All chili samples in batch 4 were extracted in round 2. All samples were
Table 1 Species, type, SHU and batch (form and vendor) of chilies used in the study. Batch 1
Batch 2
Batch 3
Batch 4
Species
Chili type
Form
Vendor
Form
Vendor
Form
Vendor
C. chinense
Carolina Reaper SHU 1 569 000 Carboruga SHU 1 500 000 Bhut Jolokia SHU >1 000 000 7 Pot Yellow SHU 800 000–1 000 000 7 pot Bubblegum Chocolate SHU 700 000–800 000 Habanero SHU 300 000 Fatalii Brown SHU 300 000 Big Peach Mojo
Dried
Webshop 21
Dried
Webshop 11,2
Powder
Webshop 11,2
C. frutescens
Piri-Piri SHU 100 000
Dried
Store 11
Dried
Webshop 11,2
Powder
Webshop 11,2
C. annuum
Jalapeño (green) SHU 4 000–8 000 Dutch Chili (red) SHU 2 500–5 000 Padrón (green) SHU 2500 Dutch Chili (green) SHU <2 500–5 000
Fresh
Store 11,2
Fresh
Webshop 21,2
Fresh
Store 31,2
Dried
Fresh
Webshop 11,2
Store 11,2
Dried
Dried
Webshop 21,2
Webshop 11,2
Powder
Powder
Form
Vendor
Fresh
Webshop 32
Fresh
Webshop 32
Fresh
Webshop32
Fresh
Store 22
Fresh
Webshop 32
Fresh
Webshop 32
Fresh
Store 22
Fresh
Store 12
Fresh
Store 22
Webshop11,2
Webshop 11,2
SHU denotes the approximate Scoville Heat Units provided by the vendors (or information obtained from chilepeppar.com and chilli-seedz.com if absent from the vendors). The SHUs for Big Peach Mojo and Carboruga were unknown. Form shows the state, i.e., fresh, dried or powder, of the chilies at purchase. Vendor indicates the store/webshop where the chilies were purchased. The superscripted numbers (1 and 2) denotes which round/rounds of extraction the chilies were included. Batches 1–3 were extracted in triplicate in both round 1 and 2, except for batch 1 Carolina Reaper and batch 1 Piri-Piri, which were only extracted in round 1. Batch 4 was extracted in round 2.
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extracted in triplicate in both extraction rounds, except for batch 2, round 2 of Carolina Reaper and batch 1, round 2 of Bhut Jolokia (Table 1). Following extraction, the capped vials were inverted twice a day for seven days. 2.3. Sample preparation After seven days, the extracts were filtered into new vials and their volumes were reduced under nitrogen gas to a volume of 1 mL. Next, 5–150 mL (depending on the weight of chili used, 3.0 g– 0.1 g) of the extracts was added to GC vials together with GCgrade dichloromethane (Merck KGaA, Darmstadt, Germany) to a final volume of 200 mL. 2.4. GC–MS analysis The samples were analyzed in a randomized order using GC– MS. Two solvent blanks were analyzed between each sample to minimize carryover. A quality control (QC) sample containing a series of alkanes was also analyzed to determine retention indices (RIs). The instrument used was an Agilent 7890 gas chromatograph coupled to an Agilent 5975C MSD. A 30 m DB-5MS column (inner diameter 0.25 mm, film thickness 0.25 mm, from J&W Scientific) with a flow rate of 1 mL He/min was used to separate then analytes. The oven temperature was held at 40 C for 1 min, ramped at 10 C/ min to 300 C and held at 300 C for 5 min. Samples were injected (1 mL) using an autosampler (Agilent 7683B) with splitless injection (1 min splitless time) at a temperature of 200 C and ionized using electron ionization (EI). The MS was operated in full scan mode (m/z 29–550) with a transfer line temperature of 280 C and a source temperature of 230 C. 2.5. Data processing Chromatograms from the GC–MS analysis were examined using the Automatic Mass Spectral Deconvolution and Identification System (AMDIS) [29]. The analyzed QC sample was used to perform RI calibration. A library including all peaks from the chromatogram of all samples was constructed. The library consisted of both known and unidentified compounds. Detected molecular features were identified using the NIST Mass Spectral Library 2.3 (as of 2017). Thereafter, each spectrum was processed through the library, setting a minimum match factor of 70%. The acquired data were exported as a data matrix with rows corresponding to samples and columns to molecular features and area of the peaks. For each sample, the area of each peak was divided by the total area of all peaks for that sample to normalize the data and adjust for instrumental drift of sensitivity. The normalized data were imported into SIMCA (SIMCA 15.0; Sartorius Stedim Biotech Goettingen, Germany) for multivariate data analysis.
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2.6. Multivariate data analysis Prior to multivariate data analysis, the data were log transformed and UV-scaled. Initially, principal component analysis (PCA) was applied to obtain an overview of the variation of the data, observe groups and trends, and detect outliers. Thereafter, to differentiate the samples according to species, chili type, and origin (vendor), OPLS-DA was applied. Different OPLS-DA models were created, each built from one of the pre-set classes to obtain models that could be subsequently used to differentiate species, chili type and origin of the chili (vendor) (Fig. 1). Variables present only in two observations or less and variables displaying a lower significance to the class separation of interest, were excluded from the models. Variable selection was based on model weight values (w*), where variables with w* < w*average 1.5 standard deviation (SD) were excluded, or Variable Importance for the Projection (VIP) values, where variables with VIP < 1.0 were excluded. The models were validated using cross validation (CV), with replicate samples included in the same CV group to avoid overfitting. Cross-validated score scatter plots were generated for all models. All OPLS-DA models were evaluated by examining the number of components together with the measures R2X, R2Y and Q2. The latter three measures represent the fraction of the described variation in the data, the fraction of the between-class variation described, and the fraction of the between-class variation predicted estimated by cross-validation, respectively. Lastly, the most significant variables (molecular features) that differentiated the classes in each OPLSDA model were examined based on loadings and correlation coefficients. 2.7. Prediction of species, chili type and vendor/origin To validate the OPLS-DA models, we used an external test set consisting of chili extracts from the same species and chili types but purchased and extracted about 1 year after the chilies used to construct the model. Chili types included were Carolina Reaper (C. chinense), Bhut Jolokia (C. chinense), Habanero (C. chinense), PiriPiri (C. frutescens) and Jalapeño (C. annuum). The chilies were acquired from webshop 1 in dried form, except for Jalapeño, which was bought fresh from grocery store 1. The chilies were from different production batches than the ones previously analyzed, except for Habanero. The chilies were subjected to the same procedures of extraction and sample preparation as described above. In total, three replicates of Carolina Reaper, Bhut Jolokia and Habanero and 6 replicates (two different batches) of Piri-Piri were analyzed by GC–MS with the same parameters/settings as used when developing the model. The obtained data was processed as before – the spectra of all samples were processed through the same library and molecular identities in each sample with a match factor >70% were exported, normalized and imported to SIMCA, where the data set was predicted in the models. t-predicted score
Fig. 1. Route of differentiation of homemade pepper spray from unknown identity to species, type of chili and distributor of the chili.
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scatter plots and predicted Y values for all models were examined and evaluated. 2.8. Detection and identification of homemade pepper spray from a textile material Chili extracts from Carolina Reaper were added to 2 cm 3 cm pieces of a cotton textile material placed in a 7 mL vial. The extracts used were the triplicates prepared for model prediction (see above) diluted 10 in acetone before addition to the textile material. 100 mL of each triplicate was added to three pieces of textile material, giving nine samples in total. The vials with the
textile material were left open in a fume hood and samples were taken from one of each triplicate after 6 h, 3 days and 6 days by submerging the pieces of textile material in 3 mL of hypergrade acetonitrile (Merck, Darmstadt, Germany) added to the vials. For extraction of textile material, different solvents have been evaluated and based on the results acetonitrile is routinely used by our laboratory (data not shown). The vials were then capped and placed in an ultrasonic bath for 10 min. The extracts of the textile material were transferred to new vials, the volumes were reduced to 200 mL under N2 gas and analyzed by GC–MS. The same parameters/settings for GC–MS analysis were used as described above. The obtained data were processed as described and all
Fig. 2. Example chromatograms from (A) C. Chinense, (B) C. frutescens and (C) C. annuum. 8-methyl-6-nonenamide, an important molecular feature for the classification of C. chinense were detectable only in samples from C. chinense. Linoleic acid, an important molecular feature for the classification of C. frutescens were also detectable in both other species but in lower amounts. 2,4-decadienal an important molecular feature for C. annuum were only detectable in that species. The relative concentration of capsaicinoids are highest in C. Chinense and lowest in C. annuum, however, this difference were not important for the separation of species.
L. Mörén et al. / Forensic Science International 304 (2019) 109956
spectra were processed through the same library, where molecular identities with a match factor >70% were exported, normalized and imported to SIMCA. The imported data were predicted in the models and t-predicted score scatter plots and predicted Y values for the models were evaluated.
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Two models were created for types of chili in C. chinense and C. annuum, respectively. Since Piri-Piri was the only type of chili in C. frutescens included in this study, no model was created based on chili type for that species. Suppliers in Sweden use the name PiriPiri for several chili types of C. frutescens, and hence proper separation of different types was not possible.
3. Results and discussion 3.1. Data processing The data was processed through the constructed library and a total of 2043 features were detected. However, after excluding features only present in two observations or less, 1122 molecular features remained. Identification of the detected features was made using standard library searches, which gave presumed molecular identities for 173 features. Unidentified features were named according to their RI and retention time. 3.2. Differentiation between species To differentiate between the three species included in this study, i.e., C. chinense, C. frutescens and C. annuum, OPLS-DA was applied. After exclusion of variables with low significance to the class separation (w* < w*average 1.5 SD), 273 variables remained in the model that enabled complete separation between the three species (Fig. A1 A in appendix). The remaining variables constituted each species CAS. Features that distinguished the species were examined – the most prominent and significant ones are presented in Table A1 in the appendix. Some molecular features were unique for a certain species, i.e., detectable only in one species, whereas others were detected in all species but in varying amounts. Example chromatograms from each species are shown in Fig. 2. 8-Methyl-6-nonenamide, an important molecular feature for the classification of C. chinense were detectable only in samples from that species while linoleic acid, an important feature for the classification of C. frutescens were detectable in both other species but in lower amounts. 2,4-decadienal an important feature for C. annuum were only detectable in that species. Fig. 2 also show the great difference in capsaicinoid concentration and to ensure that the differentiation between species was not solely based on pungency, the capsaicinoids were excluded from the model. The resulting model contained the same number of components and values of R2X, R2Y and Q2 as the model with capsaicinoids included. This indicated that the amount of capsaicinoids (pungency) did not contribute to the observed differences between the species. 3.3. Differentiation between chili types within species To differentiate chili types within each species, OPLS-DA models were generated with type of chili as pre-defined classes.
3.3.1. C. chinense When differentiation between chili types was evaluated for C. chinense, the chili types Carolina Reaper, Bhut Jolokia and Habanero were included as they were hypothesized to be likely selected for preparation of homemade pepper sprays due to their pungency and availability. The other five chili types included in the species C. chinense were hard to acquire, and therefore only extracted once and in one batch, resulting in too few replicates. The cross validated score plot of the model is presented in Figure A1 B in the appendix. Complete separation was obtained between the three chili types. The final model consisted of 227 variables – the most prominent and significant ones are listed in Table A1 in the appendix. Some molecular features were unique to a specific chili type, whereas other molecular features were shared but differed in the amounts. For instance, benzyl benzoate, which can function as an insect repellant [30], was only identified in Carolina Reaper. Certain fatty acids and esters were also found to be unique for specific chili types. 3.3.2. C. annuum For the chili types in C. annuum (Fig. A1 C and D in appendix), a clear separation was obtained between the red and green chilies (the first predictive component), most likely due to molecular features present at different stages of ripening. In addition, all the three green chili types were completely separated (the second and third predictive component). The final OPLS-DA model consisted of 102 variables – the most prominent and significant ones are presented in Table A1 in the appendix. All chili types contained unique molecular features. Unique molecular features detected in the red Dutch chili included vitamin E and some fatty acids, together with molecular features with unknown identities. Padrón, Jalapeño and green Dutch chili also exhibited unique molecular features that were not identified. 3.4. Differentiation of chili type acquired from different vendors/origin To examine if the characteristics of a certain chili type differed depending on its origin, OPLS-DA models were generated with vendors (the different webshops and grocery stores) as predefined classes. Models were attempted for all chili types acquired from more than one vendor, i.e., Carolina Reaper, Bhut Jolokia, Habanero, Piri-Piri and Jalapeño. However, models could not be constructed for Bhut Jolokia and Jalapeño, i.e. between-vendorvariation could not be extracted. In contrast, models could be
Table 2 Summary of OPLS-DA models and their performance. Model based on
Including
Components
R2X(cum)
R2Y(cum)
Q2(cum)
Species
C. chinense C. frutescens C. annuum
2+2+0
0.48
0.94
0.84
Chili type
C. chinense* C. annuum
2+4+0 3+1+0
0.58 0.71
0.98 0.97
0.85 0.91
Vendor
Carolina Reaper Habanero Piri-Piri
1+1+0 2+1+0 1+2+0
0.71 0.69 0.76
0.99 0.99 0.99
0.92 0.97 0.98
*
Includes three chili types: Carolina Reaper, Bhut Jolokia and Habanero.
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L. Mörén et al. / Forensic Science International 304 (2019) 109956 Table 3 Confusion matrix for the test set prediction of species. Species
C. annuum
C. chinense
C. frutescens
C. annuum (Jalapeño) C. chinense (Bhut Jolokia, Carolina Reaper, Habanero) C. frutescens (Piri-Piri)
3 0
0 8
0 0
0
0
6
created for Piri-Piri, Carolina Reaper and Habanero (crossvalidated score plots of the models are presented in Figure A2 in the appendix), for which complete separation was possible depending on the vendor. The cross-validated score plots from the Piri-Piri model and the Carolina Reaper model showed a withinvendor-variation in the orthogonal component, which were lesser than the between-vendor-variation. The within-vendor-variation depended on round of analysis, as there was a small analytical variation between the two rounds of analysis. The final models included 134 variables for Piri-Piri, 185 for Carolina Reaper and 134 for Habanero – the most prominent and significant variables are presented in Table A1 in the appendix. An example of a molecular feature found in Piri-Piri from only one vendor was 8-methylnonanoic acid, which is a precursor in the biosynthesis of dihydrocapsaicin [31] and a possible degradation product of dihydrocapsaicin. Molecular features for Carolina Reaper that differed between vendors were mostly unidentifiable, with the exception of certain esters that were found to be specific for each vendor. Habanero from one distributor contained specific amides and fatty acids, whereas from another vendor contained specific esters and unidentified molecular features. Habanero acquired from a third vendor contained only one specific, unidentified molecular feature. The observed differences depending on vendor were likely due to different cultivars sold under the same tradename. For example, there are a large number of cultivars of Habanero available [32], and Piri-Piri and Birds Eye are sold under the same tradename PiriPiri in grocery stores. Furthermore, countries of origin, stages of ripening of the chili or drying temperatures during production might contribute to variation. A summary of the final OPLS-DA models is presented in Table 2. The results showed that extracts of the three species included in the study could be differentiated according to species, type and vendor. Several chili types within C. chinense and all analyzed chili types within C. annuum could be differentiated. In addition, three out of five chili types acquired from more than one vendor could be separated depending on their origin. 3.5. Prediction of species and chili type
Fig. 3. t-predicted score scatterplot of OPLS-DA model based on (A) species and type of chili in (B) C. chinense and (C and D) C. annuum. A) 18 replicates (white symbols with shapes according to their classification) predicted for the three species C. chinense (red circles), C. frutescens (yellow triangles) and C. annuum (green squares). B) Nine replicates (white symbols with shapes according to their classification) predicted for the three chili types Carolina Reaper (red triangles), Bhut Jolokia (orange circles) and Habanero (yellow squares). C and D) Three replicates (white circles) of Jalapeño predicted for the chili types red Dutch chili (red squares), Padrón (blue inverted triangles), green Dutch chili (green triangles) and
For further validation of the models, a prediction set was applied. Data for five chili types from a third round of analysis were used as a prediction set in all OPLS-DA models. The results of prediction using the models based on species and chili type are presented in Fig. 3. As shown in Table 3 and Table 4, the confusion matrices based on the predicted Y values, all samples were correctly classified based on the species and chili type. This indicates that it may be possible to extract specific CASs that could be used to predict the species and chili type of seized homemade pepper sprays. However, there is always a risk of obtaining false positive and false negative results, which could be limited by continuously extending the models with more reference material. Jalapeño (yellow circles). The three predictive components of the model were required to achieve complete separation of the four types of chili (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
L. Mörén et al. / Forensic Science International 304 (2019) 109956
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Table 4 Confusion matrix for the test set prediction of chili type. Chili type
Bhut Jolokia
Carolina Reaper
Habanero
Jalapeño
Dutch chili (green)
Padrón
Dutch chili (red)
Bhut Jolokia Carolina Reaper Habanero Jalapeño
3 0 0 –
0 3 0 –
0 0 3 –
– – – 3
– – – 0
– – – 0
– – – 0
Bhut Jolokia, Carolina Reaper and Habanero were predicted in the chili type model only including chilies from C. chinense while Jalapeño were predicted in the chili type model only including chilies from C. annuum. – denotes that the chili type was not included in the model.
The chosen method of modelling will reduce the risk of obtaining false positive results, as this method does not force the unknown observation into any of the classes.
3.6. Prediction of vendor The origins of Carolina Reaper, Habanero and Piri-Piri were all correctly predicted (Fig. 4, Table 5). Interestingly, as stated above, Carolina Reaper and Piri-Piri used in the prediction set were from different production batches than the ones used to develop the models, showing that the origin of these chili types could be classified correctly even when they originated from different production batches. The systematic variation between the vendors could be due to different cultivars sold under the same tradename, stages of ripening of the chili before harvesting as well as different growing environments, which could indicate different geographical locations. 3.7. Detection and identification of homemade pepper spray from a textile material Extracts from the textile material treated with Carolina Reaper (C. chinense, purchased from webshop 1) displayed, in addition to capsaicinoids, the characteristic chemical profile. This was the case for all replicates and time points analyzed. Furthermore, the species of chili was correctly predicted for all replicates and time points (Fig. 5, Table 6). However, prediction of the type of chili showed that the prediction performance declines with time. After Table 5 Confusion matrix for the test set predictions of vendor. Chili type
Vendor
Webshop 1
Store 1
Store 2
Webshop 2
Habanero Piri-Piri Carolina Reaper
Webshop 1 Webshop 1 Webshop 1
3 6 6
0 0 –
0 – –
– – 0
The test sets of Habanero, Carolina Reaper and Piri-Piri were predicted in three different models, one for each chili type.
Fig. 4. t-predicted score scatterplot of OPLS-DA model based on vendor for (A) PiriPiri, (B) Carolina Reaper and (C) Habanero. The prediction set is represented as white symbols with shapes indicating their classification. A) Six replicates of Piripiri predicted to two vendors webshop 1 (blue circles) and store 1 (red triangles). B) Three replicates of Carolina Reaper predicted to two vendors webshop 1 (blue circles) and webshop 2 (orange squares). C) Three replicates of Habanero predicted to three vendors webshop 1 (blue circles), store 1 (yellow squares) and store 2 (green triangles) (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 5. t-predicted score scatterplot of OPLS-DA model based on species. 9 replicates of Carolina Reaper extracted from textile material were predicted to three species C. chinense (red circles), C. frutescens (yellow triangles) and C. annuum (green squares). The prediction set (white circles) consisted of sampled material after 6 h, 3 days and 6 days (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
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L. Mörén et al. / Forensic Science International 304 (2019) 109956
Table 6 Confusion matrix for the textile material predictions of species, type and vendor. Species
Days
C. annuum
C. chinense
C. frutescens
Uncertain
C. chinense (Carolina Reaper)
0 3 6
0 0 0
3 3 3
0 0 0
0 0 0
Chili type Carolina Reaper
0 3 6
Vendor Webshop 1
0 3 6
Bhut Jolokia
Carolina Reaper
Habanero
Uncertain
0 0 0
2 1 0
0 0 0
1 2 3
Webshop 1
Webshop 2
Uncertain
3 3 3
0 0 0
0 0 0
An observation is considered uncertain if the Y predicted values are equally high for two or more classes.
Fig. 6. Sequential order of prediction of species, chili type and origin to track and match seized homemade pepper spray. The prediction set is represented as white symbols with shapes indicating their classification and the sequential order shows prediction of a chili pepper extract of Habanero chili from species, chili type within C. chinense, to vendor.
6 days, all three replicates show an uncertain prediction meaning that the predicted observations appears to belong to more than one class. Although chili type could not be conclusively predicted for all replicates, the vendor of the Carolina Reaper chilies was correctly predicted (Table 6). Even though information about chili type declines with time, there were still information in the sample that could be used as forensic intelligence. These results suggest that the species of chili used for a homemade pepper spray could be predicted from extracts of the textile material independently of time of application up to at least six days. This could be useful in cases where residues of suspected homemade pepper spray are present on the clothing of victims. 3.8. Forensic applicability The results demonstrate that OPLS-DA models could be used sequentially to predict first the species, then chili type and finally origin/vendor of chilies used in homemade pepper sprays. Data in Fig. 6 show that a new extract from chilies purchased approximately one year after the models were constructed was correctly classified as C. chinense in the species model and as Habanero in the model of chili type. Lastly, it was correctly classified as originating from the vendor webshop 1, in the model differentiating Habaneros from different vendors (Fig. 6). The results indicate that seized homemade pepper sprays could be analyzed by GC–MS, followed by data processing, and then classified based on the created models to acquire information on the species, chili type used and origin/vendor of the chili. Thus, chili pepper contains specific variation that could be of forensic interest as such information may be useful in investigations for law enforcement to track and match seized samples.
4. Conclusion The results of this study showed that it was possible to extract CASs that contained information about species, type and origin/ vendor of chilies that could be used to prepare homemade pepper sprays. Models differentiating species, type of chili and vendor were constructed that could be used for prediction of homemade pepper sprays. All the generated models gave correct classification of an external test set consisting of chili acquired one year after development of the models. Additionally, residues from homemade pepper spray on textile material were identified and the species of chili correctly classified. The high predictability of the models would be useful for prediction of the identity and origin of seized homemade pepper sprays and residues on clothing. Thus, modeling of the chili plant material chemical composition could aid the investigator in match and classify seizures and samples collected from victims. The models as presented here could provide useful investigative information and if extended provide source predictions to be made in a legal framework. CRediT authorship contribution statement Lina Mörén: Methodology, Software, Validation, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Sebastian Jonsson: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing original draft, Visualization. Tobias Tengel: Methodology, Investigation, Writing - review & editing. Anders Östin: Conceptualization, Writing - review & editing, Supervision, Project administration, Funding acquisition.
L. Mörén et al. / Forensic Science International 304 (2019) 109956
Declaration of Competing Interest None. Acknowledgement This work was financed by The Swedish Ministry of Defence.
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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.forsciint.2019.109956.
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