Talanta 225 (2021) 122069

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Talanta

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Real-time authentication of animal species origin of products using rapid evaporative ionization mass spectrometry and chemometric analysis

Haiyan Gao a,b, Jihong Lin c, Xiaofei Jia c, Yang Zhao d, Songying Wang b, Hua Bai a, Qiang Ma a,* a Chinese Academy of Inspection and Quarantine, Beijing 100176, China b Inner Mongolia Autonomous Region Institute of Product Quality Inspection, Huhhot 010070, China c Waters Corporation, Beijing 100176, China d National Quality Supervision and Testing Center for Leather Products, Beijing 100015, China

ARTICLE INFO ABSTRACT

Keywords: Increasing accounts of fraud and persistent labeling problems have brought the authenticity of leather products Rapid evaporative ionization mass into question. In this study, we developed an extremely simplified workflow for real-time, in situ, and unam­ spectrometry biguous authentication of leather samples using rapid evaporative ionization mass spectrometry (REIMS) Leather coupled with an electric soldering iron. Initially, authentic leather samples from , sheep, pig, deer, ostrich, Authentication crocodile, and snake were used to create a chemometric model based on principal component analysis and linear Scanning electron microscopy discriminant analysis algorithms. The validated multivariate statistical model was then used to analyze and generate live classificationsof commercial leather samples. In addition to REIMS analysis, the microstructures of were characterized by scanning electron microscopy to provide complementary information. The current study is expected to provide a high-throughput tool with superior efficiencyand accuracy for authenticating the identity of leathers and other consumer products of biogenic origin.

1. Introduction origin of leather products can help maintain transparency in trade and detect counterfeit leather goods. Leather is ubiquitous in consumer goods, serving as an important raw The physical properties and morphological features of leather material in the production of various lifestyle accessories such as products can be checked by sensory inspection (thickness, size, color, handbags, belts, wallets, gloves, shoes, clothing, and watch . The gloss, pore pattern, and feel) and microscopic observation (hair follicle global demand for products and household items containing leather has array, cross sections, leather grain, and medulla) [6,7], giving some grown significantly in recent years. Leather is produced from animal clues to distinguishing their animal origin. Unfortunately, these skins and hides by physical and chemical treatments, which provide methods are time- and labor-intensive, and rely heavily on operator high mechanical strength and stability against heat and moisture [1,2]. experience. Moreover, the surface and fiber structure of leather are The animal source of leather products must be specified by manufac­ sometimes disrupted during the manufacturing process, making it turers, however, there is an ongoing problem with fraudulent mis­ difficultto identify its origin [8]. Spectroscopic methods have also been labeling of the origin of leather products [3]. Furthermore, with used for the analysis of leather samples, such as Fourier transform economic development and technological advancements, artificial or infrared (FTIR) [6,9], near-infrared (NIR) [10,11], Raman [9], and synthetic leather has been mass produced as a cheap alternative that terahertz (THz) [12] spectroscopy. However, these measurements exhibits similar outward appearance and physical characteristics as depend on subtle spectral differences between test samples, which are natural leather [1,4]. However, artificial leather has low water vapor prone to interferences from leather surface coatings. In addition, permeability, poor dye fastness, and may contain harmful chemicals that DNA-based approaches have been employed for the authentication of potentially pose significant health risks [4,5]. In order to maximize leather species, allowing high sensitivity and high taxonomic specificity profits, dishonest businesses have been misrepresenting fake merchan­ [13–17]. However, some concerns exist with regard to leather analysis, dise, infringing on the legitimate interests of consumers. Thus, an un­ such as degradation or modificationof DNA caused by various chemical ambiguous analytical method to differentiate and verify the animal treatments (acid, alkaline, , and dyeing) for leather production

* Corresponding author. E-mail address: [email protected] (Q. Ma). https://doi.org/10.1016/j.talanta.2020.122069 Received 29 November 2020; Received in revised form 22 December 2020; Accepted 25 December 2020 Available online 29 December 2020 0039-9140/© 2020 Elsevier B.V. All rights reserved. H. Gao et al. Talanta 225 (2021) 122069 and inhibition of polymerase chain reaction (PCR) amplification by collided with a heated helical coil, disrupting the solvent-ion clusters coextracted compounds during the DNA extraction process from leather and liberating the gas-phase ions. The REIMS source was mounted on a specimens [13,15,18], which may reduce confidence in analytical Waters Xevo G2-XS hybrid quadrupole time-of-flightmass spectrometer results. (Milford, MA, USA). A heater bias of 60 V and a cone voltage of 40 V Mass spectrometry (MS) is a highly sensitive and specific analytical were set for the analysis. Mass spectra were acquired in negative ion tool that enables definitive animal source identification based on the polarity and sensitivity mode over m/z 50–1200 with a scan time of 1 s. species-specific detection pattern of selected marker peptides from The mass spectrometer was calibrated with a sodium formate solution trypsin-digested leathers when coupled with liquid chromatography (5 mM in isopropanol/water, 90:10, v/v) at an infusion flowrate of 200 (LC) [3,8,19–21]. Although these MS-based methods can provide valu­ μL/min before analysis. For lock-mass correction, a solution of leucine able information for detecting interspecies differences, they may still enkephalin (200 ng/mL in isopropanol) was continuously infused into suffer from complicated and time-consuming sample pretreatment the REIMS source at a constant flowrate of 150 μL/min using a syringe procedures prior to instrumental analysis, such as dechroming, heat pump. Mass drift was corrected based on the reference peak at m/z denaturation, and enzyme digestion. One of the holy grails of MS has 554.2615 corresponding to the deprotonated leucine enkephalin always been minimizing or eliminating sample preparation altogether molecule. [22]. Ambient ionization mass spectrometry [23–27] is a novel tech­ nique allowing rapid, in situ, and high-throughput analysis of a wide 2.3. Statistical analysis and multivariate modeling variety of samples. It requires little to no sample preparation and ionizes analytes from their native environment. Starting with desorption elec­ High-resolution accurate-mass data were collected using the Mas­ trospray ionization (DESI) [28] and direct analysis in real time () sLynx software (Watres, Milford, MA, USA). The information-rich, [29], dozens of ambient ionization methods have been developed [26, leather-specific REIMS spectra from the authenticated leather samples 30,31]. Rapid evaporative ionization mass spectrometry (REIMS) [32, of different animal origins (cattle, sheep, pig, deer, ostrich, crocodile, 33] is a novel ambient ionization approach that enables the formation of and snake) were imported into the LiveID software (Watres, Milford, aerosols containing a considerable quantity of gaseous ions directly from MA, USA), where the acquired raw data were transformed to centroid the sample surface using electrosurgical tools. REIMS offers possibilities mass spectra via automatic peak detection, background subtracted, then for rapid profilinganalysis of target samples, revealing critical attributes normalized against the total ion current (TIC). A multivariate statistical such as authenticity, species, quality, or phenotypic trait. Various algorithm of principal component analysis-linear discriminant analysis studies report successful applications of REIMS in many fields,including (PCA-LDA) was used for chemometric model building. The Progenesis tissue detection [34–38], microorganism characterization [33,39,40], QI software with the extended EZinfo statistical module (Nonlinear and food analysis [41–50]. Dynamics, Newcastle upon Tyne, UK) was used to discover discrimi­ In this study, the capability of REIMS coupled with an electric sol­ nating marker compounds between leather species. The established dering iron for contact heating to quickly and precisely identify the model was validated and applied to the analysis of commercial leather animal source of leather products was explored without the need for products under examination, enabling real-time sample recognition and lengthy or complicated sample pretreatment. Authentic samples of differentiation. cattle, sheep, pig, deer, ostrich, crocodile, and snake leathers were investigated. The aerosols produced by the cauterization and dissection 2.4. Microstructure characterization by scanning electron microscopy of the leather samples were found to contain significant gaseous ions (SEM) including the biological signature of the specific tissue area being vaporized. A multivariate model was established, validated, and then Cross sections of the leather samples were cut, adhered to the used to analyze and discriminate commercial leather samples. conductive adhesive of a sample plate, then placed in a Cressington 108auto sputter coater (Watford, UK) for gold sputter coating. A 2. Experimental conductive coating of gold was deposited onto the outer layer of the fibers with a spray current of 40 mA and a spray time of 160 s. The 2.1. Chemicals and reagents microstructures of the leather samples were characterized using a Hitachi S-4800 scanning electron microscope (Tokyo, Japan) operated Seventeen authenticated leather samples from cattle, sheep, pig, at a working voltage of 15 kV and a working distance of 10–13 mm. deer, ostrich, crocodile, and snake were obtained from the National Quality Supervision and Testing Center for Leather Products (Beijing, 3. Results and discussion China). LC-MS grade isopropanol, methanol, and acetone were pur­ chased from Fisher Scientific(Pittsburgh, PA, USA). Leucine enkephalin 3.1. Optimization of REIMS parameters was purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water was prepared using a Millipore Direct-Q water purificationsystem The REIMS analysis of leather samples was carried out based on the (Bedford, MA, USA). Nitrogen (ultra-high purity of 99.999%) was pro­ aerosols produced by the electric soldering iron. In an attempt to obtain vided by Beijing Beiyang United Gas Co., Ltd (Beijing, China). optimal ionization efficiency,a comprehensive investigation into critical parameters was conducted, including heater bias, cone voltage, auxil­ 2.2. REIMS workflow iary solvent flow rate, and leather cutting length. The heater bias was optimized from 20 to 120 V (Fig. S1). It was found that the overall in­ The REIMS sampling was carried out using a CS-20 hand-held elec­ tensity of the total ion current chromatogram (TICC) increased incre­ ◦ tric soldering iron device (Taiyo Electric, Tokyo, Japan) heated at 380 C mentally from 20 to 60 V, then significantly declined thereafter, for each leather sample. The heated metal tip was used to cut a 10-mm resulting in a maximum signal intensity at a heater bias of 60 V. Simi­ line on each leather sample, instantly producing a stream of aerosols larly, the cone voltage was optimized from 10 to 80 V (Fig. S2). The from the contact surface. A wet sponge, supplied with the electric sol­ signal intensity increased proportionately with increasing voltage from dering iron device, was used to clean the metal tip after each sampling. 10 to 40 V, reaching a maximum at 40 V, after which the intensity For each sample, 10–15 analytical repeats were recorded. An air-driven decreased. The isopropanol auxiliary solvent containing leucine Venturi pump actively transported the resulting aerosol through a pol­ enkephalin was infused into the mass spectrometer for lock-mass ytetrafluoroethylene(PTFE) tube (4 m length, 4.11 mm o.d., 2.53 mm i. correction and signal enhancement. The flow rate was adjusted from d.) to the REIMS atmospherc interface chamber, where the aerosols 50 to 250 μL/min. Fig. S3 demonstrated that the signal intensity

2 H. Gao et al. Talanta 225 (2021) 122069 increased gradually as the flow rate increased from 50 to 150 μL/min the prominent marker compounds that were significantly altered be­ and steadily declined as the flowrate further increased. Therefore, 150 tween different leather species and were responsible for the predictive μL/min was chosen as the optimum flowrate for the auxiliary solvent. In variation. A partial least squares-discriminant analysis (PLS-DA) statis­ addition, the leather cutting length was optimized to achieve better peak tical algorithm was performed in a supervised fashion to identify the shape and analysis reproducibility. The effect of cutting length was compounds of interest discriminating between leather species. The data tested from 5 to 20 mm. The experimental results shown in Fig. S4 were exported to the Progenesis QI software with the extended EZinfo illustrated that there was a growth in signal intensity from 5 to 10 mm statistical module, where the features that most significantlycontributed but plateaued as the cutting length increased from 10 to 20 mm. Thus, a to the discrimination between groups were calculated based on the cutting length of 10 mm was chosen for the REIMS process. following criteria: variable importance in projection (VIP) > 2, analysis of variance (ANOVA) p-value < 0.05, and max fold change > 2. As listed 3.2. Multivariate statistical analysis in Table 1 according to decreasing VIP scores, the eight main composi­ tional species identified include saturated, monounsaturated, poly­ The REIMS spectra obtained from 17 authenticated leather samples unsaturated, and hydroxylated fatty acids, with mass accuracy ranging from cattle (n = 5), sheep (n = 6), pig (n = 2), deer (n = 1), ostrich (n = from 1.4–1.5 ppm (parts per million). The distribution of the fatty 1), crocodile (n = 1), and snake (n = 1) were processed with the LiveID acids detected varied from leather to leather (Table S2), which could software for chemometric model building (Fig. S5). PCA is an unsu­ serve as characteristic fingerprints for the discrimination of leather pervised algorithm for dimension reduction by ignoring class labels and specificity. The PLS-DA plot, VIP versus coefficients plot, and VIP plot finding principal components to maximize the variance in a dataset; for the multivariate statistical analysis are shown in Figs. S6–S8, whereas LDA is a supervised data analysis that maximizes the separation revealing the interspecies differences that highly contribute to the between multiple classes and minimizes the variance within the cate­ discriminant separation model. gories. The combination of unsupervised PCA followed by supervised The identity of chemical components observed from leather samples LDA has been found to reduce the chance of over-fittingthat may occur was analyzed using the Progenesis QI software by setting a mass error with a pure LDA model [51]. Thus, PCA-LDA was used to establish a tolerance of 5 ppm. Observation of the mass spectra in both ion detec­ multivariate statistical model to differentiate the animal leather samples tion modes indicated that the negative ion mode could provide more based on their molecular fingerprints. The three-dimensional PCA-LDA informative data for subsequent multivariate statistical analysis. Thus, scatter plot is shown in Fig. 1, featuring the complete separation and for further analysis of leather samples, negative ion mode was adopted. accurate discrimination between individual animal leather categories. The mass spectra collected in the range of m/z 100–550 for cattle, sheep, All the leather samples were clustered clearly into seven groups, while pig, deer, ostrich, crocodile, and snake leathers with varying signal in­ leather samples from the same animal source exhibited good clustering. tensities are shown in Fig. 2. Within the REIMS spectra of leather sam­ The validity of the created chemometric model was evaluated with a ples analyzed, fatty acids were found to be major characteristic leave-20%-out cross-validation test. Four partitions (80%) of the dataset constituents. The ions at m/z 157.1236 (pelargonic acid, FA 9:0), were used to build a training model, while the remaining one partition 255.2334 (palmitic acid, FA 16:0), 271.2279 (FA 16:0; O), 279.2342 (FA (20%) was used to validate the classification model. This was repeated 18:2), 281.2496 (FA 18:1), 283.2639 (stearic acid, FA 18:0), 297.2420 five times and each partition was predicted once by the model trained (FA 18:1; O), and 299.2579 (FA 18:0; O) were tentatively identified by from the other four partitions [51]. A correctness score of 98.22% was searching against the LIPID MAPS Structure Database (https://www. achieved with only one classification failure and four outliers out of a lipidmaps.org/data/structure/) based on the acquired accurate masses total of 281 mass spectra (Table S1). Moreover, an additional authentic of the candidate peaks. The peak at m/z 281.2496 appeared as the base sheep leather sample, which had not been previously used to generate peak in sheep, deer, crocodile, and snake leathers. The ions at m/z the chemometric model, was correctly identified based on the estab­ 297.2420 and 299.2579 were the most abundant ions in cattle and pig lished model with a 100% confidence value, indicating satisfactory leathers, respectively. In addition to the presence of fatty acids, the ions model predictive accuracy. at m/z 311.1677 and 325.1868 were identified as N-undecylbenzene­ sulfonic acid and 2-dodecylbenzenesulfonic acid, respectively. These 3.3. HRMS profiling and discovery of marker compounds chemicals have been commonly used for degreasing during leather processing. An in-depth analysis of the REIMS raw data was conducted to explore

Table 1 The discriminating marker compounds identified in different animal leathers.

No. VIP Exact m/ Measured Mass Elemental Identity z m/z error composition (ppm)

1 5.72 283.2643 283.2639 1.4 C18H36O2 FA 18:0 (stearic acid) 2 5.34 281.2492 281.2491 0.4 C18H34O2 FA 18:1 3 3.87 279.2332 279.2334 0.7 C18H32O2 FA 18:2 4 3.38 297.2435 297.2433 0.7 C18H34O3 FA 18:1; O 5 3.28 299.2589 299.2589 0.0 C18H36O3 FA 18:0; O 6 2.91 255.2333 255.2334 0.4 C16H32O2 FA 16:0 (palmitic acid) 7 2.63 271.2279 271.2283 1.5 C16H32O3 FA 16:0; O 8 1.18 157.1234 157.1236 1.3 C9H18O2 FA 9:0 Fig. 1. PCA-LDA chemometric model based on REIMS analysis of seven cate­ (pelargonic acid) gories of authentic animal leathers (cattle, sheep, pig, deer, ostrich, crocodile, and snake). VIP: variable importance in projection.

3 H. Gao et al. Talanta 225 (2021) 122069

Fig. 2. Representative REIMS spectra for the analysis of seven categories of animal leathers in the negative ionization mode.

3.4. SEM characterization local stores. Real-time authentication of the animal source of the leatherwas achieved using the live-recognition function of the LiveID In addition to REIMS analysis, SEM was used to characterize the fiber software, which performed a mass spectral comparison with the data­ structure and cross sections of the different kinds of animal leathers, base and calculated the confidencevalue of the tested sample. As shown providing complementary information. As shown in Fig. 3, the cross in Fig. S9, two out of the 10 commercial samples were identifiedas cattle sections of all leathers exhibited tightly woven collagen fibers with a leather with confidence values of 99.89% and 93.92%. The other eight thin grain layer. The meshwork and muscle surface layers were rela­ commercial samples were assigned as outliers as they did not match the tively thick, with loosely woven collagen fibers. Moreover, each source characteristics of any of the animal leathers investigated in this study of leather had distinct features that were observed. For example, the (Fig. S10). In contrast to authentic cattle leather (Fig. 4a) and com­ cattle leather was generally thicker, with a natural transition from mercial cattle leather (Fig. 4b), a characteristic cluster of ion peaks (m/z compactly (bottom layer) to loosely (top layer) woven collagen fibers, 749.4818, 807.5206, 865.5656, 923.6074, 981.6519, 1039.6947, and the fibers of the muscle surface layer showed braided patterns in 1097.7279, etc.), with an equal mass difference of 58 Da corresponding different directions. On the other hand, the leather and muscle face fi­ to the mass of a propylene oxide unit, was observed in the mass spectrum bers of the sheep leather were slender and arranged along the same of an identified artificial leather (Fig. 4c), which was attributed to the direction. The pig leather displayed relatively looser woven collagen possible presence of surfactants. The SEM images also exhibited a fiberson the bottom layer compared to the upper layer, and the middle marked difference in microstructures between natural and artificial reticular layer had many fiber bundles similar to the columnar trans­ leathers (Fig. 4). The experimental results revealed discrepancies be­ verse section. The deer leather had a distinct layering pattern where the tween the labels of the tested commercial products and their real lower layer was comprised of thin fibersthat were tightly woven parallel identity. to the grain surface. The middle network layer was tightly woven par­ allel to the grain surface and perpendicular to the vertical section, and 4. Conclusions the upper muscle surface layer was slightly loose with irregular weaving directions. The was relatively thin and gradually turned In this study, we developed a high-throughput workflow for real- sparse from bottom to top, with long and messy fibers on the muscle time, in situ, and unambiguous authentication of leather products surface. The crocodile leather was dense and neat, with a muscle surface using REIMS and chemometric analysis. The REIMS analytical parame­ comprised of a thin layer of fluffy fibers. Finally, the snake leather was ters were optimized, allowing great insights into the chemical infor­ relatively thin. The boundary between the meshwork layer and the mation of the investigated leathers. A multivariate statistical model was muscle surface layer was bevelled, and the cross sections had a promi­ built to differentiate the animal species origin of leather samples. The nent scaly structure. leather microstructures were also characterized by SEM for identity verification. The proposed methodology significantly improved the ac­ curacy and efficiencyof leather product testing, providing an enormous 3.5. Analysis of commercial samples potential for the authentication of leather goods.

The PCA-LDA chemometric model built with authentic leather samples was used to analyze 10 leather belts purchased online or from

4 H. Gao et al. Talanta 225 (2021) 122069

Fig. 3. SEM images for the characterization of (a) cattle, (b) sheep, (c) pig, (d) deer, (e) ostrich, (f) crocodile, and (g) snake leathers.

Fig. 4. REIMS spectra and SEM images of (a) an authentic cattle leather, (b) a commercial cattle leather, and (c) an identified artificial leather.

Author contributions – review & editing.

Haiyan Gao: Investigation, Formal analysis, Writing – original draft. Declaration of competing interest Jihong Lin: Investigation. Xiaofei Jia: Investigation. Yang Zhao: Re­ sources. Songying Wang: Investigation. Hua Bai: Supervision. Qiang Ma: The authors declare that they have no known competing financial Conceptualization, Funding acquisition, Project administration, Writing interests or personal relationships that could have appeared to influence

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