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A novel FTIR discrimination based on genomic DNA for species-specific analysis of meat and bone meal
Food Chemistry 294 (2019) 526–532
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A novel FTIR discrimination based on genomic DNA for species-specific analysis of meat and bone meal Yahong Han, Xinlei Wang, Ye Liu, Lujia Han, Zengling Yang, Xian Liu
T
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College of Engineering, China Agricultural University, Beijing 100083, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Genomic DNA FTIR Meat and bone meal PLS-DA Chemometrics
Fourier transform infrared (FTIR) spectroscopy was applied for the species-specific identification of meat and bone meal (MBM), based on genomic DNA characteristics. A total of 51 source-reliable MBM samples, including porcine, poultry, bovine, and ovine MBM, were analysed. Genomic DNA was extracted using an optimized procedure for FTIR scanning. The results showed that a series of discriminatory FTIR spectral bands were closely related to DNA characteristics of MBM. The spectral intensity difference at 1651 cm−1 was identified as a key peak for discriminating ruminant from non-ruminant MBM. Combining FTIR data with chemometrics, a two-step protocol for discriminant analysis was established. An initial identification model of porcine, poultry, and ruminant MBM and a second model of bovine and ovine MBM were established. The results obtained using two models showed that the correct classification rate was 100%. This method could assist governments in confirming the authenticity of species to ensure feed safety.
1. Introduction
MBM with a low false positive rate, but its detection efficiency is quite low and it cannot identify the species of MBM. In 2013, PCR was authorized as another official technique in view of its ability to distinguish MBM species based on the characteristics of genomic DNA. However, the PCR method has a high detection cost, a long analysis period and a high false positive rate. Therefore, a promising and innovative hypothesis was proposed in this study for the discrimination of speciesspecific MBM involving a combination of FTIR spectroscopy and genomic DNA characteristics. FTIR spectroscopy is a rapid, relatively cheap and convenient analytical method, that can reflect that DNA composition and structure (Song et al., 2014). FTIR spectroscopy can not only be used to analyse the DNA structure (Agarwal, Jangir, & Mehrotra, 2013; Kondepati et al., 2008; Stefan, Muntean, Tripon, Halmagyi, & Valimareanu, 2014; Whelan et al., 2011) but can also be used for species identification based on genomic DNA (Emura, Yamanaka, Isoda, & Watanabe, 2006). For example, Emura et al. (2006) initially suggested that FTIR spectroscopy was a powerful tool for distinguishing the japonica and the indica rice varieties based on the structural differences of their DNA. Subsequently, several researchers have successfully applied FTIR spectral analysis of DNA to differentiate sample species including five varieties of Chinese cabbages (Song et al., 2014), three varieties of Nicotiana tabacum L. (Qiu et al., 2014), and 10 varieties of Camellia
Meat and bone meal (MBM) was an important animal-origin protein material used commonly in feeding formulations. However, the feeding of ruminant MBM (contaminated with prions) to ruminants is now generally acknowledged to be responsible for the transmission of bovine spongiform encephalopathy. Therefore, the use of MBM in feedstuffs has been strictly prohibited globally since 2001 (Ec, 2001; Shinoda et al., 2008). In 2013, the European Commission partially lifted restrictions on porcine and poultry MBM in aquaculture (EC, 2013). This reintroduction of non-ruminant MBM in fish feed may be due to the significant progress in the field of analytical methodology (Baeten et al., 2005; De la Roza-Delgado et al., 2007; Fernández-Ibáñez, Fearn, Soldado, & Roza-Delgado, 2009; Mandrile, Amato, Marchis, Martra, & Rossi, 2017). However, the use of MBM in terrestrial animal feed is still prohibited. This ban can only be lifted if a sensitive and reliable species-specific method for efficient surveillance and removal of bovine MBM from the human food chain, is developed. In the European Union (EU), there are two official methods for detecting the presence of MBM, namely, light microscopy and polymerase chain reaction (PCR) (EC, 2013). The light microscopy method was the first official method established by the European Union in 1998 (Commission, 1998; EC, 2009). This method can detect the presence of
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Corresponding author at: Box 191, No. 17 Tsinghua East Road, Haidian District, Beijing 100083, China. E-mail addresses: [email protected] (Y. Han), [email protected] (X. Wang), [email protected] (Y. Liu), [email protected] (L. Han), [email protected] (Z. Yang), [email protected] (X. Liu). https://doi.org/10.1016/j.foodchem.2019.05.088 Received 2 April 2018; Received in revised form 29 March 2019; Accepted 10 May 2019 Available online 11 May 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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adjusted to the same concentration (100 ng/μL) using a nuclease-free water. The concentration and purity of the extracted DNA were measured using an ultramicro UV spectrophotometer (Nanodrop, Thermo Fisher Scientific, San Jose, USA). The purity of the DNA was evaluated using the absorbance ratio at the A260/A280 and A260/A230 ratios. Additionally, the quality of the DNA solution was analysed using a capillary gel electrophoresis-based DNA analyser (Qsep100, Bioptic, Taiwan, China) and agarose gel electrophoresis. All the genomic DNA samples were stored at −20 °C until further analysis.
reticulata Lindl. from the Chuxiong population (Qiu et al., 2015). However, to the best of our knowledge, species-specific identification of MBM based on FTIR spectroscopic analysis of DNA has not been previously reported. In the present study, a rapid, sensitive, and reliable FTIR spectroscopic method was developed for the identification of MBM samples. Five DNA extraction methods were compared for FTIR scanning, and then a high-quality infrared spectrum (IR) of genomic DNA was obtained. Combined with chemometrics, a two-step protocol for determining the origin of MBM was proposed. 2. Materials and methods
2.3. FTIR spectroscopy 2.1. Sample preparation The DNA solution (15 μL) was dried on a 96-well high throughput extension (HTS-XT) microplate at 30 °C for FTIR examination over the wavenumber range from 4000 to 400 cm−1. The spectra were recorded by 64 scans with a resolution of 4 cm−1 (Han, Han, Yao, Li, & Liu, 2018). Analysis of each sample was performed in triplicate.
A total of 51 MBM samples were prepared in this study, including 12 porcine MBM samples, 11 poultry MBM samples, 14 bovine MBM samples and 14 ovine MBM samples. Raw animal byproducts (mainly bone with a small amount of meat) were collected from different markets in Beijing. The MBM samples were prepared according to the standard procedures approved by the European Commission (European, 2011). Briefly, all the raw materials were heated (133 °C, 20 min, 300 kPa), dried, pressed, and defatted. Subsequently, the samples were ground using an ultra-centrifugal mill (ZM200, Retsch, Haan, Germany) with a mesh size of 0.5 mm and were then characterized by the realtime PCR method to ensure purity (Fumière, Dubois, Baeten, von Holst, & Berben, 2006). All the MBM samples were then stored at −20 °C for further analysis. To compare the genomic DNA extracted from the MBM samples, double-stranded DNA derived from calf thymus (calf DNA) was obtained from Sigma–Aldrich (St. Louis, MO, USA). A solution with 100 ng/μL calf DNA was prepared with nuclease-free water (Promega, Madison, USA) and then stored at 4 °C until further analysis.
2.4. Statistical analysis The FTIR spectral data in the region (1800–800 cm−1, a span of 520 wavenumbers) were analysed by MATLAB version R2012 (Mathworks, Natick, USA) and the SPSS 12.0 software package for Windows (SPSS Inc., Chicago, USA). Area normalization and second derivation were used to optimize data preprocessing. The Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity were carried out prior to the principal component analysis (PCA). PCA and partial least squares-discriminant analysis (PLS-DA) were performed with the PLS Toolbox 8.0 (Eigenvector Research, USA). Furthermore, the classification rates of the PLS-DA model were measured according to the sensitivity, specificity, classification error and the correct classification rate, which were determined as follows (Górski, Sordoń, Ciepiela, Kubiak, & Jakubowska, 2016; Mazivila et al., 2015):
2.2. DNA extraction protocols To obtain high-quality infrared spectra of genomic DNA, five DNA extraction methods were compared, which were denoted as Methods15. Method 1 involved the use of the Wizard Magnetic DNA Purification Kit (Promega, Madison, USA), according to the EURL-AP standard operating procedure (Procedure, 2013). Method 2 followed Method 1, but with further purification. Specifically, Method 2 involved purifying the DNA extracted using Method 1 with an Amicon Ultra 10 KDa (Millipore, Bedford, USA) filter device. Method 3 was similar to Method 2 and included purification using magnetic-particle technology (FineMag DNA Clean Beads; Genfine Co. Ltd., Beijing, China). The purification procedure was performed in accordance with the supplier’s instructions. Method 4 featured spin-column technology, using the TIANamp Feedstuff Animal DNA Kit (Tiangen, Beijing, China) following the supplier’s instructions. Method 5 was based on a combination of the procedures of Saitoh, Togashi, Arie, and Teraoka (2006) and Zhou (2011) with some modifications. Specifically, a sample (200 mg) was transferred to 980 μL Nlauroylsarcosine sodium salt buffer [100 mM Tris-HCl (pH 8.0), 100 mM EDTA (pH 8.0), 200 mM NaCl, 1% n-lauroylsarcosine sodium salt (pH 8.0)]. Subsequently, β-mercaptoethanol (20 μL) was added to the mixture, and incubated for 10 min at room temperature. DNA solutions were extracted with an equal volume of phenol/chloroform/ isoamyl alcohol (25:24:1) and were then extracted with an equal volume of chloroform. In addition, RNase A (1 μL, 10 mg/ml) was added, and the DNA solution was incubated in a water bath (37 °C, 30 min). An equal volume of ice-cold isoamyl alcohol was later added and mixed. The mixture was centrifuged at 13000 rpm (4 °C, 15 min) and the supernatant was discarded. Subsequently, DNA was washed twice with 1 mL of 70% ethanol, and then it was air-dried and dissolved in 50 μL nuclease-free water. The final concentration of the DNA samples was
Sensitivity =
TP × 100% TP + FN
(1)
Specificity =
TN × 100% TN + FP
(2)
Classification error =
FP + FN × 100% TP +TN + FP+FN
Correct classification rate = 1 −
FP + FN × 100% TP +TN + FP+FN
(3)
(4)
where the true positive (TP) denotes the number of correct positive results, the true negative (TN) denotes the number of correct negative results, the false positive (FP) denotes the number of false positive results, and the false negative (FN) denotes the number of false negative results. Furthermore, the mechanism of the PLS-DA model was explored by a combination of difference spectral analysis and significant differences analysis of the infrared intensities at each wavenumber. For each wavenumber, the significant differences (p < 0.05) between the infrared intensities of the MBM samples (12 porcine, 11 poultry, 14 bovine, and 14 ovine MBM samples) were determined using the nonparametric Kruskal–Wallis test with Bonferroni correction. Furthermore, the significant differences (p < 0.05) between the infrared intensities of the two groups samples (14 of bovine and 14 of ovine MBM) were determined using the t test at each wavenumber. Five hundred-twenty p values were calculated. 527
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2013; Tan & Yiap, 2009). The concentrations of DNA that were extracted using Methods 2 and 3 were 41.25 ng/μL and 20.2 ng/μL, respectively. The infrared spectra of DNA extracted using Methods 2 and 3 were presented in Fig. 1C and Fig. 1D, respectively. The band at 1674 cm−1 (Fig. 1C) was sharper than the band at 1655 cm−1 (Fig. 1D), while there were more characteristic bands in the region of 1500–1250 cm−1 (Fig. 1D). However, the bands at 966 and 833 cm−1, which were associated with the backbone of DNA, disappeared in Fig. 1C and D. Subsequently, to develop a procedure that could obtain high-quality infrared spectra of DNA, Methods 4 and 5 were investigated and compared. The concentrations of DNA extracted using Methods 4 and 5 were higher than those of DNA extracted using other methods (p < 0.05). In particular, the concentration of DNA extracted using Method 5 was the highest (680.5 ng/μL). Furthermore, compared with the infrared spectra of DNA extracted using Methods 1, 2 and 3 (Fig. 1BD), many sharp bands were observed for DNA extracted using Methods 4 and 5 (Fig. 1E-F), indicating that the purity of DNA extracted using Methods 4 and 5 was higher, possibly because Method 4 was based on the spin-column extraction technology, enabling greater specificity of binding with nucleic acids (Tan & Yiap, 2009). However, the bands in the region of 1000–800 cm−1 disappeared (Fig. 1E), while the principal infrared bands of DNA base vibration including the bands at 1661, 1607, 1241, 1094, and 966 cm−1 were obtained, as shown in Fig. 1F. Taking all the relevant factors into consideration, Method 5 was selected because of the high concentration, purity, and high-quality infrared spectra of the DNA obtained by this method.
Fig. 1. Infrared spectra of calf thymus DNA and the bovine DNA obtained by different methods (A: calf DNA, B: DNA from Method 1, C: DNA from Method 2, D: DNA from Method 3, E: DNA from Method 4, F: DNA from Method 5).
3. Results and discussion 3.1. Comparison of the DNA extraction procedures
3.2. FTIR spectroscopic characteristics of genomic DNA of MBM It is well-known that FTIR spectroscopy has several limitations, such as high requirements for sample purity and water interference (Jin, Zhang, & Martin, 2017). Therefore, high-quality DNA samples are required for FTIR analysis. To obtain high- quality infrared spectra of DNA, the FTIR spectra of the DNA standard (calf DNA) and DNA extracted using five methods were analysed, with the obtained FTIR spectra presented in Fig. 1. The concentration of genomic DNA extracted using Method 1 was 32.3 ng/μL. The A260/A280 ratios of all samples were distributed from 1.8 to 2.0, indicating that the DNA was sufficiently pure for PCR analysis. For the spectra shown in Fig. 1B, there were no other characteristic peaks of functional groups of DNA, except for the peak at 1653 cm−1. However, for the spectra shown in Fig. 1A, strong bands were observed at 1661, 1241, 1094 and 966 cm−1, which can be assigned to distinct functional groups of DNA. In addition, a PLS-DA model based on the FTIR spectral data of genomic DNA extracted from all the MBM samples using Method 1 was developed. The results of this PLS-DA model showed that porcine, poultry, bovine and ovine MBM could not be discriminated (data not shown), indicating that the DNA sample extracted using Method 1 was inadequate for FTIR spectroscopic analysis. The lack of sharp and strong bands could account for the unsatisfactory results, which may be caused by the lower concentration of the DNA sample and interference by impurities such as guanidine salt. Therefore, in Methods 2 and 3, two purification procedures were used for the genomic DNA extracted using Method 1. In Method 2, an ultrafiltration process was used; this process has been proven to remove low-molecular-weight substances, such as salt, and to increase the DNA concentration (Norén, Hedell, Ansell, & Hedman, 2013). In addition, it was found that the size distribution of DNA molecules ranged from 50 to 500 bp, because a high processing temperature may lead to DNA fragmentation (Gryson, 2010). Thus a molecular weight of 10 KDa was selected. In Method 3, a magnetic purification method was added. The magnetic beads could specifically bind to nucleic acids and then be easily separated without any endogenous impurities (Norén et al.,
The FTIR spectra of the DNA of the MBM samples (porcine, poultry, bovine, and ovine) were presented in Fig. S1. The frequencies of the DNA bands were summarized in Table S1. Comparison of FTIR spectra of genomic DNA from different MBM samples showed characteristic spectral variations in three regions: 1800–1500 cm−1, −1 −1 1500–1250 cm , and 1250–800 cm (Banyay, Sarkar, & Gräslund, 2003; Mello & Vidal, 2012). The IR absorption bands in the region between 1800 and 1500 cm−1 were mainly assigned to the C]C, C]N, and C]O stretching vibrations (Banyay et al., 2003; Jangir, Tyagi, Mehrotra, & Kundu, 2010). The strong absorption bands at 1692 and 1660 cm−1 were essentially due to the C]O stretching vibrations of adenine and thymine, respectively (Mello & Vidal, 2012). The other strong bands at 1600 and 1530 cm−1 could be assigned to nucleobase in-plane vibrations (Froehlich, Mandeville, Weinert, Kreplak, & Tajmir-Riahi, 2010). Furthermore, it was found that the IR absorption bands in the region from 1800 to 1500 cm−1 were sensitive to the effects of base pairing and base stacking. In the second region, the prominent absorption bands at 1489 and 1420 cm−1 were due to the ring vibrations of adenine and guanine (Froehlich et al., 2010; Mello & Vidal, 2012). Additionally, the FTIR marker bands in the 1500–1250 cm−1 region may be due to the effects of the backbone conformation, sugar puckering modes, and glycosidic bond rotation (Banyay et al., 2003). Finally, the last region between 1250 and 800 cm−1 was extremely sensitive to the DNA conformation (Banyay et al., 2003; Song et al., 2014). Anti-symmetric PO2− stretching is known to be a main marker for nucleic acid backbone conformation and usually appears at approximately 1240 cm−1 in the A-form and at approximately 1225 cm−1 in the B-form (Banyay et al., 2003; Taillandier & Liquier, 1991; Whelan et al., 2011). In this work, anti-symmetric PO2− stretching appears at approximately 1232 cm−1, indicating a conformational change from B-form to A-form in the DNA sample. Moreover, IR absorption bands at 1080 and 968 cm−1 were attributed to symmetric PO2− stretching and OePeO bending vibration, respectively. Other absorption bands at 1038, 886, and 835 cm−1 were also sensitive to the sugar conformation. 528
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Fig. 2. PCA (A) and PLS-DA (B) score plots of the four types of MBMs based on FTIR spectral analysis of DNA.
sensitivity and specificity for the classification model of the porcine and poultry MBM samples were higher than 90% during the cross-validation test. The results of the external validation test showed that the correct classification rates of all the samples were 100%, indicating the clear separation of porcine, poultry, and ruminant MBM samples. A second classification step was then performed for bovine and ovine MBM samples. This classification model was established considering eight principal components. During the cross-validation test, the sensitivity and specificity of the bovine/ovine discriminant model were higher than 90%. The results of the external validation test showed that no misclassified samples were obtained. It was demonstrated that porcine, poultry, bovine and ovine MBM samples could be well discriminated by the established two-step protocol.
3.3. Discriminant analysis based on DNA spectroscopic characteristics Multivariate analysis was used to further identify the FTIR spectra of genomic DNA from different MBM samples. Initially, PCA was performed to assess the potential of FTIR spectral analysis of DNA to differentiate MBM samples. Prior to PCA analysis, KMO and Bartlett's test of sphericity were carried out. The results showed that the KMO measure was 0.97, and the P value of Bartlett's test of sphericity was 0.000 (< 0.05), indicating that the data were suitable for PCA analysis (Granato et al., 2018). A PCA score plot was created by using the first three principal components (PCs), which accounted for 78.32%, 8.12%, and 3.75% of the total variation, respectively. As presented in Fig. 2A, genomic DNA of different animal origins completely overlapped in the plot, showing that PCA was unable to classify the different types of MBM samples. Therefore, a PLS-DA score plot was constructed as a three-dimensional view based on the first, second, and fifth latent variables, and was used to achieve better discriminant grouping than that obtained with PCA (Fig. 2A and B). However, satisfactory separation was not achieved when all the species (porcine, poultry, bovine and ovine) were considered simultaneously. Therefore, a two-step protocol of discriminant analysis was developed: (i) an initial identification model of porcine, poultry, and ruminant MBM; (ii) a subsequent discrimination model of bovine and ovine MBM. For these two models, all the obtained spectra were split into a calibration set (75%) and an external validation set (25%) using the Kennard-Stone algorithm. For each model, two different validation tests were performed, namely, the leave-one-out cross validation and external validation tests. The initial classification model was calculated considering twelve principal components. As shown in Table 1, the results of the cross-validation test showed that the identification of the ruminant MBM samples was the best, with sensitivity, specificity, and classification error values of 100%, 100%, and 0%, respectively. The
3.4. The mechanism of the discriminant model The difference spectra of DNA between different MBM samples were analysed to explore the mechanism of the discriminant model. Four FTIR difference spectra of DNA from different MBM samples, including porcine and ruminant MBM; poultry and ruminant MBM, porcine and poultry MBM, and bovine and ovine MBM, were generated. As illustrated in Fig. 3, the most significant spectral variations were in a concentrated distribution in the region of 1800–1500 cm−1 in all of the difference spectra. It was inferred that the absorption bands in this region were directly associated with the base pairing, base stacking, and propeller twist of DNA, greatly contributed to the identification model (Qiu et al., 2015; Song et al., 2014). A similar result was also reported by Dina et al. (2016), who suggested that C]O stretching, C] N stretching, and NH2 deformation of DNA bases made the greatest contributions in the identification of seven different grapevine varieties. In fact, the alteration of the base structure, including base stacking and base pairing, may be due to the different base sequences of DNA from different species. It has been proven that the free energy of base stacking decreased in the following order for base sequences: purine–purine > purine–pyrimidine > pyrimidine–pyrimidine (Egli & Saenger, 2013). Different base sequences could also induce varying degrees of propeller twist (Nelson, Finch, Luisi, & Klug, 1987). Furthermore, the GC contents of different species markedly varied, which could have an influence on the base pairing associated with hydrogen bonds (Adams, 2012). Therefore, it was concluded that the difference in the FTIR spectra of DNA can be due to the DNA base sequences of the different MBM samples. To explore the contribution of each characteristic peak in the porcine/poultry/ruminant discriminant model, further data analysis of the spectral wavenumbers was performed. Twenty-five types of analysis results (No. 1-No. 25) from non-parametric Kruskal-Wallis tests were obtained, as illustrated in Fig. 4. In total, there were significant differences (p < 0.05) among the four species of MBM samples, at 337
Table 1 Results of PLS-DA discrimination based on the FT-IR spectra of genomic DNA. Class
Sensitivity(CV) Specificity(CV) Sensitivity(Val) Specificity(Val) Classification error(Val) Correct classification rate (Val)
Discrimination 1
Discrimination 2
Porcine
Poultry
Ruminant
Bovine
Ovine
MBM
MBM
MBM
MBM
MBM
91.7 92.3 100 100 0.00 100
100 92.5 100 100 0.00 100
100 100 100 100 0.00 100
92.3 100 100 100 0.00 100
93.3 92.3 100 100 0.00 100
CV refers cross-validation; Val refers external validation. 529
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Fig. 3. Four FT-IR difference spectra of DNAs from different MBM samples, including porcine and ruminant MBM (A), poultry and ruminant MBM (B), porcine and poultry MBM (C), and bovine and ovine MBM (D).
presented in the analysis results (No. 2–4, No. 8, No. 10). Specifically, there were significant differences (p < 0.05) between the ruminant MBM and the other species at 57 wavenumbers, which was a greater number than those for the porcine and poultry MBM samples. This result may explain why the discrimination values (sensitivity and specificity) of the ruminant MBM samples were higher than those of the porcine and poultry MBM samples, in the cross-validation test. A significant difference (p < 0.05) between the non-ruminant and ruminant MBM samples was observed in the absorption bands at approximately 1694 cm−1 (C]O vibration, thymine), 1656 cm−1 (C]O vibration,
sites of 520 wavenumbers, indicating that infrared intensities of 337 wavenumbers contributed to the discriminant model. Specifically, these contributing characteristic peaks had a concentrated distribution (71.97%) in the region of 1800–1500 cm−1. This result is consistent with the difference spectroscopic analysis described in Section 3.4. For the 520 spectral wavenumbers, there was no frequency that could simultaneously discriminate among the four species of MBM, explaining why the four species could not be simultaneously separated by a single model, as described in Section 3.3. However, 59 wavenumbers contributed to the porcine/ poultry/ruminant discriminant model, as
Fig. 4. Twenty-five analysis results of the significant difference (p < 0.05) among meat and bone meal (MBM, porcine, poultry, bovine and ovine) samples and the scatter plot of twenty-five analysis results (Y-axis) in the spectral region of 1800–800 cm−1 (X-axis). 530
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Fig. 5. Statistical significance of IR intensities of 520 of wavenumbers (1800–800 cm−1) of the two groups (bovine and ovine meat and bone meal), based on the t test.
thymine), 1490 cm−1 (C]N vibration, adenine and guanine), 1080 cm−1 (backbone) and 839 cm−1 (deoxyribose vibration) (No. 3 in Fig. 4). A similar phenomenon was observed in a previous work by Dina et al. (2016), who concluded that the C]O, C]N and deoxyribose stretching vibrations made the greatest contributions for differentiating seven grapevine varieties. Based on the abovementioned analysis, a decision rule was established that the sample can be a ruminant MBM, if the IR intensities of the pretreated spectra (area normalization and second derivation) at 1651 cm−1 are greater than 0: otherwise, it is a non-ruminant MBM. Applying the decision rule to all of the MBM samples (28 ruminant and 23 non-ruminant samples), 27 ruminant and 23 non-ruminant MBM samples were correctly classified. The result showed that the correct classification rate was 98%. A t-test of the difference between the bovine and ovine MBM absorbances was also performed at each spectral wavenumber, as presented in Fig. 5. Upon comparing the infrared spectral intensities of the DNA extracted from the bovine and ovine MBM samples, it was found that there were significant differences (p < 0.05) in 205 sites of 520 wavenumbers, indicating the ability to differentiate bovine MBM from ovine MBM. It was also revealed that there were significant differences (p < 0.01) in the absorption bands at 1599 cm−1 (adenine, in-plane vibration), 1531 cm−1 (cytosine, guanine, in-plane vibration), 1494 cm−1 (adenine, guanine, C]N vibration), 1050 cm−1 (CeO deoxyribose), and 881 cm−1 (deoxyribose ring vibration). It was inferred that, together, these bands influence the bovine/ovine classification model.
Declaration of Competing Interest The authors have declared no conflict of interest. Acknowledgements This research was supported by National Key R&D Program of China (2017YFE0115400), International S&T Cooperation Program of China (2015DFG32170) and China Agriculture Research System (China Agricultural Research System-36). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.05.088. References Adams, R. L. (2012). The biochemistry of the nucleic acids. New York: Springer Science & Business Media (Chapter 2). Agarwal, S., Jangir, D. K., & Mehrotra, R. (2013). Spectroscopic studies of the effects of anticancer drug mitoxantrone interaction with calf-thymus DNA. Journal of Photochemistry and Photobiology B: Biology, 120(5), 177–182. Baeten, V., Von, H. C., Garrido, A., Vancutsem, J., Michotte, R. A., & Dardenne, P. (2005). Detection of banned meat and bone meal in feedstuffs by near-infrared microscopic analysis of the dense sediment fraction. Analytical & Bioanalytical Chemistry, 382(1), 149–157. Banyay, M., Sarkar, M., & Gräslund, A. (2003). A library of IR bands of nucleic acids in solution. Biophysical Chemistry, 104(2), 477–488. Commission, (1998). Directive 98/88/EC of 13 November 1998 establishing guidelines for the microscopic identification and estimation of constituents of animal origin for the official control of feedingstuffs. Official Journal of the European Communities, L318, 45–50. De la Roza-Delgado, B., Soldado, A., MartíNezfernáNdez, A., Vicente, F., Garridovaro, A., PéRezmaríN, D., Mjdela, H., & Guerreroginel, J. E. (2007). Application of near-infrared microscopy (NIRM) for the detection of meat and bone meals in animal feeds: A tool for food and feed safety. Food Chemistry, 105(3), 1164–1170. Dina, N. E., Muntean, C. M., Leopold, N., Fălămaș, A., Halmagyi, A., & Coste, A. (2016). Structural changes induced in grapevine (Vitis vinifera L.) DNA by femtosecond IR laser pulses: A surface-enhanced Raman spectroscopic study. Nanomaterials, 6(6), 96. EC, R. (2001). No 999/2001 of the European Parliament and of the Council laying down rules for the prevention, control and eradication of certain transmissible spongiform encephalopathies. Official Journal of the European Union, L147, 1–40. EC, R. (2009). No 1069/2009 of the European Parliament and of the Council of 21 October 2009, laying down health rules as regards animal by-products and derived products not intended for human consumption and repealing Regulation (EC) No 1774/2002 (Animal by-products Regulation). Official Journal of the European Union, 14, L300. EC, R. (2013). No 51/2013 of 16 January 2013 amending Regulation (EC) No 152/2009 as regards the methods of analysis for the determination of constituents of animal origin for the official control of feed. Official Journal of the European Union, L20, 33–43. Egli, M., & Saenger, W. (2013). Principles of nucleic acid structure. New York: Springer Science & Business Media (Chapter 6).
4. Conclusions In summary, a novel and promising FTIR spectroscopic method was developed to identify MBM species based on genomic DNA characteristics. Five DNA extraction methods were compared, and the modified sodium laurate extraction method was selected for FTIR scanning. Combined with PLS-DA, the results of a two-step protocol for discriminant analysis showed that the MBM samples of porcine, poultry, and ruminant origin were successfully discriminated. The correct classification rates of the external validation test for all samples were 100%. Furthermore, important discriminatory spectral characteristics were identified and proven to be related to subtle changes in the structure and base sequences of DNA from different sources. Based on these results, this approach was demonstrated to be a reliable, rapid, and sensitive spectroscopic method for species-specific identification of MBM. In the future, efforts can be made to discriminate mixtures of MBM samples, followed by an evaluation of the efficacy of this technique in feed authenticity studies. 531
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A novel FTIR discrimination based on genomic DNA for species-specific analysis of meat and bone meal Yahong Han, Xinlei Wang, Ye Liu, Lujia Han, Zengling Yang, Xian Liu
T
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College of Engineering, China Agricultural University, Beijing 100083, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Genomic DNA FTIR Meat and bone meal PLS-DA Chemometrics
Fourier transform infrared (FTIR) spectroscopy was applied for the species-specific identification of meat and bone meal (MBM), based on genomic DNA characteristics. A total of 51 source-reliable MBM samples, including porcine, poultry, bovine, and ovine MBM, were analysed. Genomic DNA was extracted using an optimized procedure for FTIR scanning. The results showed that a series of discriminatory FTIR spectral bands were closely related to DNA characteristics of MBM. The spectral intensity difference at 1651 cm−1 was identified as a key peak for discriminating ruminant from non-ruminant MBM. Combining FTIR data with chemometrics, a two-step protocol for discriminant analysis was established. An initial identification model of porcine, poultry, and ruminant MBM and a second model of bovine and ovine MBM were established. The results obtained using two models showed that the correct classification rate was 100%. This method could assist governments in confirming the authenticity of species to ensure feed safety.
1. Introduction
MBM with a low false positive rate, but its detection efficiency is quite low and it cannot identify the species of MBM. In 2013, PCR was authorized as another official technique in view of its ability to distinguish MBM species based on the characteristics of genomic DNA. However, the PCR method has a high detection cost, a long analysis period and a high false positive rate. Therefore, a promising and innovative hypothesis was proposed in this study for the discrimination of speciesspecific MBM involving a combination of FTIR spectroscopy and genomic DNA characteristics. FTIR spectroscopy is a rapid, relatively cheap and convenient analytical method, that can reflect that DNA composition and structure (Song et al., 2014). FTIR spectroscopy can not only be used to analyse the DNA structure (Agarwal, Jangir, & Mehrotra, 2013; Kondepati et al., 2008; Stefan, Muntean, Tripon, Halmagyi, & Valimareanu, 2014; Whelan et al., 2011) but can also be used for species identification based on genomic DNA (Emura, Yamanaka, Isoda, & Watanabe, 2006). For example, Emura et al. (2006) initially suggested that FTIR spectroscopy was a powerful tool for distinguishing the japonica and the indica rice varieties based on the structural differences of their DNA. Subsequently, several researchers have successfully applied FTIR spectral analysis of DNA to differentiate sample species including five varieties of Chinese cabbages (Song et al., 2014), three varieties of Nicotiana tabacum L. (Qiu et al., 2014), and 10 varieties of Camellia
Meat and bone meal (MBM) was an important animal-origin protein material used commonly in feeding formulations. However, the feeding of ruminant MBM (contaminated with prions) to ruminants is now generally acknowledged to be responsible for the transmission of bovine spongiform encephalopathy. Therefore, the use of MBM in feedstuffs has been strictly prohibited globally since 2001 (Ec, 2001; Shinoda et al., 2008). In 2013, the European Commission partially lifted restrictions on porcine and poultry MBM in aquaculture (EC, 2013). This reintroduction of non-ruminant MBM in fish feed may be due to the significant progress in the field of analytical methodology (Baeten et al., 2005; De la Roza-Delgado et al., 2007; Fernández-Ibáñez, Fearn, Soldado, & Roza-Delgado, 2009; Mandrile, Amato, Marchis, Martra, & Rossi, 2017). However, the use of MBM in terrestrial animal feed is still prohibited. This ban can only be lifted if a sensitive and reliable species-specific method for efficient surveillance and removal of bovine MBM from the human food chain, is developed. In the European Union (EU), there are two official methods for detecting the presence of MBM, namely, light microscopy and polymerase chain reaction (PCR) (EC, 2013). The light microscopy method was the first official method established by the European Union in 1998 (Commission, 1998; EC, 2009). This method can detect the presence of
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Corresponding author at: Box 191, No. 17 Tsinghua East Road, Haidian District, Beijing 100083, China. E-mail addresses: [email protected] (Y. Han), [email protected] (X. Wang), [email protected] (Y. Liu), [email protected] (L. Han), [email protected] (Z. Yang), [email protected] (X. Liu). https://doi.org/10.1016/j.foodchem.2019.05.088 Received 2 April 2018; Received in revised form 29 March 2019; Accepted 10 May 2019 Available online 11 May 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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adjusted to the same concentration (100 ng/μL) using a nuclease-free water. The concentration and purity of the extracted DNA were measured using an ultramicro UV spectrophotometer (Nanodrop, Thermo Fisher Scientific, San Jose, USA). The purity of the DNA was evaluated using the absorbance ratio at the A260/A280 and A260/A230 ratios. Additionally, the quality of the DNA solution was analysed using a capillary gel electrophoresis-based DNA analyser (Qsep100, Bioptic, Taiwan, China) and agarose gel electrophoresis. All the genomic DNA samples were stored at −20 °C until further analysis.
reticulata Lindl. from the Chuxiong population (Qiu et al., 2015). However, to the best of our knowledge, species-specific identification of MBM based on FTIR spectroscopic analysis of DNA has not been previously reported. In the present study, a rapid, sensitive, and reliable FTIR spectroscopic method was developed for the identification of MBM samples. Five DNA extraction methods were compared for FTIR scanning, and then a high-quality infrared spectrum (IR) of genomic DNA was obtained. Combined with chemometrics, a two-step protocol for determining the origin of MBM was proposed. 2. Materials and methods
2.3. FTIR spectroscopy 2.1. Sample preparation The DNA solution (15 μL) was dried on a 96-well high throughput extension (HTS-XT) microplate at 30 °C for FTIR examination over the wavenumber range from 4000 to 400 cm−1. The spectra were recorded by 64 scans with a resolution of 4 cm−1 (Han, Han, Yao, Li, & Liu, 2018). Analysis of each sample was performed in triplicate.
A total of 51 MBM samples were prepared in this study, including 12 porcine MBM samples, 11 poultry MBM samples, 14 bovine MBM samples and 14 ovine MBM samples. Raw animal byproducts (mainly bone with a small amount of meat) were collected from different markets in Beijing. The MBM samples were prepared according to the standard procedures approved by the European Commission (European, 2011). Briefly, all the raw materials were heated (133 °C, 20 min, 300 kPa), dried, pressed, and defatted. Subsequently, the samples were ground using an ultra-centrifugal mill (ZM200, Retsch, Haan, Germany) with a mesh size of 0.5 mm and were then characterized by the realtime PCR method to ensure purity (Fumière, Dubois, Baeten, von Holst, & Berben, 2006). All the MBM samples were then stored at −20 °C for further analysis. To compare the genomic DNA extracted from the MBM samples, double-stranded DNA derived from calf thymus (calf DNA) was obtained from Sigma–Aldrich (St. Louis, MO, USA). A solution with 100 ng/μL calf DNA was prepared with nuclease-free water (Promega, Madison, USA) and then stored at 4 °C until further analysis.
2.4. Statistical analysis The FTIR spectral data in the region (1800–800 cm−1, a span of 520 wavenumbers) were analysed by MATLAB version R2012 (Mathworks, Natick, USA) and the SPSS 12.0 software package for Windows (SPSS Inc., Chicago, USA). Area normalization and second derivation were used to optimize data preprocessing. The Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity were carried out prior to the principal component analysis (PCA). PCA and partial least squares-discriminant analysis (PLS-DA) were performed with the PLS Toolbox 8.0 (Eigenvector Research, USA). Furthermore, the classification rates of the PLS-DA model were measured according to the sensitivity, specificity, classification error and the correct classification rate, which were determined as follows (Górski, Sordoń, Ciepiela, Kubiak, & Jakubowska, 2016; Mazivila et al., 2015):
2.2. DNA extraction protocols To obtain high-quality infrared spectra of genomic DNA, five DNA extraction methods were compared, which were denoted as Methods15. Method 1 involved the use of the Wizard Magnetic DNA Purification Kit (Promega, Madison, USA), according to the EURL-AP standard operating procedure (Procedure, 2013). Method 2 followed Method 1, but with further purification. Specifically, Method 2 involved purifying the DNA extracted using Method 1 with an Amicon Ultra 10 KDa (Millipore, Bedford, USA) filter device. Method 3 was similar to Method 2 and included purification using magnetic-particle technology (FineMag DNA Clean Beads; Genfine Co. Ltd., Beijing, China). The purification procedure was performed in accordance with the supplier’s instructions. Method 4 featured spin-column technology, using the TIANamp Feedstuff Animal DNA Kit (Tiangen, Beijing, China) following the supplier’s instructions. Method 5 was based on a combination of the procedures of Saitoh, Togashi, Arie, and Teraoka (2006) and Zhou (2011) with some modifications. Specifically, a sample (200 mg) was transferred to 980 μL Nlauroylsarcosine sodium salt buffer [100 mM Tris-HCl (pH 8.0), 100 mM EDTA (pH 8.0), 200 mM NaCl, 1% n-lauroylsarcosine sodium salt (pH 8.0)]. Subsequently, β-mercaptoethanol (20 μL) was added to the mixture, and incubated for 10 min at room temperature. DNA solutions were extracted with an equal volume of phenol/chloroform/ isoamyl alcohol (25:24:1) and were then extracted with an equal volume of chloroform. In addition, RNase A (1 μL, 10 mg/ml) was added, and the DNA solution was incubated in a water bath (37 °C, 30 min). An equal volume of ice-cold isoamyl alcohol was later added and mixed. The mixture was centrifuged at 13000 rpm (4 °C, 15 min) and the supernatant was discarded. Subsequently, DNA was washed twice with 1 mL of 70% ethanol, and then it was air-dried and dissolved in 50 μL nuclease-free water. The final concentration of the DNA samples was
Sensitivity =
TP × 100% TP + FN
(1)
Specificity =
TN × 100% TN + FP
(2)
Classification error =
FP + FN × 100% TP +TN + FP+FN
Correct classification rate = 1 −
FP + FN × 100% TP +TN + FP+FN
(3)
(4)
where the true positive (TP) denotes the number of correct positive results, the true negative (TN) denotes the number of correct negative results, the false positive (FP) denotes the number of false positive results, and the false negative (FN) denotes the number of false negative results. Furthermore, the mechanism of the PLS-DA model was explored by a combination of difference spectral analysis and significant differences analysis of the infrared intensities at each wavenumber. For each wavenumber, the significant differences (p < 0.05) between the infrared intensities of the MBM samples (12 porcine, 11 poultry, 14 bovine, and 14 ovine MBM samples) were determined using the nonparametric Kruskal–Wallis test with Bonferroni correction. Furthermore, the significant differences (p < 0.05) between the infrared intensities of the two groups samples (14 of bovine and 14 of ovine MBM) were determined using the t test at each wavenumber. Five hundred-twenty p values were calculated. 527
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2013; Tan & Yiap, 2009). The concentrations of DNA that were extracted using Methods 2 and 3 were 41.25 ng/μL and 20.2 ng/μL, respectively. The infrared spectra of DNA extracted using Methods 2 and 3 were presented in Fig. 1C and Fig. 1D, respectively. The band at 1674 cm−1 (Fig. 1C) was sharper than the band at 1655 cm−1 (Fig. 1D), while there were more characteristic bands in the region of 1500–1250 cm−1 (Fig. 1D). However, the bands at 966 and 833 cm−1, which were associated with the backbone of DNA, disappeared in Fig. 1C and D. Subsequently, to develop a procedure that could obtain high-quality infrared spectra of DNA, Methods 4 and 5 were investigated and compared. The concentrations of DNA extracted using Methods 4 and 5 were higher than those of DNA extracted using other methods (p < 0.05). In particular, the concentration of DNA extracted using Method 5 was the highest (680.5 ng/μL). Furthermore, compared with the infrared spectra of DNA extracted using Methods 1, 2 and 3 (Fig. 1BD), many sharp bands were observed for DNA extracted using Methods 4 and 5 (Fig. 1E-F), indicating that the purity of DNA extracted using Methods 4 and 5 was higher, possibly because Method 4 was based on the spin-column extraction technology, enabling greater specificity of binding with nucleic acids (Tan & Yiap, 2009). However, the bands in the region of 1000–800 cm−1 disappeared (Fig. 1E), while the principal infrared bands of DNA base vibration including the bands at 1661, 1607, 1241, 1094, and 966 cm−1 were obtained, as shown in Fig. 1F. Taking all the relevant factors into consideration, Method 5 was selected because of the high concentration, purity, and high-quality infrared spectra of the DNA obtained by this method.
Fig. 1. Infrared spectra of calf thymus DNA and the bovine DNA obtained by different methods (A: calf DNA, B: DNA from Method 1, C: DNA from Method 2, D: DNA from Method 3, E: DNA from Method 4, F: DNA from Method 5).
3. Results and discussion 3.1. Comparison of the DNA extraction procedures
3.2. FTIR spectroscopic characteristics of genomic DNA of MBM It is well-known that FTIR spectroscopy has several limitations, such as high requirements for sample purity and water interference (Jin, Zhang, & Martin, 2017). Therefore, high-quality DNA samples are required for FTIR analysis. To obtain high- quality infrared spectra of DNA, the FTIR spectra of the DNA standard (calf DNA) and DNA extracted using five methods were analysed, with the obtained FTIR spectra presented in Fig. 1. The concentration of genomic DNA extracted using Method 1 was 32.3 ng/μL. The A260/A280 ratios of all samples were distributed from 1.8 to 2.0, indicating that the DNA was sufficiently pure for PCR analysis. For the spectra shown in Fig. 1B, there were no other characteristic peaks of functional groups of DNA, except for the peak at 1653 cm−1. However, for the spectra shown in Fig. 1A, strong bands were observed at 1661, 1241, 1094 and 966 cm−1, which can be assigned to distinct functional groups of DNA. In addition, a PLS-DA model based on the FTIR spectral data of genomic DNA extracted from all the MBM samples using Method 1 was developed. The results of this PLS-DA model showed that porcine, poultry, bovine and ovine MBM could not be discriminated (data not shown), indicating that the DNA sample extracted using Method 1 was inadequate for FTIR spectroscopic analysis. The lack of sharp and strong bands could account for the unsatisfactory results, which may be caused by the lower concentration of the DNA sample and interference by impurities such as guanidine salt. Therefore, in Methods 2 and 3, two purification procedures were used for the genomic DNA extracted using Method 1. In Method 2, an ultrafiltration process was used; this process has been proven to remove low-molecular-weight substances, such as salt, and to increase the DNA concentration (Norén, Hedell, Ansell, & Hedman, 2013). In addition, it was found that the size distribution of DNA molecules ranged from 50 to 500 bp, because a high processing temperature may lead to DNA fragmentation (Gryson, 2010). Thus a molecular weight of 10 KDa was selected. In Method 3, a magnetic purification method was added. The magnetic beads could specifically bind to nucleic acids and then be easily separated without any endogenous impurities (Norén et al.,
The FTIR spectra of the DNA of the MBM samples (porcine, poultry, bovine, and ovine) were presented in Fig. S1. The frequencies of the DNA bands were summarized in Table S1. Comparison of FTIR spectra of genomic DNA from different MBM samples showed characteristic spectral variations in three regions: 1800–1500 cm−1, −1 −1 1500–1250 cm , and 1250–800 cm (Banyay, Sarkar, & Gräslund, 2003; Mello & Vidal, 2012). The IR absorption bands in the region between 1800 and 1500 cm−1 were mainly assigned to the C]C, C]N, and C]O stretching vibrations (Banyay et al., 2003; Jangir, Tyagi, Mehrotra, & Kundu, 2010). The strong absorption bands at 1692 and 1660 cm−1 were essentially due to the C]O stretching vibrations of adenine and thymine, respectively (Mello & Vidal, 2012). The other strong bands at 1600 and 1530 cm−1 could be assigned to nucleobase in-plane vibrations (Froehlich, Mandeville, Weinert, Kreplak, & Tajmir-Riahi, 2010). Furthermore, it was found that the IR absorption bands in the region from 1800 to 1500 cm−1 were sensitive to the effects of base pairing and base stacking. In the second region, the prominent absorption bands at 1489 and 1420 cm−1 were due to the ring vibrations of adenine and guanine (Froehlich et al., 2010; Mello & Vidal, 2012). Additionally, the FTIR marker bands in the 1500–1250 cm−1 region may be due to the effects of the backbone conformation, sugar puckering modes, and glycosidic bond rotation (Banyay et al., 2003). Finally, the last region between 1250 and 800 cm−1 was extremely sensitive to the DNA conformation (Banyay et al., 2003; Song et al., 2014). Anti-symmetric PO2− stretching is known to be a main marker for nucleic acid backbone conformation and usually appears at approximately 1240 cm−1 in the A-form and at approximately 1225 cm−1 in the B-form (Banyay et al., 2003; Taillandier & Liquier, 1991; Whelan et al., 2011). In this work, anti-symmetric PO2− stretching appears at approximately 1232 cm−1, indicating a conformational change from B-form to A-form in the DNA sample. Moreover, IR absorption bands at 1080 and 968 cm−1 were attributed to symmetric PO2− stretching and OePeO bending vibration, respectively. Other absorption bands at 1038, 886, and 835 cm−1 were also sensitive to the sugar conformation. 528
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Fig. 2. PCA (A) and PLS-DA (B) score plots of the four types of MBMs based on FTIR spectral analysis of DNA.
sensitivity and specificity for the classification model of the porcine and poultry MBM samples were higher than 90% during the cross-validation test. The results of the external validation test showed that the correct classification rates of all the samples were 100%, indicating the clear separation of porcine, poultry, and ruminant MBM samples. A second classification step was then performed for bovine and ovine MBM samples. This classification model was established considering eight principal components. During the cross-validation test, the sensitivity and specificity of the bovine/ovine discriminant model were higher than 90%. The results of the external validation test showed that no misclassified samples were obtained. It was demonstrated that porcine, poultry, bovine and ovine MBM samples could be well discriminated by the established two-step protocol.
3.3. Discriminant analysis based on DNA spectroscopic characteristics Multivariate analysis was used to further identify the FTIR spectra of genomic DNA from different MBM samples. Initially, PCA was performed to assess the potential of FTIR spectral analysis of DNA to differentiate MBM samples. Prior to PCA analysis, KMO and Bartlett's test of sphericity were carried out. The results showed that the KMO measure was 0.97, and the P value of Bartlett's test of sphericity was 0.000 (< 0.05), indicating that the data were suitable for PCA analysis (Granato et al., 2018). A PCA score plot was created by using the first three principal components (PCs), which accounted for 78.32%, 8.12%, and 3.75% of the total variation, respectively. As presented in Fig. 2A, genomic DNA of different animal origins completely overlapped in the plot, showing that PCA was unable to classify the different types of MBM samples. Therefore, a PLS-DA score plot was constructed as a three-dimensional view based on the first, second, and fifth latent variables, and was used to achieve better discriminant grouping than that obtained with PCA (Fig. 2A and B). However, satisfactory separation was not achieved when all the species (porcine, poultry, bovine and ovine) were considered simultaneously. Therefore, a two-step protocol of discriminant analysis was developed: (i) an initial identification model of porcine, poultry, and ruminant MBM; (ii) a subsequent discrimination model of bovine and ovine MBM. For these two models, all the obtained spectra were split into a calibration set (75%) and an external validation set (25%) using the Kennard-Stone algorithm. For each model, two different validation tests were performed, namely, the leave-one-out cross validation and external validation tests. The initial classification model was calculated considering twelve principal components. As shown in Table 1, the results of the cross-validation test showed that the identification of the ruminant MBM samples was the best, with sensitivity, specificity, and classification error values of 100%, 100%, and 0%, respectively. The
3.4. The mechanism of the discriminant model The difference spectra of DNA between different MBM samples were analysed to explore the mechanism of the discriminant model. Four FTIR difference spectra of DNA from different MBM samples, including porcine and ruminant MBM; poultry and ruminant MBM, porcine and poultry MBM, and bovine and ovine MBM, were generated. As illustrated in Fig. 3, the most significant spectral variations were in a concentrated distribution in the region of 1800–1500 cm−1 in all of the difference spectra. It was inferred that the absorption bands in this region were directly associated with the base pairing, base stacking, and propeller twist of DNA, greatly contributed to the identification model (Qiu et al., 2015; Song et al., 2014). A similar result was also reported by Dina et al. (2016), who suggested that C]O stretching, C] N stretching, and NH2 deformation of DNA bases made the greatest contributions in the identification of seven different grapevine varieties. In fact, the alteration of the base structure, including base stacking and base pairing, may be due to the different base sequences of DNA from different species. It has been proven that the free energy of base stacking decreased in the following order for base sequences: purine–purine > purine–pyrimidine > pyrimidine–pyrimidine (Egli & Saenger, 2013). Different base sequences could also induce varying degrees of propeller twist (Nelson, Finch, Luisi, & Klug, 1987). Furthermore, the GC contents of different species markedly varied, which could have an influence on the base pairing associated with hydrogen bonds (Adams, 2012). Therefore, it was concluded that the difference in the FTIR spectra of DNA can be due to the DNA base sequences of the different MBM samples. To explore the contribution of each characteristic peak in the porcine/poultry/ruminant discriminant model, further data analysis of the spectral wavenumbers was performed. Twenty-five types of analysis results (No. 1-No. 25) from non-parametric Kruskal-Wallis tests were obtained, as illustrated in Fig. 4. In total, there were significant differences (p < 0.05) among the four species of MBM samples, at 337
Table 1 Results of PLS-DA discrimination based on the FT-IR spectra of genomic DNA. Class
Sensitivity(CV) Specificity(CV) Sensitivity(Val) Specificity(Val) Classification error(Val) Correct classification rate (Val)
Discrimination 1
Discrimination 2
Porcine
Poultry
Ruminant
Bovine
Ovine
MBM
MBM
MBM
MBM
MBM
91.7 92.3 100 100 0.00 100
100 92.5 100 100 0.00 100
100 100 100 100 0.00 100
92.3 100 100 100 0.00 100
93.3 92.3 100 100 0.00 100
CV refers cross-validation; Val refers external validation. 529
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Fig. 3. Four FT-IR difference spectra of DNAs from different MBM samples, including porcine and ruminant MBM (A), poultry and ruminant MBM (B), porcine and poultry MBM (C), and bovine and ovine MBM (D).
presented in the analysis results (No. 2–4, No. 8, No. 10). Specifically, there were significant differences (p < 0.05) between the ruminant MBM and the other species at 57 wavenumbers, which was a greater number than those for the porcine and poultry MBM samples. This result may explain why the discrimination values (sensitivity and specificity) of the ruminant MBM samples were higher than those of the porcine and poultry MBM samples, in the cross-validation test. A significant difference (p < 0.05) between the non-ruminant and ruminant MBM samples was observed in the absorption bands at approximately 1694 cm−1 (C]O vibration, thymine), 1656 cm−1 (C]O vibration,
sites of 520 wavenumbers, indicating that infrared intensities of 337 wavenumbers contributed to the discriminant model. Specifically, these contributing characteristic peaks had a concentrated distribution (71.97%) in the region of 1800–1500 cm−1. This result is consistent with the difference spectroscopic analysis described in Section 3.4. For the 520 spectral wavenumbers, there was no frequency that could simultaneously discriminate among the four species of MBM, explaining why the four species could not be simultaneously separated by a single model, as described in Section 3.3. However, 59 wavenumbers contributed to the porcine/ poultry/ruminant discriminant model, as
Fig. 4. Twenty-five analysis results of the significant difference (p < 0.05) among meat and bone meal (MBM, porcine, poultry, bovine and ovine) samples and the scatter plot of twenty-five analysis results (Y-axis) in the spectral region of 1800–800 cm−1 (X-axis). 530
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Fig. 5. Statistical significance of IR intensities of 520 of wavenumbers (1800–800 cm−1) of the two groups (bovine and ovine meat and bone meal), based on the t test.
thymine), 1490 cm−1 (C]N vibration, adenine and guanine), 1080 cm−1 (backbone) and 839 cm−1 (deoxyribose vibration) (No. 3 in Fig. 4). A similar phenomenon was observed in a previous work by Dina et al. (2016), who concluded that the C]O, C]N and deoxyribose stretching vibrations made the greatest contributions for differentiating seven grapevine varieties. Based on the abovementioned analysis, a decision rule was established that the sample can be a ruminant MBM, if the IR intensities of the pretreated spectra (area normalization and second derivation) at 1651 cm−1 are greater than 0: otherwise, it is a non-ruminant MBM. Applying the decision rule to all of the MBM samples (28 ruminant and 23 non-ruminant samples), 27 ruminant and 23 non-ruminant MBM samples were correctly classified. The result showed that the correct classification rate was 98%. A t-test of the difference between the bovine and ovine MBM absorbances was also performed at each spectral wavenumber, as presented in Fig. 5. Upon comparing the infrared spectral intensities of the DNA extracted from the bovine and ovine MBM samples, it was found that there were significant differences (p < 0.05) in 205 sites of 520 wavenumbers, indicating the ability to differentiate bovine MBM from ovine MBM. It was also revealed that there were significant differences (p < 0.01) in the absorption bands at 1599 cm−1 (adenine, in-plane vibration), 1531 cm−1 (cytosine, guanine, in-plane vibration), 1494 cm−1 (adenine, guanine, C]N vibration), 1050 cm−1 (CeO deoxyribose), and 881 cm−1 (deoxyribose ring vibration). It was inferred that, together, these bands influence the bovine/ovine classification model.
Declaration of Competing Interest The authors have declared no conflict of interest. Acknowledgements This research was supported by National Key R&D Program of China (2017YFE0115400), International S&T Cooperation Program of China (2015DFG32170) and China Agriculture Research System (China Agricultural Research System-36). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.05.088. References Adams, R. L. (2012). The biochemistry of the nucleic acids. New York: Springer Science & Business Media (Chapter 2). Agarwal, S., Jangir, D. K., & Mehrotra, R. (2013). Spectroscopic studies of the effects of anticancer drug mitoxantrone interaction with calf-thymus DNA. Journal of Photochemistry and Photobiology B: Biology, 120(5), 177–182. Baeten, V., Von, H. C., Garrido, A., Vancutsem, J., Michotte, R. A., & Dardenne, P. (2005). Detection of banned meat and bone meal in feedstuffs by near-infrared microscopic analysis of the dense sediment fraction. Analytical & Bioanalytical Chemistry, 382(1), 149–157. Banyay, M., Sarkar, M., & Gräslund, A. (2003). A library of IR bands of nucleic acids in solution. Biophysical Chemistry, 104(2), 477–488. Commission, (1998). Directive 98/88/EC of 13 November 1998 establishing guidelines for the microscopic identification and estimation of constituents of animal origin for the official control of feedingstuffs. Official Journal of the European Communities, L318, 45–50. De la Roza-Delgado, B., Soldado, A., MartíNezfernáNdez, A., Vicente, F., Garridovaro, A., PéRezmaríN, D., Mjdela, H., & Guerreroginel, J. E. (2007). Application of near-infrared microscopy (NIRM) for the detection of meat and bone meals in animal feeds: A tool for food and feed safety. Food Chemistry, 105(3), 1164–1170. Dina, N. E., Muntean, C. M., Leopold, N., Fălămaș, A., Halmagyi, A., & Coste, A. (2016). Structural changes induced in grapevine (Vitis vinifera L.) DNA by femtosecond IR laser pulses: A surface-enhanced Raman spectroscopic study. Nanomaterials, 6(6), 96. EC, R. (2001). No 999/2001 of the European Parliament and of the Council laying down rules for the prevention, control and eradication of certain transmissible spongiform encephalopathies. Official Journal of the European Union, L147, 1–40. EC, R. (2009). No 1069/2009 of the European Parliament and of the Council of 21 October 2009, laying down health rules as regards animal by-products and derived products not intended for human consumption and repealing Regulation (EC) No 1774/2002 (Animal by-products Regulation). Official Journal of the European Union, 14, L300. EC, R. (2013). No 51/2013 of 16 January 2013 amending Regulation (EC) No 152/2009 as regards the methods of analysis for the determination of constituents of animal origin for the official control of feed. Official Journal of the European Union, L20, 33–43. Egli, M., & Saenger, W. (2013). Principles of nucleic acid structure. New York: Springer Science & Business Media (Chapter 6).
4. Conclusions In summary, a novel and promising FTIR spectroscopic method was developed to identify MBM species based on genomic DNA characteristics. Five DNA extraction methods were compared, and the modified sodium laurate extraction method was selected for FTIR scanning. Combined with PLS-DA, the results of a two-step protocol for discriminant analysis showed that the MBM samples of porcine, poultry, and ruminant origin were successfully discriminated. The correct classification rates of the external validation test for all samples were 100%. Furthermore, important discriminatory spectral characteristics were identified and proven to be related to subtle changes in the structure and base sequences of DNA from different sources. Based on these results, this approach was demonstrated to be a reliable, rapid, and sensitive spectroscopic method for species-specific identification of MBM. In the future, efforts can be made to discriminate mixtures of MBM samples, followed by an evaluation of the efficacy of this technique in feed authenticity studies. 531
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