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Identification of transgenic foods using NIR spectroscopy: A review
Spectrochimica Acta Part A 75 (2010) 1–7
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Review
Identification of transgenic foods using NIR spectroscopy: A review A. Alishahi a , H. Farahmand a,∗ , N. Prieto b , D. Cozzolino c a
Department of Fisheries and Environmental Science, University of Tehran, Karaj, PO BOX: 31587-77871, Iran Estación Agrícola Experimental, CSIC, Finca Marzanas, 24346 Grulleros, León, Spain c Animal Biology Division, Scottish Agricultural College, Ferguson Building, Craibstone State, Aberdeen AB21 9YA, Scotland, UK b
a r t i c l e
i n f o
Article history: Received 22 February 2009 Accepted 3 October 2009 Keywords: NIRS Food Transgenic Discrimination
a b s t r a c t The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA. © 2009 Elsevier B.V. All rights reserved.
Contents 1. 2. 3. 4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The procedure of evaluation of transgenic products by NIR spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The application of NIRS in transgenic foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Nowadays, the genetic has so many applications in various fields of sciences, being utilized by various techniques and methods; hence its growth rate is very high. Consequently, the production of transgenic products is increasing in the global market. Nevertheless, the transgenic foods are severely limited in most regions of the world, especially in Europe, unfortunately due to the most of people are frightened of consuming transgenic products. Therefore, the regulatory organizations force legal pressures to control the production of transgenic products. In this sense, there are several methods in the world market to identify transgenic products which are depicted in Table 1. As a whole, DNA methods for identification of transgenic products have sufficient confidence and its reliability in comparison with the other methods is higher [1,5]. However, these techniques are destructive, time-consuming, tedious and high cost, thus unsuitable for on-line application [1,2].
∗ Corresponding author. Tel.: +98 261 2223044; fax: +98 261 2245908. E-mail address: [email protected] (H. Farahmand). 1386-1425/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2009.10.001
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On the contrary, near infrared spectroscopy (NIRS) is a fast and no tedious technology, which has been introduced as a nondestructive method to identify genetically modified organism (GMO). Low cost, no preparation of samples and avoiding timeconsuming are the most important privileges of this technology in comparison with the antecedent procedures of the study of GMO. However, the application of NIRS technology in the genetic field and especially in transgenic foods is almost new. This is obvious due to the said technology is susceptible to the chemical bonding of organic matter molecules in foods and feeds (C–H, O–H and N–H); so its precision is confined because the NIR spectroscopy can directly recognize neither DNA structure changes nor genotypic changes in the GMO products. However, the main structural changes coming about as consequence of the changes in the DNA structure are detected by NIR spectroscopy. It is worth mentioning that the phenotypic changes are the best indicators for expressing the changes on the genotypic structure [3]. Thus, the basis of this technology is that it could identify phenotypic changes caused by genotypic changes that ultimately bring about changes on molecular bonds such as C–H, C–N and C–O. In summary, with
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Table 1 The identification methods of transgenic products.
the application of NIR spectroscopy it would be possible to evaluate the specific gene expression based on the phenotypic changes. In addition, the NIR spectra are composed of overtones and combination regions which emanate from the bonds exposed to vacillational energy changes when the samples are irradiated by NIR frequencies. As consequence, the vibration patterns are composed of stretch vibration and bent vibration [4]. Therefore, these realms could contribute to better analysis of various components in the transgenic foods as result of genotypic changes.
In order to build the said calibration models, the matrix X and the matrix Y are correlated; being the matrix X composed of chemical analysis data and the matrix Y composed of spectra data in the used wavelengths. On the one hand, it is obvious that the more complex relationship between two matrices is, the more difficult the calibration model building and precision are. On the other hand, the more complex this relationship is, the utilization of complex models such as ANN nature gives better calibration models and higher reliability, due to its non-linearity, than the models built by using the linearity multivariate analysis such as PCA and PLS-2 in the same conditions [35]. At last, after building of the calibration model, its reliability should be evaluated. For this purpose, the regression relationship between the reference data (chemical analysis) and the predicted data obtained by the calibration model is considered on basis of the determination coefficient (R2 ). It is obvious that the higher determination coefficient, the more reliable calibration model. Fig. 3 shows the determination coefficients obtained in the calibration models which were built according to the linearity (PLS) and non-linearity models (KPLS). Regarding the efficiency of various multivariate methods used to classify the transgenic and non-transgenic foods, it is worth mentioning that it is different. Thereby, Rossel et al. [36] applied various multivariate analyses for discriminating the transgenic and conventional soybeans by PLS, LWR (locally weighted regression) and ANN using NIR spectroscopy. Their results showed that the classification power of non-linearity model, such as LWR, was much better than the linearity model one, such as PLS. Locally Weighted Regression was 93% accurate to distinguish modified soybeans from unmodified ones, using a database of almost 8000 samples.
2. The procedure of evaluation of transgenic products by NIR spectroscopy
3. The application of NIRS in transgenic foods
The general trend of the transgenic foods identification by NIR spectroscopy is described in the following section. At first, the spectra of samples are obtained by NIR spectrometer. After obtaining the data, chemical analysis take place to build the calibration model for assessing the target component(s) by NIRS. This stage is very important, even it is the most important one due to the precision in this step causes that the final calibration model(s) presents high reliability [12]. Therefore, the most important limitation of NIRS is its indirect nature, being the implementation of chemical analysis (such as PCR or microscope) imperative to build the calibrations models [5]. Generally, after obtaining the spectra from spectrometer, the following steps take place: (1) pretreatment or pre-processing of the spectra, (2) building of the calibration models, (3) model transfer [6]. For each one of the above portions exist models and procedures which are as follows. In the first stage, the obtained spectra should be pre-treated. The main purpose of this stage is to remove noise and rectify the background [7]. There are various models that facilitate the next stages of the calibration models building, being the pre-processing spectra, the standard normal variates transform (SNV) and the first and second derivatives the most used [8–18]. Fig. 1 is showed the mean centered spectra and SNV of fish meal spectra. As it can be observed, the original spectra of fish meal changed when the SNV was used, so that the multiplicative interference, the change of light distance, the particle size, the curvilinearity and the additive impacts were eliminated [13,19]. In this sense, Fig. 2 shows how the first and second derivatives pre-processing model treated the original spectra of animal feeds [11,15]. Indeed, most of the calibration models for qualitative and quantitative analysis are usually made according to one of three above mentioned models [11–13,20–34].
In the best of our knowledge and research in the literature, the number of applications of NIRS to recognize transgenic foods has been few. The first application of the NIRS technology for identification and classification purposes was reported in bone and meat meals in the diets of farmed animals [57]. The results of the mentioned research showed that the efficiency of this technology to identify bone and meat meals was appropriate, being a suitable option to replace the common genetic procedures recognizing these meals. Then, other researchers applied this technique in other genetic fields and especially in the transgenic products. Hurburgh et al. [37] used NIR spectroscopy to distinguish transgenic grains. They showed that PCA method had a good performance to separate completely transgenic from non-transgenic grains. Hence, they concluded that the utilization of NIRS technology as a non-destructive method had a suitable efficiency to detect transgenic grains. On the other hand, Munck et al. [38] applied the NIRS technology to discriminate barley flour with high content of lysine amino acids from barley flour with normal content of this amino acid. They manifested that the discrepancies between these two variants of barley flour were detected using PCA clustering. Thereby, these two variants of barley flour were classified correctly in two groups according to the different spectra obtained by the spectrometer. Also, this technology was used successfully to classify different mutant endosperms of maize [39] and to detect adulteration of fish meal with meat and bone meal [40]. Later, Munck et al. [3] classified transgenic and non-transgenic barley by NIR spectroscopy and PCR. The clustering and spectra models of the transgenic and non-transgenic barley are showed in Fig. 4. These clusters consisted of four portions reflecting four different genotypic groups. As it has been diagnosed in Fig. 4, these four genotypes consisted of common genotypes, LYS3 (including four
Methods
Ease of useEquipment sensitivityDuration
Protein-based methods Western blot Difficult High ELISA Moderate High Lateral flow strip Simple High
2 days 30–90 min 10 min
DNA-based methods Southern blot Difficult Qualitative PCR Difficult Real time PCR Difficult
Moderate Very high High
6h 1.5 h 1 days
Spectroscopy NIR spectroscopy
Low
Within several seconds
Microscopy Classical microscopyDifficult
High
1 day
Chromatography HPLC and GC mass Difficult
Very high
1–2 days
Easy
ELISA: Enzyme Linked Immunosorbant Assay; PCR: poly chain reaction; NIR: near infrared spectroscopy; HPLC: high performance liquid chromatography; GC: gas chromatography.
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Fig. 1. (a) The original near infrared spectra of all 120 samples, (b) mean centered spectra, and (c) the corresponding standard normal variates transform (SNV) of mean centered spectra (Alishahi et al., unpublished data).
alleles of a, b, c, and m) in the trend of the PC1 axe, LYS5 (including two alleles of g and f) in the trend of the PC2 axe and the recessive genotypes of LYS3 and LYS5 which were intermediate of the two mentioned genotypes. Fig. 4a was showed the original spectra of genetically modified barley and in Fig. 4b was showed the four clusters of the said genotypes. These results showed that even though NIR spectroscopy could not detect the changes in both structure of DNA and genotypes, it can identify the phenotypic changes as consequence of the genotypic changes. Fig. 4c was obvious that the two genotypes of LYS3 and LYS5 were separated suitably by using NIR spectra, while LYS3a5g was intermediate of two genotypes of LYS3 and LYS5. Fig. 4d were indicated the four alleles of LYS3 (LYS3a, LYS3b, LYS3c and LYS3m) and the two alleles of LYS5 (LYS5f
and LYS5g). The main phenotypic changes caused by these alleles were recognized comfortably by NIRS. For example, the genotype of LYS3a was characterized with a low relation of protein–amide (P/A = 11.4) in comparison with maternal varieties. Probably as a consequence of a low content of horde which was brimful of amides content and this fact was distinguished correctly by NIRS [3,38]. In addition, Munck et al. [3] evaluated successfully the NIRS technology to study the effects of environment on the transgenic and non-transgenic food. It is known that environmental changes affected on the genotype structure and on the phenotypic characteristics as well. This subject is very important in the study of population genetics and environmental variance. In this way, the molecular methods based on protein and DNA have been used com-
Fig. 2. (A) the original spectra, (B) the first derivative of the original spectra, and (C) the second derivative of the original spectra [56].
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Fig. 3. The regression coefficients in the KPLS and PLS models for proximate compositions of fish meal. (A) RSQ for calcium of fish meal in the PLS model, (B) RSQ for calcium of fish meal in the KPLS model, (C) RSQ for protein of fish meal in the PLS, (D) RSQ for protein of fish meal in the KPLS (Alishahi et al., unpublished data).
monly in the studies of the effects of environmental variance on genetic and the interaction between them. However, they are timeconsuming and high cost, as in the former section it was discussed. Conversely, the easy application of NIR spectroscopy (even in the field) overcomes to those problems [1]. Also, in aquaculture, the environmental conditions have very considerable effects on the farming environment because the
aquatic organisms are poikilotherms, i.e. their body temperature is regulated by the environment. Thus, this subject causes that the variance of genotype is very important in the genetic studies. Furthermore, most of the aquaculture studies take place in the field, so that the utilization of NIR spectroscopy as a portable system could be a very useful method to consider genotypic changes, especially in the transgenic aquatic creatures in the field [41,42].
Fig. 4. Clustering and spectral models of transgenic and non-transgenic barley [3].
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Cozzolino and Murray [26] showed that NIRS technology was able to distinguish adequately the meat of various animals, being a suitable replacement of the traditional method of poly reaction chain (PCR). Certainly, the distinction of different meats by means of PCR was very efficient but this method was cost, time-consuming and needed of qualificated persons; however the using of NIRS and the building of models on basis of chemometric procedures got over the difficulties of PCR method. According to Cozzolino and Murray [26], the spectra obtained from beef, sheep, pork and chicken samples were different in the two regions of visible and near infrared. On the one hand, the discrepancy in the visible region was mainly due to respiratory pigments in meat such as hemoglobin, which produces regions of overtones, particularly in the range of short wavelengths. In addition, other researches showed that the absorption band in the meat and processed meat in the short wavelengths was mostly due to oxymioglobin absorption. On the other hand, it is well known that the main portion of muscle is constituted by water (70–85%) and the different meats contain various amounts of water. As consequence, it brought about the distinction of meats according to the different spectra from the near infrared region, which were emanated from various contents of water in meat. This is in agreement with other researches showing that the absorption bands in the near infrared region from meat samples were mostly on the account of the existence of OH overtones. Mainly including the first, second and third overtones, and to less extent due to C–H bonds [43–48,5]. The other components in meat give rising the different forms of spectra are fat and protein, which facilitate the diagnosis among various meats [15]. In conclusion, the discrepancies among animal meats are due to the changes in the structure of genes Hence, NIRS technology can discriminate successfully the genetic changes in animal meats. Rui et al. [49] segregated transgenic corns from parental varieties by NIR diffuse reflectance and back propagation (BP) algorithm. Their outcomes showed that the discrimination of transgenic corns by this technology was very useful due to it got compre-
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hensive and complementary information to distinguish transgenic corns with different genotypic structures. Also, these researches manifested that the said technology is uncontaminated and inexpensive compared with PCR and ELISA, so it is a very promising detection method for GMO foods. According to Osorio et al. [50], NIRS and PLS technique could discriminate completely (100%) carcasses from suckling lambs cultivated by ewe milk or milk replacers. They showed that the main discrepancy to classify the carcasses according to NIR spectra was C–H bonds, and to less extent the group cis CH CH existing in the region of 2140 nm. These results and the outcomes obtained by other researchers manifested that the identification of carcasses and transgenic foods could take place according to the specific realms on the NIR spectra emanated from the chemical bonds [48,51]. Yamada et al. [52] distinguished transgenic plants by using NIRS. In this research, the results showed that the application of NIR spectroscopy was a powerful and quick tool to recognize the transgenic plants. On the other hand, Chen et al. [53] identified green, black and Oolong teas by using NIR spectroscopy and presented recently this technique in the economical scale. One of the best and current researches on the context of the application of NIR spectroscopy to differentiate the transgenic food was implemented by Xie et al. [54] who considered the potential of this technology to classify the transgenic tomatoes. At first, they transferred into the tomatoes a specific gene responsible for procrastinating the maturity of tomatoes, namely LeETR2, using a bacteria-mediated (Agrobacberium tumefaciens). At last, they used various methods to evaluate the transgenic tomatoes and presented the advantages and disadvantages of the said methods. In order to differentiate the transgenic foods in the chemometric methods are usually used various procedures, being some of these methods showed in Fig. 5. It is completely obvious that the accuracy and efficiency of clustering methods in the multivariate analysis separated adequately the transgenic (pink triangles) and non-transgenic (dark square) tomatoes. In the PCA model
Fig. 5. Various methods to classify the transgenic (pink triangles) and non-transgenic tomatoes (dark square) [54]. (A) Principle component analysis (two-dimensional score plot), (B) three-dimensional score plot in the PC model, (C) discriminate analysis for clustering the transgenic (pink triangles) and non-transgenic tomatoes (dark square), and (D) partial least square distance analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
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(Fig. 5a and b), the two- and three-dimensional PC implemented that the discrimination between the transgenic and non-transgenic tomatoes took well placed by PCA method. Also in Fig. 5c was depicted that the plot of Mahalanobis distance of every sample to the two classes was diagnosed correctly by the discriminant analysis and only some few samples of non-transgenic tomatoes was misclassified. Fig. 5d showed that the PLSDA model could be used successfully to classify the GMO tomatoes with the condition of using the derivative spectra and not the raw spectra, so that the noise and background interference were eliminated by the first and second derivatives in the PLSDA. Regarding above mentioned, in this research the discrepancies among the various varieties of transgenic and non-transgenic tomatoes were recognized suitably by NIRS, and they concluded that the utilization of NIRS compared with other methods (i.e. PCR, ELISA) was much better. In addition, Esteban-Diez et al. [55] could build powerful calibration models by means of NIR spectroscopy and direct orthogonal signal correction (DOSC) pre-processing method to differentiate the coffee varieties. Also they expressed that this technology had more advantages than the antecedent methods, such as PCR and ELISA, for the identification of coffee varieties. As a whole, the results of different researches showed that NIR spectroscopy was a suitable replacement of the antecedent procedures used in the transgenic products. Furthermore, those researches showed that the precision of NIR spectroscopy to differentiate genotypic changes was very high and this point is very interesting. Therefore, it is expected that in the future this technology will be applied to evaluate mutations and genotypic structure changes instead of the antecedent methods such as PCR, ELISA and electrophoresis. 4. Conclusion Near infrared spectroscopy is a very promising technology to discriminate transgenic foods due to it allows a fast and accurate detection of transgenic foods in market and generally in the commercial scale. In addition, it is important to highlight its noncontaminating nature in comparison with the antecedent methods and with respect of environmental issues of government and global warning about the pollution of environment. Hence, the NIRS technology could be in the future an excellent replacement of the antecedent procedures, such as PCR and ELISA, to identificate mutations and genotypic structure changes. Generally NIR method based on structural and compositional changes at a larger level than could be detected in gene changes alone. Acknowledgements The authors are grateful to the head of Fisheries and Environmental Sciences, Dr. G. Rafee and the chair of the food and drug laboratory, Dr. M. Pirali. Also the authors appreciate to Dr. M. Shekarchi and Dr. S. Masoum. References [1] F.E. Ahmed, Detection of genetically modified organisms in foods, Trends in Biotechnology 20 (2002) 215–223. [2] Y. Liu, B.G. Lyon, W.R. Windham, C.E. Lyon, E.M. Savage, Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy, Poultry Science 83 (2004) 1467–1474. [3] L. Munck, B. Møller, S. Jacobsen, S. Søndergaard, Near infrared spectra indicate specific mutant endosperm genes and reveal a new mechanism for substituting starch with (1/3, 1/4)-b-glucan in barley, Journal of Cereal Science 40 (2004) 213–222. [4] G.W. Small, Chemometrics and near-infrared spectroscopy: avoiding the pitfalls, Trends in Analytical Chemistry 11 (2006) 1057–1066. [5] L.W.D. Raamsdonk, C. Holst, V. Baeten, G. Berben, A. Boix, J. Jong, New developments in the detection and identification of processed animal proteins in feeds, Animal Feed Science and Technology 133 (2007) 63–83.
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Review
Identification of transgenic foods using NIR spectroscopy: A review A. Alishahi a , H. Farahmand a,∗ , N. Prieto b , D. Cozzolino c a
Department of Fisheries and Environmental Science, University of Tehran, Karaj, PO BOX: 31587-77871, Iran Estación Agrícola Experimental, CSIC, Finca Marzanas, 24346 Grulleros, León, Spain c Animal Biology Division, Scottish Agricultural College, Ferguson Building, Craibstone State, Aberdeen AB21 9YA, Scotland, UK b
a r t i c l e
i n f o
Article history: Received 22 February 2009 Accepted 3 October 2009 Keywords: NIRS Food Transgenic Discrimination
a b s t r a c t The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA. © 2009 Elsevier B.V. All rights reserved.
Contents 1. 2. 3. 4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The procedure of evaluation of transgenic products by NIR spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The application of NIRS in transgenic foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Nowadays, the genetic has so many applications in various fields of sciences, being utilized by various techniques and methods; hence its growth rate is very high. Consequently, the production of transgenic products is increasing in the global market. Nevertheless, the transgenic foods are severely limited in most regions of the world, especially in Europe, unfortunately due to the most of people are frightened of consuming transgenic products. Therefore, the regulatory organizations force legal pressures to control the production of transgenic products. In this sense, there are several methods in the world market to identify transgenic products which are depicted in Table 1. As a whole, DNA methods for identification of transgenic products have sufficient confidence and its reliability in comparison with the other methods is higher [1,5]. However, these techniques are destructive, time-consuming, tedious and high cost, thus unsuitable for on-line application [1,2].
∗ Corresponding author. Tel.: +98 261 2223044; fax: +98 261 2245908. E-mail address: [email protected] (H. Farahmand). 1386-1425/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2009.10.001
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On the contrary, near infrared spectroscopy (NIRS) is a fast and no tedious technology, which has been introduced as a nondestructive method to identify genetically modified organism (GMO). Low cost, no preparation of samples and avoiding timeconsuming are the most important privileges of this technology in comparison with the antecedent procedures of the study of GMO. However, the application of NIRS technology in the genetic field and especially in transgenic foods is almost new. This is obvious due to the said technology is susceptible to the chemical bonding of organic matter molecules in foods and feeds (C–H, O–H and N–H); so its precision is confined because the NIR spectroscopy can directly recognize neither DNA structure changes nor genotypic changes in the GMO products. However, the main structural changes coming about as consequence of the changes in the DNA structure are detected by NIR spectroscopy. It is worth mentioning that the phenotypic changes are the best indicators for expressing the changes on the genotypic structure [3]. Thus, the basis of this technology is that it could identify phenotypic changes caused by genotypic changes that ultimately bring about changes on molecular bonds such as C–H, C–N and C–O. In summary, with
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Table 1 The identification methods of transgenic products.
the application of NIR spectroscopy it would be possible to evaluate the specific gene expression based on the phenotypic changes. In addition, the NIR spectra are composed of overtones and combination regions which emanate from the bonds exposed to vacillational energy changes when the samples are irradiated by NIR frequencies. As consequence, the vibration patterns are composed of stretch vibration and bent vibration [4]. Therefore, these realms could contribute to better analysis of various components in the transgenic foods as result of genotypic changes.
In order to build the said calibration models, the matrix X and the matrix Y are correlated; being the matrix X composed of chemical analysis data and the matrix Y composed of spectra data in the used wavelengths. On the one hand, it is obvious that the more complex relationship between two matrices is, the more difficult the calibration model building and precision are. On the other hand, the more complex this relationship is, the utilization of complex models such as ANN nature gives better calibration models and higher reliability, due to its non-linearity, than the models built by using the linearity multivariate analysis such as PCA and PLS-2 in the same conditions [35]. At last, after building of the calibration model, its reliability should be evaluated. For this purpose, the regression relationship between the reference data (chemical analysis) and the predicted data obtained by the calibration model is considered on basis of the determination coefficient (R2 ). It is obvious that the higher determination coefficient, the more reliable calibration model. Fig. 3 shows the determination coefficients obtained in the calibration models which were built according to the linearity (PLS) and non-linearity models (KPLS). Regarding the efficiency of various multivariate methods used to classify the transgenic and non-transgenic foods, it is worth mentioning that it is different. Thereby, Rossel et al. [36] applied various multivariate analyses for discriminating the transgenic and conventional soybeans by PLS, LWR (locally weighted regression) and ANN using NIR spectroscopy. Their results showed that the classification power of non-linearity model, such as LWR, was much better than the linearity model one, such as PLS. Locally Weighted Regression was 93% accurate to distinguish modified soybeans from unmodified ones, using a database of almost 8000 samples.
2. The procedure of evaluation of transgenic products by NIR spectroscopy
3. The application of NIRS in transgenic foods
The general trend of the transgenic foods identification by NIR spectroscopy is described in the following section. At first, the spectra of samples are obtained by NIR spectrometer. After obtaining the data, chemical analysis take place to build the calibration model for assessing the target component(s) by NIRS. This stage is very important, even it is the most important one due to the precision in this step causes that the final calibration model(s) presents high reliability [12]. Therefore, the most important limitation of NIRS is its indirect nature, being the implementation of chemical analysis (such as PCR or microscope) imperative to build the calibrations models [5]. Generally, after obtaining the spectra from spectrometer, the following steps take place: (1) pretreatment or pre-processing of the spectra, (2) building of the calibration models, (3) model transfer [6]. For each one of the above portions exist models and procedures which are as follows. In the first stage, the obtained spectra should be pre-treated. The main purpose of this stage is to remove noise and rectify the background [7]. There are various models that facilitate the next stages of the calibration models building, being the pre-processing spectra, the standard normal variates transform (SNV) and the first and second derivatives the most used [8–18]. Fig. 1 is showed the mean centered spectra and SNV of fish meal spectra. As it can be observed, the original spectra of fish meal changed when the SNV was used, so that the multiplicative interference, the change of light distance, the particle size, the curvilinearity and the additive impacts were eliminated [13,19]. In this sense, Fig. 2 shows how the first and second derivatives pre-processing model treated the original spectra of animal feeds [11,15]. Indeed, most of the calibration models for qualitative and quantitative analysis are usually made according to one of three above mentioned models [11–13,20–34].
In the best of our knowledge and research in the literature, the number of applications of NIRS to recognize transgenic foods has been few. The first application of the NIRS technology for identification and classification purposes was reported in bone and meat meals in the diets of farmed animals [57]. The results of the mentioned research showed that the efficiency of this technology to identify bone and meat meals was appropriate, being a suitable option to replace the common genetic procedures recognizing these meals. Then, other researchers applied this technique in other genetic fields and especially in the transgenic products. Hurburgh et al. [37] used NIR spectroscopy to distinguish transgenic grains. They showed that PCA method had a good performance to separate completely transgenic from non-transgenic grains. Hence, they concluded that the utilization of NIRS technology as a non-destructive method had a suitable efficiency to detect transgenic grains. On the other hand, Munck et al. [38] applied the NIRS technology to discriminate barley flour with high content of lysine amino acids from barley flour with normal content of this amino acid. They manifested that the discrepancies between these two variants of barley flour were detected using PCA clustering. Thereby, these two variants of barley flour were classified correctly in two groups according to the different spectra obtained by the spectrometer. Also, this technology was used successfully to classify different mutant endosperms of maize [39] and to detect adulteration of fish meal with meat and bone meal [40]. Later, Munck et al. [3] classified transgenic and non-transgenic barley by NIR spectroscopy and PCR. The clustering and spectra models of the transgenic and non-transgenic barley are showed in Fig. 4. These clusters consisted of four portions reflecting four different genotypic groups. As it has been diagnosed in Fig. 4, these four genotypes consisted of common genotypes, LYS3 (including four
Methods
Ease of useEquipment sensitivityDuration
Protein-based methods Western blot Difficult High ELISA Moderate High Lateral flow strip Simple High
2 days 30–90 min 10 min
DNA-based methods Southern blot Difficult Qualitative PCR Difficult Real time PCR Difficult
Moderate Very high High
6h 1.5 h 1 days
Spectroscopy NIR spectroscopy
Low
Within several seconds
Microscopy Classical microscopyDifficult
High
1 day
Chromatography HPLC and GC mass Difficult
Very high
1–2 days
Easy
ELISA: Enzyme Linked Immunosorbant Assay; PCR: poly chain reaction; NIR: near infrared spectroscopy; HPLC: high performance liquid chromatography; GC: gas chromatography.
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Fig. 1. (a) The original near infrared spectra of all 120 samples, (b) mean centered spectra, and (c) the corresponding standard normal variates transform (SNV) of mean centered spectra (Alishahi et al., unpublished data).
alleles of a, b, c, and m) in the trend of the PC1 axe, LYS5 (including two alleles of g and f) in the trend of the PC2 axe and the recessive genotypes of LYS3 and LYS5 which were intermediate of the two mentioned genotypes. Fig. 4a was showed the original spectra of genetically modified barley and in Fig. 4b was showed the four clusters of the said genotypes. These results showed that even though NIR spectroscopy could not detect the changes in both structure of DNA and genotypes, it can identify the phenotypic changes as consequence of the genotypic changes. Fig. 4c was obvious that the two genotypes of LYS3 and LYS5 were separated suitably by using NIR spectra, while LYS3a5g was intermediate of two genotypes of LYS3 and LYS5. Fig. 4d were indicated the four alleles of LYS3 (LYS3a, LYS3b, LYS3c and LYS3m) and the two alleles of LYS5 (LYS5f
and LYS5g). The main phenotypic changes caused by these alleles were recognized comfortably by NIRS. For example, the genotype of LYS3a was characterized with a low relation of protein–amide (P/A = 11.4) in comparison with maternal varieties. Probably as a consequence of a low content of horde which was brimful of amides content and this fact was distinguished correctly by NIRS [3,38]. In addition, Munck et al. [3] evaluated successfully the NIRS technology to study the effects of environment on the transgenic and non-transgenic food. It is known that environmental changes affected on the genotype structure and on the phenotypic characteristics as well. This subject is very important in the study of population genetics and environmental variance. In this way, the molecular methods based on protein and DNA have been used com-
Fig. 2. (A) the original spectra, (B) the first derivative of the original spectra, and (C) the second derivative of the original spectra [56].
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Fig. 3. The regression coefficients in the KPLS and PLS models for proximate compositions of fish meal. (A) RSQ for calcium of fish meal in the PLS model, (B) RSQ for calcium of fish meal in the KPLS model, (C) RSQ for protein of fish meal in the PLS, (D) RSQ for protein of fish meal in the KPLS (Alishahi et al., unpublished data).
monly in the studies of the effects of environmental variance on genetic and the interaction between them. However, they are timeconsuming and high cost, as in the former section it was discussed. Conversely, the easy application of NIR spectroscopy (even in the field) overcomes to those problems [1]. Also, in aquaculture, the environmental conditions have very considerable effects on the farming environment because the
aquatic organisms are poikilotherms, i.e. their body temperature is regulated by the environment. Thus, this subject causes that the variance of genotype is very important in the genetic studies. Furthermore, most of the aquaculture studies take place in the field, so that the utilization of NIR spectroscopy as a portable system could be a very useful method to consider genotypic changes, especially in the transgenic aquatic creatures in the field [41,42].
Fig. 4. Clustering and spectral models of transgenic and non-transgenic barley [3].
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Cozzolino and Murray [26] showed that NIRS technology was able to distinguish adequately the meat of various animals, being a suitable replacement of the traditional method of poly reaction chain (PCR). Certainly, the distinction of different meats by means of PCR was very efficient but this method was cost, time-consuming and needed of qualificated persons; however the using of NIRS and the building of models on basis of chemometric procedures got over the difficulties of PCR method. According to Cozzolino and Murray [26], the spectra obtained from beef, sheep, pork and chicken samples were different in the two regions of visible and near infrared. On the one hand, the discrepancy in the visible region was mainly due to respiratory pigments in meat such as hemoglobin, which produces regions of overtones, particularly in the range of short wavelengths. In addition, other researches showed that the absorption band in the meat and processed meat in the short wavelengths was mostly due to oxymioglobin absorption. On the other hand, it is well known that the main portion of muscle is constituted by water (70–85%) and the different meats contain various amounts of water. As consequence, it brought about the distinction of meats according to the different spectra from the near infrared region, which were emanated from various contents of water in meat. This is in agreement with other researches showing that the absorption bands in the near infrared region from meat samples were mostly on the account of the existence of OH overtones. Mainly including the first, second and third overtones, and to less extent due to C–H bonds [43–48,5]. The other components in meat give rising the different forms of spectra are fat and protein, which facilitate the diagnosis among various meats [15]. In conclusion, the discrepancies among animal meats are due to the changes in the structure of genes Hence, NIRS technology can discriminate successfully the genetic changes in animal meats. Rui et al. [49] segregated transgenic corns from parental varieties by NIR diffuse reflectance and back propagation (BP) algorithm. Their outcomes showed that the discrimination of transgenic corns by this technology was very useful due to it got compre-
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hensive and complementary information to distinguish transgenic corns with different genotypic structures. Also, these researches manifested that the said technology is uncontaminated and inexpensive compared with PCR and ELISA, so it is a very promising detection method for GMO foods. According to Osorio et al. [50], NIRS and PLS technique could discriminate completely (100%) carcasses from suckling lambs cultivated by ewe milk or milk replacers. They showed that the main discrepancy to classify the carcasses according to NIR spectra was C–H bonds, and to less extent the group cis CH CH existing in the region of 2140 nm. These results and the outcomes obtained by other researchers manifested that the identification of carcasses and transgenic foods could take place according to the specific realms on the NIR spectra emanated from the chemical bonds [48,51]. Yamada et al. [52] distinguished transgenic plants by using NIRS. In this research, the results showed that the application of NIR spectroscopy was a powerful and quick tool to recognize the transgenic plants. On the other hand, Chen et al. [53] identified green, black and Oolong teas by using NIR spectroscopy and presented recently this technique in the economical scale. One of the best and current researches on the context of the application of NIR spectroscopy to differentiate the transgenic food was implemented by Xie et al. [54] who considered the potential of this technology to classify the transgenic tomatoes. At first, they transferred into the tomatoes a specific gene responsible for procrastinating the maturity of tomatoes, namely LeETR2, using a bacteria-mediated (Agrobacberium tumefaciens). At last, they used various methods to evaluate the transgenic tomatoes and presented the advantages and disadvantages of the said methods. In order to differentiate the transgenic foods in the chemometric methods are usually used various procedures, being some of these methods showed in Fig. 5. It is completely obvious that the accuracy and efficiency of clustering methods in the multivariate analysis separated adequately the transgenic (pink triangles) and non-transgenic (dark square) tomatoes. In the PCA model
Fig. 5. Various methods to classify the transgenic (pink triangles) and non-transgenic tomatoes (dark square) [54]. (A) Principle component analysis (two-dimensional score plot), (B) three-dimensional score plot in the PC model, (C) discriminate analysis for clustering the transgenic (pink triangles) and non-transgenic tomatoes (dark square), and (D) partial least square distance analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
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(Fig. 5a and b), the two- and three-dimensional PC implemented that the discrimination between the transgenic and non-transgenic tomatoes took well placed by PCA method. Also in Fig. 5c was depicted that the plot of Mahalanobis distance of every sample to the two classes was diagnosed correctly by the discriminant analysis and only some few samples of non-transgenic tomatoes was misclassified. Fig. 5d showed that the PLSDA model could be used successfully to classify the GMO tomatoes with the condition of using the derivative spectra and not the raw spectra, so that the noise and background interference were eliminated by the first and second derivatives in the PLSDA. Regarding above mentioned, in this research the discrepancies among the various varieties of transgenic and non-transgenic tomatoes were recognized suitably by NIRS, and they concluded that the utilization of NIRS compared with other methods (i.e. PCR, ELISA) was much better. In addition, Esteban-Diez et al. [55] could build powerful calibration models by means of NIR spectroscopy and direct orthogonal signal correction (DOSC) pre-processing method to differentiate the coffee varieties. Also they expressed that this technology had more advantages than the antecedent methods, such as PCR and ELISA, for the identification of coffee varieties. As a whole, the results of different researches showed that NIR spectroscopy was a suitable replacement of the antecedent procedures used in the transgenic products. Furthermore, those researches showed that the precision of NIR spectroscopy to differentiate genotypic changes was very high and this point is very interesting. Therefore, it is expected that in the future this technology will be applied to evaluate mutations and genotypic structure changes instead of the antecedent methods such as PCR, ELISA and electrophoresis. 4. Conclusion Near infrared spectroscopy is a very promising technology to discriminate transgenic foods due to it allows a fast and accurate detection of transgenic foods in market and generally in the commercial scale. In addition, it is important to highlight its noncontaminating nature in comparison with the antecedent methods and with respect of environmental issues of government and global warning about the pollution of environment. Hence, the NIRS technology could be in the future an excellent replacement of the antecedent procedures, such as PCR and ELISA, to identificate mutations and genotypic structure changes. Generally NIR method based on structural and compositional changes at a larger level than could be detected in gene changes alone. Acknowledgements The authors are grateful to the head of Fisheries and Environmental Sciences, Dr. G. Rafee and the chair of the food and drug laboratory, Dr. M. Pirali. Also the authors appreciate to Dr. M. Shekarchi and Dr. S. Masoum. References [1] F.E. Ahmed, Detection of genetically modified organisms in foods, Trends in Biotechnology 20 (2002) 215–223. [2] Y. Liu, B.G. Lyon, W.R. Windham, C.E. Lyon, E.M. Savage, Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy, Poultry Science 83 (2004) 1467–1474. [3] L. Munck, B. Møller, S. Jacobsen, S. Søndergaard, Near infrared spectra indicate specific mutant endosperm genes and reveal a new mechanism for substituting starch with (1/3, 1/4)-b-glucan in barley, Journal of Cereal Science 40 (2004) 213–222. [4] G.W. Small, Chemometrics and near-infrared spectroscopy: avoiding the pitfalls, Trends in Analytical Chemistry 11 (2006) 1057–1066. [5] L.W.D. Raamsdonk, C. Holst, V. Baeten, G. Berben, A. Boix, J. Jong, New developments in the detection and identification of processed animal proteins in feeds, Animal Feed Science and Technology 133 (2007) 63–83.
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