Distinguishing cotton seed genotypes by means of vibrational spectroscopic methods (NIR and Raman) and chemometrics
Mayara Macedo da Mata
a, Priscila Dantas Rocha
a, Ingrid Kelly Teles de Farias
a,
Juliana Lima Brasil da Silva
a, Everaldo Paulo Medeiros:
b, Carolina Santos Silva
c,d, Simone da Silva Simões
a,⇑aGraduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil
bDepartment of Chemistry Engineering, Federal University of Pernambuco, Av. da Arquitetura, Cidade Universitária, Recife, Pernambuco, 50740-540, Brazil
cDepartment of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta
dBrazilian Agricultural Research Corporation, Embrapa Cotton, Rua Osvaldo Cruz, 1143, Bairro Centenário, Campina Grande, Paraiba, 58428-095, Brazil
h i g h l i g h t s
Spectroscopic methods to distinguish cotton seeds in fast and efficient way.
The performance of a portable Raman spectrometer was evaluated.
PLS-DA-NIR model showed high sensitivity and specificity.
PLS-DA-Raman model achieved high sensitivity and specificity values.
Both technics achieved few
classification errors (2.3% for NIR and 0.0% for Raman).
g r a p h i c a l a b s t r a c t
a r t i c l e i n f o
Article history:
Received 29 April 2021
Received in revised form 7 September 2021 Accepted 10 September 2021
Available online 14 September 2021
Keywords:
Genotypes Classification Transgenic DNA
a b s t r a c t
The use of vibrational spectroscopy, such as near infrared (NIR) and Raman, combined with multivariate analysis methods to analyze agricultural products are promising for investigating genetically modified organisms (GMO). In Brazil, cotton is grown under humid tropical conditions and is highly affected by pests and diseases, requiring the use of large amounts of phytosanitary chemicals. To avoid the use of those pesticides, genetic improvement can be carried out to produce species tolerant to herbicides, resis- tant to fungi and insects, or even to provide greater productivity and better quality. Even with these advantages, it is necessary to manage and limit the contact of transgenic species with native ones, avoid- ing possible contamination or even extinction of conventional species. The identification of the presence of GMOs is based on complex DNA-based analysis, which is usually laborious, expensive, time- consuming, destructive, and generally unavailable. In the present study, a new methodology to identify GMOs using partial least squares discriminant analysis (PLS-DA) on NIR and Raman data is proposed to distinguish conventional and transgenic cotton seed genotypes, providing classification errors for predic- tion set of 2.23% for NIR and 0.0% for Raman.
Published by Elsevier B.V.
1. Introduction
Brazil is one of the largest producers of cotton under humid tropical conditions and employs sophisticated technology in its
production systems. The main direct impact on cultivated areas in the country involves the control of pests and diseases, requiring the adoption of chemical or biological-based measures[1]. To con- trol these destructive pests, large amounts of phytosanitary chem- icals are often used, leading to a reduction in the population of beneficial organisms and driving the spread of species that are more resistant to conventional pesticides. Genetically modified
https://doi.org/10.1016/j.saa.2021.120399 1386-1425/Published by Elsevier B.V.
⇑Corresponding author.
E-mail address:simonesimoes@servidor.uepb.edu.br(S.S. Simões).
Contents lists available atScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m/ l o c a t e / s a a
tion are often laborious, expensive, time-consuming, destructive or unavailable[3].
The detection of transgenic material using vibrational spec- troscopy, such as near infrared (NIR) and Raman, has been reported in publications that feature the success of these technologies[4].
Particularly NIR spectroscopy combined with chemometric meth- ods has proven to be useful for the identification of transgenics [5,6]due to its non-destructive, fast, and relatively low-cost char- acteristics for a large number of samples[7]. Additionally, when applied to the analysis of transgenic species, it can identify molec- ular links, such as CAH, CAN, OAH, and SAH, related to changes caused by genotypic modifications carried out in the transgenic procedure [5]. Cui and co-workers[5]evaluated the influence of different pre-processing methods on NIR spectra in the perfor- mance of forecasting models to discriminate genotypes among cot- ton seeds and leaves. The results obtained demonstrated that a robust Principal Component Analysis (rPCA)[6]was able to distin- guish transgenic from non-transgenic soybean oils using NIR spec- troscopy and chemometric methods for multivariate classification.
Hao et al.[3]identified rice varieties and their transgenic charac- teristics, using near-infrared diffuse reflectance spectroscopy (NIR-DRS) combined with Principal Component Analysis (PCA), PLS-DA, and support vector machines (SVM).
Baranski and Baranska [8] applied Fourier transform Raman spectroscopy and cluster analysis to identify plant tissues from transgenic carrots and tobacco leaves. From this study, the authors concluded that the main advantage of Raman spectroscopy is the possibility of analyzing the samples in a non-destructive way. Jian- jun Liu[9]used chemometrics and Terahertz spectroscopy to iden- tify transgenic corn oil, showing that PLS-DA models can accurately discriminate transgenic from non-transgenic oils. Xu and co- authors[10]successfully employed Raman and multivariate anal- ysis to identify transgenic organisms to differentiate transgenic and conventional rice. In this context, the present work aims to develop and compare methods based on NIR and Raman spectro- scopies combined with supervised pattern recognition methods to distinguish between conventional and transgenic genotypes cot- ton seeds in a reliable, fast, simple, and inexpensive way.
2. Experimental section
2.1. Sampling and spectral acquisition
Two genotypes of cotton seeds, Gossypium L., one transgenic and one of a conventional variety, were analyzed in the present study, using a benchtop NIR spectrometer and a portable Raman device. The transgenic seeds studied in this work had been devel- oped to be tolerant to an herbicide; the transformation carried out is expressed in protein 5-enolpyruvylshikimate-3 phosphate syn- thase (EPSPS) from Agrobacterium tumefaciens CP4 (CP4 EPSPS) [11].
The collected seeds were cleaned to remove the fiber residues.
The samples were stored in a room with controlled temperature (20°C) and relative humidity (64%) for at least one hour before the spectral acquisition.Fig. 1depicts the visual aspects of both types of seeds, transgenic and conventional, illustrating the diffi- culty of distinguishing them by visual inspection. Analysis on the
lenging due the high variability and complexity of samples. There- fore, to build reliable models, 2390 cotton seeds were analyzed in a benchtop NIR spectrometer, with 1195 seeds per phenotype. The benchtop VIS/NIR spectrometer XDS Analyzer (Foss Analytical, Hogans, Sweden) was employed to acquire diffuse reflectance spectra of the samples. Each spectrum was acquired in the spectral range of 400 to 2500 nm, 32 scans and an increment of 0.5 nm.
Due to the high fluorescence in Raman profiles, 20 seeds (10 conventional and 10 genetically modified) were each sectioned in half, longitudinally for spectral acquisition (seeFig. 1). Four spectra were obtained for each seed, with two replicates for each face of the seed. Each side of the seed was represented by the average of the two spectra. The data matrix was composed of 20 spectra from each genotype variety.
The spectral measurements were acquired using the portable Raman spectrometer Metrohm Mira Ra DS, with a spectral range from 400 to 2300 cm 1, spectral resolution from 8 to 10 cm 1. The instrument has a single 785 nm (±0.5 nm) laser and laser power of approximately 100 mW and USB 2.0 interface with data transmission power supply with a USB cable.
2.2. Data analysis
Due the high number of samples able to be analyzed by NIR spectroscopy, it was possible to use the sample selection algorithm Sample set Partitioning based on joint X-y distances (SPXY)[13].
From the 2402 seeds analyzed, 1590 were selected for the training set (being 871 for conventional and 719 for transgenic classes) and 812 for prediction set (of which 331 were from conventional seeds and 481 from transgenic).
For the Raman data, the selection of these sets was made so that there was no possibility of spectra of the same seed belonging to both the training and the prediction set. Both training and predic- tion sets for Raman analysis were composed of 10 seeds of each type. The reduced number of seeds analyzed by Raman in compar- ison with NIR was due the destructive approach needed in this par- ticular case. High fluorescence profiles were acquired during spectral acquisition of the seeds
The data acquired using the benchtop NIR and Raman spec- trometer was initially analyzed and the preprocessing techniques were evaluated to attenuate typical spectral scattering, baseline shifts and other unwanted spectral characteristics unrelated to the problem of interest. A common problem in Raman scattering spectroscopy and reflectance measurements in solid materials is the scattering of radiation, causing displacement at the baseline of the spectra. This physical effect results from such things as the size and shape of the particles, fluorescence, incident wavelength, and sample refraction index[14]. To correct these effects, the pre- processing techniques normalization by the unit vector (NUV), together with baseline correction with the Weight Least Squares (WLS) algorithm were applied as standard pre-processing in all studies. Other pre-processing methods, such as standard normal variate (SNV), Savitzky-Golay smoothing filter and derivatives, were tested to verify which one would obtain the best performance for the distinction between the studied classes.
The presence of samples with anomalous behavior was verified in the data set and, when detected, these samples were excluded from the model using the residual plots to accomplish this task.
The unsupervised learning technique, PCA, was employed to explore natural trends in the dataset. Before building the classifica- tion model, the training set and prediction sets were defined.
It is important to emphasize here the seeds analyzed can only belong to one of the two types of phenotypes studied, which justi- fies the use of techniques such as partial least squares discriminant analysis (PLS-DA). All models built in the present study were devel- oped using MatLabÒ (Natick, Massachusetts, USA) environment using the software PLS toolboxÒ 8.6 (Eigenvector Research 278 Inc., Wenatchee, USA).
3. Results and discussion 3.1. Near infrared data
As depicted inFig. 1, the cotton seeds from the conventional and those from genetically modified cultivars are indistinguishable both on the outside and on the inside. It is also not possible to find
evident differences from the visual analysis of the NIR or Raman spectra (Fig. 2). After spectral acquisition, preprocessing tech- niques were employed to attenuate unwanted variations on both NIR and Raman datasets. For the NIR data, SNV and Savitzky- Golay derivatives were tested to correct additive effects present in the raw spectra (Figure S1ofsupplementary material).Fig. 2a and b show the training set NIR spectra composed by 1590 seeds after preprocessing with SNV and derivative, respectively. The best performance according to exploratory analysis was obtained from 1st order derivative (2nd order polynomial and 7-point window width).
Even though NIR band assignment is a difficult task due its broad and highly superimposed bands, it is possible to identify important spectral regions.Table 1summarizes common spectral regions present in cotton seeds with NIR profiles described else- where[15], including starch, cellulose and protein contributions (Fig. 2a). In contrast, derivative spectra (Fig. 2b) show other impor- tant features that are relevant for genotype discrimination. The spectral region around 2532–2270 nm is related to CAH Fig. 1.Cotton seeds genotypes from (A) conventional and (B) modified genetically used in NIR analysis; and (C) conventional and (D) genetically modified used for Raman analysis.
Fig. 2.(a) SNV and (b) 1st derivative (7-point window width, 2nd order polynomial) preprocessed of cotton seeds training set. Conventional (red lines) and transgenic (blue lines) with the main NIR spectral bands.
stretch + CH deformation for peptides groups; 1450 nm 2 OH stretch + deformation of water; 1483 NH (2
m
), CONH2.Fig. 2shows that there are no noticeable spectral differences between the spectral profiles of conventional and transgenic cot- ton seeds, which makes it impossible to distinguish between them without the use of multivariate approaches (chemometrics). To explore potential differences between the two genotypes and iden-
tify the presence of outliers a PCA was performed. For the NIR data, a 2-component PCA model was built explaining 82.61% of data variation and the main results, as shown inFig. 3.
The scores plot inFig. 3a does not evidence well-defined clus- ters for either of the two studied genotypes. However, it is possible to observe trends suggesting that most of conventional samples (red diamonds) are in the positive scores of PC1 and PC2, while most genetically modified samples (green) tend to be in the nega- tive scores of both PC1 and PC2.
The loadings for PC1 and PC2 are shown inFig. 3c and d. Even though the absence of clear clusters in the scores plot hampers the assignment of specific NIR regions related to each genotype, the loadings show influences of important bands. PC1 loadings shows two regions around 1390 and 1890 nm related to methyl group and OH groups, respectively. On the other hand, PC2 shows that the bands around 2051, 2264 and 2466 nm are related to the differences between the genotypes as seen in the PC2 scores. Inter- estingly, those specific bands are related tob-sheet information in Combination
1950 ACO2R C@O stretch 2nd overtone 2090 Oil OAH combination
2100 Starch OAH bend and CAO stretch combination Starch or cellulose Asym CAOAO stretch 3rd overtone 2180 Protein NAH bend 2nd overtone
Oil CAH stretch C@O stretch combination 2282 Starch CAH stretch and CH2 deformation
2344 Cellulose CAH stretch/ CH deformation and CH2bend 2nd overtone
2445 CONH2, especially due to the hydrogen bonding C@O with BH of the peptide called alpha helix structure.
Fig. 3.PCA model main results. (a) PC1PC2 scores plot; (b) Hotelling T2Q residuals plot, colored by KNN scores distances; (c) PC1 loadings plot, (d) PC2 loadings plot.
Fig. 4.ROC curves of prediction set for conventional (top left) and transgenic (bottom left) classes. Sensitivity and specificity curves and model threshold for conventional (top right) and transgenic (bottom right) classes.
Fig. 5.PLS-DA residual plot (top row) and sample prediction plot (bottom row) with training and prediction sets.
the spectra of proteins, which is related to the process of genetic manipulation.
It is important to highlight here that, although PC1 is related to water content, it is not the only component that shows important trends to differentiate the two genotypes. In fact, PC2 is also informative with regard to both cotton varieties and the spectral range that is associated to this trend is related to protein content, as previously mentioned. This can be highlighted in the histograms of the scores scatter plot shown inFigure S2, in the supplementary material, that shows slightly different distributions in both PC1 and PC2 (Supplememtary material Figure S2).
For outlier detection, the Hotelling T2 versusQ-residuals plot was evaluated (Fig. 3b) and the samples were color-coded accord-
ing to the k-nearest neighbors’ distances with blue color for smal- ler distances and yellow for larger. It is possible to observe two behaviors: regular extreme samples, which are inherent to any object population, and a few outliers with high values of Hotelling T2and Q-residuals. The reader can find further discussion on out- liers and extreme samples elsewhere [16]. The four samples beyond the 95% confidence level threshold for both Hotelling T2 and Q-residuals were evaluated and eliminated from the training dataset.
Figures S2 and S3 (Supplementary materialsection) show the probability plots for genetically modified and conventional sam- ples, which were built to check if both classes followed normal dis- tribution. Both classes showed normal behavior, with the conventional class (Figure S2) presenting a slight deviation from normality. This, however, did not hamper the use of supervised pattern recognition models that assumed normal behavior. There- fore, partial least squares discriminant analysis (PLS-DA) was employed for genotype classification.
The preprocessed NIR data were used to build a 6-latent vari- able (LV) PLS-DA model (Figure S4of Supplementary material).
Fig. 6.Variable importance in projection (VIP) scores plot for the PLS-DA model of NIR data.
Fig. 7.(a) Raw Raman spectra of all seeds, and (c) preprocessed spectral range employed in the analyses with important bands.
1443–1443 Aliphatic
1650–1680 Amide I (proteins)
1748 Esters, aldehydes, carboxylic acids and ketones
Cross-validation of random subsets was employed with 15 itera- tions to assess data variability. The model built was employed to predict the remaining 812 samples of the prediction set. The sum- mary of results obtained for the conventional class is depicted for training, cross-validation and prediction sets. Bear in mind that, since only two classes are being analyzed, the specificity value of conventional seeds class corresponds to the sensitivity of trans- genic class and vice-versa (Snconv = Sptrans and Sntrans = Spconv).
Values for sensitivity (Sn) and specificity (Sp) are similar in all steps of the model building, with a slightly better performance, although not significantly different, for the prediction set due the sample selection step. The model showed sensitivity values for the validation and prediction sets for conventional seeds equal to 0.953 and 0.978, respectively (seeTable 2).
Since sensitivity is a parameter that measures the true positive rates in classification, it tends to avoid false positives (i.e., trans- genic seeds classified as conventional). The true negative rates (specificity) for validation and prediction sets of conventional class were also high, showing values of 0.956 and 0.977, respectively. In general, the models were able to discriminate between the two modeled classes, as observed inFig. 4, which depicts the receiver
operating characteristic (ROC) curve for the prediction set. The per- centage of incorrect classification in the validation step was 4% and 2% for validation and prediction sets, respectively.
Fig. 5 shows the residuals and the samples prediction values plots for conventional (red symbols) and transgenic (green sym- bols) classes. Both training (diamond symbols) and prediction (squares) sets are depicted in the figure, where samples above the discriminant function threshold (red dashed line) are assigned to the conventional class and assigned to the transgenic class when below the threshold.
The variable importance in projection (VIP) of the scores plot depicted inFig. 6shows the main spectral regions for the discrim- ination of both cotton seed genotypes. As expected, the spectral regions identified as significant for the discrimination are consis- tent with the NIR bands associated to the protein structure as observed in PCA loading plots. These results are consistent with those founded by Rocha and co-workers[17]using hyperspectral image for distinguish conventional and GM cotton seeds.
3.2. Raman data
Fig. 7a shows the raw Raman spectral range from 400 to 2400 cm 1 for all analyzed seeds. The characteristic baseline is shifted due to the influence of the fluorescence signal, and the presence of regions with a low signal-to-noise ratio[18]. Thus, the non-linear variation in the extreme regions were removed to facilitated spectral pre-processing.Fig. 7b shows the spectral range used for the final analysis after preprocessing with normalization and WLS for baseline correction.
Fig. 8.PCA model main results for Raman data. (a) PC1PC2 scores plot; (b) PC1PC3 scores plot; (c) PC1and PC2 loadings plot, (d) PC1 and PC3 loadings plot.
Table 4
PLS-DA figures of merit for cotton seeds classification of Raman data.
Snconv(Sptrans) Spconv(Sntrans) Class. Error
Training 1.00 1.00 0
Cross-validation 0.90 0.90 0.10
Prediction 1.00 1.00 0
In general, band assignment of Raman spectral profiles can also be complex and challenging depending on the samples analyzed.
Important attributions for Raman bands for cotton seeds were gathered from literature and are listed inTable 3 [19,20].
A 3-component principal component analysis (PCA) model was built explaining 88.81% of data variation. The main results are shown in Fig. 8, which depicts the scores and loading plots for the first 3 PCs. The residual plot for the PCA model is shown in Supplementary material Figure S5, where in the Hotelling T2versus Q residuals plot, we can observe the samples color-coded accord- ing to k-nearest neighbors (KNN) scores distances. Although it is possible to see 2 samples with high values for Hotelling T2and high KNN scores distances. However, as a compromise with the reduced number of samples, all spectra were kept for further analysis.
In contrast with NIR data, the PCA built with Raman data in Fig. 8highlights the differences between the two class mainly in the PC3 scores plot (Fig. 8b). We can see that most conventional samples (red diamonds) have PC3 negative scores, while most transgenic samples (green squares) are positive. The spectral range associated to PC3 covers the interval between 1290 and 1320 cm 1, related to amide III (b-sheets) as well as the interval between 1600 and 1700 cm- 1, related to amide I. These two regions are useful bands for obtaining data on the secondary structures of proteins, which are composed of
a
-helices,b-sheets, turns and random coil structures. The b-sheet information in the spectra of proteins undergoes modification after the process of genetic manipulation.These bands could be a reliable indication of the structural hetero- geneity of proteins that are involved in the process of genetic mod- ifications. Mutations, on the other hand, alter the structure of DNA, which is composed of non-coding regions and the so-called coding regions, that is, stretches that designate information for the adjust- ment of proteins. Mutation change can result in the synthesis of a protein with altered function. Thus, the expression of the genetic Fig. 9.Predicted class for the samples of the training set and test set.
Fig. 10.Variable importance in projection (VIP) scores plot for the PLS-DA model of Raman data.
modification may be related to changes in the Raman signal of the protein. The Raman band at 1615 cm 1is related to tyrosine, or 4- hydroxyphenylalanine, that is one of the 20 amino acids that are part of proteins. This is an
a
-amino acid with a side chain formed by CH2linked to a phenol group that constitutes an important part of proteins that intervene in signal transduction processes in the cell[21].Raman spectra can vary considerably with respect to unwanted variation. This means that spectral preprocessing step must be carefully carried out. Given this, in order to choose the best prepro- cessing methods, a screening step for 24 different models was car- ried out and the choice was made based on the performance of the PLS-DA. The models results obtained with different combinations of normalization, baseline correction, derivatives and smoothing filter can be seen in Table S in thesupplementary material. The preprocessing that gave rise to the best result was built after nor- malization, smoothing (order: 2, window: 15 points), and mean centering the data. Afterwards, a final model with 4 latent vari- ables was built; its performance related to sensitivity, specificity and classification error can be seen inTable 4. Although the num- ber of samples of Raman data are reduced in comparison to NIR due experimental limitations, a classification error of 0% was achieved for prediction set.
Fig. 9shows the predicted values of samples of the training (di- amonds) and test (squares) sets. The red samples represent the conventional class, while the green ones represent transgenic.
The dashed blue line stands for the 95% of confidence level, and the dashed red line represents the discrimination threshold between the two classes.
The VIP scores and the selectivity ratio (Fig. 10), indicate which important spectral regions make a clear distinction between the two classes. The wavelengths marked with an asterisk represent spectral regions related to vibrations of functional amino acid groups with the participation of tyrosine (1620 cm 1) and amide I (1650–1690 cm 1)[19].
4. Conclusions
The NIR and Raman methodologies developed in this work are very promising for discriminating between conventional and transgenic cotton seeds. Both achieved adequate values of sensitiv- ity and specificity and few classification errors (2.3% for NIR and 0.0% for Raman). The methodology developed using the benchtop NIR spectrometer is non-destructive and fast, enabling the analysis of several seed samples at once. However, this may present a lim- itation regarding the identification of individual seeds, which is overcome by the proposed method using the portable Raman spec- trometer. Although defined as a destructive technique for the pre- sent study, it was demonstrated that this method can provide individual information about the seeds as well as useful for field analysis. The results obtained for the two techniques were similar and proved their applicability for the classification of conventional and genetically modified cotton seed quickly and efficiently.
CRediT authorship contribution statement
Mayara Macedo da Mata:Data curation, Methodology, Investi- gation.Priscila Dantas Rocha:Data curation, Methodology, Inves- tigation.Ingrid Kelly Teles de Farias:Data curation.Juliana Lima Brasil da Silva:Data curation.Everaldo Paulo Medeiros::Concep- tualization, Writing – review & editing.Carolina Santos Silva:Val- idation, Conceptualization, Writing – original draft. Simone da Silva Simões: Conceptualization, Validation, Writing – original draft, Funding acquisition, Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing finan- cial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to thank the Brazilian Embrapa (SEG 20.20.00.120.00.00 and 30.19.00.135.00.00), Brazilian agencies CNPq, CAPES and FACEPE (BFP-0800-1.06/17) for scholarships sup- port for this work. PROPESQ/UEPB (1.06.04.00-6-398/2017-1) and NUQAAPE FACEPE (APQ-0346-1.06/14) for the funds granted for the research.
The English text of this paper has been revised by Sidney Pratt, Canadian, MAT (The Johns Hopkins University), RSAdip - TESL (Cambridge University).
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1016/j.saa.2021.120399.
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