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Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods

  • A. J. Steidle Neto (a1), D. C. Lopes (a1), J. V. Toledo (a2), S. Zolnier (a2) and T. G. F. Silva (a3)...

Abstract

The use of fast and non-destructive techniques for identifying sugarcane varieties enables the development of automatic sorting systems, contributing towards improving pre-processing steps in the alcohol and sugar industries. In this context, principal component analysis (PCA), factorial discriminant analysis (FDA), stepwise forward discriminant analysis (SFDA) and partial least-squares discriminant analysis (PLS-DA) were used to classify four Brazilian sugarcane varieties based on visible/near infrared (Vis/NIR) spectral reflectance measurements (450–1000 nm range) of stalks. All wavelengths contributed towards discriminating the sugarcane varieties, but the 600–750 nm range was most relevant. When evaluating PCA results considering the four sugarcane varieties, two of them overlapped and it was only possible to use classifiers of three varieties. Factorial discriminant analysis, PLS-DA and SFDA reached correct classifications of 0.81, 0.82 and 0.74, respectively, when considering the external validation data and the four sugarcane varieties evaluated. Results showed that Vis/NIR spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast sugarcane variety classification, which can be used in the agro-food industry or directly in the field.

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Corresponding author

Author for correspondence: D. C. Lopes, E-mail: danielalopes@ufsj.edu.br

References

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Ballabio, D and Consonni, V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods 5, 37903798.
Ballabio, D and Todeschini, R (2009) Multivariate classification for qualitative analysis. In Sun, DW (ed.) Infrared Spectroscopy for Food Quality Analysis and Control. New York: Elsevier, pp. 83104.
Berrueta, LA, Alonso-Salces, RM and Héberger, K (2007) Supervised pattern recognition in food analysis. Journal of Chromatography A 1158, 196214.
Bertrand, D, Courcoux, P, Autran, JC, Meritan, R and Robert, P (1990) Stepwise canonical discriminant analysis of continuous digitalized signals: application to chromatograms of wheat proteins. Journal of Chemometrics 4, 413427.
Bourennane, H, Couturier, A, Pasquier, C, Chartin, C, Hinschberger, F, Macaire, JJ and Salvador-Blanes, S (2014) Comparative performance of classification algorithms for the development of models of spatial distribution of landscape structures. Geoderma 219–220, 136144.
Bro, R and Smilde, AK (2014) Principal component analysis. Analytical Methods 6, 28122831.
Cheavegatti-Gianotto, A, de Abreu, HMC, Arruda, P, Bespalhok Filho, JC, Burnquist, WL, Creste, S, di Ciero, L, Ferro, JA, Figueira, AVO, Filgueiras, TS, Grossi-de-Sá, MF, Guzzo, EC and Hoffman, HP (2011) Sugarcane (Saccharum x officinarum): a reference study for the regulation of genetically modified cultivars in Brazil. Tropical Plant Biology 4, 6289.
Cozzolino, D, Cynkar, WU, Shah, N and Smith, P (2011) Multivariate data analysis applied to spectroscopy: potential application to juice and fruit quality. Food Research International 44, 18881896.
de Carvalho, LC, de Morais, CDLM, de Lima, KMG, Júnior, LCC, Nascimento, PAM, de Faria, JB and de Almeida Teixeira, GH (2016) Determination of the geographical origin and ethanol content of Brazilian sugarcane spirit using near-infrared spectroscopy coupled with discriminant analysis. Analytical Methods 8, 56585666.
Devaux, MF, Bertrand, D, Robert, P and Qannari, M (1988) Application of multidimensional analyses to the extraction of discriminant spectral patterns from NIR spectra. Applied Spectroscopy 42, 10151019.
dos Santos, JM, Duarte Filho, LSC, Soriano, ML, da Silva, PP, Nascimento, VX, Barbosa, GVS, Todaro, AR, Ramalho Neto, CE and Almeida, C (2012) Genetic diversity of the main progenitors of sugarcane from the RIDESA germplasm bank using SSR markers. Industrial Crops and Products 40, 145150.
Everingham, YL, Lowe, KH, Donald, DA, Coomans, DH and Markley, J (2007) Advanced satellite imagery to classify sugarcane crop characteristics. Agronomy for Sustainable Development 27, 111117.
Fortes, C and Demattê, JAM (2006) Discrimination of sugarcane varieties using Landsat 7 ETM+ spectral data. International Journal of Remote Sensing 27, 13951412.
Galvão, LS, Formaggio, AR and Tisot, DA (2005) Discrimination of sugarcane varieties in southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment 94, 523534.
Giambanelli, E, Ferioli, F, Koçaoglu, B, Jorjadze, M, Alexieva, I, Darbinyan, N and D'Antuono, LF (2014) A comparative study of bioactive compounds in primitive wheat populations from Italy, Turkey, Georgia, Bulgaria and Armenia. Journal of the Science of Food and Agriculture 93, 34903501.
Gitelson, AA and Merzlyak, MN (2004) Non-destructive assessment of chlorophyll, carotenoid and anthocyanin content in higher plant leaves: principles and algorithms. In Stamatiadis, S, Lynch, JM and Schepers, JS (eds), Remote Sensing for Agriculture and the Environment. Larissa, Greece: Ella, pp. 7894.
Johnson, RM, Viator, RP, Veremis, JC, Richard, EPR Jr and Zimba, PV (2008) Discrimination of sugarcane varieties with pigment profiles and high resolution, hyperspectral leaf reflectance data. Journal of the American Society of Sugar Cane Technologists 28, 6375.
Karoui, R, Dufour, E and De Baerdemaeker, J (2007) Front face fluorescence spectroscopy coupled with chemometric tools for monitoring the oxidation of semi-hard cheeses throughout ripening. Food Chemistry 101, 13051314.
Kottek, M, Grieser, J, Beck, C, Rudolf, B and Rubel, F (2006) World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15, 259263.
Kramer, R (1998) Chemometric Techniques for Quantitative Analysis. New York, USA: CRC Press.
Li, X and He, Y (2008) Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks. Biosystems Engineering 99, 313321.
Martens, H and Naes, T (1992) Multivariate Calibration. New York, USA: John Wiley & Sons.
Martínez-Pinilla, O, Guadalupe, Z, Ayestarán, B, Pérez-Magarino, S and Ortega-Heras, M (2013) Characterization of volatile compounds and olfactory profile of red minority varietal wines from La Rioja. Journal of the Science of Food and Agriculture 93, 37203729.
Martini, DZ, de Aragão, LEOC, Sanches, ID, Galdos, MV, da Silva, CRU and Dalla-Nora, EL (2018) Land availability for sugarcane derived jet-biofuels in São Paulo – Brazil. Land Use Policy 70, 256262.
Misaki, M, Kim, Y, Bandettini, PA and Kriegeskorte, N (2010) Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage 53, 103118.
Monakhova, YB, Godelmann, R, Hermann, A, Kuballa, T, Cannet, C, Schäfer, H, Spraul, M and Rutledge, DN (2014) Synergistic effect of the simultaneous chemometric analysis of 1H NMR spectroscopic and stable isotope (SNIF-NMR, 18O, 13C) data: application to wine analysis. Analytica Chimica Acta 833, 2939.
Moscetti, R, Haff, RP, Stella, E, Contini, M, Monarca, D, Cecchini, M and Massantini, R (2015) Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae. Postharvest Biology and Technology 99, 5862.
Pholpho, T, Pathaveerat, S and Sirisomboon, P (2011) Classification of longan fruit bruising using visible spectroscopy. Journal of Food Engineering 104, 169172.
RIDESA (2010) Rede Interuniversitária para Desenvolvimento do Setor Sucroalcooleiro. Catálogo Nacional de Variedades ‘RB’ de Cana-de-açúcar. Curitiba, Brazil: Rede Interuniversitária para o Desenvolvimento do Setor Sucroalcooleiro. Available at http://www.canaufv.com.br/catalogo/catalogo-2010.pdf (Accessed 14 June 2018).
Santiago, TR, Pereira, VM, de Souza, WR, Steindorff, AS, Cunha, BADB, Gaspar, M, Fávaro, LCL, Formighieri, EF, Kobayashi, AK and Molinari, HBC (2018) Genome-wide identification, characterization and expression profile analysis of expansins gene family in sugarcane (Saccharum spp.). PLoS ONE 13, e0191081.
Saporta, G (2006) Probabilités, Analyse des Données et Statistique. Paris, France: Editions Technip.
Savitzky, A and Golay, MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36, 16271639.
Serranti, S, Cesare, D, Marini, F and Bonifazi, G (2013) Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta 103, 276284.
Silva, ALBO, Pires, RCM, Ribeiro, RV, Machado, EC, Blain, GC and Ohashi, AYP (2016) Development, yield and quality attributes of sugarcane cultivars fertigated by subsurface drip irrigation. Revista Brasileira de Engenharia Agrícola e Ambiental 20, 525532.
Steidle Neto, AJ, Toledo, JV, Zolnier, S, Lopes, DC, Pires, CV and da Silva, TGF (2017) Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance. Biosystems Engineering 156, 1726.
Su, WH, He, HJ and Sun, DW (2017) Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review. Critical Reviews in Food Science and Nutrition 57, 10391051.
Verma, AK, Garg, PK and Prasad, KSH (2017) Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach. Arabian Journal of Geosciences 10, 16.
Wagih, ME, Musa, Y and Ala, A (2004) Fundamental botanical and agronomical characterisation of sugarcane cultivars for clonal identification and monitoring genetic variations. Sugar Tech 6, 127140.
Wanitchang, P, Terdwongworakul, A, Wanitchang, J and Nakawajana, N (2011) Non-destructive maturity classification of mango based on physical, mechanical and optical properties. Journal of Food Engineering 105, 477484.
Yuan, L, Huang, Y, Loraamm, RW, Nie, C, Wang, J and Zhang, J (2014) Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crops Research 156, 199207.
Zhou, Z, Huang, J, Wang, J, Zhang, K, Kuang, Z, Zhong, S and Song, X (2015). Object-oriented classification of sugarcane using time-series middle-resolution Remote Sensing data based on adaboost. PLoS ONE 10, e0142069.

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