Abstract
Wheat (Triticum aestivum L.) carries three waxy loci (Wx-A1, Wx-B1, and Wx-D1) encoding granule-bound starch synthase (GBSS) which are related to amylose synthesis. The present study was to investigate the possibility of using near-infrared (NIR) hyperspectral spectroscopy to differentiate waxy wheat and three partial waxy wheats from wild-type wheat. Nearly 267 seeds from the same harvest year were used to obtain hyperspectral imaging maps in the near-infrared range (930–2548 nm), and then the data were analyzed based on chemometric methods. The first derivative, standard normal variable (SNV) transformation, and multivariate scattering correction (MSC) were used for spectral pretreatment. Support vector machine (SVM) and partial least square discriminant analysis (PLS-DA) and backpropagation neural network (BPNN) were applied to build discrimination models for wheat line classification. The results demonstrate that the highest classification accuracy of 98.51% for the prediction set was achieved by SVM model based on raw spectral data in full NIR region, and the classification accuracy of wild-type and partial waxy wheats all reached 100%. SVM models’ prediction accuracy (98.51%) is higher than PLS-DA and BPNN models’ (75.76% and 82.10%). The above results show that NIR hyperspectral spectroscopy combined SVM model was useful in identification of waxy and partial waxy wheats from wild-type wheat.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Barbedo JGA, Tibola CS, Fernandes JMC (2015) Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst Eng 131:65–76. https://doi.org/10.1016/j.biosystemseng.2015.01.003
Barker M, Rayens W (2003) Partial least squares for discrimination. J Chemom 17:166–173. https://doi.org/10.1002/cem.785
Esbensen K, Geladi P (1989) Strategy of multivariate image analysis (MIA). Chemom Intell Lab Syst 7:67–86. https://doi.org/10.1016/0169-7439(89)80112-1
Ferreira MMC, Antunes AM, Melgo MS, Volpe PLO (1999) Chemometrics I: multivariate calibration, a tutorial. Quím Nova 22:724–731. https://doi.org/10.1590/S0100-40421999000500016
Forina M, Lanteri S, Casale M (2007) Multivariate calibration. J Chromatogr A 1158:61–93. https://doi.org/10.1080/00401706.1991.10484852
Galvao RKH, Araujo MCU, Fragoso WD, Silva EC, Jose GE, Soares SFC, Paiva HM (2008) A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemom Intell Lab Syst 92:83–91. https://doi.org/10.1016/j.chemolab.2007.12.004
García-Molina MD, García-Olmo J, Barro F (2006) Effective identification of low-gliadin wheat lines by near infrared spectroscopy (NIRS): implications for the development and analysis of foodstuffs suitable for celiac patients. PLoS One 11:e0152292. https://doi.org/10.1371/journal.pone.0152292
Graybosch RA, Hansen LE (2016) Functionality of chemically modified waxy, partial waxy and wild-type starches from common wheat. Starch Starke 68:496–504. https://doi.org/10.1002/star.201500241
Guo X, Jiang X, Zhu Y, Zhuang S (2019) Unified description on principles of fourier transform infrared spectroscopy and terahertz time-domain spectroscopy. Infrared Phys Technol 101:105–109. https://doi.org/10.1016/j.infrared.2019.06.005
Hansen LE, Jackson DS, Wehling RL, Graybosch RA (2010) Functionality of chemically modified wild-type, partial waxy and waxy starches from tetraploid wheats. J Cereal Sci 51:409–414. https://doi.org/10.1016/j.jcs.2010.02.010
Hoshino T, Ito S, Hatta K, Nakamura T, Yamamori M (1996) Development of waxy common wheat by haploid breeding. Breed Sci 46:185–188. https://doi.org/10.1270/jsbbs1951.46.185
Kim W, Johnson JW, Graybosch RA, Gaines CS (2003) Physicochemical properties and end-use quality of wheat starch as a function of waxy protein alleles. J Cereal Sci 37:195–204. https://doi.org/10.1006/jcrs.2002.0494
Kiribuchi-Otobe C, Fujita M, Matsunaka H, Sekine M (2006) Properties of cross-linked starch from waxy mutant wheat Tanikei A6599-4. Cereal Chem 83:590–594. https://doi.org/10.1094/CC-83-0590
Li C, Park SC (2009) Combination of modified BPNN algorithms and an efficient feature selection method for text categorization. Inf Manag 45:329–340. https://doi.org/10.1016/j.ipm.2008.09.004
Liu D, Sun D, Zeng X (2014a) Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol 7:307–323. https://doi.org/10.1007/s11947-013-1193-6
Liu D, Wang L, Sun D, Zeng X, Qu J, Ma J (2014b) Lychee variety discrimination by hyperspectral imaging coupled with multivariate classification. Food Anal Methods 7:1848–1857. https://doi.org/10.1007/s12161-014-9826-6
Liu D, Wu Y, Gao Z, Yun Y (2019) Comparative non-destructive classification of partial waxy wheats using near-infrared and Raman spectroscopy. Crop Pasture Sci 70:437–441. https://doi.org/10.1071/CP18499
Madden HH (1978) Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data. Anal Chem 50:1383–1386. https://doi.org/10.1021/ac50031a048
Mavroforakis ME, Theodoridis S (2006) A geometric approach to support vector machine (SVM) classification. IEEE Trans Neural Netw 17:671–682. https://doi.org/10.1109/tnn.2006.873281
Morris CF, Konzak CF (2000) Registration of D-Null ‘Bai Huo’ waxy wheat germplasm. Crop Sci 40:304–305. https://doi.org/10.2135/cropsci2000.0011rgp
Mouazen AM, Karoui R, Baerdemaeker JD, Ramon H (2006) Classification of soils into different moisture content levels based on VIS-NIR Spectra, Proceedings of the 2006 ASBE Annual International Meeting, Portland
Nakamura T, Yamamori M, Hidaka S, Hoshino T (1992) Expression of HMW Wx protein in Japanese common wheat (Triticum aestivum L.) cultivars. Jpn J Breed 42:681–685. https://doi.org/10.1270/jsbbs1951.42.681
Nakamura T, Yamamori M, Hirano H, Hidaka S, Nagamine T (1995) Production of waxy (amylose-free) wheats. Mol Gen Genomics 3:253–259. https://doi.org/10.1007/bf02191591
Nansen C, Zhao GP, Dakin N, Zhao CH, Turner SR (2015) Using hyperspectral imaging to determine germination of native Australian plant seeds. J Photochem Photobiol B 145:19–24. https://doi.org/10.1016/j.jphotobiol.2015.02.015
Saerens M, Fouss F, Yen L, Dupont P (2004) The principal components analysis of a graph, and its relationships to spectral clustering. Mach Learn:371–383
Salse J, Bolot S, Throude M, Jouffe V, Piegu B (2008) Identification and characterization of shared duplications between rice and wheat provide new insight into grass genome evolution. Plant Cell 20:11–24. https://doi.org/10.1105/tpc.107.056309
Sasaki T, Yasui T, Matsuki J (2000) Effect of amylose content on gelatinization, retrogradation, and pasting properties of starches from waxy and nonwaxy wheat and their F1 Seeds. Cereal Chem 77:58–63. https://doi.org/10.1094/CCHEM.2000.77.1.58
Wang L, Liu D, Pu H, Sun D, Gao W, Xiong Z (2015) Use of hyperspectral imaging to discriminate the variety and qality of rice. Food Anal Methods 8:515–523. https://doi.org/10.1007/s12161-014-9916-5
Yan L, Bhave M (2001) Sequences of the waxy loci of wheat: utility in analysis of waxy proteins and developing molecular markers. Biochem Genet 38:391–411. https://doi.org/10.1023/A:1026436831777
Yang X, Hong H, You Z, Cheng F (2015) Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification. Sensors 15:15578–15594. https://doi.org/10.3390/s150715578
Zhang X, Liu F, Nie P, He Y, Bao Y (2014) Rapid detection of nitrogen content and distribution in oilseed rape leaves based on hyperspectral imaging. Spectrosc Spectr Anal 34:2513–2518. https://doi.org/10.3964/j.issn.1000-0593(2014)09-2513-06
Funding
This work was supported by the National Natural Science Foundation of China (No. 31601426) and the Scientific Research Funds of Hainan University (KYQD (ZQ)-1844).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed Consent
Informed consent not applicable.
Conflict of Interest
Yixuan Wu declares that she has no conflict of interest. Yonghuan Yun declares that he has no conflict of interest. Jian Chen declares that he has no conflict of interest. Wen Chen declares that she has no conflict of interest. Dongli Liu declares that she has no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, Y., Yun, Y., Chen, J. et al. Discrimination of Waxy Wheats Using Near-Infrared Hyperspectral Spectroscopy. Food Anal. Methods 14, 1704–1713 (2021). https://doi.org/10.1007/s12161-021-02008-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-021-02008-1