Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging

A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

[1]  Wei Wang,et al.  Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B 1 contaminated maize kernels , 2018 .

[2]  S. S. Chen,et al.  PREDICTION OF MOISTURE CONTENT IN CORN LEAVES BASED ON HYPERSPECTRAL IMAGING AND CHEMOMETRIC ANALYSIS , 2015 .

[3]  G. Fox,et al.  Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.) , 2017 .

[4]  Kurt C. Lawrence,et al.  Utilisation of visible/near-infrared hyperspectral images to classify aflatoxin B1 contaminated maize kernels , 2018 .

[5]  Anderson Rodrigo da Silva,et al.  Temperature and seed moisture content affect electrical conductivity test in pea seeds. , 2017 .

[6]  B. A. Weinstock,et al.  Prediction of Oil and Oleic Acid Concentrations in Individual Corn (Zea Mays L.) Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Multivariate Analysis , 2006, Applied spectroscopy.

[7]  Song Xiaoyan,et al.  Monitoring and evaluation in freeze stress of winter wheat (Triticum aestivum L.) through canopy hyperspectrum reflectance and multiple statistical analysis , 2018 .

[8]  Huang Min,et al.  MODEL UPDATING OF HYPERSPECTRAL IMAGING DATA FOR VARIETY DISCRIMINATION OF MAIZE SEEDS HARVESTED IN DIFFERENT YEARS BY CLUSTERING ALGORITHM , 2016 .

[9]  Da-Wen Sun,et al.  Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds , 2015, Food Analytical Methods.

[10]  Min Huang,et al.  Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel , 2015 .

[11]  Fang Cheng,et al.  Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification , 2015, Sensors.

[12]  G Bonifazi,et al.  Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. , 2010, International journal of food microbiology.

[13]  Xin Zhao,et al.  Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis , 2017 .

[14]  Seung-Chul Yoon,et al.  Detection of aflatoxin B 1 (AFB 1 ) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging , 2017 .

[15]  Marena Manley,et al.  Classification of Maize Kernel Hardness Using near Infrared Hyperspectral Imaging , 2012 .

[16]  Giyoung Kim,et al.  Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging , 2014, Sensors.

[17]  Huazhong Lü,et al.  Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis , 2018, Sensors.

[18]  I. Kavdir,et al.  Analysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods , 2016 .

[19]  Sergios Theodoridis,et al.  A geometric approach to Support Vector Machine (SVM) classification , 2006, IEEE Transactions on Neural Networks.

[20]  Paul J. Williams,et al.  Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis , 2012 .

[21]  Kang Tu,et al.  Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches , 2017 .

[22]  Huazhou Chen,et al.  The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models , 2013 .

[23]  Jihua Wang,et al.  Diagnosis of freezing stress in wheat seedlings using hyperspectral imaging , 2012 .

[24]  Yong He,et al.  Application of hyperspectral imaging and chemometrics for variety classification of maize seeds , 2018, RSC advances.

[25]  Mary-Grace C. Danao,et al.  Analysis and prediction of unreacted starch content in corn using FT-NIR spectroscopy. , 2013 .

[26]  Min Huang,et al.  Classification of maize seeds of different years based on hyperspectral imaging and model updating , 2016, Comput. Electron. Agric..

[27]  T. C. Pearson,et al.  Detection of Fungus-Infected Corn Kernels using Near-Infrared Reflectance Spectroscopy and Color Imaging , 2011 .

[28]  R. D. Vieira,et al.  Electrical conductivity testing of corn seeds as influenced by temperature and period of storage , 2006 .

[29]  Yong He,et al.  Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds , 2012, Sensors.

[30]  S. Wold,et al.  Partial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study , 1987 .

[31]  Da-Wen Sun,et al.  Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry , 2014, Food and Bioprocess Technology.

[32]  Min Huang,et al.  Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method , 2017, Comput. Electron. Agric..

[33]  Chu Zhang,et al.  Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis , 2017, Sensors.

[34]  Paul J. Williams,et al.  Classification of maize kernels using NIR hyperspectral imaging. , 2016, Food chemistry.

[35]  Xiaolin Qin,et al.  Rapid Quantitative Analysis of Corn Starch Adulteration in Konjac Glucomannan by Chemometrics-Assisted FT-NIR Spectroscopy , 2015, Food Analytical Methods.

[36]  Deepak Bhatnagar,et al.  Integration of Fluorescence and Reflectance Visible Near-Infrared (VNIR) Hyperspectral Images for Detection of Aflatoxins in Corn Kernels , 2016 .

[37]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[38]  Haibo Yao,et al.  Hyperspectral Image Classification and Development of Fluorescence Index for Single Corn Kernels Infected with Aspergillus flavus , 2013 .

[39]  D. Massart,et al.  The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. , 1999, Journal of Pharmaceutical and Biomedical Analysis.

[40]  G. Fitzgerald,et al.  In-field methods for rapid detection of frost damage in Australian dryland wheat during the reproductive and grain-filling phase , 2017, Crop and Pasture Science.

[41]  M. Kim,et al.  Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics , 2018 .

[42]  Lalit Mohan Kandpal,et al.  High speed measurement of corn seed viability using hyperspectral imaging , 2016 .

[43]  C. Levasseur-Garcia,et al.  Assessing Risk of Fumonisin Contamination in Maize Using Near-Infrared Spectroscopy , 2015 .

[44]  Haikuan Feng,et al.  Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis. , 2016, Plant physiology and biochemistry : PPB.

[45]  Jordi-Roger Riba Ruiz,et al.  Comparative Study of Multivariate Methods to Identify Paper Finishes Using Infrared Spectroscopy , 2012, IEEE Transactions on Instrumentation and Measurement.

[46]  Emanuela Gobbi,et al.  Prediction of milled maize fumonisin contamination by multispectral image analysis. , 2010 .

[47]  Paul J. Williams,et al.  Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. , 2009, Analytica chimica acta.

[48]  D. M. Tekrony,et al.  Handbook Of Vigour Test Methods , 2004 .