Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

Hyperspectral images in the spectral range of 874–1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.

[1]  K. Shadan,et al.  Available online: , 2012 .

[2]  Yali Zhang,et al.  Phenolic compounds and antioxidant properties of different grape cultivars grown in China , 2010 .

[3]  B. Aliakbarian,et al.  Extraction of phenolics from Vitis vinifera wastes using non-conventional techniques , 2010 .

[4]  J. Hernández-Hierro,et al.  A novel method for evaluating flavanols in grape seeds by near infrared hyperspectral imaging. , 2014, Talanta.

[5]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[6]  Cheng Zhong,et al.  Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods. , 2014, Food chemistry.

[7]  J. Lachman,et al.  Towards complex utilisation of winemaking residues: Characterisation of grape seeds by total phenols, tocols and essential elements content as a by-product of winemaking , 2013 .

[8]  Santosh Lohumi,et al.  Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds , 2016 .

[9]  Ponnadurai Ramasami,et al.  Chemical and near-infrared determination of moisture, fat and protein in tuna fishes , 2007 .

[10]  Ludovic Duponchel,et al.  Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. , 2014, Food chemistry.

[11]  Chu Zhang,et al.  Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine , 2016 .

[12]  R. Peces,et al.  Phenolic compounds in skins and seeds of ten grape Vitis vinifera varieties grown in a warm climate , 2006 .

[13]  Douglas Fernandes Barbin,et al.  Grape seed characterization by NIR hyperspectral imaging , 2013 .

[14]  H. Byrne,et al.  Study of phenolic extractability in grape seeds by means of ATR-FTIR and Raman spectroscopy. , 2017, Food chemistry.

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

[16]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans , 2016, Food and Bioprocess Technology.

[17]  Da-Wen Sun,et al.  Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion , 2016, Comput. Electron. Agric..

[18]  J. S. Ribeiro,et al.  Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. , 2011, Talanta.

[19]  Yong He,et al.  Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. , 2018, Food chemistry.

[20]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[21]  M. Kim,et al.  Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging. , 2016, Journal of the science of food and agriculture.

[22]  M. Chevalier,et al.  Anatomical, histological, and histochemical changes in grape seeds from Vitis vinifera L. cv Cabernet franc during fruit development. , 2006, Journal of agricultural and food chemistry.

[23]  Fengle Zhu,et al.  Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds , 2013 .

[24]  M. I. Dias,et al.  Grape pomace as a source of phenolic compounds and diverse bioactive properties. , 2018, Food chemistry.

[25]  Chu Zhang,et al.  Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems , 2018, Sensors.

[26]  O. Grillo,et al.  Morphological characterisation of Vitis vinifera L. seeds by image analysis and comparison with archaeological remains , 2013, Vegetation History and Archaeobotany.

[27]  Á. Peña-Neira,et al.  Comparative study of the phenolic composition of seeds and skins from Carménère and Cabernet Sauvignon grape varieties (Vitis vinifera L.) during ripening. , 2010, Journal of agricultural and food chemistry.

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

[29]  Silvia Serranti,et al.  Hyperspectral Imaging for Process and Quality Control in Recycling Plants of Polyolefin Flakes , 2012 .

[30]  Identification of seeds based on molecular markers and secondary metabolites in Senna obtusifolia and Senna occidentalis , 2017, Botanical Studies.