Application of Multispectral Imaging to Determine Quality Attributes and Ripeness Stage in Strawberry Fruit

Multispectral imaging with 19 wavelengths in the range of 405–970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  B. R. MacKay,et al.  Applications of Canonical Discriminant Analysis in Horticultural Research , 1994 .

[3]  Ah Chung Tsoi,et al.  Lessons in Neural Network Training: Overfitting May be Harder than Expected , 1997, AAAI/IAAI.

[4]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[5]  M. S. Kim,et al.  MULTISPECTRAL DETECTION OF FECAL CONTAMINATION ON APPLES BASED ON HYPERSPECTRAL IMAGERY: PART I. APPLICATION OF VISIBLE AND NEAR–INFRARED REFLECTANCE IMAGING , 2002 .

[6]  H. Ito.  Potential of near infrared spectroscopy for nondestructive estimation of degrees Brix in strawberries , 2002 .

[7]  Franci Stampar,et al.  The composition of fruit of different strawberry varieties depending on maturity stage , 2003 .

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  Jasper G. Tallada,et al.  Predicting Maturity Quality Parameters of Strawberries Using Hyperspectral Imaging , 2004 .

[10]  R. Lu.  Multispectral imaging for predicting firmness and soluble solids content of apple fruit , 2004 .

[11]  David Heber,et al.  Identification of phenolic compounds in strawberries by liquid chromatography electrospray ionization mass spectroscopy , 2005 .

[12]  Yankun Peng,et al.  Improving apple fruit firmness predictions by effective correction of multispectral scattering images , 2006 .

[13]  S. Shouche,et al.  Potential of Artificial Neural Networks in Varietal Identification using Morphometry of Wheat Grains , 2006 .

[14]  Jasper G. Tallada,et al.  Non-Destructive Estimation of Firmness of Strawberries (Fragaria*ananassa Duch.) Using NIR Hyperspectral Imaging , 2006 .

[15]  David Delgado-Gómez,et al.  Precise acquisition and unsupervised segmentation of multi-spectral images , 2007, Comput. Vis. Image Underst..

[16]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[17]  M. Ngadi,et al.  Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .

[18]  P Jancsók,et al.  Stem-end/Calyx Identification on Apples using Contour Analysis in Multispectral Images , 2007 .

[19]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[20]  Yankun Peng,et al.  Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images , 2007 .

[21]  Yong He,et al.  Nondestructive Measurement of Acidity of Strawberry Using Vis/NIR Spectroscopy , 2008 .

[22]  Motoyasu Natsuga,et al.  Non-destructive analysis of soluble sugar components in strawberry fruits using near-infrared spectroscopy. , 2009 .

[23]  M. Ruiz-Altisent,et al.  Multispectral images of peach related to firmness and maturity at harvest , 2009 .

[24]  L. Duponchel,et al.  Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation , 2009 .

[25]  R. Lu,et al.  Technologies for Nondestructive Quality Evaluation of Fruits and Vegetables , 2010 .

[26]  J. Blasco,et al.  Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.

[27]  Lourdes Lleó,et al.  A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices , 2011 .

[28]  Stina Frosch,et al.  Multispectral Imaging for Determination of Astaxanthin Concentration in Salmonids , 2011, PloS one.

[29]  Jens Michael Carstensen,et al.  Using Multispectral Imaging for Spoilage Detection of Pork Meat , 2013, Food and Bioprocess Technology.

[30]  Da-Wen Sun,et al.  Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review , 2012, Critical reviews in food science and nutrition.

[31]  K. Chen,et al.  Predicting beef tenderness using color and multispectral image texture features. , 2012, Meat science.

[32]  Margarita Ruiz-Altisent,et al.  Monitoring of fresh-cut spinach leaves through a multispectral vision system , 2012 .

[33]  Dolores Pérez-Marín,et al.  Non-destructive characterization and quality control of intact strawberries based on NIR spectral data , 2012 .

[34]  Merete Edelenbos,et al.  Color and textural quality of packaged wild rocket measured by multispectral imaging , 2013 .