Nondestructive Determination of Soluble Solids Content of ‘Fuji’ Apples Produced in Different Areas and Bagged with Different Materials During Ripening

To investigate the feasibility of hyperspectral imaging technique in nondestructive determination of soluble solids content (SSC) of fruits produced in different places and bagged with different materials during ripening, the near infrared hyperspectral reflectance images were acquired on 196 ‘Fuji’ apples picked from four orchards in different areas and bagged with polyethylene film or light-impermeable paper. Mean reflectance spectrum from the regions of interest in the hyperspectral image of each apple was extracted. Standard normal variate (SNV) was used to eliminate the effect of instrument and environment on spectra. The sample set partitioning based on joint x–y distances method was applied to divide the samples into calibration set and prediction set as the ratio of 3:1. Successive projection algorithm (SPA) and uninformative variable elimination (UVE) method were used to select effective wavelengths (EWs) from the full spectra. Partial least squares (PLS), least squares support vector machine (LSSVM), and extreme learning machine (ELM) were used to develop SSC determination models. The results showed that 24 and 122 EWs were selected by SPA and UVE, respectively. The selection of EWs was helpful to SSC determination performance improvement. The optimal SSC prediction model was LSSVM based on selected EWs by SPA, with the correlation coefficient and root-mean-square error of prediction set of 0.878 and 0.908 °Brix, respectively. This study indicates that hyperspectral imaging technique could be used to determine SSC of intact apples produced in different places and bagged with different materials during ripening.

[1]  Jiewen Zhao,et al.  Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools. , 2012, Food chemistry.

[2]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

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

[4]  Yidan Bao,et al.  Visible/Near-Infrared Spectra for Linear and Nonlinear Calibrations: A Case to Predict Soluble Solids Contents and pH Value in Peach , 2011 .

[5]  Renfu Lu,et al.  Assessing Multiple Quality Attributes of Peaches Using Optical Absorption and Scattering Properties , 2012 .

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

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

[8]  Zou Xiaobo,et al.  Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models , 2007 .

[9]  Renfu Lu,et al.  Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging , 2013 .

[10]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[11]  Di Wu,et al.  Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM , 2008 .

[12]  R. Lu,et al.  Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content , 2008 .

[13]  Renfu Lu,et al.  OPTIMAL WAVELENGTH SELECTION FOR HYPERSPECTRAL SCATTERING PREDICTION OF APPLE FIRMNESS AND SOLUBLE SOLIDS CONTENT , 2010 .

[14]  Renfu Lu,et al.  Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality , 2007 .

[15]  Fei Liu,et al.  Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. , 2009 .

[16]  Yuanwen Teng,et al.  Effects of fruit bagging on coloring and related physiology, and qualities of red Chinese sand pears during fruit maturation , 2009 .

[17]  Jiewen Zhao,et al.  Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of ‘Fuji’ apple based on BiPLS and FiPLS models , 2007 .

[18]  Baohua Zhang,et al.  A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy , 2013 .

[19]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[20]  Yuan Lan,et al.  Constructive hidden nodes selection of extreme learning machine for regression , 2010, Neurocomputing.

[21]  Renfu Lu,et al.  Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content , 2011 .

[22]  Roberto Kawakami Harrop Galvão,et al.  A method for calibration and validation subset partitioning. , 2005, Talanta.

[23]  L. Bodria,et al.  Evaluation of Grape Quality Parameters by a Simple Vis/NIR System , 2010 .

[24]  Renfu Lu,et al.  Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images , 2007 .

[25]  Roberto Kawakami Harrop Galvão,et al.  A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm , 2008 .