An analytical method for determination of quality parameters in cotton plumes by digital image and chemometrics

Display Omitted A simple and inexpensive analytical method based on digital image.Determination of quality parameters in cotton plumes.Comparative evaluation of two multivariate calibration techniques.Fast analysis of cotton plume without destroying the sample.No significant difference was found between the predicted and reference values. This paper proposes an analytical method based on the use of digital images and multivariate calibration for the determination of degree of yellowness (+b), reflectance (Rd) and content of wax (WAX) in samples of white cotton plumes and naturally colored plumes. Digital images acquisition of cotton plumes was carried out through a webcam and histograms containing frequency distributions of color index in red-green-blue (RGB), hue (H), saturation (S), value (V), and grayscale channels were obtained. Models of multivariate calibration based on Regression by partial least squares (PLS) using the complete histogram, as well as multiple linear regression (MLR) with the selection of variables by successive projections algorithm (SPA) were developed, validated and compared. Satisfactory prediction results were obtained for both models with Root Mean Square Error of Prediction (RMSEP) values varying from 0.76-0.83, 2.49-2.53% and 0.30-0.33% for +b, Rd and wax, respectively. According to a paired t-test at a 95% confidence level, no statistically significant difference was found between the predicted and reference values. An F-test at 95% confidence level does not indicate significant differences between the RMSEP values obtained with the full-histogram PLS and MLR-SPA models. It is a simple and low-cost method that does not use reagent, does not destroy the sample and performs analysis with comparable results to the reference method.

[1]  Xiongming Du,et al.  Effects of fiber wax and cellulose content on colored cotton fiber quality , 2010, Euphytica.

[2]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[3]  Toby P. Breckon,et al.  Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab , 2011 .

[4]  X. D. Wang,et al.  Breeding for high yield and fibre quality in coloured cotton , 2004 .

[5]  M. Matusiak,et al.  Important Aspects of Cotton Colour Measurement , 2010 .

[6]  Svante Wold,et al.  Personal memories of the early PLS development , 2001 .

[7]  Roberto Kawakami Harrop Galvão,et al.  Cross-validation for the selection of spectral variables using the successive projections algorithm , 2007 .

[8]  James Rodgers,et al.  TEXTILE TECHNOLOGY Portable Color Spectrophotometer Measurements of Cotton Color in Remote Locations , 2013 .

[9]  A. Olivieri,et al.  Teaching Chemometrics with a Bioprocess: Analytical Methods Comparison Using Bivariate Linear Regression , 2002 .

[10]  M. E. Centurión,et al.  Application of digital images to determine color in honey samples from Argentina , 2015 .

[11]  Franco Pedreschi,et al.  A non-destructive digital imaging method to predict immobilized yeast-biomass , 2009 .

[12]  Peter D. Wentzell,et al.  Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk , 2012, Food Analytical Methods.

[14]  S. Gordon,et al.  3 – Cotton fibre quality , 2007 .

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

[16]  Hossein Ghorashi,et al.  Color Grading of Cotton Part I: Spectral and Color Image Analysis , 1999 .

[17]  Timothy A. Calamari,et al.  Cotton Wax and Its Relationship with Fiber and Yarn Properties , 2002 .

[18]  Bruno G Botelho,et al.  Development and analytical validation of a simple multivariate calibration method using digital scanner images for sunset yellow determination in soft beverages. , 2014, Food chemistry.

[19]  C. Conrad.  Determination of Wax in Cotton Fiber A New Alcohol Extraction Method , 1944 .

[20]  Y. Liu,et al.  UV/visible/near-infrared reflectance models for the rapid and non-destructive prediction and classification of cotton color and physical indices. , 2010 .

[21]  Tae Jin Kang,et al.  Objective Evaluation of the Trash and Color of Raw Cotton by Image Processing and Neural Network , 2002 .

[22]  K. Plataniotis,et al.  Color Image Processing and Applications , 2000 .

[23]  María S. Di Nezio,et al.  Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water , 2007 .

[24]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .