Abstract
In this method, a numerical matrix comprised of ten color scales (RGB, HSV, L, and rgb) as independent variables from digitalized images was used as a proof of concept for the prediction of the mass, apparent volume, and bulk density parameters of grains for quality control considering post-harvest purposes. The goal was to develop a high throughput multivariate regression model using partial least squares (PLS) combined with the information from color images to assess the raw product. The data set of external samples was successfully evaluated with standard error of cross-validation (SECV) values of 1.23 g (16.4–28.9), 2.03 cm3 (20.5–40.5), and 0.018 g cm−3 (0.68–0.85) for the mass, apparent volume, and bulk density, respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Russ JC (1992) Image processing handbook. CRC Press, New York
Wojnar L (1999) Image analysis: applications in materials engineering. CRC Press, Boca Raton
Liu JJ, MacGregor JF (2007) On the extraction of spectral and spatial information from images. Chemom Intell Lab Syst 85:119–130
Pereira FMV, Bueno MIMS (2007) Image evaluation with chemometric strategies for quality control of paints. Anal Chim Acta 588:184–191
Geladi P, Sethson B, Nyström J, Lillhonga T, Torbjörn L, Burger J (2004) Chemometrics in spectroscopy. Part 2. Examples. Spectrochim Acta B 59:1347–1357
Walker CK, Panozzo JF (2012) Measuring volume and density of a barley grain using ellipsoid approximation from a 2-D digital image. J Cereal Sci 55:61–68
Borin A, Ferrão MF, Mello C, Cordi L, Pataca LCM, Durán N, Poppi RJ (2007) Quantification of Lactobacillus in fermented milk by multivariate image analysis with least-squares support-vector machines. Anal Bioanal Chem 387:1105–1112
Antonelli A, Cocchi M, Fava P, Foca G, Franchini GC, Manzini D, Ulrici A (2004) Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Anal Chim Acta 515:3–13
Santos PM, Wentzell PD, Pereira-Filho ER (2012) Scanner digital images combined with color parameters: a case study to detect adulterations in liquid cow’s milk. Food Anal Methods 5:89–95
Santos PM, Pereira-Filho ER (2013) Digital image analysis—an alternative tool for monitoring milk authenticity. Anal Methods 5:3669–3674
Daszykowski M, Walczak B, Massart DL (2002) Representative subset selection. Anal Chim Acta 468:91–103
Boynton RM (1960) J Opt Soc Am 50:929–944
Acknowledgments
The authors are grateful to the Fundunesp process number 0268/001/14, The National Council for Scientific and Technological Development (CNPq) process number 445729/2014-7, The São Paulo Research Foundation (FAPESP, 2016/00779-6), and the PROPe process number 39229 - L.J.S. grant fellowship).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Fabíola Manhas Verbi Pereira declares that she has no conflict of interest. Vanessa Rodrigues de Camargo declares that she has no conflict of interest. Lucas Janoni dos Santos declares that he has no conflict of interest.
Ethics Approval
This article does not contain any studies with human or animal subjects performed by any of the authors.
Consent for Publication
Publication has been approved by all individual participants.
Electronic Supplementary Material
ESM 1
(DOCX 23 kb)
Rights and permissions
About this article
Cite this article
de Camargo, V.R., dos Santos, L.J. & Pereira, F.M.V. A Proof of Concept Study for the Parameters of Corn Grains Using Digital Images and a Multivariate Regression Model. Food Anal. Methods 11, 1852–1856 (2018). https://doi.org/10.1007/s12161-017-1028-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-017-1028-6