A Proof of Concept Study for the Parameters of Corn Grains Using Digital Images and a Multivariate Regression Model

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 postharvest 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 cm 3 (20.5-40.5), and 0.018 g cm −3 (0.68-0.85) for the mass, apparent volume, and bulk density, respectively.

Color approach to the analysis of white crystal cane sugar for the detection of solid impurities

“…The square images were converted to color histograms using a laboratory‐made code 16 in MATLAB R2020a software (MathWorks, Natick, MA, USA). In a color histogram, each color descriptor comprises an interval of 256 value intensities.…”

“…Analytical methods involving digital images have been widely applied in many types of samples, such as cold meats, 10 sugarcane spirits, 11 citrus, 12,13 wine, 14 grain, 14 sugarcane, 4 and endoglucanase activity, 15 as far as the authors are aware. For instance, Camargo et al 16 . and Floriam et al 17 .…”

“…Analytical methods involving digital images have been widely applied in many types of samples, such as cold meats, 10 sugarcane spirits, 11 citrus, 12,13 wine, 14 grain, 14 sugarcane, 4 and endoglucanase activity, 15 as far as the authors are aware. For instance, Camargo et al 16 and Floriam et al 17 estimated the bulk density of grains using ten color descriptors, red (R), green (G), blue (B), hue (H), saturation (S), value (V), relative colors of red (Rr), green (Rg) and (Rb), and luminosity (L), using multivariate methods. 18 Those previous studies reported a non-invasive, high-throughput, low-cost, and suitably sensitive method that demanded minimal or no chemical treatment and that could be a strong alternative technique for food analysis.…”

Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

“…Afterward, the images were converted into color histograms using another function, 'imhist' in MATLAB. The average color values, which were comprised of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity), using a laboratory-made MATLAB code was available in the study of Camargo et al (2018).…”