Rapid discrimination of three classes of safflowers, dyed safflower, adulterated safflower, and pure safflower using computer vision and image processing algorithms.

A low cost computer vision system (CVS) was designed to measure the color of safflowers in the RGB (red, green, blue), L^∗a^∗b^∗, and HSV (hue, saturation, vale) color spaces. The color moments in these three color spaces were extracted from the acquired images as color features of safflower. In addition, five kinds of pigments that are commonly used to dye safflowers were identified by high-performance liquid chromatography as a reference. Pattern recognition methods were investigated for rapid discrimination, including an unsupervised principal component analysis (PCA) algorithm and a supervised partial least squares discriminant analysis (PLS-DA) algorithm.

The mean error (e) between color values measured with the colorimeter and calculated with the CVS was 2.4%, with a high correlation coefficient (r) of 0.9905. This result indicated that the established CVS was reliable for color estimation of safflowers. The PLS-DA model, which had a total accuracy of 91.89%, outperformed the PCA model in classifying pure, adulterated, and dyed safflowers.

The color objectification is a promising tool for rapid identification of dyed and adulterated safflowers.

In this study, safflower (Carthamus tinctorius L.) was taken as a representative example to examine the application of color characteristics to evaluate quality.

A computer vision system was established for the objective and nondestructive assessment of color using image processing algorithms. Color parameters were investigated based on the RGB, L*a*b and HSV color spaces. The content of hydroxysafflor yellow A (HSYA), a major bioactive constituent of safflower, was determined by high-performance liquid chromatography. The relationship between HSYA content and color values was investigated by Pearson correlation analysis. A multiple linear regression model was established to predict the HSYA content from color values.

The red color and lightness of safflower were found to be significantly related to HSYA content. The prediction equation obtained by multiple regression was reliable with an R² value of 0.805 (P <.01).

The results suggest that the computer vision technique could be used as a promising and non-destructive technology for color measurement and quality evaluation of CHM.