Estimation of percentage of impurities in coffee using a computer vision system

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

Heritability Estimates of L*a*b* Color Space Values in Winter Squash (Cucurbita spp.)

Carotenoids serve as protective antioxidants, and function in normal vision, bone growth, cell division and differentiation, and reproduction. Winter squash (Cucurbita spp.) is an excellent dietary source of carotenoids. The range of colors from yellow to red in Cucurbita species indicates that increasing carotenoid levels through plant breeding is possible. The objective of this research was to determine the heritability of flesh color in winter squash in both Cucurbita moschata Duchesne and Cucurbita pepo L. Segregating families representing F2, BC1P1 and BC1P2 populations were created in two families of C. pepo (‘Table Gold Acorn’ × PI 314806 and ‘Table King Bush’ × PI 314806) and one family of C. moschata (‘Butterbush’ × ‘Sucrine DuBerry’). Broad-sense heritabilities were calculated for the F2, BC1P1, and BC1P2 populations within each of the three families. Heritabilities ranged from 0.19 to 0.82 for L*, 0.28 to 0.97 for chroma, and 0.12 to 0.87 for hue across all families. Transgressive segregation for color space values L* was identified in the ‘Table King Bush’ × PI 314806 C. pepo population. Our results indicate that it is possible to breed for improved flesh color in Cucurbita, but the population size and number of test locations for evaluation need to be increased to provide better heritability estimates. Cucurbita species are grown throughout the world and their availability and low price makes them an important potential source of carotenoids for human nutrition and health for all ages.

Color-Based Feature Extraction Method for Skin Segmentation Based on SVM

Different colored digital images can be represented in a variety of color spaces. Red-Green-Blue is the most commonly used color space. That can be transformed into Luminance, Blue difference, Red difference. These color pixels' defined features provide strong information about whether they belong to human skin or not. A novel color-based feature extraction method is proposed in this paper, which makes use of both red, green, blue, luminance, hue, and saturation information. The proposed method is used on an image database that contains people of various ages, races, and genders. The obtained features are used to segment the human skin using the Support-Vector- Machine algorithm, and the promising performance results of 89.86% accuracy are then compared to the most commonly used methods in the literature. Keywords: Skin segmentation, SVM, feature extraction, digital images

A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

There always exists subjective and objective color differences between digital wood grain and real wood grain, making it difficult to replicate the color of natural timber. Therefore, we described a novel method of correcting the chromatic aberration of scanned wood grain to maximally restore the objective color information of the real wood grain. A point-to-point correction model of chromatic aberration between the scanned wood grain and the measured wood grain was established based on Circle 1 by adjusting the three channels (sR, sG, and sB) of the scanned images. A conversion of the color space was conducted using the mutual conversion formulas. The color change of the scanned images before and after the correction was evaluated through the L* a* b* color-mode-based ΔE* and the lαβ color-model-based CIQI (Color Image Quality Index) and CQE (Color Quality Enhancement). The experimental results showed that the chromatic aberration ΔE* between the scanned wood grain and the measured wood grain decreased and the colorfulness index CIQI of the scanned wood grain increased for most wood specimens after the correction. The values of ΔE* of the twenty kinds of wood specimens decreased by an average of 3.1 in Circle 1 and 2.3 in Circle 2, thus the correction model established based on Circle 1 was effective. The color of the scanned wood grain was more consistent with that of the originals after the correction, which would provide a more accurate color information for the reproductions of wood grain and had an important practical significance.

Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence

The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.

Development of a Screening Method for Sulfamethoxazole in Environmental Water by Digital Colorimetry Using a Mobile Device

Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82–101% at 0.1 µg and 0.5 µg of sulfamethoxazole (25 mL), providing a detection limit of 0.08 µg and a quantification limit of 0.26 µg. As a proof of concept, application to in-field analysis was successfully implemented.

Influence of UV Radiation on the Color Change of the Surface of Steamed Maple Wood with Saturated Water Steam

The wood of maple (Acer Pseudopatanus L.) was steamed with a saturated steam-air mixture at a temperature of t = 95 °C or saturated steam at t = 115 °C and t = 135 °C, in order to give a pale pink-brown, pale brown, and brown-red color. Subsequently, samples of unsteamed and steamed maple wood were irradiated with a UV lamp in a Xenotest Q-SUN Xe-3-H after drying, in order to test the color stability of steamed maple wood. The color change of the wood surface was evaluated by means of measured values on the coordinates of the color space CIE L* a* b*. The results show that the surface of unsteamed maple wood changes color markedly under the influence of UV radiation than the surface of steamed maple wood. The greater the darkening and browning color of the maple wood by steaming, the smaller the changes in the values at the coordinates L*, a*, b* of the steamed maple wood caused by UV radiation. The positive effect of steaming on UV resistance is evidenced by the decrease in the overall color difference ∆E*. While the value of the total color diffusion of unsteamed maple wood induced by UV radiation is ∆E* = 18.5, for maple wood steamed with a saturated steam-air mixture at temperature t = 95 °C the ∆E* decreases to 12.6, for steamed maple wood with saturated water steam with temperature t = 115 °C the ∆E* decreases to 10.4, and for saturated water steam with temperature t = 135 °C the ∆E* decreases to 7.2. Differential ATR-FTIR spectra declare the effect of UV radiation on unsteamed and steamed maple wood and confirm the higher color stability of steamed maple wood.

A precise method of color space conversion in the digital printing process based on PSO-DBN

Neural networks have been widely used in color space conversion in the digital printing process. The shallow neural network easily obtains the local optimal solution when establishing multi-dimensional nonlinear mapping. In this paper, an improved high-precision deep belief network (DBN) algorithm is proposed to achieve the color space conversion from CMYK to L*a*b*. First, the PANTONE TCX color card is used as sample data, in which the CMYK value of the color block is used as input and the L*a*b* value is used as output; then, the conversion model from CMYK to L*a*b* color space is established by using DBN. To obtain better weight and threshold, DBN is optimized by a particle swarm optimization algorithm. Experimental results show that the proposed method has the highest conversion accuracy compared with Back Propagation Neural Network, Generalized Regression Neural Network, and traditional DBN color space conversion methods. It can also adapt to the actual production demand of color management in digital printing.