Nix Pro Color Sensor provides comparable color measurements to HunterLab colorimeter for fresh beef

Author(s):  
Conrad S. Schelkopf ◽  
Emily A. Rice ◽  
Joanna K. Swenson ◽  
Ann M. Hess ◽  
Ifigenia Geornaras ◽  
...  
2020 ◽  
Vol 66 (6) ◽  
pp. 601-606 ◽  
Author(s):  
Alex D Potash ◽  
Daniel U Greene ◽  
Gabrielle A Foursa ◽  
Verity L Mathis ◽  
L Mike Conner ◽  
...  

Abstract An animal’s pelage, feather, or skin color can serve a variety of functions, so it is important to have multiple standardized methods for measuring color. One of the most common and reliable methods for measuring animal coloration is the use of standardized digital photographs of animals. New technology in the form of a commercially available handheld digital color sensor could provide an alternative to photography-based animal color measurements. To determine whether a digital color sensor could be used to measure animal coloration, we tested the ability of a digital color sensor to measure coloration of mammalian, avian, and lepidopteran museums specimens. We compared results from the sensor to measurements taken using traditional photography methods. Our study yielded significant differences between photography-based and digital color sensor measurements of brightness (light to dark) and colors along the green to red spectrum. There was no difference between photographs and the digital color sensor measurements for colors along the blue to yellow spectrum. The average difference in recorded color (ΔE) by the 2 methods was above the threshold at which humans can perceive a difference. There were significant correlations between the sensor and photographs for all measurements indicating that the sensor is an effective animal coloration measuring tool. However, the sensor’s small aperture and narrow light spectrum range designed for human-vision limit its value for ecological research. We discuss the conditions in which a digital color sensor can be an effective tool for measuring animal coloration in both laboratory settings and in the field.


2019 ◽  
Vol 3 (2) ◽  
Author(s):  
C. Schelkopf ◽  
J. Swenson ◽  
A. Hess ◽  
K. E. Belk ◽  
M. N. Nair

ObjectivesMeat color is the most important quality attribute that influences consumer purchase decisions. Monitoring color to maximize shelf life and consumer acceptability is routinely used in meat science research. The HunterLab MiniScan EZ (HunterLab) colorimeter is the widely used industry standard for objectively measuring meat color. This device can collect tristimulus values of CIE L* (lightness), a* (redness), and b* (yellowness) for color measurements based on the light reflectance from the meat surface. While the HunterLab colorimeter serves as an accurate measure of meat color, it is relatively expensive and bulky. The Nix Pro Color Sensor (Nix) colorimeter is a less expensive and smaller handheld device that can capture the CIE L*, a*, b* values which can be downloaded to a smartphone app. However, limited research has been performed to compare the efficiency of these colorimeters for measuring beef color. Therefore, the objective of this study was to investigate the capabilities of the Nix colorimeter as an additional resource for objective fresh beef color measurements.Materials and MethodsThe longissimus dorsi muscle from one side of A maturity beef carcasses (n = 200) were evaluated using the HunterLab and Nix colorimeters. Carcasses were allowed approximately 1 h to bloom after being ribbed (between the 12th and 13th rib) prior to color measurements. Three (technical replicate) scans were obtained using the HunterLab colorimeter (illuminant A and 10° standard observer) and the mean readings were recorded. A series of independent technical replication (3, 5, 7, and 9) scans were obtained using the Nix colorimeter with illuminant A and 10° standard observer as well. The differences in color measurements between colorimeters were analyzed by using the Bland Altman Limits of Agreement and CORR (correlation) procedure of SAS with α < 0.05.ResultsCorrelation between the HunterLab and Nix was highest for a* value (redness) with 3 scans (r = 0.85, P < 0.01), followed by 7, 5, and 9 scans (r = 0.84, 0.82, and 0.82, respectively; P < 0.01). Additionally, L* values (lightness) were highly correlated for all the scanning series (r = 0.79–0.81; P < 0.01). Similar to a* values, 3 scans with the Nix for b* values (yellowness) demonstrated the best correlation with HunterLab (r = 0.83; P < 0.01), whereas the 5, 7, and 9 scans were still highly correlated (r = 0.79–0.82; P < 0.01). The Bland Altman Limits of Agreement analysis indicated that the mean difference in a* values using 3 scans of both colorimeters was –1.68, whereas it was –0.91 for L* values and 0.25 for b* values. Moreover, the analysis indicated good agreement between the Nix and the Hunterlab colorimeters for all the color parameters.ConclusionThree replicate scans using the Nix was highly correlated with color measurements using the HunterLab colorimeter and can serve as an acceptable additional resource for objectively measuring beef color. The Nix provides an opportunity for a less expensive, more mobile, and multipurpose device. Although these colorimeters are not equivalent, the Nix could be an adequate method for objective beef color measurements and is comparable to the HunterLab.


2019 ◽  
Vol 3 (2) ◽  
pp. 178-178
Author(s):  
C. Schelkopf ◽  
J. Swenson ◽  
A. Hess ◽  
K. E. Belk ◽  
M. N. Nair

2008 ◽  
Vol 56 (2) ◽  
pp. 518-531 ◽  
Author(s):  
Silvia Zuffi ◽  
Simone Santini ◽  
Raimondo Schettini

2003 ◽  
Vol 29 (1) ◽  
pp. 38-42 ◽  
Author(s):  
Maria E. Nadal ◽  
Edward A. Early
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 986
Author(s):  
Daun Seol ◽  
Daeil Jang ◽  
Kyungjoon Cha ◽  
Jin-Woo Oh ◽  
Hoeil Chung

A single M13 bacteriophage color sensor was previously utilized for discriminating the geographical origins of agricultural products (garlic, onion, and perilla). The resulting discrimination accuracy was acceptable, ranging from 88.6% to 94.0%. To improve the accuracy further, the use of three separate M13 bacteriophage color sensors containing different amino acid residues providing unique individual color changes (Wild sensor: glutamic acid (E)-glycine (G)-aspartic acid (D), WHW sensor: tryptophan (W)-histidine (H)-tryptophan (W), 4E sensor: four repeating glutamic acids (E)) was proposed. This study was driven by the possibility of enhancing sample discrimination by combining mutually characteristic and complimentary RGB signals obtained from each color sensor, which resulted from dissimilar interactions of sample odors with the employed color sensors. When each color sensor was used individually, the discrimination accuracy based on support vector machine (SVM) ranged from 91.8–94.0%, 88.6–90.3%, and 89.8–92.1% for garlic, onion, and perilla samples, respectively. Accuracy improved to 98.0%, 97.5%, and 97.1%, respectively, by integrating all of the RGB signals acquired from the three color sensors. Therefore, the proposed strategy was effective for improving sample discriminability. To further examine the dissimilar responses of each color sensor to odor molecules, typical odor components in the samples (allyl disulfide, allyl methyl disulfide, and perillaldehyde) were measured using each color sensor, and differences in RGB signals were analyzed.


Author(s):  
Akshay Dhawan ◽  
Pauroosh Kaushal ◽  
R. P. Mudhalwadkar
Keyword(s):  
Low Cost ◽  

1997 ◽  
Vol 67 (12) ◽  
pp. 881-890 ◽  
Author(s):  
Bugao Xu ◽  
Chaoying Fang ◽  
Robin Huang ◽  
Michael D. Watson

The U.S. cotton classification system has been undergoing significant changes, moving from human classing to the use of precise instruments. Along with this trend, the current research is an effort to develop a new computer vision system to measure detailed trash and color attributes of raw cotton. The system primarily consists of a color ccd camera, xenon flash light, and customized software. In this paper, we introduce a new trash and spot identification method, multidimension thresholding, and the methods for characterizing size, spatial density, shape, and color of trash and spots present in cotton samples. We report on the trash and color measurements of twelve cotton samples, including statistical data and distribution curves, and we compare the results from this system with those from other instruments such as the Spinlab and Motion Control hvi machines and the Minolta Chroma Meter CR-210. Finally, we investigate the influence of trash and spots on cotton color values.


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