Digital image analyses as an alternative tool for chicken quality assessment

Barbin, Douglas Fernandes; Mastelini, Saulo Martiello; Barbon, Sylvio; Campos, Gabriel Fillipe Centini; Barbon, Ana Paula A. C.; Shimokomaki, Massami

doi:10.1016/j.biosystemseng.2016.01.015

Cited by 58 publications

(44 citation statements)

References 33 publications

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“…Colour lightness ( L *), redness ( a *) and yellowness ( b *) values ranged from 59.39 to 60.94, 6.10 to 7.37 and 10.61 to 12.63, respectively. Although statistical analysis showed some significant differences in colour of frankfurter formulations, the calculated total colour difference values (Δ E , data no shown) of all possible sample pairs were <2.3; therefore, these statistical differences may not be practically important as the colour difference may not be discernable by human vision (Barbin et al , ). As reported by previous studies, NaCl replacement by only KCl in meat products did not significantly alter L *, a * and b * values (Campagnol et al , ; Santos et al , ).…”

Section: Resultsmentioning

confidence: 99%

Texture and colour characteristics, and optimisation of sodium chloride, potassium chloride and glycine of reduced‐sodium frankfurter

Wilailux

Sriwattana

Chokumnoyporn

et al. 2019

Int J of Food Sci Tech

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The three-components mixture design was applied to optimise a ratio of NaCl (0%-65%), KCl (35%-100%) and glycine (0%-20%) in reduced-sodium frankfurters. Fourteen frankfurters were analysed for texture and colour, and consumer (n = 100) acceptability. Results indicated that NaCl levels affected consumer acceptability of reduced-sodium frankfurters. Increasing NaCl generally increased texture hardness. Optimisation of a salt mixture was performed by superimposing contour plots of predicted acceptability scores (≥5.5 on a 9-points hedonic scale) of all sensory attributes and revealed the optimal salt mixture: 40.03%-63.66% NaCl, 35.00%-55.90% KCl and 0.00%-20.00% glycine. The optimal salt mixture contained 220-340 mg Na/100g frankfurter compared with 540 mg Na/100 g of the control formulation (100% NaCl). The mean overall liking score (5.9 vs. 5.9) of the optimal reduced-sodium frankfurter was not different from the control (100% NaCl). This optimal formulation had >25% sodium reduction and could be claimed as 'reduced-sodium' according to US Food and Drugs Administration regulation.

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Section: Resultsmentioning

confidence: 99%

Texture and colour characteristics, and optimisation of sodium chloride, potassium chloride and glycine of reduced‐sodium frankfurter

Wilailux

Sriwattana

Chokumnoyporn

et al. 2019

Int J of Food Sci Tech

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“…Color properties ( L * , a * , b * , chroma, and Hue angle) were significantly different among samples (Table ). The Δ E ranged from 6.0 to 15.6, and considering that Δ E ≤ 2.3 are not discernable by human vision (Barbin et al., ), the eight samples would be likely discernable visually by their colors. The Un samples were more reddish (44.1 to 50.3 for hue angle; 26.1 to 34.5 for a * ), whereas Ro samples were more less reddish/more yellowish (53.2 to 63.6 for Hue angle; 18.2 to 22.0 for a * ).…”

Section: Resultsmentioning

confidence: 99%

Effects of Visual Cues on Consumer Expectation, Emotion and Wellness Responses, and Purchase Intent of Red Chili Powders

Ngoenchai

Alonso

Suwonsichon

et al. 2019

Journal of Food Science

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The effects of visual cues on familiarity, expected heat intensity, liking of appearance, emotional and wellness responses, and purchase intent (PI) before and after disclosing information associated with red chili powders were determined using a 3‐point scale, a 15‐cm line scale, a 9‐point hedonic scale, a 15‐cm line scale, and a binomial scale, respectively. In this study, consumers only visually evaluated red chili powder samples without sniffing nor tasting. Eight chili powders were prepared according to a 23 factorial design: roasted (Ro) compared with unroasted (Un); whole pod with seeds (Wh) compared with seedless (Sl); coarsely (Cr) compared with finely ground (Gr). Thai consumers (N = 230) were generally more familiar with samples having coarse particles and more reddish color (lower hue angle and higher a* values) than samples having finely ground particles and less reddish/more yellowish color (higher hue angle and lower a* values). The expected heat intensity and liking scores for appearance were lower for samples with higher hue values, particularly RoWhGr and RoSIGr samples. All scores for emotion/wellness terms, except curious, were generally higher for samples with lower hue angle and higher a* values (redness). The consumer familiarity to the appearance of the samples influenced expected heat intensity, liking of appearance, and emotion/wellness responses. PI increased by >10% after presenting “organic,” “aflatoxin free,” and “organic and aflatoxin free” product statements to consumers. Results showed that familiarity, overall liking of appearance, color liking, fine particles liking, and healthy as well as wild terms were significant predictors for PI (odds ratio = 1.282, 1.519, 1.314, 1.158, 1.056, and 0.939, respectively) of red chili powders. Practical Application This study showed that consumer familiarity to the appearance (visual cues) of red chili powder affected expected heat intensity, liking of appearance, and emotion/wellness responses, which, in turn, affected purchase intent (PI). Samples with more reddish color and coarse particles were perceived to be more familiar than those with more yellowish color and finer particles. Familiarity, overall liking of appearance, color liking, fine particles liking, and healthy and wild terms were significant predictors for PI of chili power. The results demonstrated the importance of visual cues on the consumers’ expectation and PI of red chili powder, thus offering valuable information for manufacturers.

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“…This situation is observed in biscuit which has the dark and light tones of the yellow colour and sudjuk which contains fat having white tones. Barbin et al (2016) e ∆ values ranged from 1.7 to 4.6. In our work, * ab e ∆ value for the fresh meat was found to be 3.25 while the * ab e ∆ value for the processed meat (salami) was 3.10.…”

Section: Resultsmentioning

confidence: 99%

Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods

2016

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The colour of food is one of the most important factors affecting consumers' purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L * a * b * colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as "computer vision system" is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be classified with regard to their colours in a fast, inexpensive and simple way. This system could also be used to distinguish the defectives from the non-defectives. Quality parameters of meat and dairy products could be monitored without any physical contact, which causes contamination during sampling.Keywords: computer vision system; food; RGB; L * a * b * .Practical Application: A computer vision system, a simple and alternative way to conventional colour measurement techniques, can be used to measure colour of foods and as a quality control tool in the food industry.

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Digital image analyses as an alternative tool for chicken quality assessment

Cited by 58 publications

References 33 publications

Texture and colour characteristics, and optimisation of sodium chloride, potassium chloride and glycine of reduced‐sodium frankfurter

Texture and colour characteristics, and optimisation of sodium chloride, potassium chloride and glycine of reduced‐sodium frankfurter

Effects of Visual Cues on Consumer Expectation, Emotion and Wellness Responses, and Purchase Intent of Red Chili Powders

Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods

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