Developing a computer vision system for real-time color measurement – A case study with color characterization of roasted rice

Nguyen, Chanh-Nghiem; Vo, Van-Thoai; Ha, Nam Chul

doi:10.1016/j.jfoodeng.2021.110821

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…To evaluate the effect of different treatments on the catfish's color, the mean (L*, a*, b*) color value of the bighead catfish for each treatment was compared to that of the control group. The comparison metric was the color difference defined by the International Commission on Illumination (CIE) in 1976, which has been extensively applied in various studies related to food color measurement and comparison [24][25][26]. Accordingly, the mean color difference between a non-control catfish with the mean color c(L*, a*, b*) and a treated catfish with the mean color c(L t *, a t *, b t *) in their L*, a*, b* color space was calculated as seen in Equation (4).…”

Section: Colorimeter Methodsmentioning

confidence: 99%

Dietary Effects of Carotenoid on Growth Performance and Pigmentation in Bighead Catfish (Clarias macrocephalus Günther, 1864)

Hien

Loc

et al. 2022

Fishes

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This study investigates the effects of supplemental carotenoid pigments on growth and color performance in bighead catfish (Clarias macrocephalus). Two experiments were undertaken to determine the appropriate types, feed duration, and dose of astaxanthin (As), canthaxanthin (Ca), and xanthophyll (Xa) pigments individually and in combination. In the first experiment, fish were fed with one control diet (basic diet), six experimental diets comprised of three diets of As, Ca, and Xa at a 100 mg/kg rate of supplementation, respectively, and three diets combinations of As + Ca, As + Xa, and Ca + Xa at a supplement rate of 50 mg + 50 mg/kg. The results showed no significant difference in weight gain (WG), specific growth rate (SGR), survival rate (SR), and feed conversion ratio of fish among treatments (p > 0.05) after 6 weeks. The L* (Lightness) and a* (redness) values in the Xa diet were significantly lower than other treatments, while b* (yellowness) was significantly higher than in the control and others treatments (p < 0.05). These values peaked after 4 weeks and remained stable until the end of the experiment. Consistently, the highest muscle carotenoid content (16.89 ± 0.60 mg/100 g) was found in the fish fed with the Xa diet. The Xa diet was selected for the second experiment. This experiment consisted of four Xa supplemented diets at rates of 25, 50, 75, and 100 mg/kg and a basal diet without any Xa supplementation. The results showed that there was no difference in the SGR or SR of fish fed various Xa levels (p > 0.05). Fish fed the Xa diet of 75 mg/kg were the most preferred by consumers for the natural “yellowness” of muscle. Thus, the results suggested that additional carotenoid pigments did not affect the growth performance of fish. Farmers and feed producers could utilize Xa at an optimal dose of 75 mg/kg to enhance color performance in the market size of bighead catfish for at least 4 weeks prior to harvest.

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Section: Colorimeter Methodsmentioning

confidence: 99%

Dietary Effects of Carotenoid on Growth Performance and Pigmentation in Bighead Catfish (Clarias macrocephalus Günther, 1864)

Hien

Loc

et al. 2022

Fishes

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show abstract

“…The observation of the dehydration procedure involved more than simply measuring weight and moisture content. It also entailed the use of computer vision to visually analyze [17,18] alterations in the food's appearance. The advantage of utilizing these techniques is that they are non-invasive and can be put into practice without requiring costly laboratory equipment.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Networks for Non-Destructive Detection of Food Irradiation

Nada¹,

Omer²,

Esmaiel³

et al. 2023

RIA

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The authenticity of food and the guarantee of its validity has become of great importance nowadays. One of the most crucial methods for getting rid of food pollutants is food irradiation. Therefore, utilising deep learning algorithms, this work proposed an effective and non-destructive way for identifying foods that have been exposed to gamma radiation. In place of the conventional destructive method for irradiation detection, deep learning technology is suggested in this research as a quick and non-destructive alternative. The proposed method is based on the detection of the changes of the spectrum of the radiation samples. The method is tested over apple images samples, which are irradiated by 0.5, 1, 1.5, 2 and 2.5 KGy. The findings demonstrated that employing imaging processing technology, it was possible to accurately identify food that had been exposed to radiation. The deep learning boosts the score to 94% -100%, thus improving machine learning method results from 85%. The suggested method is of great importance due to its usability, fast measurement and no need for special skills or sample preparation.

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“…[52], where authors used the algorithm to recognize leaf color differences. By Nguyen et al [35] in the context of rice color recognition and by López et al [30] for mortar color differences. Usage of KL = 1, KC = 1, and KH = 1 parametric values is the most common approach when using Fig.…”

Section: Related Workmentioning

confidence: 99%

Use of Functional Ink in a Smart Tag for Fast-Moving Consumer Goods Industry

Isohanni

2022

J Package Technol Res

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In the fast-moving consumer goods (FMCG) industry, current labelling solutions have challenges to meet the track & trace requirements. Currently, FMCG items use mainly paper-based self-adhesive labels with traditional barcodes. These labels are low priced and technically easy to produce and deploy. The shift towards advanced solutions, like radio frequency identification (RFID) or near field communication (NFC) tags, still does not offer a good enough cost/benefit ratio. These advanced solutions have a high unit price or require costly changes in production lines. Still, the industry recognizes the possibilities of smart tags. Recent research has shown that functional inks can operate as cheap sensors. However, more research is needed to take functional inks into the operational FMCG environment. This paper presents one technical solution for an FMCG smart tag. The proposed smart tag builds on traditional QR-Code and Datamatrix markers, printed with standard inks. However, it also has functional ink embedded inside the marker as a sensor. This research experiments how embedding impacts the overall performance of the smart tag decoding. And if the CIEDE2000 color difference algorithm can calculate the state of the sensor. Three different parameter combinations, CIEDE2000(1, 1, 1), CIEDE2000(2, 1, 1), CIEDE2000(2.76, 1.58, 1), and their accuracy are compared. Experiments show that the proposed approach does not negatively affect the decoding performance. And that a color comparison can detect sensor states, especially when the functional ink has high enough color intensity. Between different parameters, CIEDE2000(2.76, 1.58, 1) performed best, especially in the low-intensity test. However, some future research needs to address absolute color value detection and the accuracy of color recognition; especially when if the color has low intensity.

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Developing a computer vision system for real-time color measurement – A case study with color characterization of roasted rice

Cited by 9 publications

References 42 publications

Dietary Effects of Carotenoid on Growth Performance and Pigmentation in Bighead Catfish (Clarias macrocephalus Günther, 1864)

Dietary Effects of Carotenoid on Growth Performance and Pigmentation in Bighead Catfish (Clarias macrocephalus Günther, 1864)

Deep Learning Networks for Non-Destructive Detection of Food Irradiation

Use of Functional Ink in a Smart Tag for Fast-Moving Consumer Goods Industry

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