Meat quality classification based on color intensity measurement method

Yulianti, Titin; Yudamson, Afri; Septama, Hery Dian; Sulistiyanti, Sri Ratna; Setiawan, Febryan; Telaumbanua, Mareli

doi:10.1109/isesd.2016.7886727

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Research by [30] showed that OPEFB pellets after torrefaction at 280ºC for 20 minutes have water resistance for 12 hours. The development of torrefaction research could measure the quality of pellets without immersion for 24 hours, but the quality of torrefaction pellets could be measured through the color intensity average from Red, Green, and Blue channel [31].…”

Section: Figurementioning

confidence: 99%

Design of an automatic electric torrefaction reactor for oil palm empty fruit bunch pellets

Telaumbanua,

Fitni,

Wisnu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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One of the technologies for utilizing waste into high-quality fuel is torrefaction. Torefaction of empty palm fruit bunches is a thermochemical process without the presence of oxygen by heating the biomass slowly and holding it for a certain time. Torrefaction degrades the hemicellulose content to optimize mass and energy yields. The purpose of this study was to design a temperature control system using a electrical heater on a rotary torrefaction reactor for Oil Palm Empty Fruit Bunch (OPEFB) pellets. The validation temperature sensor has shown a coefficient of determination (R2) 0.9999 using the model coefficient y = 1.0029x + 0.4017. The performance results of the electric reactor control system for torrefaction show a temperature control accuracy of 85.77% at various setting points. The response of the temperature control system within 10 minutes can reach a temperature of 213°C and in the 13th minute it reaches 301.6°C. The stability of the tool produces good performance. The electric power consumption for 6 kg OPEFB pellets was 2.98 kWh at a cost of USD 0.33. The water content in the torrefaction pellets is 1-2%. The results of the hydrophobicity test showed that the torrefactive samples at temperatures above 200 °C did not change their physical form after immersion in water for 24 hours. These results indicate that the electric torrefaction reactor automatic has succeeded in increasing the caloric ratio of the material, resistance to water such as charcoal, but still has the characteristics and properties of biomass.

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Section: Figurementioning

confidence: 99%

Design of an automatic electric torrefaction reactor for oil palm empty fruit bunch pellets

Telaumbanua,

Fitni,

Wisnu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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“…Selain itu, berdasarkan hasil pengembangan metode penilaian kualitas daging sapi segar dan busuk telah dilakukan sebelumnya (Yulianti et al, 2016;Yudamson et al, 2016) yaitu dengan menggunakan persen rerata intensitas warna kanal R, G, dan B. Berdasarkan hasil penelitian tersebut diketahui bahwa kesegaran daging sapi dipengaruhi oleh perubahan persen rerata intensitas warna kanal R. Namun pada penelitian ini ketiga fitur tersebut akan digunakan dan diseleksi dengan metode F-Score untuk mendapatkan fitur yang paling relevan. Dengan demikian terdapat total sebanyak 14 fitur yang diekstraksi dan diseleksi.…”

Section: Abstrakunclassified

Pengaruh Seleksi Fitur Citra Terhadap Klasifikasi Tingkat Kesegaran Daging Sapi Lokal

Yulianti

Telaumbanua

Septama

et al. 2021

JTEP-L

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Identifying beef manually has some drawbacks because human visual has limitations and there are differences of human perception in assessing object quality. Several researches developed beef quality assessment methods based on image feature extraction. However, not all features support for obtaining the classification results that have high accuracy. The efficiency will be achieved if the classification analyzes only the relevant features. Therefore, a feature selection process is required to select relevant features and to eliminate irrelevant features to obtain more accurate and faster classification results. One of the feature selection algorithms is the F-Score which is a simple technique that measures the discrimination of two sets of real numbers. The features with the lowest ranking from the F-Score will be eliminated one by one until the most relevant features are obtained. The test is carried out by analyzing the classification results in the form of sensitivity, specificity, and accuracy values. The results of this research showed that by using the F-Score feature, the most relevant features for the classification of freshness level of local beef are obtained using the K-Nearest Neighbor (KNN) method. These features include the average color intensity R and standard deviation with a sensitivity of 0.8, a specificity of 0.93, and an accuracy of 86%. Keywords: Classification, Fiture Selection, F-Score, K-Nearest Neighbor, Local beef

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“…Due to carrageenan being the most widely used hydrocolloid in meat products (Bartlová et al, 2021), this study selected carrageenan as the experimental subject to explore the feasibility of smartphone-based computer vision for quantitatively detecting hydrocolloid adulteration. Previous studies on meat quality assessment often categorized meat into high quality and low quality (Qiu et al, 2024;Yulianti et al, 2016), or further subdivided it into categories such as premium, average, and poor (Díaz et al, 2005;Monroy et al, 2010). However, in order to achieve quantitative analysis, a multi-classification approach is needed that can distinguish subtle differences.…”

Section: Introductionmentioning

confidence: 99%

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

Gao,

Chen,

Huang

et al. 2024

Journal of Food Science

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Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real‐time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone‐based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre‐training and fine‐tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin‐Tiny (Swin‐T) achieved the highest trade‐off performance, with a Top‐1 accuracy of 0.997, a detection speed of 3.2 ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin‐T model was also capable of distinguishing between different types of hydrocolloids with a Top‐1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real‐time quantitative information about meat adulteration anytime, anywhere.Practical ApplicationThis research provides a practical solution for regulators and consumers to non‐destructively and quantitatively detect the content and type of hydrocolloids in beef in real‐time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.

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Meat quality classification based on color intensity measurement method

Cited by 6 publications

References 3 publications

Design of an automatic electric torrefaction reactor for oil palm empty fruit bunch pellets

Design of an automatic electric torrefaction reactor for oil palm empty fruit bunch pellets

Pengaruh Seleksi Fitur Citra Terhadap Klasifikasi Tingkat Kesegaran Daging Sapi Lokal

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

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