Beef freshness classification by using color analysis, multi-wavelet transformation, and artificial neural network

Trientin, Danika; Hidayat, Bambang; Darana, Sjafril

doi:10.1109/icacomit.2015.7440202

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Although the comparisons with other works is not easy since the types of the meat, what to predict, number of the samples, and classes affect the accuracy of the prediction, the proposed scheme of combination of image classification and EIS is comparable with the state-of-the-art works using image classification [ 17 , 53 ], and moisture content prediction using EIS [ 23 ].…”

Section: Image Classification With Eis Resultsmentioning

confidence: 99%

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

Huh

Kim

Lee

et al. 2021

Sensors

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This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.

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Section: Image Classification With Eis Resultsmentioning

confidence: 99%

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

Huh

Kim

Lee

et al. 2021

Sensors

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show abstract

“…For defining the region of interest in tuna meat samples pictures, the script uses a three-dimensional range of H, S, and V minimum (160, 0, 0) and maximum (180, 255, 255), representing variations of red tones. For the images of salmon meat samples, the range varied from (5,50,50) to (15,255,255), representing variations in shades of orange, the predominant color in the samples. The script then applies a grayscale mask to the red/orange-coloured pixels found in the range, generating a Gaussian noisy grayscale image.…”

Section: Preprocessing Of Imagesmentioning

confidence: 99%

“…Trientin, Hidayat, and Darana [15] proposed the classification of beef's freshness through the sensory analysis of the samples' color, using two models: K-nearest neighbors (KNN) [16] model and artificial neural network (ANN) [17] with retro propagation. The authors captured the samples' images in a controlled environment, using a digital camera.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

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Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

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Beef freshness classification by using color analysis, multi-wavelet transformation, and artificial neural network

Cited by 2 publications

References 3 publications

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

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