Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Moon, Eui Jung; Kim, Young‐Sik; Xu, Yu; Na, Yeul; Giaccia, Amato J.; Lee, Jae Hyung

doi:10.3390/s20154299

Cited by 25 publications

(15 citation statements)

References 28 publications

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“…achieved an accuracy of 84% for Atlantic salmon and 85% for Pacific salmon. Our research presents classification models with greater accuracy than the studies [36,37].…”

Section: Discussionmentioning

confidence: 92%

“…[36] presented a KNN-based solution with 86.6% accuracy. Moon, Kim, Xu, Na, Giaccia, and Lee [37] proposed a CNN for the same task, obtaining an accuracy of 88%. For the classification of salmon freshness, the authors.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, Moon, Kim, Xu, Na, Giaccia, and Lee [37] proposed assessing the freshness of salmon, beef, and tuna meat using spectral data obtained from the samples using a portable visible/near-infrared (VIS/NIR) spectrometer. The authors used a convolutional neural network (CNN) [38] to analyze the spectral response data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…achieved an accuracy of 84% for Atlantic salmon and 85% for Pacific salmon. Our research presents classification models with greater accuracy than the studies [36,37].…”

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

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“…In addition, it is important to note that portable instruments are quite suitable for traditional product analysis, as reported in previous studies [11]. Portable NIR devices have been used to face different food-related issues, such as the determination of fish freshness [12] or the prediction of lycopene content in tomato [13]. Among different portable devices, MicroNIR is one of the most reliable due to its high resolution and broad spectral range.…”

Section: Introductionmentioning

confidence: 87%

Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

González‐Mohíno

Pérez‐Palacios

Antequera

et al. 2020

Foods

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This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

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“…To measure the efficiency of minimal processing on structure and quality, in many studies scientists use non-destructive measurement techniques. There are many non-destructive techniques that can be used to measure quality properties of food such as electronic nose [ 20 , 21 ], ultrasound [ 22 ], near-infrared spectroscopy [ 23 , 24 ], ultraviolet-visible spectroscopy [ 25 ] and hyperspectral imaging [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra

Nagy

Felföldi²,

Brückner³

et al. 2021

Sensors

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In this study, liquid egg, albumen, and egg yolk were artificially inoculated with E. coli. Ultrasound equipment (20/40 kHz, 180/300 W; 30/45/60 min) with a circulation cooling system was used to lower the colony forming units (CFU) of E. coli samples. Frequency, absorbed power, energy dose, and duration of sonication showed a significant impact on E. coli with 0.5 log CFU/mL in albumen, 0.7 log CFU/mL in yolk and 0.5 log CFU/mL decrease at 40 kHz and 6.9 W absorbed power level. Significant linear correlation (p < 0.001) was observed between the energy dose of sonication and the decrease of E. coli. The results showed that sonication can be a useful tool as a supplementary method to reduce the number of microorganism in egg products. With near-infrared (NIR) spectra analysis we were able to detect the structural changes of the egg samples, due to ultrasonic treatment. Principal component analysis (PCA) showed that sonication can alter C–H, C–N, –OH and N–H bonds in egg. The aquagrams showed that sonication can alter the properties of H2O structure in egg products. The observed data showed that the absorbance of free water (1412 nm), water molecules with one (1440 nm), two (1462 nm), three (1472 nm) and four (1488 nm) hydrogen bonds, water solvation shell (1452 nm) and strongly bonded water (1512 nm) of the egg samples have been changed during ultrasonic treatment.

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Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Cited by 25 publications

References 28 publications

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra

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