Using electronic nose to recognize fish spoilage with an optimum classifier

Vajdi, Meisam; Varidi, Mohammad Javad; Varidi, Mehdi; Mohebbi, Mohebbat

doi:10.1007/s11694-019-00036-4

Cited by 41 publications

(28 citation statements)

References 29 publications

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“…Fish and seafood flavors are derived from both volatile and nonvolatile components. The concentration of both volatile and nonvolatile components in fish and seafood vary with species, age of the aquatic organism, environmental conditions, and product freshness [ 21 , 52 ]. The volatile compounds include mainly carbonyls and alcohols, which are formed as a result of long-chain polyunsaturated fatty acids oxidation by lipoxygenases, and organic acids.…”

Section: Textural and Sensorial Properties Of Fishmentioning

confidence: 99%

Plant-Based Seafood Analogs

Kazir

Livney

2021

Molecules

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There is a growing global need to shift from animal- towards plant-based diets. The main motivations are environmental/sustainability-, human health- and animal welfare concerns. The aim is to replace traditional animal-based food with various alternatives, predominantly plant-based analogs. The elevated consumption of fish and seafood, leads to negative impacts on the ecosystem, due to dwindling biodiversity, environmental damage and fish diseases related to large-scale marine farming, and increased intake of toxic substances, particularly heavy metals, which accumulate in fish due to water pollution. While these facts lead to increased awareness and rising dietary shifts towards vegetarian and vegan lifestyles, still the majority of seafood consumers seek traditional products. This encourages the development of plant-based analogs for fish and seafood, mimicking the texture and sensorial properties of fish-meat, seafood, or processed fish products. Mimicking the internal structure and texture of fish or seafood requires simulating their nanometric fibrous-gel structure. Common techniques of structuring plant-based proteins into such textures include hydrospinning, electrospinning, extrusion, and 3D printing. The conditions required in each technique, the physicochemical and functional properties of the proteins, along with the use of other non-protein functional ingredients are reviewed. Trends and possible future developments are discussed.

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Section: Textural and Sensorial Properties Of Fishmentioning

confidence: 99%

Plant-Based Seafood Analogs

Kazir

Livney

2021

Molecules

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“…Hence, the raw data patterns generated by the E‐nose in response to cane shoot samples carry plenty of overlapping information. Based on previous work reported on the E‐nose (Amari et al, 2006; Vajdi, Varidi, Varidi, & Mohebbi, 2019), five different feature parameters were extracted from each sensor response G i : Average of the conductance in the interval (0–100) s. Steady‐state conductance from the average of the conductance during the last 30 s. Slope of the dynamic conductance from the conductance curve within the interval (20–60) s. Area below the conductance curve between 20s and 70s calculated by the “trapezoidal” integration method. Difference between first and last conductance. …”

Section: Resultsmentioning

confidence: 99%

An electronic nose system for the monitoring of water cane shoots quality with swarm clustering algorithm

Zhou

Zhu

2020

Journal of Food Safety

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A fast, portable, nondestructive, and simple‐to‐operate method for determination of water cane shoot quality via an electronic nose (E‐nose) system is presented. The responses of E‐nose sensors to cane shoot samples during storage time are measured. Considering that one of the most common problems with fresh aquatic vegetables is the nearly negligible change in volatile organic compounds, a novel swarm clustering based on particle swarm optimization (SWC) algorithm is proposed to extract the effective features of the sensors responses. In SWC, the number of clusters is automatically estimated based on the neighborhood radius of the particles, which reduces the difficulty in determining the number of cluster. Traditional clustering algorithms, such as K‐means clustering (KMC) and hierarchical clustering analysis (HCA), are used for the purposes of comparison, and the changes in quality of cane shoot samples are quantitatively determined according to the hardness and pH. The results suggest that the E‐nose‐based SWC methodology is capable of accurately classifying the cane shoots freshness during the 20‐day storage time, which is much better than that of the comparative KMC and HCA methods. Additionally，quantitative analysis of the sensory evaluation, hardness, and pH is shown to have good agreement with the E‐nose system analysis results. Validation experiments show that unknown samples are successfully grouped into several different categories with a high correlation (R2 = .982) between unknown and known samples, demonstrating the potential use of E‐nose system to predict the freshness of the cane shoots.

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“…The olfactory sensor is a detection instrument developed to simulate animals’ olfactory organs and can recognize simple or complex odor information volatized by organic substances. Currently, this technology has been successfully applied to detection and analysis in many fields, such as food and agricultural products [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ], including successful applications in the detection of food storage quality [ 30 , 31 , 32 ]. During the storage of rice, due to the loss of the protection of the outer shell, the rice is directly exposed to air and is easily affected by the external environment, resulting in poor storage stability.…”

Section: Introductionmentioning

confidence: 99%

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

Jiang

Chen

2021

Sensors

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This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

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Using electronic nose to recognize fish spoilage with an optimum classifier

Cited by 41 publications

References 29 publications

Plant-Based Seafood Analogs

Plant-Based Seafood Analogs

An electronic nose system for the monitoring of water cane shoots quality with swarm clustering algorithm

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

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