Rapid Identification between Two Fish Species Using UV-Vis Spectroscopy for Substitution Detection

Chai, Zhaoliang; Wang, Chengyu; Bi, Hongyan

doi:10.3390/molecules26216529

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…The current research on fish species identification is mainly aimed at the static images of fish in marine and aquatic fields (Pepe et al, 2007;Bingpeng et al, 2018;Chai et al, 2021;Pereira et al, 2021). Although some achievements have been made, however, the actual situation is that the fish swimming through fishway is a dynamic process, and the body of fish is often in a torsional state of deformation, making it difficult to directly carry out research on the identification of passing fish targets in real fishways engineering scenarios using existing algorithms.…”

Section: Research Backgroundmentioning

confidence: 99%

Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

Liu

Jiang

et al. 2023

Front. Environ. Sci.

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In this paper, based on the improved DeepSORT algorithm, four target species of passing fish (Schizothorax o’connori, Schizothorax waltoni, Oxygymnocypris stewartii and Schizopygopsis younghusbandi) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii, respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.

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Section: Research Backgroundmentioning

confidence: 99%

Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

Liu

Jiang

et al. 2023

Front. Environ. Sci.

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“…As a result, meeting this demand necessitates the use of appropriate analytical instruments both before and after food processing and production. Quick, efficient, simple, user friendly, limited or nil sample preparation, and a very small amount or no wastage sample are all desirable aspects of such technologies and these features are peculiar characteristics of different spectroscopic technologies including mid-infrared, near-infrared and visible spectroscopy (111). Ghidini, Varrà (105) described qualitative spectroscopy in combination with chemometrics in seafood products.…”

Section: Eggsmentioning

confidence: 99%

Spectroscopic techniques for authentication of animal origin foods

Chaudhary

Kajla²,

Dewan³

et al. 2022

Front. Nutr.

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Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

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“…2 Fraud in seafood products is a common problem, and such topics are frequently classified among the top food product categories that are susceptible to fraud owing to their high national production, importation, and consumption. 1,3 According to a report issued by the EU Food Fraud Network in 2019, fish and fish products, occupied the second position in the ranking, with 42 study requests of possible cases of food fraud in Europe, ranking only behind fats and oils with 44 requests. 4 Fraud in products of animal origin includes species substitution, the addition of non-declared substances, as well as fraudulent treatments and non-declaration of processes, such as previous freezing, irradiation, and microwave heating, discrepancies in the production method and farming or breading technique, the addition of non-declared substances and mislabeling of the provenance (geographical or botanical origin).…”

Section: Introductionmentioning

confidence: 99%

Fraud detection in the fishing sector using hyperspectral imaging

Esplandiú,

Marín-Méndez,

Alonso-Santamaría

et al. 2024

Journal of Near Infrared Spectroscopy

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Currently, more and more consumers are interested in the quality, safety, and authenticity of food products. The fishing sector is the second food category with the highest risk of fraud and the greatest presence of authentication problems. There are non-destructive, fast and accurate techniques for real-time authentication, with hyperspectral imaging (HSI) standing out among these. In this context, the main aim of this study is to explore the viability of HSI in the visible and near infrared (VIS-NIR) and near infrared (NIR) ranges for the detection of fraud by origin and by non-declaration of the previous freezing process, in anchovies. The spectral pretreatment methods used were the standard normal variate method, the Savitzky-Golay 1st derivate and the Savitzky-Golay 2nd derivate, always followed by mean centering (MC). In addition, the impact of using a previous step of smoothing prior to pretreatment was also evaluated. Two classification algorithms: soft independent modeling of class analogy, and partial least squares discriminant analysis (PLS-DA) were used to build the classification model. After analysis, it was found that the modelling results using the VIS-NIR region were always better than those using the NIR region, and the best performing model was by PLS-DA with a recall of 0.97 for fresh and 0.98 for frozen-thawed anchovies and 0.98 for Cantabrian anchovies and 0.96 for Mediterranean anchovies. One advantage of the model obtained is the ability to classify the anchovies measuring on the skin side of fish without the need for sample preparation. Overall, the results showed that HSI combined with PLS-DA is a favorable solution for rapid, and non-destructive recognition of adulteration regarding freshness and origin in anchovies.

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Rapid Identification between Two Fish Species Using UV-Vis Spectroscopy for Substitution Detection

Cited by 9 publications

References 32 publications

Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

Spectroscopic techniques for authentication of animal origin foods

Fraud detection in the fishing sector using hyperspectral imaging

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