Natural Additives as Quality Promoters in Surimi: a Brief Review

Sousa, Thamyres César de Albuquerque; Ferreira, Valquíria Cardoso da Silva; Araújo, Íris Braz da Silva; Silva, Fábio Anderson Pereira da

doi:10.1080/10498850.2022.2092434

Cited by 4 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous studies, adding natural additives to surimi could increase myofibril and gel strength due to its high water-holding capacity (Sousa et al, 2022). Sharma et al (2023) also showed that adding a natural extract from Citrus limetta could manipulate the protein structure and its bonds to improve the gel and texture quality of surimi.…”

Section: Water-holding Capacitymentioning

confidence: 97%

Effectiveness of washing with atung (Parinarium glaberimum Hassk) solution on quantity and quality of dark meat yellowfin tuna (Thunnus albacares) surimi

Pattipeilohy,

Moniharapon,

Supit

2024

IFRJ

View full text Add to dashboard Cite

Yellowfin tuna (Thunnus albacares) is the world's second-largest commodity with good nutrition. After processing, some wastes / residues / by-products can be used to produce surimi which still retains the tuna meat properties. The manufacturing of surimi requires safe preservatives, such as the natural preservative, atung (Parinarium glaberimum Hassk), which contains antibacterial substances. Therefore, the present work aimed to determine the effectiveness of 4% (w/v) atung solution on the quantity and quality of yellowfin tuna surimi produced from tuna by-products. The fish meat was treated with a washing agent of ice water and 4% atung solution with several washing frequencies. The parameters assessed were the yield and quality of surimi by analysing the protein content, myofibrillar protein content (salt soluble), water-holding capacity, Salmonella spp., and E. coli. In addition, the parameters included teeth-cutting, ashi strength, and folding. The 4% (w/v) atung solution effectively produced tuna fish surimi with 68.50% yield, 20.62% protein, 8.87% myofibrillar protein, and free from Salmonella spp. and E. coli. The water-holding capacity of the surimi was recorded at 68.9% and increased to 73.3% after three washes. The use of 4% (b/v) atung solution effectively produced surimi with a teeth-cutting value of 7.80 - 7.92, and an ashi strength of 7.97 - 8.08, whereas the folding test was B to A (3.80) after four washes.

show abstract

Section: Water-holding Capacitymentioning

confidence: 97%

Effectiveness of washing with atung (Parinarium glaberimum Hassk) solution on quantity and quality of dark meat yellowfin tuna (Thunnus albacares) surimi

Pattipeilohy,

Moniharapon,

Supit

2024

IFRJ

View full text Add to dashboard Cite

show abstract

“…This flexibility is achieved through the application of formulated seasonings, flavors, and colors. The ultimate consumer reception of surimi-based products rests significantly upon the functional characteristics of surimi gels, particularly in terms of texture and color [4]. Central determinants influencing these functional traits encompass the selection of fish species employed in surimi production, moisture content in both surimi and surimi gels, as well as the integration of supplementary ingredients.…”

Section: Introductionmentioning

confidence: 99%

“…The moisture content within both surimi and surimi gels plays a vital role in managing the texture and color of these products. Assessing the texture properties and the whiteness of surimi gels produced with varying moisture contents allows for the evaluation of both the quality and quantity of proteins in the surimi [4,9]. Water within surimi gels becomes entrapped within three-dimensional networks, thus contributing to the elastic properties closely tied to the texture characteristics of the gels.…”

Section: Introductionmentioning

confidence: 99%

“…Maintaining a well-regulated moisture content within surimi gels serves as a crucial parameter and a valuable tool for ensuring the quality of surimi-based seafood products. While specific moisture content ranges can vary depending on the desired product characteristics and the specific surimi formulation, a typical range might fall between 75% and 82%, but this can be adjusted based on the product's intended texture and other factors [1,[4][5][6]. Ultimately, a well-controlled moisture content serves as a valuable tool for achieving consistent quality in surimi-based seafood products.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of surimi seafood, there exist variations in quality due to the diversity of fish species comprising the raw surimi, the grade of raw surimi, moisture content in the included surimi, and the quantity of additives. Research on mechanical measurement methods and sensory evaluation to analyze these differences has been extensively reported [4,28,29]. However, compared to other processed foods, there is a lack of reported studies on quality classification methods utilizing image analysis and artificial intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Quality Characteristics of Surimi Gels from Different Species Using Images and Convolutional Neural Network

Yoon,

Oyinloye,

Kim

2023

Processes

View full text Add to dashboard Cite

In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an AI algorithm on labeled images as a non-destructive method to classify surimi gels. Gels were made with different moisture (76–82%) and corn starch (5–16%) levels from Alaska pollock and Threadfin breams. In surimi gelation, interactions among surimi, starch, and moisture caused color and quality shifts. Color changes are indicative of structural and quality variations in surimi. Traditional color measuring techniques using colorimeter showed insignificant differences (p < 0.05) in color values and whiteness among treatments. This complexity hindered effective grading, especially in intricate formulations. Despite insignificant color differences, they signify structural changes. The Convolutional Neural Network (CNN) predicts the visual impact of moisture and starch on gel attributes prepared with different surimi species. Automated machine learning assesses AI algorithms; and CNN’s 70:30 training/validation ratio involves 400–700 images per category. CNN’s architecture, including input, convolutional, normalization, Rectified Linear Unit (ReLU) activation, and max-pooling layers, detects subtle structural changes in treated images. Model test accuracies exceed 95%, validating CNN’s precision in species and moisture classification. It excels in starch concentrations, yielding > 90% accuracy. Average precision (>0.9395), recall (>0.8738), and F1-score (>0.8731) highlight CNN’s high performance. This study demonstrates CNN’s value in non-destructively classifying surimi gels with varying moisture and starch contents across species, and it provides a solid foundation for advancing our understanding of surimi production processes and their optimization in the pursuit of high-quality surimi products.

show abstract

Collagens for surimi gel fortification: Type-dependent effects and the difference between type I and type II

Zhao

Piao

et al. 2023

Food Chemistry

View full text Add to dashboard Cite

Natural Additives as Quality Promoters in Surimi: a Brief Review

Cited by 4 publications

References 55 publications

Effectiveness of washing with atung (Parinarium glaberimum Hassk) solution on quantity and quality of dark meat yellowfin tuna (Thunnus albacares) surimi

Effectiveness of washing with atung (Parinarium glaberimum Hassk) solution on quantity and quality of dark meat yellowfin tuna (Thunnus albacares) surimi

Classification of Quality Characteristics of Surimi Gels from Different Species Using Images and Convolutional Neural Network

Collagens for surimi gel fortification: Type-dependent effects and the difference between type I and type II

Contact Info

Product

Resources

About