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Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics
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Xiaodong JIANG1, 2, Shuaizhong ZHANG1, 2, Bo PENG3, Ying WANG1, 2, *, Yuanqin SUN1, 2, Xiao LI1, 2, Lei JI1, 2
Science and Technology of Food Industry | 2026, 47(17) : 260 - 272
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Science and Technology of Food Industry | 2026, 47(17): 260-272
• Processing Technology •
Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics
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Xiaodong JIANG1, 2, Shuaizhong ZHANG1, 2, Bo PENG3, Ying WANG1, 2, *, Yuanqin SUN1, 2, Xiao LI1, 2, Lei JI1, 2
Affiliations
  • 1Marine Science Research Institute of Shandong Province, Qingdao 266104, China
  • 2Municipal Engineering Research Center of Aquatic Biological Quality Evaluation and Application, Qingdao 266104, China
  • 3College of Food Science and Engineering, Ocean University of China, Qingdao 266100, China
Published: 2026-09-01 doi: 10.13386/j.issn1002-0306.2025080046
Outline
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To explore the potential of Artemia to be developed as a food resource, promote the diversified development of the Artemia industry, and expand the biscuit food market. Biscuits containing Artemia flavor were developed. Single-factor experiments were performed to evaluate the effects of the Artemia addition, quinoa flour supplementation, and baking time and temperature on the sensory scores of the biscuits. Subsequently, the optimal processing parameters were determined using both response surface methodology (RSM) and artificial neural network (ANN) modeling. Furthermore, the nutritional and flavor profiles of the Artemia biscuits were characterized by means of nutritional composition and electronic tongue analyses as well as headspace solid-phase microextraction-coupled gas chromatography-mass spectrometry (SPME-GC-MS). Compared with the RSM model, the ANN model exhibited superior predictive and optimization capabilities, as demonstrated by its lower root mean square error (RMSE) of 1.8614, higher correlation coefficient (r) of 0.9602, and smaller errors between the measured and predicted values. The ANN-optimized biscuit formulation was as follows: Artemia (13.56 g), quinoa flour (47.26 g), low-gluten wheat flour (100 g), milk (40 g), corn oil (30 g), butter (60 g), salt (2 g), and xylitol (25 g), baked at 150 °C for 20.70 min. This optimized process yielded the highest sensory score (73.00). Additionally, the biscuits contained 6.46 g/100 g protein and 30.30 g/100 g fat. Electronic tongue analysis indicated that the taste profile was dominated by umami and saltiness. Moreover, GC-MS identified 48 volatile compounds, primarily aldehydes, acids, esters, furans, and pyrazines, with (E,E)-2,4-decadienal exhibiting the highest content (103.7 µg/kg) and odor activity value (3840.74). The development of Artemia biscuits enriches the variety of products in the biscuit market and offers a novel approach and theoretical foundation for utilizing Artemia resources in the snack-food industry.

Artemia  /  biscuit  /  artificial neural network model  /  technique  /  flavor characteristics  /  sensory scores
姜晓东, 张帅中, 彭博, 王颖, 孙元芹, 李晓, 纪蕾. 卤虫饼干研制及其营养与风味分析. 食品工业科技, 2026 , 47 (17) : 260 -272 . DOI: 10.13386/j.issn1002-0306.2025080046
Xiaodong JIANG, Shuaizhong ZHANG, Bo PENG, Ying WANG, Yuanqin SUN, Xiao LI, Lei JI. Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics[J]. Science and Technology of Food Industry, 2026 , 47 (17) : 260 -272 . DOI: 10.13386/j.issn1002-0306.2025080046
Year 2026 volume 47 Issue 17
139
55
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doi: 10.13386/j.issn1002-0306.2025080046
  • Receive Date:2025-08-11
  • Online Date:2026-10-24
  • Published:2026-09-01
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  • Received:2025-08-11
Affiliations
    1Marine Science Research Institute of Shandong Province, Qingdao 266104, China
    2Municipal Engineering Research Center of Aquatic Biological Quality Evaluation and Application, Qingdao 266104, China
    3College of Food Science and Engineering, Ocean University of China, Qingdao 266100, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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