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FOOD CHEMISTRY · 2022 · IF 9.231

Halophilic bacteria as starter cultures: A new strategy to accelerate fermentation and enhance flavor of shrimp paste

Jing Yu,Kuan Lu,Jiwei Zi,Xihong Yang,Zuoxing Zheng,Wancui Xie

Retaining the traditional flavor while shortening the fermentation cycle is the current research focus for shrimp paste fermentation technology. The present study investigated the effect of combined use of halophilic bacteria as starters on the sensory and flavor characteristics of rapidly fermented shrimp paste. Sensory evaluation indicated that the starter-inoculated samples had high texture, appearance, and overall quality scores. Headspace gas chromatography-ion mobility spectrometry/mass spectrometry (HS-GC-IMS/MS) identified 95 volatile compounds, the fingerprint profiles of the starter-inoculated samples were similar to those of the traditional sample. Notably, the content of benzaldehyde, phenylethylaldehyde, and 3-methylbutyraldehyde increased significantly in the starter-inoculated samples ( p  < 0.05), which may provide an intense malt, caramel, and pleasant odor. Although the content of certain flavor substances in the starter-inoculated samples was lower than those of traditional sample, the use significantly reduced the fermentation time and mimicked the flavor profile of traditional shrimp paste to some extent.

研究领域: 食品科学 · 微生物学
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JOURNAL OF FOOD PROCESSING AND PRESERVATION · 2024 ·Vol. 2024 ·期 1 ·4506851 · IF 2

Quality Analysis and Shelf-Life Prediction of Antarctic Krill (Euphausia superba) Sauce Based on Kinetic Model and Back Propagation Neural Network Model

Hai Chi,Yuanxing Zhang,Lukai Zhao,Na Lin,Wei Kang

The study is aimed at determining how the quality of Antarctic krill ( Euphausia superba ) sauce (AkS) changed over time, including changes in color, moisture content, acid value (AV), peroxide value (POV), thiobarbituric acid reactive substance (TBARS), aerobic plate count, and sensory score. Quality variations and shelf life of AkS were estimated using kinetic model and back propagation (BP) neural network model. The results showed that sensory score, moisture content, and a ∗ values of AkS declined as storage temperature increased at 4, 25, and 37°C. In addition, the L ∗ values, b ∗ values, AV, POV, and TBARS of AkS increased as storage duration increased, indicating that high storage temperature of the samples accelerated quality degradation. The primary reason for AkS degradation was the oxidation of proteins and lipids. The POV, TBARS, and total sensory evaluation rating exhibited a highly significant correlation, and therefore, POV and TBARS were selected as the indicators for the two models. The BP neural network outperformed the kinetic model in predicting quality changes over the whole storage period, with relative errors of less than 10%. In terms of shelf-life prediction, the BP neural network’s relative errors were 11.76% and 13.39% in POV and TBARS, respectively. POV and TBARS had experimental shelf lengths of 119 and 142 d, respectively. Compared with the kinetic model, the BP neural network model predicted the quality changes and shelf life of AkS with greater accuracy and stability. The findings offer fundamental insights and innovative concepts for the production of high-value Antarctic krill products, as well as the exploitation of Antarctic krill resources.

研究领域: 食品科学 · 海洋生物学
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FOOD CHEMISTRY · 2025 · IF 9.8

Using machine learning method to predict the quality of fermented krill sauce based on the freshness of Antarctic krill

Wenhui Qu,Luying Tang,Ruoshu Li,Weijia Liu,Xiaoming Jiang,Qing Kong,Yunqi Wen,Changhu Xue

Antarctic krill, a shrimp-like crustacean, whose quality strongly influences the quality of fermented shrimp sauce, yet the relationship and prediction methods remain unclear. This study evaluated 75 krill samples of varying freshness and their corresponding shrimp sauces, and successfully developed a high-performance predictive model linking raw material quality to product quality. Shrimp spoiled significantly after only 24 h at room temperature, exhibiting darkened color, increased spoilage-related compounds, with yield and conversion rate sharply decreasing from 80.4 % and 87.1 % to 58.9 % and 63.7 %, respectively. Machine learning with multiple feature selection methods and models showed that Lasso regression effectively reduced collinearity, and its integration with a linear model enhanced prediction accuracy, with nearly 80 % of features well-fitted and about 70 % showing mean absolute percentage error below 5 %. This study successfully established an accurate model linking krill freshness to shrimp sauce quality, which offered guidance for krill utilization and intelligent fermentation.

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SENSORS AND ACTUATORS B-CHEMICAL · 2022 · IF 9.221

Simultaneous determination of three food contaminants in shrimp paste by hollow porous pentagonal Au/Ag alloy nanotubes with excellent SERS activity

Yan-ling Liu,Jian Zhu,Guo-jun Weng,Jian-jun Li,Jun-wu Zhao

Herein, we reported a robust and sensitive SERS sensing platform based on hollow porous pentagonal Au/Ag alloy nanotubes (Au/AgANTs) for simultaneous determination of three food contaminants. With the increase amount of etching agent, the morphology of the nanoparticles changed from solid to hollow porous structures , and then the disappearance of pores, accompanied by a trend of first increasing and then decreasing SERS activity. The Au/AgANTs with pentagonal cross section, hollow and pore structure provide stronger local electric field. Under optimized experimental conditions, they were utilized to achieve the simultaneous determination of rhodamine B (RhB), methylene blue (MB) and malachite green (MG) in shrimp paste with low detection limit (5.3 ×10 −11 M for RhB, 1.27 ×10 −10 M for MB and 4.07 ×10 −12 M for MG), wide linear detection range (1.0 ×10 −8 ∼1.0 ×10 −4 M for RhB, 1.0 ×10 −9 ∼1.0 ×10 −5 M for MB, and 1.0 ×10 −9 ∼1.0 ×10 −5 M for MG) and satisfactory recoveries (97.24–102.66%), suggesting that the developed method is a promising tool for accurate quantification of the three food contaminants at trace level.

研究领域: 食品科学 · 分析化学 · 纳米材料
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