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Rapid Quality Assessment of Antarctic Krill Based on Near-Infrared Spectroscopy
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Lin LI1, 2, Huihui SUN1, Rong CAO1, Yong SUN1, *, Zhaohui ZHANG2, *
Science and Technology of Food Industry | 2026, 47(1) : 318 - 325
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Science and Technology of Food Industry | 2026, 47(1): 318-325
• Analysis and Determination •
Rapid Quality Assessment of Antarctic Krill Based on Near-Infrared Spectroscopy
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Lin LI1, 2, Huihui SUN1, Rong CAO1, Yong SUN1, *, Zhaohui ZHANG2, *
Affiliations
  • 1Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
  • 2College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
Published: 2026-01-01 doi: 10.13386/j.issn1002-0306.2024120219
Outline
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The aim of this study was to develop a rapid quality assessment method for Antarctic krill (Euphausia superba) by integrating near-infrared spectroscopy (NIRS) with partial least squares (PLS) regression. Quantitative models were established to predict two critical quality indicators: non-protein nitrogen (NPN) and total volatile base nitrogen (TVB-N) contents. Following spectral acquisition, the key model parameters, including the preprocessing methods, characteristic spectral ranges, and principal factor numbers, were systematically optimized. Model performance was evaluated using the coefficient of determination (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). For the NPN model, multiplicative scatter correction (MSC) was selected as the optimal preprocessing method, with a characteristic spectral range of 8887.1 to 7774.2 cm−1. The TVB-N model utilized a combination of MSC and Savitzky-Golay smoothing (SG), with the full spectral band employed for modeling. Both models adopted five principal factors. After optimization and external validation, the optimized NPN model demonstrated a robust performance, with R2=0.9384, RMSEC=0.279, and RMSEP=0.443, whereas the TVB-N model achieved R2=0.8685, RMSEC=3.800, and RMSEP=4.070. These results indicated that both models exhibit high predictive accuracy (R2>0.85) and stability, with the NPN model outperforming the TVB-N model in terms of predictive capability. In conclusion, quantitative analysis models constructed by combining NIRS and PLS can predict the NPN and TVB-N content in Antarctic krill. This approach provides a rapid solution for Antarctic krill quality assessment, addressing the growing demand for the efficient monitoring of Antarctic krill resource utilization.

Antarctic krill  /  near infrared spectroscopy  /  partial least squares  /  non-protein nitrogen  /  total volatile base nitrogen
李琳, 孙慧慧, 曹荣, 孙永, 张朝辉. 基于近红外光谱技术的南极磷虾品质快速评定. 食品工业科技, 2026 , 47 (1) : 318 -325 . DOI: 10.13386/j.issn1002-0306.2024120219
Lin LI, Huihui SUN, Rong CAO, Yong SUN, Zhaohui ZHANG. Rapid Quality Assessment of Antarctic Krill Based on Near-Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2026 , 47 (1) : 318 -325 . DOI: 10.13386/j.issn1002-0306.2024120219
Year 2026 volume 47 Issue 1
141
64
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doi: 10.13386/j.issn1002-0306.2024120219
  • Receive Date:2024-12-17
  • Online Date:2026-10-24
  • Published:2026-01-01
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  • Received:2024-12-17
Affiliations
    1Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
    2College of Food Science and Engineering, Ocean University of China, Qingdao 266003, 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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