Application Research of NIR Technology on the Fast Quantification of the Key Quality Indicators of Antarctic Krill Oil

MIAO Junkui; ZHANG Yating; JIN Yongpei; LIU Xiaofang; YU Yuan; LENG Kailiang; YANG Zengguang; JIANG Yongyi

doi:10.13386/j.issn1002-0306.2021120098

Volume 43 Issue 14

Jul. 2022

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Science and Technology of Food Industry > 2022 > 43(14): 10-17. > DOI: 10.13386/j.issn1002-0306.2021120098

MIAO Junkui, ZHANG Yating, JIN Yongpei, et al. Application Research of NIR Technology on the Fast Quantification of the Key Quality Indicators of Antarctic Krill Oil[J]. Science and Technology of Food Industry, 2022, 43(14): 10−17. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021120098.

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Application Research of NIR Technology on the Fast Quantification of the Key Quality Indicators of Antarctic Krill Oil

1.
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Key Laboratory of Sustainable Development of Polar Fishery, Ministry of Agriculture and Rural Affairs, Qingdao Engineering Research Center for Polar Fishery Resources Development, Qingdao 266071, China
2.
Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266200, China
3.
Qingdao Future Testing Technology Co., Ltd., Qingdao 266111, China

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Received Date: December 08, 2021
Available Online: May 05, 2022

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Abstract

Abstract

In this paper, near-infrared spectroscopy technology was used to establish a rapid determination of the content of phospholipids, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), astaxanthin and acid value in Antarctic krill oil. Partial least squares (PLS) was used as modeling method. The NIR spectra of krill oil were treated by first-order difference (FD), FD+Savitzky-Golay (SG), FD+Norris (N), second-order difference (SD), SD+SG and SD+N. And root mean square error of cross validation (RMSECV), root mean square error of external prediction (RMSEP), the ratio of the RMSEP to standard deviation of reference data in the prediction (RPDEV), correlation coefficient in calibration (RC), correlation coefficient in cross validation (RCV) and correlation coefficient in external validation (REV) of the prediction models were compared. It was determined that the best treatment methods for phospholipid, EPA and DHA in krill oil were FD, FD and SD+N, and the acid value model did not need to be treated. Under the optimal conditions, RC, RCV and REV of the four components in the NIR prediction model were all above 0.95, except that RCV of acid value was slightly lower (0.917). Meanwhile, the RPDCV and RPDEV of the four components, except for the acid value, which was 2.365, slightly less than 2.5, the rest meet the requirements of greater than 2.5. It showed that prediction model of phospholipid, EPA and DHA of krill oil using near infrared spectroscopy had a good prediction accuracy. The difference between RMSEC and RMSECV was not significant, indicating that the model had good stability. Due to the low content and complex composition of astaxanthin in krill oil, the RC, RCV and REV of the NIR quantification model were all under 0.60, near infrared spectroscopy (NIR) was not suitable for the rapid quantification of astaxanthin in krill oil. In this study, it was confirmed that NIR could be used for the rapid quantification of phospholipids, EPA, DHA and acid value in krill oil and was able to be used as an effective substitute and supplement for traditional chemical detection methods.
- Antarctic krill oil,
- phospholipid,
- EPA,
- DHA,
- astaxanthin,
- acid value,
- near infrared spectroscopy (NIR),
- fast quantification

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References

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