DOI QR코드

DOI QR Code

A study on the use of FT-NIR spectophotometer for dried laver quality evaluation

마른김 품질 평가를 위한 FT-NIR 분광기 활용 연구

  • 이경인 (목포수산식품수출센터 품질관리인증팀) ;
  • 이근직 (목포수산식품수출센터) ;
  • 윤영승 (목포과학대학교 해양수산식품융합과)
  • Received : 2022.10.12
  • Accepted : 2022.11.23
  • Published : 2022.12.30

Abstract

The micro-Kjeldahl method, a common technique for analyzing crude proteins, is time-consuming and dangerous due to the employment of reagents such as sulfuric acid and sodium hydroxide. However, a Fourier transform near-infrared (FT-NIR) spectrophotometer analysis can be completed in under a minute after simple pre-processing if data has been gathered using sufficient reference material in advance. Furthermore, the use of safe reagents in this technique ensures the safety of the experimenter and the environment. In addition, a portable FT-NIR spectrophotometer enables real-time measurement at processing or distribution sites and has recently gained popularity. The standard errors of calibration and regression (r2) for the calibration result for estimating the crude protein content of dried laver were 0.9775 and 1.2526, respectively. The standard error of prediction was 1.1814, and the r2 was 0.9303 in the validation results, which was a good level. In the present study, a method for predicting the crude protein content of dried laver using an FT-NIR spectrophotometer in the range of 29%-40% crude protein content has been reported.

Keywords

References

  1. Fisheries Outlook Center of Korea Maritime Institute. 2022. Monthly outlook report, https://www.foc.re.kr/web/obsbook/list.do?rbsIdx=37&cs_category=6 
  2. Baek, E. Y. 2020. A study on the current state and problems of laver drying-processing industry. J. F. M. S. E. 32, 713-724. 
  3. Park, H. J., Kim, J. U., Jang, Y. S. 2018. A study on the management efficiency of laver drying-processing company. J. Fish. Bus. Adm. 49, 37-50.  https://doi.org/10.12939/FBA.2018.49.4.037
  4. National Fishery Products Quality Management Service. 2022. http://www.nfqs.go.kr/hpmg/qumg/actionQualityCertificationForm.do?menuId=M0000202 
  5. Choi, M. S., Kim, J. Y., Jeon, E. B., Park, S. Y. 2020. Predictive growth models of Bacillus cereus on dried laver Pyropia pseudolinearis as function of storage temperature. Korean J. Fish Aquat. Sci. 53, 699-706.  https://doi.org/10.5657/KFAS.2020.0699
  6. Kwon, K., Ryu, D. G., Jeong, M. C., Kang, E. H., Jang, Y., Kwon, J. Y., Kim, J. M., Shin, I. S., Kim, Y. M. 2018. Analysis of microbial contaminants and microbial changes during dried-laver Pyropia spp. processing. Korean J. Fish Aquat. Sci. 51, 8-14.  https://doi.org/10.5657/KFAS.2018.0008
  7. Lee, K. H., Song, S. H., Jeong, I. H. 1987. Quality changes of dried lavers during processing and storage 1. Quality evaluation of different grades of dried lavers and its changes during storage. Bull. Korean Fish. Soc. 20, 408-418. 
  8. AACC. 2000. Approved methods of the American association of cereal chemists In : St. Paul, MN, 10th edition. USA. 
  9. Oh, S. J., Choi, Y. M., Yoon, H. M., Lee, S. K., Yoo, E. A., Hyun, D. Y., Shin, M. J., Lee, M. C., Chae B. S. 2019. Statistical analysis of protein content in wheat germplasm based on near-infrared reflectance spectroscopy. Korean J. Crop Sci. 64, 353-365. 
  10. Kim, J. S., Song, M. H., Choi, J. E., Lee, H. B., Ahn, S. N. 2008. Quantification of protein and amylose contents by near infrared reflectance spectroscopy in aroma rice. Korean J. Food Sci. Technol. 40, 603-610. 
  11. Kim, J. S., Cho, Y. H., Gwag, J. G., Ma, K. H., Choi, Y. M., Kim, J. B., Lee, J. H., Kim, T. S., Cho, J. K., Lee, S. Y. 2008. Quantitative analysis of amylose and protein content of rice germplasm in RDA-genebank by near infrared reflectance spectroscopy. Korean J. Crop Sci. 53, 217-223. 
  12. Oh, S. J., Lee, M. C., Choi, Y. M., Lee, S. K., Oh, M. W., Asjad, A., Chae, B. S., Hyun, D. Y. 2017. Development of near-infrared reflectance spectroscopy (NIRS) model for amylose and crude protein contents analysis in rice germplasm. Korean J. Plant Res. 30, 38-49.  https://doi.org/10.7732/KJPR.2016.30.1.038
  13. Park, H. S., Kim, J. H., Choi, K. C., Oh, M. R., Lee, K. W., Lee, H. H. 2019. Evaluation of feed values for imported hay using near infrared reflectance spectroscopy. J. Kor. Grassl. Forage Sci. 39, 258-263.  https://doi.org/10.5333/KGFS.2019.39.4.258
  14. Lee, J. C., Yoon, Y. H., Kim, S. M., Pyo, B. S., Hsieh, F. H., Kim, H. J., Eun, J. B. 2007. Rapid prediction of amylose content of polished rice by Fourier transform near-infrared spectroscopy. Food Sci. Biotechnol. 16, 477-481. 
  15. Lee, J. C., Yoon, Y. H., Kim, S. M., Pyo, B. S., Eun, J. B. 2006. Development of prediction model for total dietary fiber content in brown rice by Fourier transform-near infrared spectroscopy. Korean J. Food Sci. Technol. 38, 165-168. 
  16. Ryu, S. N., Yang, J. J., Park, S. Z. 2005. Development of rapid prediction model of C3G content in black pigmented rice. Korean J. Crop Sci. 50, 1-3. 
  17. Joo, J. Y., Yeo, Y. H., Lee, N. R. 2017. Comparison of quality characteristics of sesame oil and blend oil by using component analysis and NIR spectroscopy. J. Korean Soc. Food Sci. Nutr. 46, 739-743.  https://doi.org/10.3746/JKFN.2017.46.6.739
  18. Lee, K. W., Song, Y., Kim, J. H., Rahman, M. A., Oh, M., Park, H. S. 2020. Variey discrimination of Sorghum-Sudangrass hybrids seed using near infrared spectroscopy. J. Kor. Grassl. Forage Sci. 40, 259-64.  https://doi.org/10.5333/KGFS.2020.40.4.259
  19. Kim, K. J., Eom, T. J. 2016. Classification of papers using IR and NIR spectra and principal component analysis. J. Korea TAPPI. 48, 34-42.  https://doi.org/10.7584/ktappi.2016.48.1.034
  20. Jin, J. H., Baek, O. H., Shin, J. Y., Ha, H. Y., Choi, D. S., Park, S. E., Ihm, Y. B., Hong, J. H. 2015. Study of feasibility test: FT-NIR spectrometer for discrimination analysis of agrochemical products. Korean J. Pestic. Sci. 19, 241-247.  https://doi.org/10.7585/kjps.2015.19.3.241
  21. Kim, J. H., Lee, K. W., Oh, M., Park, H. S. 2019. Evaluation of feed values for whole crop rice using near infrared reflectance spectroscopy. J. Kor. Grassl. Forage Sci. 39, 292-297.  https://doi.org/10.5333/KGFS.2019.39.4.292
  22. Cho, B. M., Lee, Y. J., Park, J. W., Park, I. B., Cho, J. Y., Moon, J. H. 2021. Constituents and antioxidant activities of lavers (Pyropia spp.) bred at the southwestern coastal area of Korea. Korean J. Food Sci. Technol. 53, 669-681. 
  23. Walder, F. T., Smith, M. J. 1991. Quantitative aspects of near-infrared Fourier transform Raman spectroscopy. Spectrochim Acta. 47, 1202-1216. 
  24. Ahn, C. K., Cho, B. K., Kang, J. S., Lee, K. J. 2012. Study on non-destructive sorting technique for lettuce(Lactuca sativa L) seed using fourier transform near-Infrared spectrometer. Korean J. Agric. Sci. 39, 111-116.  https://doi.org/10.7744/CNUJAS.2012.39.1.111
  25. Hruschka, W. R. 1987. Data analysis: wavelength selection methods. In P. Williams and K. Norris (eds.) Near-infrared technology in the agricultural and food industries. St. Paul, MN: Am. Assoc. Cereal Chemists Inc. 35-55. 
  26. Shenk, J. S., Westerhaus, M. O. 1991. Population structuring of near infrared spectra and modified partial least regression. Crop Science. 31, 1548-1555. https://doi.org/10.2135/cropsci1991.0011183X003100060034x