• Title/Summary/Keyword: 근적외선 스펙트럼

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Development of Prediction Model for Sugar Content of Strawberry Using NIR Spectroscopy (근적외선 분광을 이용한 딸기의 당도예측모델 개발)

  • Son, Jaeryong;Lee, Kangjin;Kang, Sukwon;Yang, Gilmo;Seo, Youngwook
    • Food Engineering Progress
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    • v.13 no.4
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    • pp.297-301
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    • 2009
  • This study was performed to develop a prediction model of sugar content for strawberry. Near-infrared (NIR) spectroscopy has been prevailed for on-line and portable applications for non-invasive quality assessment of intact fruit. This work presents effects of illumination method and coating of reflection surface of light source on prediction result of sugar content. Effect of preprocessing methods was also examined. A low-cost commercially available VIS/NIR spectrometer was used for estimation of total soluble solids content (Brix). To predict sugar contents of strawberry, the best results were obtained with the spectrum data measured under intensive illuminations at three locations induced from the light source with fiber optic bundles. Gold coating of reflection surface of light source lamp gave favorable effect to prediction result. The best results in validation of PLSR model were $r_{SEP}$ = 0.891 and SEP = 0.443 Brix under OSC preprocessing and those of PCR were $r_{SEP}$ = 0.845, SEP $r_{SEP}$= 0.520 Brix, under no preprocessing.

Statistical Analysis of Protein Content in Wheat Germplasm Based on Near-infrared Reflectance Spectroscopy (밀 유전자원의 근적외선분광분석 예측모델에 의한 단백질 함량 변이분석)

  • Oh, Sejong;Choi, Yu Mi;Yoon, Hyemyeong;Lee, Sukyeung;Yoo, Eunae;Hyun, Do Yoon;Shin, Myoung-Jae;Lee, Myung Chul;Chae, Byungsoo
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.64 no.4
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    • pp.353-365
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    • 2019
  • A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R2, and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.