FT-IR 스펙트럼 기반 다변량통계분석을 이용한 작물의 품종 및 형질 예측 모델링 확립과 적용

Abstract

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate major leguminous plant at metabolic level, seeds of six major leguminous plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from seeds were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). The hierarchical dendrogram based on PLS-DA separated the six leguminous plants into four major groups. The first group consisted of Vigna radiata var. radiate, Vigna angularis var. angularis and Vigna unguiculata subsp. unguiculata, whereas Pisum sativum var. sativum, Glycine max L and Phaseolus vulgaris var. vulgaris were clustered into a separate group respectively. These results showed that metabolic classification system were in accordance with known phylogenic taxonomy. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from seeds represented the most probable chemotaxonomical relationship between six leguminous plants. Furthermore these metabolic discrimination systems could be applied for rapid selection and classification of useful leguminous cultivars. We established a high throughput screening system of African yam tuber lines which contain higher contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to 0.91mg·g-1 dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to 229mg·g-1 and from 0.29 to 5.2mg·g-1 dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700–1,500, 1,500–1,300 and 1,100-950 cm-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700–1,500cm-1), phosphodiester groups from nucleic acid and phospholipid (1,500–1,300cm-1) and carbohydrate compounds (1,100-950cm-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients(R2) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines. This work examined the feasibility of prediction of carotenoid content without HPLC analysis using prediction modeling of carotenoid content by multivariate analysis combined with FT-IR and HPLC data. FT-IR spectra from peels and flesh of citrus (Citrusunshiu Marc. cv. Miyagawa) fruit were measured at monthly intervals. Quantitative analysis of carotenoids from the same citrus fruit was confirmed by quantitative HPLC analysis. FT-IR spectroscopy showed that remarkable increase in carbohydrate region (1,000-1,200 cm-1) and decrease in amide region (1,500-1,700 cm-1). HPLC also showed that increase in β-cryptoxanthin and decrease in lutein content during citrus fruit development. Reliable prediction of antheraxanthin (R2=0.9117), β-carotene (R2=0.8816), β-cryptoxanthin (R2=0.8856), and violaxanthin (R2=0.7305) was obtained from partial least square (PLS) regression modeling. Considering these results, FT-IR might be applied for metabolic evaluation of citrus fruit during ripening. Fourier transform infrared spectroscopy (FT-IR) spectroscopy in combination with multivariate analysis was used to discriminate two different F1 hybrid lines from their parental inbred lines. Genomic DNA was isolated from leaves of three parental lines and two F1 hybrid lines of Brassica campestris subsp. pekinensis. Purified genomic DNA was analyzed by FT-IR spectroscopy in the spectral region from 4,000 to 400 cm-1. FT-IR spectra confirmed typical spectral differences between the frequency regions of N–H stretching (amide I) and C=O stretching vibrations (amide II) as well as PO2- ionized asymmetric and symmetric stretching. Principal component analysis was able to discriminate between F1 hybrid progenies depending on their parental lineages, even though they share the same maternal background. Partial least squares discriminant analysis gave a more clear discrimination between the two parental lines and their hybrid progenies. These FT-IR spectral differences might be directly related to subtle changes in the base functional group and backbone structures of genomic DNA. Considering these results, this technique could provide a solid research foundation for FT-IR spectral-based rapid diagnosis, selection, and discrimination of parental lines from their progenies. Furthermore, this technique could be applied to test purity in the hybrid seed industry. We established a high throughput screening system of Citrus fruits lines which contain higher contents of sugar content and acidity content using HORIBA and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The sugar contents from 5 citrus fruits varied from 0.84 to 10.3˚Brix. The acidity contents also varied from 0.61 to 0.99%. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700–1,500, 1,500–1,300 and 1,100-950 cm-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700–1,500cm-1), phosphodiester groups from nucleic acid and phospholipid (1,500–1,300cm-1) and carbohydrate compounds (1,100-950cm-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate 5 citrus fruits lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of sugar contents and acidity contents from citrus fruits lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients(R2) between predicted values and estimated values of sugar contents and acidity contents were 0.99 respectively. These results showed that quantitative predictions of sugar contents and acidity contents were possible from FT-IR spectra of citrus fruits lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding citrus fruits lines.