본 연구에서는 FT-IR 스펙트럼데이터의 다변량 통계분석 기법을 활용하여 인도수집 옥수수 계통 및 품종으로부터 단백질 함량이 높은 옥수수를 신속하게 선발할 수 있는 선발체계를 확립함과 동시에 lysine과 tryptophan의 함량분석을 목적으로 연구를 수행하였다. 총 48시료의 인도수집 옥수수 계통 및 품종과 국내산 품종을 이용하여 종자로부터 FT-IR 스펙트럼을 조사하였으며, 무작위로 선발된 24시 료를 대상으로 총 단백질 함량을 조사하였다. 대조구로 사용한 광평옥 모계(GPO1)의 경우 단백질 함량 이 9.34 ± 0.3mg/g dw인데 비하여 H4 계통의 경우 단백질 함량이 10.26 ± 0.5mg/g dw로 48개 옥 수수 시료 중에서 가장 높게 나타났다. 특히 옥수수 H4, H6, H11, 그리고 H12 계통의 경우 총 단백질 함량이 각각 10mg/g dw 이상으로 측정되어 광평옥 모계(9.34mg/g dw)와 부계(9.36mg/g dw) 및 이 들의 F1(9.14mg/g dw)보다 총 단백질 함량이 높은 계통으로 판명되었다. Cross-validation test에서 옥수수 종자 내 총 단백질 함량예측 PLS regression model의 regression coefficient(R2) 는 0.77로 비교적 정확하게 총 단백질 함량예측이 가능한 것으로 나타났다. 따라서 본 PLS regression model을 이용하여 단백질 함량이 높은 사일리지 옥수수 계통의 선발이 가능할 것으로 기대되며, 더 나아가 다양 한 옥수수 계통의 신속한 대사체 수준 평가가 가능할 것으로 예상된다.

Floral scents and metabolites from cut flowers of 14 peony cultivars (Paeonia lactiflora Pall.) were analyzed to discriminate different cultivars and to compare the Korean cultivar with the other cut peonies imported to Korea using electronic nose (E-nose) and Fourier transform infrared (FT-IR) spectroscopy combined with multivariate analysis, respectively. Principal component analysis (PCA) and discriminant function analysis (DFA) dendrogram of peony floral scents were not precisely same but there were 3 groups including same cultivars. PCA and partial least squares-discriminant analysis (PLS-DA) dendrograms of peony metabolites showed that different cut peony cultivars were clustered into two major groups including same cultivars. Fragrance pattern of Korean ‘Taebaek’ was classified to same group with ‘Jubilee’ on the PCA and DFA results and its metabolite pattern was clearly discriminated by the PCA and PLS-DA compared to the other cultivars. These results show that the 14 peony cut flowers could be discriminated corresponding to their chemical relationship and the metabolic profile of Korean ‘Taebaek’ has distinctive characteristics. Furthermore, we suggest that these results could be used as the preliminary data for breeding new cut peony cultivars and for improving the availability of Korean cut peony in cosmetic industry.

In this study we established the high throughput screening system of high functional soybean cultivars using PLS modeling from FT-IR spectral data of soybean(Glycine max L) seeds. Crude extract of 20% methanol from soybean seed powders (153 lines) were used for FT-IR spectroscopy. Total fatty acid, carotenoids, flavonoids and phenolic compounds contents from soybean seed powders were analyzed using UV-spectrum and GC analysis respectively. PCA analysis showed that 153 soybean lines formed a single clusters with a few outlier. PC score 1 and 2 represented 39.5, 16.4% of total variation, respectively. And than showed change patten from the middle to outside for PCA plot. We conducted PLS regression analysis between FT-IR spectral data and fatty acids data. Palmitic acid showed the highest regression coefficient (R=0.78). This result implied that the content of palmitic acid could be predicted from FT-IR spectral data from soybean seed powders with relatively high fidelity. PLS modeling of total carotenoids also showed regression coefficient of 0.69. Regression coefficient of total flavnoids and phenolic compounds were 0.44, 0.39, respectively. At present, we are trying to confirm the accuracy of PLS prediction modeling using targeted metabolite analysis (GC-MS, LC-MS) from predicted soybean lines. To increase the accuracy of PLS modeling, we also trying to standardization of spectroscopy and spectral data processing. Furthermore we are going to develop PLS modeling from GC-MS, LC-MS data. The PLS prediction modeling established in this study could be applied for high throughput screening of other leguminous plant.