• Journal of the Korean Vacuum Society
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• v.17 no.3
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• pp.215-219
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• 2008

The bonding structure and composition of silicon nitride (SiNx) films were investigated by using Fourier transform infrared spectroscopy (FT-IR). SiNx films were deposited on Si substrate at $340^{\circ}C$ using a conventional PECVD system. The compositions of Si and N in SiNx films were confirmed by using Rutherford backscattering spectroscopy (RBS) and photoluminescence (PL) analysis. The surface morphology of SiNx films was also analyzed by using atomic force microscopy (AFM). It was found that the contents of NH(at. %) is the reverse related with those of SiH corresponding to the result of FT-IR. we conclude that a quantitative analysis on SiNx films can be possible through a precise detection of the contents of H in SiNx films with a FT-IR analysis only.

• Plant Biotechnology Reports
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• v.3 no.1
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• pp.87-93
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• 2009

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves and fruits of five commercial strawberry cultivars were subjected to Fourier transform infrared (FT-IR) spectroscopy. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Fisher's linear discriminant function analysis. The dendrogram based on hierarchical clustering analysis of these spectral data separated the five commercial cultivars into two major groups with originality. The first group consisted of Korean cultivars including 'Maehyang', 'Seolhyang', and 'Gumhyang', whereas in the second group, 'Ryukbo' clustered with 'Janghee', both Japanese cultivars. The results from analysis of fruits were the same as of leaves. We therefore conclude that the hierarchical dendrogram based on PCA of FT-IR data from leaves represents the most probable chemotaxonomical relationship between cultivars, enabling discrimination of cultivars in a rapid and simple manner.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Journal of Plant Biotechnology
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• v.34 no.3
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• pp.201-205
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• 2007

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts of higher plants is applied to discriminate plants genetically, leaf samples of eight cultivars of Catharanthus roseus were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR fingerprint region data were analyzed by principal component analysis (PCA). Major peaks as biomarkers were identified as the most significant contributors to distinguish samples by using genetic programming. A hierarchical dendrogram based on the results from PCA separated the eight cultivars into two major groups in the same manner as the dendrograms based on genetic fingerprinting methods such as RAPD and AFLP. A slight difference between the dendrograms was found only in branching pattern within each subgroup. Therefore, we conclude that the hierarchical dendrogram based on PCA of the FT-IR data represents the most probable chemotaxonomical relationship between cultivars, which is in general agreement with the genetic relationship determined by conventional DNA fingerprinting methods.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.383-390
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• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis could be used to discriminate Chinese cabbage breeding line at metabolic level, whole cell extracts of nine different breeding lines (three paternal, three maternal and three $F_1$ lines) were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data of Chinese cabbage plants were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). The hierarchical dendrograms based on PLS-DA from two of three cross combinations showed that paternal, maternal, and their progeny $F_1$ lines samples were perfectly separated into three branches in breeding line dependent manner. However, a cross combination failed to fully discriminate them into three branches. Thus, hierarchical dendrograms based on PLS-DA of FT-IR spectral data of Chinese cabbage breeding lines could be used to represent the most probable chemotaxonomical relationship among maternal, paternal, and $F_1$ plants. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful Chinese cabbage cultivars.

• Journal of the Korean Society of Costume
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• v.58 no.5
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• pp.211-231
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• 2008

To conserve historic textiles, analyses of textile materials, pollutants and deterioration are prerequisite steps. Based upon analytical results, guides for conservation of historic textiles are established. In analyses of textile materials, pollutants and deterioration, there are chemical methods(burning, solubility and staining), physical methods(microscopy and density) and instrumental analysis(Fourier Transform Infrared Spectroscopy (FT-IR), Fourier Transform Raman Spectroscopy(FT-Raman), Gas Chromatography(GC), Mass Spectroscopy(MS), X-Ray Fluorescence (EDXRF, WDXRF), Energy Dispersive Spectroscopy(EDS), and X-Ray Diffraction(XRD), Tensile Testing Machine etc.). Combination of qualitative and quantitative analyses makes accurate diagnosis of textile condition possible. As examples of analyses and conservation of historic textiles, Chuninsan(19 century) similar to sunshade with handing down historic textile and golden decorative skirt(17 century) with excavated costume are taken.

• Journal of Plant Biotechnology
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• v.33 no.4
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• pp.277-281
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• 2006

Fourier transform - infrared spectroscopy (FT-IR) provides biochemical profiles containing overlapping signals from a majority of the compounds that are present when whole cell extracts are analyzed. We attempted to determine the ripeness of strawberry fruit flesh by FT-IR. Fruit ripeness was divided into four developmental stages based on fruit skin color: 'yellow-green', 'pink-green', 'pink', and 'red' stages. Principal component analysis of FT-IR data of inside fruit flesh extracts clustered samples of four different developmental stages into three discrete groups: (1) 'yellow-green' group, (2) 'pink-green' group, and (3) 'pink' and 'red' group. The most remarkable difference between four different developmental stages was found in the carbohydrate fingerprint region $(1,000-1,100cm^{-1})$ of the FT-IR spectrum, indicating that differences in carbohydrate compounds represented the ripeness of strawberry fruit. Overall results indicate that FT-IR in combination with PCA enables discrimination of the ripeness of strawberry fruit flesh.

• Journal of Plant Biotechnology
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• v.38 no.1
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• pp.9-14
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• 2011

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves of nine commercial Orostachys plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA). The dendrogram based on hierarchical clustering analysis of these PLS-DA data separated the nine Orostachys species into five major groups. The first group consisted of O. iwarenge 'Yimge', 'Jeju', 'Jeongsun' and O. margaritifolius 'Jinju' whereas in the second group, 'Sacheon' was clustered with 'Busan,' both of which belong to O. malacophylla species. However, 'Samchuk', belong to O. malacophylla was not clustered with the other O. malacophylla species. In addition, O. minuta and O. japonica were separated to the other Orostachys plants. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from leaves represented the most probable chemotaxonomical relationship between commercial Orostachys plants. Furthermore these metabolic discrimination systems could be applied for reestablishment of precise taxonomic classification of commercial Orostachys plants.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1628-1628
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• 2001

Near infrared (NIR) spectroscopy is a powerful technique for non-destructive analysis that can be obtained in a wide range of environments. Recently, NIR measurements have been utilized as probe for quantitative analysis in agricultural, industrial, and medical sciences. In addition, it is also possible to make practical application on NIR for molecular structural analysis. In this work, Fourier transform near infrared (FT-NIR) measurements were carried out to utilize extensively in the relative amounts of different secondary structures were employed, such as Iysozyme, concanavalin A, silk fibroin and so on. Several broad NIR bands due to the protein absorption were observed between 4000 and $5000\;^{-1}$. In order to obtain more structural information from these featureless bands, second derivative and Fourier-self-deconvolution procedures were performed. Significant band separation was observed near the feature at $4610\;^{-1}$ ,. Particularly the peak intensity at $4525\;^{-1}$ shows a characteristic change with thermal denaturation of fibroin. The structural information can be also obtained by mid-IR and CD spectral. Correlation of NIR spectra with protein structure is discussed.

• Journal of Ginseng Research
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• v.38 no.1
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• pp.52-58
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• 2014

To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.