• Food Science of Animal Resources
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• v.40 no.6
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• pp.924-937
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• 2020

The purpose of this study was to investigate the meat metabolite profiles related to differences in beef quality attributes (i.e., high-marbled and low-marbled groups) using nuclear magnetic resonance (NMR) spectroscopy. The beef of different marbling scores showed significant differences in water content and fat content. High-marbled meat had mainly higher taste compounds than low-marbled meat. Metabolite analysis showed differences between two marbling groups based on partial least square discriminant analysis (PLS-DA). Metabolites identified by PLS-DA, such as N,N-dimethylglycine, creatine, lactate, carnosine, carnitine, sn-glycero-3-phosphocholine, betaine, glycine, glucose, alanine, tryptophan, methionine, taurine, tyrosine, could be directly linked to marbling groups. Metabolites from variable importance in projection plots were identified and estimated high sensitivity as candidate markers for beef quality attributes. These potential markers were involved in beef taste-related pathways including carbohydrate and amino acid metabolism. Among these metabolites, carnosine, creatine, glucose, and lactate had significantly higher in high-marbled meat compared to low-marbled meat (p<0.05). Therefore, these results will provide an important understanding of the roles of taste-related metabolites in beef quality attributes. Our findings suggest that metabolomics analysis of taste compounds and meat quality may be a powerful method for the discovery of novel biomarkers underlying the quality of beef products.

• Journal of Plant Biotechnology
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• v.45 no.3
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• pp.250-256
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• 2018

To determine whether metabolite fingerprinting for whole cell extracts based on Fourier transform infrared spectroscopy (FT-IR) can be used to discriminate and compare metabolic equivalence, standard medicinal parts of Cynanchum wilfordii (Maxim.) Hemsl. and their adventitious roots were subjected to FT-IR. The principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) from FT-IR spectral data showed that whole metabolic pattern from the adventitious root of Cynanchum wilfordii was highly similar to its standard medicinal parts. These results clearly showed that mass proliferation of adventitious roots could be applied for the novel supply of standard medicinal parts of medicinal plants. Furthermore, FT-IR spectroscopy combined with multivariate analysis established in this study could be applied as an alternative tool for discriminating of whole metabolic equivalence from standard medicinal parts. Thus, it is proposed that these metabolic discrimination systems from the adventitious root of Cynanchum wilfordii could be applied for metabolic standardization of in vitro grown Cynanchum wilfordii.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.105-114
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• 2014

We established a high throughput screening system of African yam tuber lines which contain high 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 $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, 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 the 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 ($R^2$) 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.

• Food Science of Animal Resources
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• v.42 no.2
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• pp.341-349
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• 2022

The objective of this study was to examine the effect of temperature abuse prior to cold storage on changes in quality and metabolites of frozen/thawed beef loin. The aerobic packaged samples were assigned to three groups: refrigeration (4℃) (CR); freezing (-18℃ for 6 d) and thawing (20±1℃ for 1 d), followed by refrigeration (4℃) (FT); temperature abuse (20℃ for 6 h) prior to freezing (-18℃ for 6 d) and thawing (20±1℃ for 1 d), followed by refrigeration (4℃) (AFT). FT and AFT resulted in higher volatile basic nitrogen (VBN) values than CR (p<0.05), and these values rapidly increased in the final 15 d. Cooking loss decreased significantly with an increase in the storage period (p<0.05). In addition, cooking loss was lower in the FT and AFT groups than in the CR owing to water loss after storage (p<0.05). A scanning electron microscope (SEM) revealed that frozen/thawed beef samples were influenced by temperature abuse in the structure of the fiber at 15 d. Metabolomic analysis showed differences among CR, FT, and AFT from partial least square discriminant analysis (PLS-DA) based on proton nuclear magnetic resonance (¹H NMR) profiling. The treatments differed slightly, with higher FT than AFT values in several metabolites (phenylalanine, isoleucine, valine, betaine, and tyrosine). Overall, temperature abuse prior to freezing and during thawing of beef loin resulted in accelerated quality changes.

• Journal of Plant Biotechnology
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• v.42 no.1
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• pp.60-70
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• 2015

The aim of this study was to investigate whether fourier transform infrared (FT-IR) spectroscopy can be applied to simultaneous determination of fatty acids contents in different soybean cultivars. Total 153 lines of soybean (Glycine max Merrill) were examined by FT-IR spectroscopy. Quantification of fatty acids from the soybean lines was confirmed by quantitative gas chromatography (GC) analysis. The quantitative spectral variation among different soybean lines was observed in the amide bond region ($1,700{\sim}1,500cm^{-1}$), phosphodiester groups ($1,500{\sim}1,300cm^{-1}$) and sugar region ($1,200{\sim}1,000cm^{-1}$) of FT-IR spectra. The quantitative prediction modeling of 5 individual fatty acids contents (palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid) from soybean lines were established using partial least square regression algorithm from FT-IR spectra. In cross validation, there were high correlations ($R^2{\geq}0.97$) between predicted content of 5 individual fatty acids by PLS regression modeling from FT-IR spectra and measured content by GC. In external validation, palmitic acid ($R^2=0.8002$), oleic acid ($R^2=0.8909$) and linoleic acid ($R^2=0.815$) were predicted with good accuracy, while prediction for stearic acid ($R^2=0.4598$), linolenic acid ($R^2=0.6868$) had relatively lower accuracy. These results clearly show that FT-IR spectra combined with multivariate analysis can be used to accurately predict fatty acids contents in soybean lines. Therefore, we suggest that the PLS prediction system for fatty acid contents using FT-IR analysis could be applied as a rapid and high throughput screening tool for the breeding for modified Fatty acid composition in soybean and contribute to accelerating the conventional breeding.