• Journal of the Korean Society of International Agriculture
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• v.31 no.4
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• pp.378-383
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• 2019

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate papaya at metabolic level. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). 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 (1,700-1,500 cm^-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm^-1) and carbohydrate compounds (1,100-950 cm^-1). The result of PCA analysis showed that papaya leaves could be separated into clusters depending on different growth temperature. In this case, showed discrimination confirmed according to metabolite content of growth condition from papaya. And PLS-DA analysis also showed more clear discrimination pattern than PCA result. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful papaya cultivars.

• Animal Bioscience
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• v.37 no.5
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• pp.918-928
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• 2024

Objective: The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Methods: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. Results: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R² >0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). Conclusion: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

• Journal of Applied Biological Chemistry
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• v.65 no.4
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• pp.439-446
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• 2022

Soybean is a crop with high-quality of protein and oil, and it is one of the most widely used genetically modified (GM) crops in the world today. In South Korea, Kwangan is the most utilized variety as a parental line for GM soybean development. In this study, untargeted LC-MS metabolomic approaches were used to compare metabolite profiles of Kwangan and three other commercial varieties cultivated in Gunwi and Jeonju in 2020 year. Metabolomic studies revealed that the 4 soybean varieties were distinct based on the partial least squares-discriminant analysis (PLS-DA) score plots; 18 metabolites contributed to variety distinction, including phenylalanine, isoflavones, and fatty acids. All varieties were clearly differentiated by location on the PLS-DA score plot, indicating that the growing environment is also attributable to metabolite variability. In particular, isoflavones and linolenic acid levels in Kwangan were significantly lower and higher, respectively compared to those of the three varieties. It was discussed that it might need to include more diverse conventional varieties as comparators in regard to metabolic characteristics of Kwangan for the assessment of substantial equivalence biogenetically engineered soybeans in a Kwangan-variety background.

• Journal of Microbiology and Biotechnology
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• v.28 no.12
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• pp.1971-1981
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• 2018

In this study, we investigated the altered enzymatic activities and metabolite profiles of koji fermented using varying permutations of Aspergillus oryzae and/or Bacillus amyloliquefaciens. Notably, the protease and ${\beta}$-glucosidase activities were manifold increased in co-inoculated (CO) koji samples (co-inoculation of A. oryzae and B. amyloliquefaciens). Furthermore, gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling indicates that levels of amino acids, organic acids, sugars, sugar alcohols, fatty acids, nucleosides, and vitamins were distinctly higher in CO, SA (sequential inoculation of A. oryzae, followed by B. amyloliquefaciens), and SB (sequential inoculation of B. amyloliquefaciens, followed by A. oryzae). The multivariate principal component analysis (PCA) plot based on GC-MS datasets indicated a clustered pattern for MA and MB (koji samples inoculated either with A. oryzae or B. amyloliquefaciens) across PC2 (20.0%). In contrast, the CO, SA, and SB metabolite profiles displayed segregated patterns across PLS1 (22.2%) and PLS2 (21.1%) in the partial least-square discriminant analysis (PLS-DA) model. Intriguingly, the observed disparity in the levels of primary metabolites was engendered largely by higher relative levels of sugars and sugar alcohols in MA, SA, and CO koji samples, which was commensurate with the relative amylase activities in respective samples. Collectively, the present study emphasizes the utility of integrated biochemical and metabolomic approaches for achieving the optimal permutation of fermentative inocula for industrial koji preparation.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.31-37
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• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

• Food Engineering Progress
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• v.15 no.2
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• pp.136-142
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• 2011

The objective of this research was to classify intact soybeans and foreign objects using near-infrared (NIR) spectroscopy. Intact soybeans and foreign objects were scanned using a NIR spectrometer equipped with scanning monochromator. NIR spectra of intact soybeans and foreign objects in the wavelength range from 900 to 1800 nm were collected. The classification of intact soybeans and foreign objects were conducted by using partial least-square discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) multivariate methods. Various types of data pretreatments were tested to develop the classification models. Intact soybeans and foreign objects were successfully classified by the PLS-DA prediction model with mean normalization pretreatment. These results showed the potential of NIR spectroscopy combined with multivariate analysis as a method for classifying intact soybeans and foreign objects.

• Journal of Plant Biotechnology
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• v.42 no.3
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• pp.257-264
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• 2015

To determine whether metabolite fingerprinting for whole cell extracts based on Fourier transform infrared (FT-IR) spectroscopy can be used to discriminate and compare metabolic equivalence, standard medicinal parts from four medicinal plants (Cynanchum wilfordii Hemsley, Atractylodes japonica Koidz, Polygonum multiflorum Thunberg and Astragalus membranaceus Bunge) and their in vitro-produced adventitious roots were analyzed by FT-IR spectroscopy. The principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) from the FT-IR spectral data showed that the whole metabolic pattern from Cynanchum wilfordii was highly similar to Astragalus membranaceus. However, Atractylodes japonica and Polygonum multiflorum showed significantly different metabolic patterns. Furthermore, adventitious roots from Cynanchum wilfordii and Astragalus membranaceus also showed similar metabolic patterns compared to their standard medicinal parts. These results clearly show that mass proliferation of adventitious roots may be applied to aquire novel supply of standard medicinal parts from medicinal plants. However, the whole metabolic pattern from adventitious roots of Atractylodes japonica and Polygonum multiflorum were not similar to their standard medicinal parts. Furthermore, FT-IR spectroscopy combined with multivariate analyses established in this study may be applied as an alternative tool to discriminate the whole metabolic equivalence from several standard medicinal parts. Thus, we suggest that these metabolic discrimination systems may be applied for metabolic standardization of herbal medicinal resources.

• 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.