The objective of this study was to evaluate the firmness and the sweetness index (SI) of tomatoes with a hyperspectral imaging (HSI) technique within the wavelength range of 1000 - 1550 nm. The hyperspectral images of 95 tomatoes were acquired with a push-broom hyperspectral reflectance imaging system, from which the mean spectra of each tomato were extracted from the regions of interest. The reference firmness and sweetness index of the same sample was measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing methods. The calibration model developed by PLS regression based on the Savitzky-Golay second-derivative preprocessed spectra resulted in a better performance for both the firmness and the SI of the tomatoes compared to models developed by other preprocessing methods. The correlation coefficients ($R_{pred}$) were 0.82, and 0.74 with a standard error of prediction of 0.86 N, and 0.63, respectively. Then, the feature wavelengths were identified using a model-based variable selection method, i.e., variable importance in projection, from the PLS regression analyses. Finally, chemical images were derived by applying the respective regression coefficients on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on the firmness and the SI of the tomatoes. The results show that the proposed HSI technique has potential for rapid and non-destructive evaluation of firmness and the sweetness index of tomatoes.
We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.
Background: The reports about valuable oligosaccharides in ginseng are quite limited. There is an urgent need to develop a practical procedure to detect and analyze ginseng oligosaccharides. Methods: The oligosaccharide extracts from ginseng were permethylated by solid-phase methylation method and then were analyzed by ultra-high-performance liquid chromatography-Q-Orbitrap/MS. The sequence, linkage, and configuration information of oligosaccharides were determined by using accurate m/z value and tandem mass information. Several standard references were used to further confirm the identification. The oligosaccharide composition in white ginseng and red ginseng was compared using a multivariate statistical analysis method. Results: The nonreducing oligosaccharide erlose among 12 oligosaccharides identified was reported for the first time in ginseng. In the comparison of the oligosaccharide extracts from white ginseng and red ginseng, a clear separation was observed in the partial least squares-discriminate analysis score plot, indicating the sugar differences in these two kinds of ginseng samples. The glycans with variable importance in the projection value large than 1.0 were considered to contribute most to the classification. The contents of oligosaccharides in red ginseng were lower than those in white ginseng, and the contents of maltose, maltotriose, maltotetraose, maltopentaose, maltohexaose, maltoheptaose, maltooctaose, maltononaose, sucrose, and erlose decreased significantly (p < 0.05) in red ginseng. Conclusion: A solid-phase methylation method combined with liquid chromatography-tandem mass spectrometry was successfully applied to analyze the oligosaccharides in ginseng extracts, which provides the possibility for holistic evaluation of ginseng oligosaccharides. The comparison of oligosaccharide composition of white ginseng and red ginseng could help understand the differences in pharmacological activities between these two kinds of ginseng samples from the perspective of glycans.
Eom, Jun Sik;Kim, Eun Tae;Kim, Hyun Sang;Choi, You Young;Lee, Shin Ja;Lee, Sang Suk;Kim, Seon Ho;Lee, Sung Sill
Animal Bioscience
/
v.34
no.2
/
pp.213-222
/
2021
Objective: The metabolites that constitute the rumen fluid and milk in dairy cattle were analyzed using proton nuclear magnetic resonance (1H-NMR) spectroscopy and compared with the results obtain for other dairy cattle herds worldwide. The aim was to provide basic dataset for facilitating research on metabolites in rumen fluid and milk. Methods: Six dairy cattle were used in this study. Rumen fluid was collected using a stomach tube, and milk was collected using a pipeline milking system. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by principal component analysis, partial least squares discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0. Results: The total numbers of metabolites in rumen fluid and milk were measured to be 186 and 184, and quantified as 72 and 109, respectively. Organic acid and carbohydrate metabolites exhibited the highest concentrations in rumen fluid and milk, respectively. Some metabolites that have been associated with metabolic diseases (acidosis and ketosis) in cows were identified in rumen fluid, and metabolites associated with ketosis, somatic cell production, and coagulation properties were identified in milk. Conclusion: The metabolites measured in rumen fluid and milk could potentially be used to detect metabolic diseases and evaluate milk quality. The results could also be useful for metabolomic research on the biofluids of ruminants in Korea, while facilitating their metabolic research.
Eom, Jun Sik;Kim, Eun Tae;Kim, Hyun Sang;Choi, You Young;Lee, Shin Ja;Lee, Sang Suk;Kim, Seon Ho;Lee, Sung Sill
Animal Bioscience
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v.34
no.12
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pp.1930-1939
/
2021
Objective: The aim of the study was to conduct metabolic profiling of dairy cattle serum and urine using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to compare the results obtained with those of other dairy cattle herds worldwide so as to provide a basic dataset to facilitate research on metabolites in serum and urine. Methods: Six dairy cattle were used in this study; all animals were fed the same diet, which was composed of total mixed ration; the fed amounts were based on voluntary intake. Blood from the jugular neck vein of each steer was collected at the same time using a separate serum tube. Urine samples were collected by hand sweeping the perineum. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by performing principal component analysis, partial least squares-discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0. Results: The total number of metabolites in the serum and urine was measured to be 115 and 193, respectively, of which 47 and 81, respectively were quantified. Lactate (classified as an organic acid) and urea (classified as an aliphatic acylic compound) exhibited the highest concentrations in serum and urine, respectively. Some metabolites that have been associated with diseases such as ketosis, bovine respiratory disease, and metritis, and metabolites associated with heat stress were also found in the serum and urine samples. Conclusion: The metabolites measured in the serum and urine could potentially be used to detect diseases and heat stress in dairy cattle. The results could also be useful for metabolomic research on the serum and urine of ruminants in Korea.
Anjar Windarsih;Nor Kartini Abu Bakar;Abdul Rohman;Nancy Dewi Yuliana;Dachriyanus Dachriyanus
Animal Bioscience
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v.37
no.5
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pp.918-928
/
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.
Jeong, Jin Young;Kim, Min Seok;Jung, Hyun Jung;Kim, Min Ji;Lee, Hyun Jeong;Lee, Sung Dae
Korean Journal of Agricultural Science
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v.45
no.3
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pp.455-461
/
2018
Deoxynivalenol (DON), a Fusarium mycotoxin, causes health hazards for both humans and livestock. Therefore, the aim of this study was to investigate the metabolic profiles of the liver, serum, and urine of piglets fed DON using proton nuclear magnetic resonance ($^1H-NMR$) spectroscopy. The $^1H-NMR$ spectra of the liver, serum, and urine samples of the piglets provided with feed containing 8 mg DON/kg for 4 weeks were aligned and identified using the icoshift algorithm of MATLAB $R^2013b$. The data were analyzed by multivariate analysis and by MetaboAnalyst 4.0. The DON-treated groups exhibited discriminating metabolites in the three different sample types. Metabolic profiling by $^1H-NMR$ spectroscopy revealed potential metabolites including lactate, glucose, taurine, alanine, glycine, glutamate, creatine, and glutamine upon mycotoxin exposure (variable importance in the projection, VIP > 1). Forty-six metabolites selected from the principal component analysis (PCA) helped to predict sixty-five pathways in the DON-treated piglets using metabolite sets containing at least two compounds. The DON treatment catalyzed the citrate synthase reactions which led to an increase in the acetate and a decrease in the glucose concentrations. Therefore, our findings suggest that glyceraldehyde-3-phosphate dehydrogenase, citrate synthase, ATP synthase, and pyruvate carboxylase should be considered important in piglets fed DON contaminated feed. Metabolomics analysis could be a powerful method for the discovery of novel indicators underlying mycotoxin treatments.
Jeong, Jin Young;Kim, Minseok;Ji, Sang-Yun;Baek, Youl-Chang;Lee, Seul;Oh, Young Kyun;Reddy, Kondreddy Eswar;Seo, Hyun-Woo;Cho, Soohyun;Lee, Hyun-Jeong
Food Science of Animal Resources
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v.40
no.6
/
pp.924-937
/
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.
Jeong, Jin Young;Baek, Youl-Chang;Ji, Sang Yun;Oh, Young Kyun;Cho, Soohyun;Seo, Hyun-Woo;Kim, Minseok;Lee, Hyun-Jeong
Journal of Animal Science and Technology
/
v.62
no.3
/
pp.321-333
/
2020
Beef quality is influenced by the fattening period. Therefore, meat metabolomics profiles from the different fattening periods (e.g., short-term vs. long-term) were analyzed for identify potential indicators using nuclear magnetic resonance. Additionally, blood, free fatty acid, sensory, and mineral compositions in Korean steers were determined. Blood, free fatty acid, and mineral concentrations showed significant differences between short-term and long-term groups that were fed different diets. However, there were no sensory differences in the two fattening groups. Additionally, the metabolic profiles of meats were clearly separated based on multivariate orthogonal partial least square discriminant analysis. Six metabolites of variable importance in the projection plot were identified and showed high sensitivity as candidate markers for meat characteristics. In particular, lactate, carnosine, and creatine could be directly linked to scientific indicators of the fattening stage (31 vs. 28 mo) of meat. Our findings suggest that the metabolomics approach could be a powerful method for the detection of novel signatures underlying the managing period of beef.
Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, ${\alpha}$-pinene, and ${\beta}$-terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.
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