Effects of sonication on the extraction of free-amino acids from moromi and application to the laboratory scale rapid fermentation of soy sauce

Soy sauce fermentation was simulated in a laboratory and subjected to 10 min of sonication. A full factorial design, including different cycles, probe size, and amplitude was used. The composition of 17 free-amino acids (FAAs) was determined by the AccQ-Tag method with fluorescent detection. Main effect plots showed total FAAs extraction was favoured under continuous sonication at 100% amplitude using a 14 mm diameter transducer probe, reaching 1214.2 ± 64.3 mg/100 ml of total FAAs. Moreover, after 7 days of fermentation, sonication treatment caused significantly higher levels (p < 0.05) of glutamic acids (343.0 ± 22.09 mg/100 g), total FAAs (1720.0 ± 70.6 mg/100 g), and essential FAAs (776.3 ± 7.0 mg/100 g) 3 days sooner than the control. Meanwhile, enzymatic and microbial behaviours remained undisturbed. Collectively, the sonication to moromi resulted in maturation 57% faster than the untreated control

Effects of natural and synthetic antioxidants on changes in 3-MCPD esters and glycidyl ester in palm olein during deep-fat frying

The effects of selected antioxidants on the changes of the quality properties and 3-monochloropropane-1,2-diol (3-MCPD) esters and glycidyl ester (GE) contents in refined, bleached, and deodorized (RBD) palm olein during the deep-fat frying (at 180 °C) of potato chips were studied. The frying duration was 100 min in five antioxidant systems for three consecutive days. The antioxidants used were butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), tert-butylhydroquinone (TBHQ), oleoresin rosemary and sage extract. Both the frying oil and the oil extracted from the fried potato chips were analyzed for the 3-MCPD esters and GE content, acylglycerol composition, free fatty acid (FFA) content, p-anisidine value (p-AV), and specific extinction coefficient K232 and K268. Generally, TBHQ and oleoresin rosemary showed significantly lower levels of 3-MCPD esters and GE. The order of effectiveness of the selected antioxidants in the frying oil and fried potato chips was BHT < BHA < sage extract < oleoresin rosemary < TBHQ. Antioxidants reduce the 3-MCPD esters and GE levels by inhibiting the formation of radical intermediates

Comparison assessment between SIM and MRM mode in the analysis of 3-MCPD ester, 2-MCPD ester and glycidyl ester

The detection of 3- and 2-MCPD ester and glycidyl ester was transformed from selected ion monitoring (SIM) mode to multiple reaction monitoring (MRM) mode by gas chromatography triple quadrupole spectrometry. The derivatization process was adapted from AOCS method Cd 29a-13. The results showed that the coefficient of determination (R2) of all detected compounds obtained from both detection mode was comparable, which falls between 0.997 and 0.999. The limit of detection and quantification (LOD and LOQ) were improved in MRM mode as compared to SIM mode. In MRM mode, the LOD of 3- and 2-MCPD ester was achieved 0.01 mg/kg while the LOQ was 0.05 mg/kg. Besides, LOD and LOQ of glycidyl ester were 0.024 and 0.06 mg/kg respectively. A blank spiked with MCPD esters (0.03, 0.10 and 0.50 mg/kg) and GE (0.06, 0.24 and 1.20 mg/kg) were chosen for repeatability and recovery tests. MRM mode showed better repeatability in area ratio and recovery with relative standard deviation (RSD %) < 5% for 2-, 3-MCPD ester at 0.5 mg/kg and GE at 1.2 mg/kg. Quantification of 22 food samples from different category were performed by repeated injections in both detection modes. Briefly, the contaminants from crude palm oil, mustard and olive oil were present in minute amount which below the LOD or LOQ in both detection modes. Sample from chocolate and infant formula products showed certain level of MCPD esters and GE, and their detection was more precisely quantitated based on MRM mode. Besides, margarine products showed a higher level of contaminations due to the high fat content in these products. MRM mode detection was proven to provide precise data with low RSD % in different food matrices. MRM mode detection was robust and selective for MCPD esters and GE analyses, it should be applied to determine the concentration of MCPD esters and GE contaminations in food

Effects of shortening and baking temperature on quality, MCPD ester and glycidyl ester content of conventional baked cake

The quality of a baked product can be greatly affected by the choice of shortening. However, a palm-based shortening can be contaminated by monochlropropanol (MCPD) ester and glycidyl ester (GE) as it is a product derived from a refined palm oil. MCPD esters and GE can be transferred into a baked product through further processing. Therefore, this study aimed to evaluate the effects of different palm-based shortening on the qualities of cake, MCPD esters and GE content during a conventional baking system. Commercial margarine, palm olein, palm mid-fraction, and soft and hard stearin were used in a cake recipe, baked at different baking temperatures (160, 180 and 200 °C) for 20 min. First, the quality characteristics of baked cake (moisture content, texture profile and surface color) was analysed. Second, the MCPD esters and GE content, acylglycerol composition and oxidation status of the fats portion from baked cake were investigated. The results showed soft stearin, palm olein and margarine delivered a similar volume, surface color, and texture to the finished product. An elevated baking temperature was detrimental to the quality characteristics of all the studied samples and delivered a finished product with extra hardness and low moisture. The free fatty acid content and specific extinction value showed that the fat portions were significantly oxidized at high baking temperatures. In addition, 2- and 3-MCPD esters were stable during baking, but GE showed that it was vulnerable to the heating process and constantly degrades when the baking temperature increased. In short, the finished products were in better quality (physical and texture properties) when lower baking temperature (160 °C) was used, especially when margarine, soft stearin and palm olein were used as the shortening. Hard stearin naturally contains lower MCPD esters and GE, but it was not able to provide similar qualities as compared to margarine sample

The detection of glycidyl ester in edible palm-based cooking oil using FTIR-chemometrics and 1H NMR analysis

Glycidyl ester (GE) is a process contaminant formed during the palm oil refining process. In this study, 156 spectra of palm-based cooking oilwere recorded by Fourier transform infrared (FTIR) spectroscopy and resulting data were processed using chemometrics approach. The relationship between spectrum data and measured data of GE content was established using Cubist, Random Forest (RF), average neural network (avNNET), and artificial neural network (nnet) model. Then, a consensus regression model was established using a fusion of those four models. GE contents measured by gas chromatography-mass spectrometer (GC-MS) were between 1.338 and 18.362 mg/kg with mean value of 6.880 ± 3.767 mg/kg and median value of 6.480 mg/kg. In this study, FTIR spectrum served as data input and calibrated using measurements from GC-MS. NMR was then applied to verify the present and structural information of GE. Prediction results of GE using the consensus model showed -high coefficient of determination (R2) value of 0.79. The contribution (in percentage) of each member model from highest to the lowest was in order Cubist > RF > avNNET > nnet. Further confirmation of the presence of GE in samples were performed using 1H NMR spectroscopy. Comprehensive analyses based on FTIR chemometrics and 1H NMR spectroscopy successfully determined GE in palm-based cooking oil

Rapid assessment of total MCPD esters in palm-based cooking oil using ATR-FTIR application and chemometric analysis

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the –CH=CH₂ or CH=CH (990–900 cm⁻¹) and C-Cl stretch (800–700 cm⁻¹) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils

Rapid quantification of 3-monochloropropane-1,2-diol in deep-fat frying using palm olein: using ATR-FTIR and chemometrics

Fourier transform infrared spectroscopy (FT-IR) was studied as an alternative technique for the estimation of the 3-monochloropropane-1,2-diol (3-MCPD) ester level in palm olein. The samples were the frying oils of potato chips with the addition of a synthetic or natural antioxidant. The same samples were evaluated by both the conventional method (GC-MS) and FTIR. Principal component analysis (PCA) was used to group the frying oils according to the level of the 3-MCPD esters. The results obtained by FTIR were consistent with the findings using an indirect determination method by GC-MS. Chemometric analysis was applied to correlate the content of 3-MCPD esters with the FTIR spectrum data. A partial least squares (PLS) model was able to predict the concentrations of 3-MCPD esters at the 95% confidence level with R2 values higher than 0.90

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

IntroductionPharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.MethodsBuffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models’ performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.ResultsOverall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.DiscussionThe development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing

Factors associated with nursing home placement of all patients admitted for inpatient rehabilitation in Singapore community hospitals from 1996 to 2005: A disease stratified analysis

10.1371/journal.pone.0082697PLoS ONE812-POLN