Comparison of Carbon Stable Isotope and Fatty Acid Analyses for the Authentication of Perilla Oil

Rapid quantification of single component oil in perilla oil blends by ultraviolet-visible spectroscopy combined with chemometrics

As a unconventional oil, perilla oil is much more expensive than conventional oils since it has the highest content of α-linolenic acid among vegetable oils. Thus the adulteration of perilla oil is serious, which needs to be solved. In this study, the single component oil in perilla oil blends were first quantitatively analyzed by ultraviolet-visible (UV-vis) spectroscopy combined with chemometric methods. Soybean oil and palm oil were added into perilla oil to form binary and ternary perilla oil blends. Partial least squares (PLS), back propagation-artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) were compared and the best model was selected for calibration. In order to improve the prediction performance of the calibration model, ten preprocessing methods and five variable selection methods were investigated. Results show that PLS was the best calibration method for binary and ternary perilla oil blends. For binary perilla oil blends, the correlation coefficients of prediction (Rp) obtained by PLS were both above 0.99, which does not need preprocessing and variable selection. For ternary perilla oil blends, after the best continuous wavelet transform (CWT) preprocessing and discretized whale optimization algorithm (WOA) variable selection, the Rp values obtained by the best model CWT-WOA-PLS were all above 0.97. This research provides a common framework for calibration of perilla oil blends, which maybe a promising method for quality control of perilla oil in industry.

Generation of volatiles from heated enzymatic hydrolysates of perilla meal with coconut oil in Maillard reaction system

Perilla meal hydrolysates (PMHs) were prepared by proteases; volatile profiles from heated mixtures of PMH and coconut oil (CO) were evaluated for their application as odor providers. Amino acids composition and degree of hydrolysis, and antioxidant activity in O/W emulsion of PMHs were assessed. PMHs were heated with different concentration of CO or with CO, xylose, and cysteine, which were non-Maillard and Maillard system, respectively. Among PMHs, double enzyme treatment using Alcalase and Flavourzyme showed higher degree of hydrolysis and antioxidant activity compared to PMHs from one type of enzymes. The presence of CO significantly increased oxygen, sulfur, and nitrogen-containing volatiles from PMHs in non-Maillard system. In case of Maillard system, PMHs with 10 % (w/w) CO contributed the formation of oxygen and nitrogen-containing volatiles such as furan and 2-methylpyrazine. PMHs might serve as an odor generator in the presence of edible oils like CO.

Perilla oil and α-linolenic acid ameliorated thrombosis in rats induced by collagen and epinephrine

Perilla frutescens is an annual herbaceous plant widely cultivated for oil production in China, Japan, and Korea. In this study, we investigated the effect of perilla oil (PO) on thrombosis induced by collagen and epinephrine (CE) in rats. The oral administration of PO significantly increased prothrombin time (PT) and activated partial thromboplastin time (aPTT) in the blood plasma and inhibited the expression of cells adhesion markers (CAMs) such as intercellular CAM-1 (ICAM-1), vascular CAM (VCAM-1), E-selectin and P-selectin in the aorta tissue. Furthermore, pulmonary occlusion induced by CE in rats was suppressed by PO. α-Linolenic acid (ALA) was quantified at 60.14 ± 2.50 g/100 g of PO, and its oral administration at the same concentration with that in PO exerted the similar effect on PT, aPTT, ICAM-1, VCAM-1, E-selectin and P-selectin in CE-induced thrombosis rats. Taken together, PO and ALA significantly ameliorated thrombosis by regulating CAMs.

Discriminant Analysis of the Geographical Origin of Asian Red Pepper Powders Using Second-Derivative FT-IR Spectroscopy

This study aimed to discriminate between the geographical origins of Asian red pepper powders distributed in Korea using Fourier-transform infrared (FT-IR) spectroscopy coupled with multivariate statistical analyses. Second-derivative spectral data were obtained from a total of 105 red pepper powder samples, 86 of which were used for statistical analysis, and the remaining 19 were used for blind testing. A one-way analysis of variance (ANOVA) test confirmed that eight peak variables exhibited significant origin-dependent differences, and the canonical discriminant functions derived from these variables were used to correctly classify all the red pepper powder samples based on their origins. The applicability of the canonical discriminant functions was examined by performing a blind test wherein the origins of 19 new red pepper powder samples were correctly classified. For simplicity, the four most significant variables were selected as discriminant indicator variables, and the applicable range for each indicator variable was set for each geographical origin. By applying the indicator variable ranges, the origins of the red pepper powders of all the statistical and blind samples were correctly identified. The study findings indicate the feasibility of using FT-IR spectroscopy in combination with multivariate analysis for identifying the geographical origins of red pepper powders.