Metabolite Fingerprinting Using 1H-NMR Spectroscopy and Chemometrics for Classification of Three Curcuma Species from Different Origins.

Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. 1H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the 1H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R2 value more than 0.8) and good predictivity (Q2 value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R2 and Q2. It can be concluded that metabolite fingerprinting using 1H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.

Analysis of Lard in Sausage Using Fourier Transform Infrared Spectrophotometer Combined with Chemometrics.

Sausage is one of foods which must be confirmed halal to consumers. Meat is commonly used in producing sausages, especially beef. However, due to high cost of meat producer usually mixes the ingredients with other cheaper meats, such as pork. This study aimed to analyze the differences in the spectral profile of lard and beef in the sausages using Fourier transform infrared (FTIR). Lard and beef tallow was extracted using Soxhlet apparatus at ±70°C for 6 h with n-hexane. After extraction, lard and beef tallow was evaporated. Then obtained fats were stored in eppendorf and analyzed using FTIR spectrophotometer. The results were then combined with chemometrics such as Partial least squares (PLS) for the quantitative analysis and principal component analysis (PCA) for classification. PLS and PCA analysis was performed on 1200-1000 cm-1. The results of the analyzed PLS provided the linear regression equation y = 0.921x + 4.623 with R 2 = 0.985 and root-mean-square error of calibration (RMSEC) = 2.094%. External validation root-mean-square error of prediction (RMSEP) was 4.77% and internal validation root-mean-square-error cross-validation (RMSECV) was 5.12%. The results of the PCA analysis showed the classification of different quadrants between 100% pork sausage and 100% beef sausage. Thus, it can be concluded that FTIR spectroscopy method combined with chemometrics can be applied to identify the presence of pork in the sausage.