The present study is intended to analyze the presence of grape seed oil (GSO) inNigella sativaL. seed oil (NSO) using Fourier transform infrared (FTIR) spectroscopy and gas chromatography (GC). FTIR spectroscopy coupled with multivariate calibration of partial least square can quantify the levels of GSO in NSO at wavelength number of 1114–1074, 1734–1382 and 3005–3030 cm–1. The coefficient of correlation (R2) obtained for the relationship between actual (x-axis) and FTIR predicted (y-axis) values are 0.981. The errors in cross validation and in prediction are 2.34% (v/v) and 2.37% (v/v), respectively.

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Free radicals, reactive nitrogen species (RNS) and reactive oxygen species (ROS) have been known to contribute several degenerative diseases such as cardiovascular diseases, cancers, rheumatoid arthritis, neurodegenerative, and diabetes mellitus. In order to overcome the negative effects of these radicals, some scientist explores natural antioxidants from plants. Curcuma longa (Turmeric) and Curcuma xanthorrhiza (Javanese Turmeric) have been known as herbs and spices with antioxidant activities due to curcuminoid contained. Antioxidant can be defined as any substances or samples capable of inhibiting free radical reactions in the oxidation reaction. Several chemical and biological methods either in vitro or in vivo have been proposed, evaluated, and used for antioxidant evaluation of studied samples. Antioxidant activities in vivo can be measured by determining antioxidant enzymes which include catalase, glutathione reductase, superoxide dismutase, glutathione peroxidase, and glutathione S-transferase. The antioxidant enzymes increased while the lipid peroxidation decreased for both Curcuma species when research using animal models. This present review highlights the potential use of C. longa and C. xanthorrhiza as natural antioxidants in vivo. Based on in vivo studies, Curcuma species are potential sources of natural antioxidants, which can be used as food supplements.

Free radicals, reactive nitrogen species (RNS) and reactive oxygen species (ROS) have been known to contribute several degenerative diseases such as cardiovascular diseases, aging, certain types of cancers, rheumatoid arthritis, neurodegenerative, and diabetes mellitus. In order to overcome the negative effects of these radicals, some scientists have explored some natural antioxidants from plants and it's by-products. The antioxidant can be defined as any substances or samples capable of inhibiting free radical reactions in the oxidation reaction. Due to curcuminoids contained, Curcuma species such as Curcuma longa, Curcuma heyneana, Curcuma mangga, and Curcuma xanthorriza were commonly used for herbal components in some traditional medicine. Several in vitro tests been introduced and used to measure antioxidant activities, namely radical scavenging assay using 2,2’-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis-(3-ethylbenzothiazoline-6 -sulfonic acid) (ABTS•+), ferric reducing antioxidant power (FRAP), ferric-thiocyanate, phosphomolybdenum method, cupric ion reducing antioxidant capacity, metal chelating power, beta-carotene bleaching linoleic-ferric-thiocyanate, and thiobarbituric acid methods. This review highlighted the antioxidant activities in vitro of C. longa, C. heyneana, C. mangga, and C. xanthorriza through several tests. To perform this review, several repute databases were analyzed and used. From this review, it can be stated that Curcuma species have powerful antioxidant activities, therefore they could be potential sources of natural antioxidants and can be used as food supplements.

Adulteration of butter with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy was developed in order to determine the lard content in butter. The multivariate calibration of partial least square model for the prediction of adulterant was developed for quantitative measurement. The model yielded the highest regression with the correlation coefficient (R2) = 0.999, its lowest root mean square error estimation = 0.0947, and its root mean square error prediction = 0.0687, respectively. Cross validation testing evaluates the predictive power of the model. Partial least square model to be effective as their intercept of R2Y and Q2Y were 0.08 and -0.34, respectively.