Dietary Guidelines and its Implications for Coconut Oil

The dietary advice that is generally followed nationally and internationally closely follows the Dietary Guidelines for Americans which was first published in 1980 and which has been through eight editions. All of the editions of the Dietary Guidelines recommend a diet that is low in fat, and most editions recommend the replacement of saturated fat with polyunsaturated fat. This recommendation is based on the saturated fat-cholesterol-heart disease hypothesis that was first proposed by Ancel Keys in the 1950s. Coconut oil was labeled as unhealthy because of its high saturated fat composition. However, this label is unwarranted. Re-analysis of the work that Keys undertook reveals that he used some inappropriate assumptions that invalidate his hypothesis. Keys undertook a large controlled feeding study, called the Minnesota Coronary Survey (MCS), to prove his hypothesis but he did not publish the results of this work. A recent re-analysis of this work has shown that his results do not support his hypothesis. Further, historical documentary evidence has revealed the significant involvement of the American sugar industry in influencing dietary policy by blaming saturated fat for heart disease. Populations that have adhered to the low-saturated fat dietary recommendation have become significantly overweight and obese. In contrast, populations that continue to follow their traditional diet which includes coconut have not had high rates of obesity. The Keys hypothesis needs to be abandoned

Improving the Value of the Coconut with Biotechnology

The fruit of the coconut tree is perhaps the most useful plant resource in the tropics. All parts of the coconut fruit have traditional uses that have been developed commercially in recent times (Foale 2003, Dayrit and Dayrit 2013). Due to its widespread household use, trade and industry statistics on coconut products reflect only part of the actual importance of the coconut. Today, coconut-based products have gone beyond the tropics and are consumed in many temperate countries and global regions such as Australia, China, Europe, North America, and the Middle East (Costello 2018). Coconut milk is the basic ingredient of traditional cuisines and desserts worldwide in the Asian tropics, while coconut flour is used in confectionery and bakery products. Coconut oil is widely used as cooking oil, hair and cosmetic oil, and domestic remedies for burns and skin ailments and in soap-making and preparation of traditional medicine. Coconut water can be either consumed fresh or converted into vinegar and nata de coco. The residues of these processes are used for animal feed and soil enhancer. The young inflorescences can be tapped directly to obtain coconut sap. This natural honey-like product can then be evaporated to prepare coco sugar or fermented to produce coconut sap wine and vinegar. These products are markedly distinct from those produced from coconut water. However, if the sap is collected, the harvest of nuts is lost. Nondairy products from the coconut, such as margarines, yoghurts, and cheese, have become more and more popular in the global market. This chapter will deal mainly with the products that can be obtained from the fruit

The Application of 13C NMR and Untargeted Multivariate Analysis for Classifying Virgin Coconut Oil

Virgin coconut oil (VCO) is produced from fresh mature coconut meat without the use of chemicals or high heat. VCO can be made using three processes: fermentation, centrifuge, and expeller. To determine quality, it is important to be able to differentiate control VCO (fresh) from old VCO, refined bleached and deodorized coconut oil (RBDCO), and VCO which has been adulterated with RBDCO. Differentiating these types of samples has remained a challenge because of their chemical similarity. This study investigated the ability of 13C NMR and multivariate analysis to differentiate these different coconut oil samples. The methodology used the standard 13C NMR pulse sequence with broadband 1H decoupling with dioxane as the internal standard (IS). After pre-processing of the spectra (alignment, bucketing/binning, normalization with respect to dioxane IS peak), untargeted multivariate analyses, both unsupervised and supervised, were done on the bins of the 13C peaks. Principal components analysis (PCA), a linear unsupervised method, was able to differentiate control VCO (n = 57) from RBDCO (n = 21), adulterated VCO (n = 9), and old VCO (n = 11). Partial least squares–discriminant analysis (PLS–DA) was used as the supervised linear binary classifier. Using overall accuracy and AUC-ROC curves (by 100 cross validation and single validation using manual holdout), the supervised dataset with an optimized model gave performances that were 99%, 95%, and 80% improved in differentiating control VCO vs. RBDCO, old VCO, and adulterated VCO (one vs. one), respectively. Predictive ability (Q2 \u3c 0.20) and overall accuracy (\u3c0.80) were poor compared to the previous models for binary classifier models (one vs. rest) to differentiate among the three VCO processes. This may be due to the variations in production conditions and methods that different VCO producers use. We conclude that 13C NMR combined with linear techniques can be used to accurately differentiate fresh VCO from RBDCO, old VCO, and adulterated VCO

Untargeted Bioassay Strategy for Medicinal Plants: In Vitro Antidiabetic Activity and 13C NMR Profiling of Extracts from Vitex negundo L

Bioassay-guided fractionation is the principal method for the identification of active constituents in medicinal plants. By design, this method aims to identify the most active compound in a complex mixture with the objective of discovering novel drug candidates. Described here is a complementary method for the identification of known bioactive compounds in medicinal plants which is untargeted and which takes advantage of the large NMR database of known natural products and availability of statistical software. This untargeted bioassay strategy is demonstrated as a proof of principle in the determination of the antidiabetic compounds in Vitex negundo L. Crude methanol and ethanol extracts, and chloroform, ethyl acetate and aqueous fractions of V. negundo L. were prepared and tested for their in vitro antidiabetic potential using the glucose diffusion retardation assay and the in vitro starch-amylase inhibition assay. The same crude extracts and fractions were profiled using 13C nuclear magnetic resonance (NMR) spectroscopy. The 13C NMR spectra of twelve known compounds from the semi-polar fraction of V. negundo – two iridoids, seven iridoid glucosides, two flavonoids and one flavonoid C-glucoside – were matched from the 13C NMR spectra of the extracts and fractions. The 13C NMR match factor values of the twelve compounds were used in the multivariate correlation analysis with antidiabetic activity using the glucose diffusion retardation activity and the starch-amylase inhibition assay. This method was able to correlate the seven iridoid glucosides with the antidiabetic activity, a result that would have been difficult to obtain using bioassay-guided fractionation

Iota-carrageenan hydrolysis by Pseudoalteromonas carrageenovora IFO12985

We report iota-carrageenan hydrolysis by Pseudoalteromonas carrageenovora IFO 12985. Kappa-carrageenase and lambda-carrageenase were previously isolated from this organism, but iota-carrageenase activity had not been reported in the literature. P. carrageenovora was grown in iota-carrageenan-based liquid medium. Using the zone of depression assay, transfer of aliquots of the culture to solid medium with 2% iota- and kappa-carrageenan showed extensive hydrolysis of iota-carrageenan. Analysis of the hydrolysates by C-13 Nuclear Magnetic Resonance spectroscopy confirmed degradation of the iota-carrageenan. Hydrolytic activity of P. carrageenovora grown in iota-carrageenan was compared with that of the same organism grown in kappa-carrageenan. Cell-free supernatants from each yielded subtle differences in hydrolytic profiles, but showed degradation patterns consistent with hydrolysis to fragments smaller than 1.4 kDa, corresponding to six or fewer monosaccharide units. Different protein expression bands on SDS-PAGE were also observed for the cell-free supernatants of P. carrageenovora grown in iota- versus kappa-carrageenan, with lower kappa-carrageenase expression observed in the organism grown in iota-carrageenan

Chemical profiling and chemical standardization of Vitex negundo using 13C NMR

Chemical profiling and standardization of the defatted methanol extract of the leaves of Vitex negundo L. were carried out using 13C nuclear magnetic resonance (NMR) analysis followed by chemometric analysis of the chemical shift data. Chemical profile was obtained using a k-means cluster profile and chemical standardization which was achieved using a multivariate control chart. The V. negundo samples were made up of four groups: the training set, submitted samples from production farms, commercial samples, such as tablets, capsules and teas, and experimental samples (samples which were allowed to degrade). Four groups were generated in k-means cluster, which generally corresponded to the four types of samples. The multivariate control chart identified samples whose quality exceeded the upper control limit, all of which were commercial samples and experimental samples. The samples were also analyzed by quantitative thin layer chromatography (qTLC) using agnuside as marker compound. Comparison of the qTLC results with the k-means cluster and the multivariate control chart showed poor correspondence. This means that a univariate analysis of a plant sample using a marker compound is useful only for quantification of the target compound. On the other hand, chemical profiling and standardization of medicinal plants should use a multivariate method

Analysis of Volatile Organic Compounds in Virgin Coconut Oil and their Sensory Attibutes

The volatile compounds in the headspace of twenty-four commercial virgin coconut oil (VCO) samples prepared by different methods (i.e. expeller, centrifugation, and fermentation with and without heat) were analyzed by solid phase microextraction-gas chromatography mass spectrometry (SPME-GCMS). The following volatile organic compounds (VOCs) were identified: ethyl acetate, acetic acid, 2-pentanone, hexanal, n-octane, 2-heptanone, limonene, nonanal, octanoic acid, ethyl octanoate, δ-octalactone, ethyl decanoate, δ-decalactone, and dodecanoic acid. Fermentation-produced samples were found to have higher levels of acetic acid and free fatty acids in the headspace compared to VCO produced using the centrifuge and expeller methods. Descriptive sensory analysis of the VCO samples by a trained panel was carried out to determine its sensory attributes and to correlate the volatile compounds that are responsible for VCO aroma. Principal components regression (PCR) of the SPME-derived analytical and sensory data indicates that lactones impart coconut-like aroma, while octanoic acid is mainly responsible for the rancid and acid aroma. SPME-GCMS can be used to differentiate VCO produced by physical means from fermentationproduced samples and can be used as a method to monitor VCO product quality

Profile of Volatile Organic Compounds (VOCs) from Cold-Processed and Heat-Treated Virgin Coconut Oil (VCO) Samples

Virgin coconut oil (VCO) can be prepared with or without heat. Fermentation and centrifuge processes can be done without the use of heat (cold process), while expelling involves heat due to friction. Volatile organic compounds (VOCs) from VCO samples prepared using these three methods were collected using solid phase microextraction (SPME) and analyzed using gas chromatography–mass spectrometry (GC-MS). Twenty-seven VCO samples from nine VCO producers were analyzed. The VOCs from refined, bleached, and deodorized coconut oil (RBDCO) were also obtained for comparison. Fourteen compounds were found to be common in more than 80% of the VCO samples analyzed. These included: Acetic acid; C6, C8, C10, C12, and C14 fatty acids, and their corresponding delta-lactones; and C8, C10 and C12 ethyl carboxylates. Fourteen minor VOCs were likewise detected which can be grouped into five types: Carboxylic acids (formic acid, butanoic acid, benzoic acid, and pentadecanoic acid), ketones (acetoin, 2-heptanone), an alcohol (ethanol), aldehydes (acetaldehyde, hexanal, benzaldehyde), esters (ethyl acetate, methyl tetradecanoate), and hydrocarbons (n-hexane and toluene). Five pyrazines were detected in expeller VCO. Various hydrocarbons from C5 to C14 were noted to be higher in old RBDCO and VCO samples. There were variations in the VOCs within each VCO process as each producer used different processing times, temperatures, and drying procedures. Principal components analysis (PCA) was able to group the samples according to the process used, but there were overlaps which may be due to variations in the specific procedures used by the manufacturers. These results may help VCO manufacturers control their production processes