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

Quality characteristics of virgin coconut oil:Comparisons with refined coconut oil

Virgin coconut oil (VCO) is a vegetable oil that is extracted from fresh coconut meat and is processed using only physical and other natural means. VCO was compared to refined, bleached, and deodorized coconut oil (RCO) using standard quality parameters, 31 P nuclear magnetic resonance (NMR) spectroscopy, and headspace solid-phase micro - extraction/gas chromatography mass spectrometry (SPME/GCMS). VCO tends to have higher free fatty acids (FFAs), moisture, and volatile matter and lower peroxide value than RCO. However, the range of values overlap and no single standard parameter alone can be 31 used to differentiate VCO from RCO. Using 31P NMR, VCO and RCO can be distinguished in terms of the total amount of diglycerides: VCO showed an average content (w/w %) of 1.55, whereas RCO gave an average of 4.10. There was no overlap in the values found for individual VCO and RCO samples. There are four common methods of producing VCO: expeller (EXP), centrifuge (CEN), and fermentation with and without heat. VCO products prepared using these four methods could not be differentiated using standard quality parameters. Sensory analysis showed that VCO produced by fermentation (with and without heat) could be distinguished from those produced using the EXP and CEN methods; this sensory differentiation correlated with the higher levels of acetic acid and octanoic acid in the VCO produced by fermentation. Studies on physicochemical deterioration of VCO showed that VCO is stable to chemical and photochemical oxidation and hydrolysis. VCO is most susceptible to microbial attack, which leads to the formation of various organic acids, in particular, lactic acid. However, at moisture levels below 0.06 %, microbial action is significantly lessened

Physico-Chemical and Microbiological Parameters in the Deterioration of Virgin Coconut Oil

The deterioration of virgin coconut oil (VCO) due to physico-chemical oxidation and hydrolysis and microbiological processes was studied. The physico-chemical oxidation of VCO in the air at room temperature was negligible. Oxidation of VCO was observed only in the presence of air, UV radiation, ferric ion (Fe3+), and high free fatty acid (FFA) content. Chemical hydrolysis was performed at varying moisture levels and temperatures. The rate of hydrolysis to produce FFAs was measured using 31P NMR under conditions of saturated water (0.22%) and 80°C was found to be 0.066 µmol/g-hr (expressed as lauric acid). At 0.084% moisture and 80°C, the rate of FFA formation was found to be 0.008 µmol/g-hr. The microbial decomposition of VCO was determined after four days of incubation at 37°C. At low moisture levels (\u3c0.06%), VCO was stable to microbial decomposition. However, at higher moisture levels, there was an increase in the formation of organic acids, in particular, lactic acid, dodecanoic acid, succinic acid, acetic acid, and fumaric acid, indicating that microbial action had occurred. The most important conditions that influence the physicochemical and microbial degradation of VCO are moisture, temperature, and the presence of microorganisms. These degradation processes can be minimized if the moisture level is maintained below 0.06%

Smartphone-based image analysis and chemometric recognition of the thin-layer chromatographic fingerprints of herbal materials

Thin-layer chromatography (TLC) is commonly used as a screening method to verify the identity and quality of dried herbal medicinal plant material. While TLC is relatively simple, the method still requires technical experience and relies on the subjective classification of sample TLC profiles as “within-specifications” or “off-specifications.” In this work, we report the development of an objective TLC-based system for the identification and quality assessment of herbal medicinal materials. Our proposed system is a miniaturized Pharmacopeia-based TLC method coupled with a smartphone app that allows for an objective interpretation of TLC profiles via multivariate image analysis and chemometric fingerprinting. An image of the TLC profile is captured using a smartphone camera interfaced with a 3D-printed photo-box, and the analysis is automated using a framework of pre-uploaded algorithms hosted on a cloud server. The TLC profile image is converted to an unfolded red, green, and blue (RGB) channel intensity profile, and classified as “within-specifications” or “off-specifications” using aggregated Soft Independent Modeling of Class Analogy (SIMCA) models. We present the application of our system to two herbal medicinal plants, Blumea balsamifera and Vitex negundo. The proposed system demonstrates 90.2% sensitivity and 86.2% specificity for B. balsamifera classification, and 81.4% sensitivity and 92.0% specificity for V. negundo classification when compared to the respective laboratory-based Pharmacopeia TLC protocols for the ability to distinguish authentic samples from non-authentic and degraded samples. The system developed in this work is a cost-effective, rapid method that can serve as a herbal material quality assessment tool in resource-limited settings