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NMR-based and chemometric approaches applicable to adulteration studies for assessment of the botanical origin of edible oils.

ABSTRACT: The health benefits of edible oils, especially promoted by ?3 and ?9 fatty acids, have been associated with their botanical origin. In order to investigate fatty acid profiles, we assessed Brazil nut, chia, linseed, sesame (toasted and raw), and soybean oils by 1H nuclear magnetic resonance (1H NMR) and chemometrics. PCA plots revealed important relationships between chemical composition and botanical origin for reference and commercial samples. Strong evidence of commercial Brazil nut oil adulteration was confirmed using a spiking procedure. Our findings show that NMR and chemometrics are successful tools for correlating fatty acid profile with botanical origin, which can be suitable for detecting sample adulteration.

SUBMITTER: Ravaglia LM

PROVIDER: S-EPMC6342785 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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NMR-based and chemometric approaches applicable to adulteration studies for assessment of the botanical origin of edible oils.

Ravaglia Luciana Marçal LM Pizzotti Ana Beatriz Coriguazi ABC Alcantara Glaucia Braz GB

Journal of food science and technology 20181204 1

The health benefits of edible oils, especially promoted by ω3 and ω9 fatty acids, have been associated with their botanical origin. In order to investigate fatty acid profiles, we assessed Brazil nut, chia, linseed, sesame (toasted and raw), and soybean oils by <sup>1</sup>H nuclear magnetic resonance (<sup>1</sup>H NMR) and chemometrics. PCA plots revealed important relationships between chemical composition and botanical origin for reference and commercial samples. Strong evidence of commercia ...[more]

PMID: 30728595

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Project description:Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (-LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%-60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and -LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and -LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.

Dataset Information

NMR-based and chemometric approaches applicable to adulteration studies for assessment of the botanical origin of edible oils.

Publications

NMR-based and chemometric approaches applicable to adulteration studies for assessment of the botanical origin of edible oils.

Similar Datasets

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