Dataset Information

Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

ABSTRACT: Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut sample characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products).

SUBMITTER: Campmajo G

PROVIDER: S-EPMC6471388 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

Campmajó Guillem G Navarro Gemma J GJ Núñez Nerea N Puignou Lluís L Saurina Javier J Núñez Oscar O

Sensors (Basel, Switzerland) 20190321 6

Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on hu ...[more]

PMID: 30901822

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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

Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

Publications

Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

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