دورية أكاديمية

Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication

التفاصيل البيبلوغرافية
العنوان: Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication
المؤلفون: Nerea Núñez, Javier Saurina, Oscar Núñez
المصدر: Molecules, Vol 29, Iss 1, p 232 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Organic chemistry
مصطلحات موضوعية: non-targeted LC-HRMS analysis, fingerprinting chemical descriptors, coffee adulteration, principal component analysis (PCA), partial least squares–discriminant analysis (PLS-DA), partial least squares (PLS) regression, Organic chemistry, QD241-441
الوصف: Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee’s origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica—Vietnam Robusta, Vietnam Arabica—Cambodia and Vietnam Robusta—Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1420-3049
العلاقة: https://www.mdpi.com/1420-3049/29/1/232Test; https://doaj.org/toc/1420-3049Test
DOI: 10.3390/molecules29010232
الوصول الحر: https://doaj.org/article/94469859e2a14902850c3d562c2ba9adTest
رقم الانضمام: edsdoj.94469859e2a14902850c3d562c2ba9ad
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14203049
DOI:10.3390/molecules29010232