Novel application of 1H NMR spectroscopy coupled with chemometrics for the authentication of dark chocolate. (15th March 2023)

• Record Type: Journal Article
• Authors: Truzzi, Eleonora; Marchetti, Lucia; Fratagnoli, Arianna; Rossi, Maria Cecilia; Bertelli, Davide
• Is Part Of: Food chemistry. Volume 404:Part A(2023)
• Publication Date: 2023-03-15
• Subjects: Principal component analysis (PCA) -- Partial least-squares discriminant analysis (PLS-DA) -- Partial least-squares regression (PLS-R) -- Variable importance in projection (VIP) score plots -- Chocolate, cocoa-butter equivalents (CBE); Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.134522
• Languages: English
• ISSNs: 0308-8146

Abstract

Graphical abstract: Highlights: 1 H NMR spectroscopy was applied for the quality control of chocolate. Peaks' area and fingerprinting datasets were used for building PLS-DA and PLS-R models. PLS-DA models could identify the coca-butter equivalent in the range of 5–50%. PLS-R models quantified coca-butter equivalent amounts with low prediction errors. Fingerprinting PLS-DA and PLS-R models was proved to represent the best approach. Abstract: The applicability of 1 H NMR spectroscopy coupled with chemometric in the quality control of dark chocolate was investigated for the first time to detect cocoa-butter equivalents (CBEs) above the allowed limit by European regulation. Blends of chocolate-fats with CBEs in the range 0–50 % were prepared and analyzed by 1 H NMR spectroscopy. Datasets composed of peaks' areas or spectral variables (fingerprinting) in glycerol region were tested for the creation of multivariate statistical models. Partial least-squares discriminant analysis (PLS-DA) and regression (PLS-R) methods were used to correctly identify the type of CBE and quantify its concentration respectively. The performances of the models created on the two datasets were evaluated in terms of chemometric indicators and compared. The robustness of models was investigated through the analysis of test sets and random permutation tests. Fingerprinting models revealed fruitful results in classifying and quantifying CBEs in blends demonstrating the applicability of NMR in chocolate quality control.