Discrimination of civet and non-civet coffee by linear discriminant analysis (LDA), partial least squares (PLS-DA), and orthogonal projection to latent structures (OPLS-DA)

• Main Authors: Datinginoo, Madelen R., Losañes, Christine Angelica L.
• Format: text
• Language: English
• Published: Animo Repository 2015
• Subjects: Coffee > Classification; Discriminant analysis; Statistics and Probability
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/14901
• Institution: De La Salle University

One of the issues faced by coffee traders and consumers is the widespread availability of adulterated civet coffee (kopi luwak) in the market. To address this problem, the industry needs a way to discriminate between civet and non-civet coffee. Metabolomics data consisting of 24 coffee beans were subjected to linear discriminant analysis (LDA), partial least squares – discriminant analysis (PLS-DA), and orthogonal projection to latent structures – discriminant analysis (OPLS-DA). LDA identified isonicotinic acid, 3-hydroxybenzoic acid, arbutin, and propane-1,3-diol NIST as discriminant markers. On the other hand, PLS-DA described three factors highly represented by: (1) sugars and organic acids (2) aroma acids and (3) taste acids as responsible for successful class separation. Lastly, OPLS-DA showed that isonicotinic acid, 5-aminovaleric acid, beta-glutamic acid, pentitol, and urea were the most significant in discriminating the data. All the fitted models yielded 0 misclassification rates. The LDA model exhibited an R2 of 88.56%, while the OPLS-DA and PLS-DA models demonstrated R2Y of 87.9%. Unlike LDA, PLS-DA is not governed by a set of assumptions. The PLS-DA model was also evaluated with a higher Q2 (62.6%) than that of the OPLS-DA model Q2 (51.5%). Hence, among the three discriminant analyses, PLS-DA is the recommended analysis tool for discriminating between civet and non-civet coffee samples.