RT journal article
T1 A Rapid Method for Authentication of Macroalgae Based on Vis-NIR Spectroscopy Data Combined with Chemometrics Approach
A1 Gumansalangi, Frysye
A1 Pérez Calle, José Luis
A1 Barea Sepúlveda, Marta
A1 Manikharda
A1 Palma Lovillo, Miguel
A1 Lideman
A1 Rafi, Mohamad
A1 Ningrum, Andriati
A1 Setyaningsih, Widiastuti
A2 Química Analítica
K1 seaweed
K1 geographical origin
K1 exploratory study
K1 support vector machine
K1 random forest
AB Macroalgae are an essential basic ingredient for many industries from which numerousderived products with great economic value are manufactured. Each macroalga has a uniquecomposition that might provide specific physical and chemical information that can be used asmarkers for authentication. Their compositions may differ depending on different factors, includinggeographical regions. Unsupervised exploratory techniques, namely principal component analysis(PCA) and hierarchical cluster analysis (HCA), and nonparametric supervised methods includingsupport vector machines (SVMs) and random forests (RFs), were applied to the Vis-NIR spectroscopicdata to standardize the quality of macroalgae based on three regional zones in Indonesia (Western,Central, Eastern). A total of 35 macroalgae samples from six islands in Indonesia were analyzed. ThePCA and HCA results present a tendency for the samples to be distributed and clustered according tothe type of their species. Meanwhile, the SVM successfully classified samples based on their regionalzones, and when combined with five-fold cross-validation, acquired an accuracy of 82%. The RFmodel algorithm obtained an accuracy of 100%, 80%, and 82% for the training, test, and five-foldcross-validation, respectively.
PB MDPI
SN 2073-4441
YR 2023
FD 2023
LK http://hdl.handle.net/10498/32480
UL http://hdl.handle.net/10498/32480
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026