A Rapid Method for Authentication of Macroalgae Based on Vis-NIR Spectroscopy Data Combined with Chemometrics Approach

Abstract

Macroalgae are an essential basic ingredient for many industries from which numerous derived products with great economic value are manufactured. Each macroalga has a unique composition that might provide specific physical and chemical information that can be used as markers for authentication. Their compositions may differ depending on different factors, including geographical regions. Unsupervised exploratory techniques, namely principal component analysis (PCA) and hierarchical cluster analysis (HCA), and nonparametric supervised methods including support vector machines (SVMs) and random forests (RFs), were applied to the Vis-NIR spectroscopic data 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. The PCA and HCA results present a tendency for the samples to be distributed and clustered according to the type of their species. Meanwhile, the SVM successfully classified samples based on their regional zones, and when combined with five-fold cross-validation, acquired an accuracy of 82%. The RF model algorithm obtained an accuracy of 100%, 80%, and 82% for the training, test, and five-fold cross-validation, respectively.

Keywords

• seaweed
• geographical origin
• exploratory study
• support vector machine
• random forest