A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

Simple item page
dc.contributor.authorDevianti, Devianti
dc.contributor.authorSufardi, Sufardi
dc.contributor.authorYusmanizar, Yusmanizar
dc.contributor.authorSiahaan, Herbert Hasudungan
dc.contributor.authorSitorus, Agustami
dc.date.accessioned2026-08-06T10:34:25Z
dc.date.available2026-08-06T10:34:25Z
dc.date.issued2022-01-01
dc.description.abstractDetermination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alphapinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel v-SVM algorithm is better than PLSR for myristicin prediction with gamma (γ), c, and nu (v) of 0.1, 1.0, and 0.99, respectively. Also, the ε-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (γ), c, and epsilon (ε) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (R<inf>p</inf> <sup>2</sup>), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (R<inf>p</inf> <sup>2</sup>), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.
dc.identifier.citationMathematical Modelling of Engineering Problems, 9(6), 1583-1588, 2022
dc.identifier.doi10.18280/MMEP.090618
dc.identifier.issn23690739
dc.identifier.other2-s2.0-85147745567
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12517
dc.sourceMathematical Modelling of Engineering Problems
dc.subjectAgro-product
dc.subjectAlpha-pinene
dc.subjectChemometrics
dc.subjectMyristicin
dc.subjectNutmeg
dc.subjectVis-nir
dc.titleA Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm
dc.typeArticle

Files

Collections

Research