Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy

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dc.contributor.authorDitcharoen, Sirirak
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorSaengprachatanarug, Khwantri
dc.contributor.authorPhuphaphud, Arthit
dc.contributor.authorRittiron, Ronnarit
dc.contributor.authorTerdwongworakul, Anupun
dc.contributor.authorMalai, Chayuttapong
dc.contributor.authorSaenphon, Chirawan
dc.contributor.authorPanduangnate, Lalita
dc.contributor.authorPosom, Jetsada
dc.date.accessioned2026-08-06T10:41:09Z
dc.date.available2026-08-06T10:41:09Z
dc.date.issued2023-03-01
dc.description.abstractThe maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.
dc.identifier.citationArtificial Intelligence in Agriculture, 7, 35-43, 2023
dc.identifier.doi10.1016/j.aiia.2023.02.002
dc.identifier.issn25897217
dc.identifier.other2-s2.0-85149415696
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14328
dc.sourceArtificial Intelligence in Agriculture
dc.subjectClassification
dc.subjectDurian fruit
dc.subjectMaturity stage
dc.subjectNear-infrared spectroscopy
dc.subjectNon-destructive method
dc.titleImproving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy
dc.typeArticle

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