Nondestructive quality assessment of longans using near infrared hyperspectral imaging

dc.contributor.authorSahachairungrueng, Woranitta
dc.contributor.authorTeerachaichayut, Sontisuk
dc.date.accessioned2026-08-06T10:36:40Z
dc.date.available2026-08-06T10:36:40Z
dc.date.issued2022-03-30
dc.description.abstractNear infrared hyperspectral imaging (NIR-HSI) is a method that can be used to evaluate the quality of fruit nondestructively. The objective of this research was to study the feasibility of NIR-HSI reflectance mode, within the wavelength of 935-1720 nm, for predicting the quality of longans. The two important factors chosen were: total soluble solids (TSS) and moisture content (MC). Each longan was assessed by first measuring its spectral data then measuring its TSS and MC to establish calibration models using multiple linear regression (MLR) compared with partial least squares regression (PLSR). Original spectra of longans gave the optimum results by PLSR for developing the models with correlation coefficients (Rp) of 0.76 for TSS and 0.88 for MC as well as root mean square error of predictions (RMSEP) of 0.42% and 0.45% respectively. By image processing, the predictive images from the models for TSS and MC were created based on color scales. They showed different colors of longans related to the level of TSS and MC and the deviation in levels in different parts of each longan by the predictive image. The results showed it could be used for grading fruit giving NIR-HSI potential to be developed in on-line systems.
dc.identifier.citationAgricultural Engineering International Cigr Journal, 24(1), 217-227, 2022
dc.identifier.issn16821130
dc.identifier.other2-s2.0-85128677659
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13133
dc.sourceAgricultural Engineering International Cigr Journal
dc.subjectcalibration
dc.subjectimages
dc.subjectnondestructive
dc.subjectprediction
dc.subjectspectra
dc.titleNondestructive quality assessment of longans using near infrared hyperspectral imaging
dc.typeArticle

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