Nondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples

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dc.contributor.authorTantinantrakun, Achiraya
dc.contributor.authorSukwanit, Supawan
dc.contributor.authorThompson, Anthony Keith
dc.contributor.authorTeerachaichayut, Sontisuk
dc.date.accessioned2026-08-06T10:40:38Z
dc.date.available2026-08-06T10:40:38Z
dc.date.issued2023-01-01
dc.description.abstractDetermination of optimum maturity and ripeness of fruit is essential in the production of processed fruit, including pineapples, but this is difficult to achieve consistently by visual grading in commercial factories. Therefore, this study tested two nondestructive techniques for predicting the maturity index of intact pineapple. These were transmittance short wavelength near infrared spectroscopy (SW-NIRS) in the wavelength range of 665–955 nm and reflectance near infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm. The number of samples used for calibration was 120 for both SW-NIRS and NIR-HSI. The maturity index and spectral information of individual pineapple fruit were acquired from both techniques and analysed using the same procedure. Then, partial least squares regression (PLSR) was used to establish the models for predicting the maturity index of each intact fruit. The leave-one-out cross validation was used for evaluating the performance of the models. The results showed that both techniques gave reliable performance in predicting the maturity index of individual fruit, with a coefficient of determination considering cross validation (R<inf>cv</inf><sup>2</sup>) for the prediction of the maturity index of 0.70 and a root mean square error in cross validation (RMSECV) of 2.16 when using SW-NIRS and R<inf>cv</inf><sup>2</sup> of 0.72 and RMSECV of 1.68 when using NIR-HSI. It was therefore concluded that both SW-NIRS and NIR-HSI had the potential for use in nondestructive analysis of the maturity of intact pineapple fruit in fruit processing factories.
dc.identifier.citationPostharvest Biology and Technology, 195, 2023
dc.identifier.doi10.1016/j.postharvbio.2022.112141
dc.identifier.issn09255214
dc.identifier.other2-s2.0-85140761162
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14192
dc.sourcePostharvest Biology and Technology
dc.subjectFruit grading
dc.subjectModel
dc.subjectNondestructive
dc.subjectPartial least square
dc.subjectSpectra
dc.titleNondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples
dc.typeArticle

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