Comparing density and NIR methods for measurement of Kiwifruit dry matter and soluble solids content

McGlone, Andrew; Jordan, R. B.; Seelye, R. J.; Martinsen, Paul

doi:10.1016/s0925-5214(02)00014-5

Cited by 107 publications

(64 citation statements)

References 9 publications

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“…The results of long-term (UR) and hybrid prediction (UU&UR&RR) were slightly inferior. The obtained results were slightly inferior to the results of PLS models performed on some selected spectral regions by McGlone et al 2,5,6 . For this reason PLS was performed to the calibrate the global model on the full spectral region (10 000-4000 cm −1 ), but some noisy spectral information inevitably weakened the modelling performance.…”

Section: Spectral Preprocessingcontrasting

confidence: 83%

“…As the kiwifruit matures, the starch, one component of the DM, is gradually converted into soluble saccharides (glucose, fructose, sucrose, etc.) 6 . Thus the characteristic wavelength regions of DM greatly changed.…”

Section: Sipls Model Resultsmentioning

confidence: 99%

“…DM (%) is actually 1 − water content. The kiwifruit DM was calculated from the final dry weight and initial wet weight of the slices, recorded as % fresh weight in this experiment 6 . The average of the DM of two slices was used in the sequence analysis.…”

Section: Determination Of Kiwifruit Dmmentioning

confidence: 99%

“…It comprises both the soluble solids (largely fructose, glucose, and sucrose) and insoluble solids (mainly the structural carbohydrates and starch). Kiwifruit are harvested unripe, although physiologically mature, but must be put in natural storage to ripen (i.e., conversion of the stored starch into soluble solids) before consumption 6 . The kiwifruit DM is reasonably constant during ripening with only small losses due to respiration.…”

Section: Introductionmentioning

confidence: 99%

“…Kiwifruit is one commodity where sorting based on pre-selected NIR spectral features at harvest can be used to grade fruit on the basis of DM. Recent research has established that NIR spectroscopic analysis can be used to assess kiwifruit DM and/or ripened soluble solid content using spectra below 1100 nm for greater penetration 2,5,6,11,12 . The spectra, ranging from 10 000 to 4000 cm −1 (1000-2500 nm), were composed of overtones and combinations of fundamental vibrations of -CH, -NH, and -OH groups from the mid-infrared.…”

Section: Introductionmentioning

confidence: 99%

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LüQiang¹,

Ming-Jie²,

Cai³

et al. 2010

ScienceAsia

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Synergy interval partial least square (siPLS) was proposed to select efficiently the characteristic wavelength regions of dry matter against kiwifruit near-infrared spectra for dry matter prediction. Four data sets (NIR spectra and dry matter of unripe fruit (UU), NIR spectra of unripe fruit and dry matter of ripe fruit (UR), NIR spectra and dry matter of ripe fruit (RR), and UU&UR&RR) were obtained in the experiment. They were used to develop models for predicting dry matter of unripe and/or ripe kiwifruits. The results of cross-validation showed that the change of characteristic wavelength regions was caused by chemical conversion of organic compounds included in the dry matter at different storage periods of kiwifruits. Compared with the global spectra data models, the siPLS method could simplify the models with efficiently selecting characteristic wavelength regions. The root mean square error of cross-validation and correlation coefficient (r) of the UR model were 0.47% and 0.92, respectively, in calibration set. The root mean square error of prediction and r were 0.53% and 0.90, respectively, in the prediction set. This study demonstrated that NIR spectroscopy of unripe kiwifruits could predict the dry matter.

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Section: Spectral Preprocessingcontrasting

confidence: 83%

Section: Sipls Model Resultsmentioning

confidence: 99%

Section: Determination Of Kiwifruit Dmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LüQiang¹,

Ming-Jie²,

Cai³

et al. 2010

ScienceAsia

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Predicting apple (cv. Elshof) postharvest dry matter and soluble solids content with near infrared spectroscopy

2013

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Model RPD values were not high enough for general quantitative predictions, although they compare well to previous work. Certain factors affected model success, including changes in fruit physiology over time and the range of reference data. The complexity of absorbance spectra for DM and SSC plus their strong correlation suggests that prediction models cannot easily distinguish between soluble and non-soluble forms of carbohydrate.

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