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Ann Peirs

Researcher at Katholieke Universiteit Leuven

Publications -  19
Citations -  2999

Ann Peirs is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Hyperspectral imaging & Calibration (statistics). The author has an hindex of 12, co-authored 19 publications receiving 2653 citations. Previous affiliations of Ann Peirs include Catholic University of Leuven & The Catholic University of America.

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Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

TL;DR: An overview of NIR spectroscopy for measuring quality attributes of horticultural produce is given in this article, where the problem of calibration transfer from one spectrophotometer to another is introduced as well as techniques for calibration transfer.
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Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment

TL;DR: In this article, a comparison was made between two optical configurations, which can be used to perform NIR-spectroscopic measurements: the bifurcated and the 0°/45° optical configuration.
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Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging

TL;DR: In this article, a hyperspectral NIR imaging system was developed to identify bitter pit lesions on apples and a discriminant PLS calibration model was constructed to discriminate between pixels of unaffected apple skin and bitter pits.
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Effect of biological variability on the robustness of NIR models for soluble solids content of apples

TL;DR: In this paper, a statistical analysis was performed on a large spectral data set to analyse the effect of orchard, season and cultivar on spectral variability, and the robustness of calibration models for soluble solids content with respect to the three factors was tested based on external validations.
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Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy

TL;DR: In this article, the use of VIS/NIR spectroscopy was evaluated to determine the internal quality and the optimal harvest dates of apples non-destructively, and the most robust model predicted the maturity with a validation correlation of 0.90 (SEP=7.4 days).