Journal
ANALYTICAL LETTERS
Volume 54, Issue 10, Pages 1547-1560Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/00032719.2020.1812622
Keywords
Field assessment; hyperspectral imaging; ripeness detection; strawberry
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Funding
- National Natural Science Foundation of China [31701325, 31671632]
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Portable hyperspectral imaging was used for field and indoor spectra acquisition of the strawberries at three ripeness stages: ripe, mid-ripe and unripe. The mean spectra were pre-processed by multiplicative scatter correction (MSC). Principal component analysis (PCA) was employed to generate score scatter plots and visualize score images for differentiating specific grouping of samples. Three methods, including X-loading weight, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were applied to extract the effective wavelengths. Two classifiers, partial least squares - discriminant analysis (PLS-DA) and least squares - support vector machine (LS-SVM) were used for ripeness assessment. The results showed that the overall accuracy of all classifiers for field assessment ranged from 91.7% to 96.7%, slightly lower than for indoor assessment. Furthermore, the LS-SVM model combined with effective wavelengths with the CARS method performed better for field assessment of strawberry ripeness, providing an accuracy of 96.7%. It can be concluded that hyperspectral imaging can be used for real-time assessment of strawberry ripeness in the field.
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