Ripening assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration models

Pires, Rosa; Guerra, Rui; Cruz, Sandra; Antunes, Maria Dulce; Brázio, António; Afonso, Andreia M.; Daniel, Mariana; Panagopoulos, Τhomas; Gonçalves, Isabel; Cavaco, Ana M.

doi:10.1016/j.postharvbio.2021.111750

Cited by 13 publications

(3 citation statements)

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“…Torres et al [ 3 ] reported that the SEP and R p 2 were 0.71 and 0.57, respectively, based on a model developed to predict the SSC of mandarins using a portable spectrometer based on reflection spectrum measurement. Pires et al [ 4 ] used the SSC prediction PLS model of ‘Ortanique’ to obtain R 2 and RMSEP values of 0.79 and 0.75%, respectively. As shown previously, citrus species result in a wide range of SSC prediction performances, depending on peel thickness.…”

Section: Resultsmentioning

confidence: 99%

“…As shown previously, citrus species result in a wide range of SSC prediction performances, depending on peel thickness. However, citrus fruit SSC prediction results for similar thicknesses showed RMSE levels ranging from 0.51 to 0.93 °Brix [ 1 , 3 , 4 , 34 ]. Riccioli et al [ 12 ] developed an artificial neural network model to predict SSC of intact orange using hyperspectral images, and the R 2 and RMSE were 0.51 and 0.86%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Citrus fruits are non-respiratory fruits; therefore, to improve industrial competitiveness Sensors 2024, 24, 1512 2 of 13 and profitability, they should be harvested at an appropriate ripeness [3]. Citrus fruits have a high correlation between ripeness and SSC, and consumers prefer sweet fruits; therefore, the prediction of SSC for citrus fruits is necessary [2,4].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm

Kim,

Yu,

Song

et al. 2024

Sensors

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Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

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Section: Resultsmentioning

confidence: 99%