Non-destructive quality control of kiwi fruits by hyperspectral imaging

Serranti, Silvia; Bonifazi, Giuseppe; Luciani, Valentina

doi:10.1117/12.2255055

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Different performance ranges have been reported in the literature for monitoring the maturity of fruits using HSI. For example, the accuracy values in this research are much higher than those reported by Nilsen et al (2021) for predicting the kiwifruit SSC and firmness using hyperspectral data, uninformative variable elimination (UVE) wavelength selection, and PLSR modeling algorithm. Meanwhile, the HSI system discriminated strawberries regarding their storage time with 100% classification accuracy (Weng et al, 2020).…”

Section: Resultscontrasting

confidence: 73%

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

Bakhshipour

2023

Food Science & Nutrition

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A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e‐nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e‐nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion‐based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion‐based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e‐nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.

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Section: Resultscontrasting

confidence: 73%

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

Bakhshipour

2023

Food Science & Nutrition

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“…Compared with the traditional image technology and spectral information collection, it realizes the fusion of spectral information and image information. In recent years, it has been widely used in detecting of the quality of food and agricultural products (Mollazade, ; Munera et al, ; Serranti, Bonifazi, & Luciani, ; Washburn, Stormo, Skjelvareid, & Heia, ). Differences in the internal components of different tea varieties will affect the change of hyperspectral image information, which makes more and more researchers identify the tea varieties by using hyperspectral imaging technology.…”

Section: Introductionmentioning

confidence: 99%

“…used in detecting of the quality of food and agricultural products (Mollazade, 2017;Munera et al, 2017;Serranti, Bonifazi, & Luciani, 2017;Washburn, Stormo, Skjelvareid, & Heia, 2017). Differences in the internal components of different tea varieties will affect the change of hyperspectral image information, which makes more and more researchers identify the tea varieties by using hyperspectral imaging technology.…”

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confidence: 99%

Nondestructive identification of green tea varieties based on hyperspectral imaging technology

Jun

Tang

et al. 2018

J Food Process Engineering

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A new method for rapid detection of green tea varieties by hyperspectral imaging technology was proposed in this article. In this experiment, five different varieties of green tea were taken as the research object, and the hyperspectral images of five different varieties of green tea were collected. In order to reduce the impact of noise and spectral scattering, the spectral data were preprocessed using Savitzky–Golay (SG) and multiple scattering correction (MSC) preprocessing. Then iteratively retaining informative variables (IRIV) and variable iterative space shrinkage approach (VISSA) variable selection method were used to make variable selection on the pre‐processed spectral data to select the best variable combination. Since the randomness of support vector machine (SVM) parameters has a certain influence on the model, the firefly algorithm (FA) was used to optimize the parameters of SVM. Finally, the SVM green tea varieties identification models were established based on the total spectral data and the spectral data selected by variables selection, and the different modeling results were compared and analyzed. The results show that the VISSA‐FA‐SVM model has the best identification effect, and the classification accuracies of the calibration set and the prediction set are 100 and 96%, respectively. Practical applications The practical application of this article is to identify the different varieties of green tea by using hyperspectral imaging technology. Compared with traditional methods, hyperspectral imaging technology can be used to identify different varieties of green tea quickly, nondestructive and accurately. Optimizing the parameters of the model in appropriate can improve the performance of the model. In this article, firefly algorithm was used to optimize SVM parameters to get the optimal parameters for modeling. In addition, the selection of preferred variables by variables selection can provide a theoretical basis for the identification of portable green tea varieties.

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Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology

et al. 2022

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Non-destructive quality control of kiwi fruits by hyperspectral imaging

Cited by 6 publications

References 14 publications

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

Nondestructive identification of green tea varieties based on hyperspectral imaging technology

Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology

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