Rapid, non-destructive fruit sorting techniques are increasingly being adopted to ensure that producers, industry, and consumers receive products that meet their quality requirements. Quality attributes typically used to assess fruit ripeness include soluble solids content (SSC) and flesh firmness (FF). In this study, hyperspectral imaging operating at 400–1000 nm (Vis/NIR) was adopted to evaluate the ripeness degree of ‘Hayward’ kiwifruit. Partial least square (PLS) regression models were developed to estimate SSC and FF, while two different types of PLS discriminant analysis (PLS-DA) were used to classify samples according to three repining classes (defined on the base of SCC and FF values). To reduce the computation complexity, and simplify the calibration models, two variable selection methods (genetic algorithm GA, and variable importance in projection VIP) were adopted. For SSC, the prediction R2 values ranged from 0.85 (RMSE = 1.10 °Brix) to 0.94 (RMSE = 0.73 °Brix), and for FF from 0.82 (RMSE = 14.51 N) to 0.92 (RMSE = 9.87 N). Classification sensitivity reached values of 97% and 93%, for the model considering the SCC and FF classes, respectively. Prediction and classification performances remained substantially unchanged by reducing the number of wavelengths. Therefore, hyperspectral imaging appears to be suitable for prediction of kiwi quality attributes and their classification.

Ripeness evaluation of kiwifruit by hyperspectral imaging / Alessandro Benelli; Chiara Cevoli; Angelo Fabbri; Luigi Ragni. - In: BIOSYSTEMS ENGINEERING. - ISSN 1537-5110. - ELETTRONICO. - 223:PArt B, November 2022(2022), pp. 42-52. [10.1016/j.biosystemseng.2021.08.009]

Ripeness evaluation of kiwifruit by hyperspectral imaging

Alessandro Benelli;Chiara Cevoli
;
Angelo Fabbri;Luigi Ragni
2022

Abstract

Rapid, non-destructive fruit sorting techniques are increasingly being adopted to ensure that producers, industry, and consumers receive products that meet their quality requirements. Quality attributes typically used to assess fruit ripeness include soluble solids content (SSC) and flesh firmness (FF). In this study, hyperspectral imaging operating at 400–1000 nm (Vis/NIR) was adopted to evaluate the ripeness degree of ‘Hayward’ kiwifruit. Partial least square (PLS) regression models were developed to estimate SSC and FF, while two different types of PLS discriminant analysis (PLS-DA) were used to classify samples according to three repining classes (defined on the base of SCC and FF values). To reduce the computation complexity, and simplify the calibration models, two variable selection methods (genetic algorithm GA, and variable importance in projection VIP) were adopted. For SSC, the prediction R2 values ranged from 0.85 (RMSE = 1.10 °Brix) to 0.94 (RMSE = 0.73 °Brix), and for FF from 0.82 (RMSE = 14.51 N) to 0.92 (RMSE = 9.87 N). Classification sensitivity reached values of 97% and 93%, for the model considering the SCC and FF classes, respectively. Prediction and classification performances remained substantially unchanged by reducing the number of wavelengths. Therefore, hyperspectral imaging appears to be suitable for prediction of kiwi quality attributes and their classification.
2022
Ripeness evaluation of kiwifruit by hyperspectral imaging / Alessandro Benelli; Chiara Cevoli; Angelo Fabbri; Luigi Ragni. - In: BIOSYSTEMS ENGINEERING. - ISSN 1537-5110. - ELETTRONICO. - 223:PArt B, November 2022(2022), pp. 42-52. [10.1016/j.biosystemseng.2021.08.009]
Alessandro Benelli; Chiara Cevoli; Angelo Fabbri; Luigi Ragni
File in questo prodotto:
File Dimensione Formato  
cevoli_merged.pdf

embargo fino al 08/11/2024

Tipo: Postprint
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 2.58 MB
Formato Adobe PDF
2.58 MB Adobe PDF   Visualizza/Apri   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/901622
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 25
social impact