Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

Nurazwin Zulkifli; Norhashila Hashim; Hazreen Haizi Harith; Mohamad Firdza Mohamad Shukery; Daniel I Onwude

doi:10.1002/jsfa.11669

Prediction of the ripening stages of papayas using discriminant analysis and support vector machine algorithms

J Sci Food Agric. 2022 Jun;102(8):3266-3276. doi: 10.1002/jsfa.11669. Epub 2021 Dec 9.

Authors

Nurazwin Zulkifli¹, Norhashila Hashim^{1

2}, Hazreen Haizi Harith^{1

2}, Mohamad Firdza Mohamad Shukery², Daniel I Onwude³

Affiliations

¹ Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
² SMART Farming Technology Research Centre (SFTRC), Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
³ Department of Agricultural and Food Engineering, Faculty of Engineering, University of Uyo, Uyo, Nigeria.

PMID: 34802158
DOI: 10.1002/jsfa.11669

Abstract

Background: Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and L*a*b* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS) and pH for the prediction of quality properties at five ripening stages of papaya (R1-R5).

Results: The mean values of colour features in RGB $(R, m, G_{m,} B_{m})$ , normalised RGB $(R, nm, G_{nm,} B_{nm})$ HSV $(H, m, S_{m,} V_{m})$ , and L*a*b* $(L, m, a_{m}, b_{m})$ were the best estimator for predicting TSS with R² ≥ 0.90. All colour channels also showed satisfactory accuracies of R² ≥ 0.80 in predicting the bioyield force, apparent modulus and mean force. The highest average classification accuracy was obtained using linear discriminant analysis (LDA) with an average accuracy of more than 82%. The study showed that LDA, linear support vector machine, quadratic discriminant analysis and quadratic support vector machine obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range 83.75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15%, respectively. This indicates that R5 colour information was obviously different from R1-R4. The mean values of the HSV channel indicated the best performance to predict the ripening stages of papaya, compared to RGB, normalised RGB and L*a*b* channels, with an average classification accuracy of more than 80%.

Conclusion: The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas. © 2021 Society of Chemical Industry.

Keywords: machine learning algorithms; machine vision system; mechanical properties; papayas; physicochemical properties; ripening stages.

MeSH terms

Algorithms
Carica* / chemistry
Discriminant Analysis
Fruit / chemistry
Support Vector Machine