Prediction of banana maturity based on the sweetness and color values of different segments during ripening

Lukai Ma; Churong Liang; Yun Cui; Huiyan Du; Huifan Liu; Lixue Zhu; Yuanshan Yu; Chuqiang Lu; Soottawat Benjakul; Charles Brennan; Margaret Anne Brennan

doi:10.1016/j.crfs.2022.08.024

Prediction of banana maturity based on the sweetness and color values of different segments during ripening

Curr Res Food Sci. 2022 Sep 6:5:1808-1817. doi: 10.1016/j.crfs.2022.08.024. eCollection 2022.

Authors

Lukai Ma^{1

2}, Churong Liang¹, Yun Cui¹, Huiyan Du¹, Huifan Liu¹, Lixue Zhu¹, Yuanshan Yu³, Chuqiang Lu⁴, Soottawat Benjakul⁵, Charles Brennan⁶, Margaret Anne Brennan⁶

Affiliations

¹ College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China.
² Institute of Agricultural Product Development and Food Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, 850000, China.
³ Institute of Sericulture and Agro-products Processing, Guangdong Academy of Agricultural Sciences, Guangzhou, 510225, China.
⁴ Guangdong Yuanfeng Food Co., LTD., Gaozhou, 525200, China.
⁵ International Center of Excellence in Seafood Science and Innovation, Prince of Songkla University, Hat Yai, 90110, Thailand.
⁶ Department of Wine, Food and Molecular Biosciences, Lincoln University, Lincoln, 7647, New Zealand.

Abstract

To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products.

Keywords: Banana; Maturity; Prediction; Segments.