Multispectral Vision for Monitoring Peach Ripeness
Article first published online: 1 FEB 2011
© 2011 Institute of Food Technologists®
Journal of Food Science
Volume 76, Issue 2, pages E178–E187, March 2011
How to Cite
Herrero-Langreo, A., Lunadei, L., Lleó, L., Diezma, B. and Ruiz-Altisent, M. (2011), Multispectral Vision for Monitoring Peach Ripeness. Journal of Food Science, 76: E178–E187. doi: 10.1111/j.1750-3841.2010.02000.x
- Issue published online: 1 MAR 2011
- Article first published online: 1 FEB 2011
- MS 20091027 Submitted 10/15/2009, Accepted 10/4/2010.
- computer vision;
Abstract: The main objective of this research was to develop an automatic procedure able to classify Rich Lady commercial peaches according to their ripeness stage through multispectral imaging techniques. A classification procedure was applied to the ratio images calculated as red (R, 680 nm) divided by infrared (IR, 800 nm), that is, R/IR images. Four image-based ripeness reference classes (A: unripe to D: overripe) were generated from 380 fruit images (season 1: 2006) by a nonsupervised classification method and evaluated according to reference measurements of the ripeness of the same samples: Magness-Taylor penetrometry firmness, low-mass impact firmness, reflectance at 680 nm (R680, and soluble solids content. The assignment of unknown sample images from those season 1 images (internal validation, n = 380) and of 240 images from the 2nd season (season 2: 2007) to the ripeness reference classes (external validation) was carried out by computing the minimum Euclidean distance (classification distance, Cd) between each unknown image histogram and the average histogram of each ripeness reference class. For both validation phases, firmness values decreased and R680 increased for increasing alphabetical order of image-based class letter, reflecting the ripening process. Moreover, 70% (season 1) and 80% (season 2) of the samples below bruise susceptibility firmness were classified into class D.
Practical Application: This work proposes and validates a procedure for assessing peach ripeness through spectral imaging. The control of ripeness in this fruit is crucial for ensuring its quality and the measurement of optimum peach ripeness at harvest and postharvest is a controversial issue, which needs to be balanced between a minimum ripeness, acceptable for the consumer, and a maximum ripeness, to minimize fruit losses during the postharvest process. The proposed method is nondestructive and quick, showing thus, a good perspective for its application in fresh fruit packing lines, either for peach ripeness assessment or for other fruits (providing adequate calibration).