Rapid Softness Prediction and Microbial Spoilage Visualization of Whole Tomatoes by Using Hyper/Multispectral Imaging

Abstract

The choice of selecting fruit for canned whole tomatoes is driven by several quality attributes including sweetness, acidity, and softness of tomatoes. Moreover, tomatoes can be contaminated with a variety of molds during cultivation, harvest, and transportation. Conventional evaluation operations for tomato softness and microbial spoilage are usually time-consuming, destructive, and high-cost. One strategy for rapid tomato sorting is to utilize hyper/multispectral imaging. This paper proposes to improve on traditional broad-band infrared imaging of existing color and dirt sorters by increasing the spectral resolution of the information collected. The findings of this study will characterize the potential of the technology in terms of predicting tomato softness and identification of tomato microbial spoilage for further development by the industry.