Storage conditions of pear affect its subsequent softening process and shelf life. Measurements of firmness have traditionally been carried out according to the Magness Taylor (MT) procedure; using a texture analyzer or penetrometer in reference texture tests. In this study, a nondestructive method using Laser Doppler vibrometer (LDV) technology was used to estimate texture firmness of pears. This technique was employed to detect responses to imposed vibration of intact fruit using a shaker. Vibration transmitted through the fruit to the upper surface was measured by LDV. A fast Fourier transform algorithm was used to process response signals and the desired results were extracted. Multiple Linear Regression models using fruit density and four parameters obtained from modal tests showed better correlation (R 2 00.803) with maximum force in Magness Taylor test compared to the models that used only modal parameters (R 2 00.798). The best polynomial regression models for pear firmness were based on elasticity index (EI) and damping ratio (η) with R 2 00.71 and R 2 0 0.64, respectively. This study shows the capability of the LDV technique and the vibration response data for predicting ripeness and modeling pear firmness and the significant advantage for commercially classifying of pears based on consumer demands.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.