Previously, pineapple texture grading methods relied on farmers’ experience as a reference, leading to differences in judgment criteria depending on the operator. In this study, we employ a nondestructive testing method to estimate the texture of pineapples. By using excitation signals from a vibration platform and measuring the response signals of pineapples, the frequency response function is calculated to analyze the dynamic characteristics of the fruit. A texture discrimination threshold is then established as a basis for classifying pineapples into “hollow-sounding” and “solid-sounding” categories. The developed algorithm was evaluated using a confusion matrix and Matthews correlation coefficient (MCC) to confirm the texture discrimination threshold. The discriminative power of the classification model was further validated using the receiver operating characteristic curve (ROC) and the area under the ROC curve, thereby establishing a technique for quantifying pineapple texture. Principal component analysis and linear discriminant analysis (LDA) were used to reduce the dimensionality of the data and extract relevant features for texture identification. The results indicated that the MCC-based classification showed a significant consistency with farmers’ texture judgments. Moreover, LDA proved effective in distinguishing different pineapple textures. The proposed methodology provides a reliable technique for pineapple texture identification, reducing human errors and costs while significantly improving the accuracy of texture classification.