A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Abstract

Featured Application: Based on principal component analysis (PCA), an evaluation and grading model for fresh peaches was developed to provide guidance for the selection of fresh peaches for the consumer market. The approach and findings presented here may be useful for effective evaluation and grading during real-world fruit production; they can potentially improve processing efficiency, reduce costs, and minimize waste in an automated quality evaluation system. Abstract: Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar-acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch "Spring Belle") were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

“…Physical attributes of agricultural products are important for the design of grading and sorting equipment, handling materials in different mechanical systems, and various packaging and processing machinery [ 7 , 8 ]. Khojastehnazhand et al [ 9 ] studied various qualities of fresh produce, as these attributes are often used to estimate water loss, quantity of pesticide applications, heat transfer, ripeness index to predict the optimal harvest time, respiration rates, evaluation of fruit growth and quality, grading, and so on.…”

Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures

“…The appearance screening of fruit by human operators may be characterized by large errors and low efficiency. Furthermore, commonly used methods allowing for the detection of the chemical and physical quality of fruit may be time-consuming or require special equipment [13]. Therefore, objective, rapid and relatively inexpensive techniques should be constantly improved.…”

Rapid estimation of apple phenotypic parameters based on 3D reconstruction

“…The traditional researches of non-contact measurement were mostly carried out based on the method of two-dimensional image processing [7,8] . Among them, they mostly focused on apple [8,10] , kiwifruit [11,12] , navel orange [13,14] and so on.…”

“…The traditional researches of non-contact measurement were mostly carried out based on the method of two-dimensional image processing [7,8] . Among them, they mostly focused on apple [8,10] , kiwifruit [11,12] , navel orange [13,14] and so on. For example, Huang et al [15] proposed an online apple classification method based on image feature fusion, which used the methods of discriminant tree and support vector machine to make fusion decision and achieve accurate classification.…”