Sensory profile of 'Douradão' peaches cold stored under controlled atmosphere

Santana, Ligia Regina Radomille de; Benedetti, Benedito Carlos; Sigrist, José Maria Monteiro

doi:10.1590/s0101-20612011000100037

Cited by 2 publications

(1 citation statement)

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“…In many cases, practical peach quality evaluation consists of appearance screening by operators, which is influenced by subjective factors and has a low efficiency and large errors [5]. As labor costs increase, labor-intensive evaluation and grading constitutes a major expense for fresh and processed peach postharvest management [6].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Zhang

et al. 2020

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%