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.

A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption

Fresh bruise on an apple is a major problem in postharvest, as it can easily be missed at the early stage and then develop into a more serious external bruise as time goes on. In this study, GC–MS was used to investigate the metabolic apple profile change within 24 h after the occurrence of fresh bruising to understand the relationship between quality change and metabolism of fresh bruises on an apple. We identified a total of 36 metabolic components and expounded the early physiological changes and metabolic mechanisms in a freshly bruised apple. Data were analyzed using partial least squares discriminant analysis (PLS-DA) and Euclidean distance and the results showed that 12 h was determined to be the optimal time for loss reduction. We observed that sugars and sugar alditols increased significantly, while amino acids and fatty acid ester decreased dramatically within 24 h. The apple metabolic disorder after fresh bruising is mainly associated with enhancement of respiration, increase of ROS, activation of secondary metabolism, and aggravation of membrane lipid peroxidation. This study highlights the apple metabolic disorder that happens after fresh bruising, providing insights that can further facilitate postharvest apple preservation and reducing the loss of its commercial value.

Metabonomics study of fresh bruises on an apple using the gas chromatography–mass spectrometry (GC–MS) method

The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method. 

Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness

Influence of frequency of electric excitation signal on dielectric property of Fuji apples with red-dot disease.

The apples fell from a height of 40 cm and 70 cm onto a smoothly stone surface were used to investigated the effects of impact bruising on endogenous jasmonic acid(JA) and activities of protective enzymes in Fuji apples.The contents of JA and activities of protective enzymes were detected within 48 hours at room temperature.The results showed that the contents of JA significantly increased.The ethylene release rate,O-·2 production rate and H2O2 content also enhanced dramatically.Moreover,the activities of protective enzymes,which included supiroxide dismutase(SOD),catalase(CAT) and peroxidase(POD),and phenylalanine ammonia lyase(PAL) also increased significantly.In conclusion,impact bruising could increase the JA contents,ethylene release rate and intensify the metabolism of reaction oxidative species(ROS) for Fuji apples in a short time,which further generated cell membrane-lipid peroxidation and injuredy the fruits.

Response of Endogenous Jasmonic Acid and Activities of Protective Enzymes in Impact Bruising Fuji Apples within 48 Hours

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Influence of frequency of electric excitation signal on dielectric property of Fuji apples with red-dot disease.

Response of Endogenous Jasmonic Acid and Activities of Protective Enzymes in Impact Bruising Fuji Apples within 48 Hours