Blueberry firmness - A review of the textural and mechanical properties used in quality evaluations

Rivera, Sebastián; Giongo, L.; Cappai, Francesco; Kerckhoffs, Huub; Sofkova-Bobcheva, Svetla; Hutchins, Daniel; East, A.R.

doi:10.1016/j.postharvbio.2022.112016

Cited by 30 publications

(13 citation statements)

References 109 publications

(427 reference statements)

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“…Mechanical properties can be a valuable tool for differentiating the maturity stages of blueberries [ 21 ]. The results of this work showed that there were significant differences both in the shape of the force–distance curves obtained from the analysis of the ten samples and in the values of the different mechanical properties derived from them ( Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…However, we have observed that sample number 5, which presented blueberries with the highest maturity index (SSC/TA:19.03), had the lowest mechanical values described above. In a previous work, [ 21 ] reported a decrease in skin breakage force and skin breakage energy as an indicator of the progress of ripening in two different blueberry cultivars (Nui and Rahi). These authors go so far as to conclude that these mechanical parameters could be used for commercial or research purposes, with the aim of being used as quality control operations or to evaluate postharvest technological treatments.…”

Section: Resultsmentioning

confidence: 99%

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Are the Blueberries We Buy Good Quality? Comparative Study of Berries Purchased from Different Outlets

Sanchez-Ballesta,

Marti-Anders,

Álvarez

et al. 2023

Foods

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Blueberries (Vaccinium corymbosum L.) are becoming increasingly popular for their nutritional and health benefits, and their economic value is therefore increasing. The loss of quality that can occur due to softening and fungal attack is an important consideration when marketing blueberries. Despite the added value of blueberries, no studies have been carried out on how the fruit arrives at the outlets just before purchase by the consumer in terms of firmness, physico-chemical parameters, phenolic compounds, and fungal growth. The aim of this work has been, therefore, to investigate possible differences in quality parameters between blueberries purchased from ten different outlets, regardless of the supplier. The results showed that all the samples were of acceptable quality, although they all had a low maturity index at the point of sale. None of the samples studied showed clear signs of fungal decay at the time of purchase, although we were able to grow and identify some pathogen specimens after cultivation. In terms of total phenolic and anthocyanin content, as well as antioxidant activity, all the samples showed low values, possibly due to their postharvest storage, but they were within the expected range for this fruit. On the other hand, differences in the measured parameters were observed between samples of the same cultivar while no differences were found between conventionally and organically grown blueberries. This suggests that preharvest (such as edaphoclimatic conditions, agricultural practices, and cultivars) and postharvest factors (such as treatments used, storage, and transport temperatures) could influence the berry quality when they reach the consumer.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Are the Blueberries We Buy Good Quality? Comparative Study of Berries Purchased from Different Outlets

Sanchez-Ballesta,

Marti-Anders,

Álvarez

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…Calcium (Ca) is a nutrient used to improve fruit quality. It is commonly applied pre-harvest to extend postharvest shelf life [ 14 , 15 , 16 ]. Calcium functions include structural roles in cell walls, membrane stability, as well as chemical messenger communication between different plant organs and tissues [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Can the Firmness, Weight, and Size of Blueberry Fruit Be Enhanced through the Application of Low Amounts of Calcium to the Soil?

Hirzel

2023

Plants

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Background: The firmness, weight, and size of blueberries are vital for commercial success of this crop. Fertilization is a key agronomic management practice that affects fruit quality, where calcium (Ca) plays a critical role. This study aimed to assess the impact of low levels of soil-dosed Ca in carboxylic acid form on fruit size, weight, firmness, and residual soil fertility. Methods: The study focused on two varieties of blueberries, Duke and Legacy, over two consecutive growing seasons on three commercial farms located in south-central Chile. This study consisted of five treatments, ranging from 0 to 4.0 kg Ca per hectare. Results: The highest firmness values observed for Duke were between 164 and 186 g mm−1, with size values ranging from 15.7 to 16.9 mm, and weight observations ranging from 1.60 to 1.76 g. On the other hand, Legacy showed firmness values between 163 and 173 g mm−1, with size values ranging from 16.2 to 17.2 mm, and weight observations ranging from 2.01 to 2.40 g. Conclusion: The application of low Ca rates to the soil did not impact the size, weight, or firmness of ‘Duke’ and ‘Legacy’ blueberries. There was a positive correlation between the Ca soil application and the concentration of exchangeable Ca.

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“…In 2020, China’s blueberry planting area was 66,400 hectares, showing a year-over-year increase of 10%; moreover, production was 347,200 tons, which was up 64% year-over-year [ 4 ]. However, blueberry fruits are soft and susceptible to pathogenic bacteria, resulting in quality deterioration and affecting their storage, circulation, and sale [ 5 ]. Therefore, the requirements related to the transportation and storage conditions of blueberries have increased; therefore, an effective method is needed to predict the quality changes of blueberries and thus prevent them from spoiling during storage.…”

Section: Introductionmentioning

confidence: 99%

Establishing a Berry Sensory Evaluation Model Based on Machine Learning

Liu,

Bai

et al. 2023

Foods

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In recent years, people’s quality of life has increased, and the requirements for fruits have also become higher; blueberries are particularly popular because of their rich nutrients. In the blueberry industry chain, sensory evaluation is an important link in determining the quality of blueberries. Therefore, to make a more objective scientific evaluation of blueberry quality and reduce the influence of human factors, on the basis of traditional sensory evaluation methods, machine learning is introduced to establish a support vector regression prediction model optimized by the particle swarm algorithm. Ten physical and chemical flavor indices of blueberries (such as catalase, flavonoids, and soluble solids) were used as input data, and sensory evaluation scores were used as output data. Three different predictive models were applied and compared: a particle swarm optimization support vector machine, a convolutional neural network, and a long short-term memory network model. To ensure reliability, the experiments with each of the three models were repeated 20 times, and the mean of each index was calculated. The experimental results showed that the root mean square error and mean absolute error of the particle swarm optimization support vector machine were 0.45 and 0.40, respectively; these values were lower than those of the convolutional neural network (0.96 and 0.78, respectively) and the long short-term memory network (1.22 and 0.97, respectively). Hence, these results highlighted the superiority of the proposed model when sample data are limited.

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Blueberry firmness - A review of the textural and mechanical properties used in quality evaluations

Cited by 30 publications

References 109 publications

Are the Blueberries We Buy Good Quality? Comparative Study of Berries Purchased from Different Outlets

Are the Blueberries We Buy Good Quality? Comparative Study of Berries Purchased from Different Outlets

Can the Firmness, Weight, and Size of Blueberry Fruit Be Enhanced through the Application of Low Amounts of Calcium to the Soil?

Establishing a Berry Sensory Evaluation Model Based on Machine Learning

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