Prediction of bruise volume propagation of pear during the storage using soft computing methods

Razavi, Mahsa; Golmohammadi, Abdollah; Sedghi, Reza; Asghari, Ali

doi:10.1002/fsn3.1365

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Sometimes, two or more independent variables have a significant effect on a dependent variable. In this case, multiple regression is performed to predict the dependent variable [25]. In this study, six multiple linear regression models were performed to study the effect of independent variable (drop height, storage temperature, and storage duration) on the dependent variables (bruise area, weight loss, firmness, redness, total soluble solids, and lycopene) at a 5% significance level.…”

Section: Regression Modelmentioning

confidence: 99%

Bruise Damage and Quality Changes in Impact-Bruised, Stored Tomatoes

Pathare

Al-Dairi

2021

Horticulturae

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This study examined three main possible effects (impact, storage temperature, and duration) that cause and extend the level of bruising and other quality attributes contributing to the deterioration of tomatoes. The impact threshold level required to cause bruising was conducted by subjecting tomato samples to a steel ball with a known mass from different drop heights (20, 40, and 60 cm). The samples were then divided and stored at 10 and 22 °C for 10 days for the further analysis of bruise area and any physiological, chemical, and nutritional changes at two day intervals. Six prediction models were constructed for the bruised area and other quality attribute changes of the tomato. Storage time, bruise area, weight loss, redness, total color change, color index, total soluble solids, and pigments content (lycopene and carotenoids) showed a significant (p < 0.05) increase with the increase of drop height (impact level) and storage temperature. After 10 days of storage, high drop impact and storage at 22 °C generated a higher reduction in firmness, lightness, yellowness, and hue° (color purity). Additionally, regression model findings showed the significant effect of storage duration, storage temperature, and drop height on the measured variables (bruise area, weight loss, firmness, redness, total soluble solids, and lycopene) at a 5% probability level with a determination coefficient (R2) ranging from 0.76 to 0.95. Bruising and other quality attributes could be reduced by reducing the temperature during storage. This study can help tomato transporters, handlers, and suppliers to understand the mechanism of bruising occurrence and how to reduce it.

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Section: Regression Modelmentioning

confidence: 99%

Bruise Damage and Quality Changes in Impact-Bruised, Stored Tomatoes

Pathare

Al-Dairi

2021

Horticulturae

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“…In this regard, several authors highlighted the potential use of different algorithms, such as principal component analysis (PCA), multiple linear regression (MLR), and artificial neural networks (ANNs) to classify and quantify specific compounds in different agricultural products [21][22][23][24][25][26][27][28]. However, there is still a lack of methods that effectively evaluate fruit quality using easily and readily measured factors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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Strawberry is a very popular fruit, appreciated for its unique flavor and many beneficial traits such as antioxidants and useful amino acids, which strongly contribute to the overall quality of the product. Indeed, the quality of fresh fruit is a fundamental aspect for consumers, and it is crucial for the success of breeding activities as well as for enhancing the competitiveness and profitability of the fruit industry. Nowadays, the entire supply chain requires simple and fast systems for quality evaluation. In this context, the pomological and chemical traits (i.e., soluble solids, firmness, titratable acidity, dry matter) as well as nutritional ones such as total phenols, total anthocyanins and antioxidant potential were evaluated and compared for seven strawberry cultivars and three harvest times. The prediction of the qualitative traits was carried out using color space coordinates (L*, a* and b*) and two statistical techniques, i.e., the multiple linear regression models (MLR) and artificial neural networks (ANNs). Unsatisfactory prediction performances were obtained for all parameters when MLR was applied. On the contrary, the good prediction of the internal quality attributes, using ANN, was observed, especially for both antioxidant activity and the total monomeric anthocyanin (R2 = 0.906, and R2 = 0.943, respectively). This study highlighted that color coordinates coupled with ANN can be successfully used to evaluate the quality of strawberry.

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“…Niu et al (2020) predicted the relationship between fruit hardness, harvest maturity, and storage time of Korla fragrant pears through the GRNN and ANFIS models. Razavi, Golmohammadi, Sedghi, and Asghari (2020) predicted the volume propagation of pear damage during storage by way of the ANFIS model. However, there are no reports on the prediction of shelf life of damaged Korla fragrant pears in combination with the three neural network models-BPNN, GRNN, and ANFIS.…”

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confidence: 99%

Prediction method of shelf life of damaged Korla fragrant pears

Lan

et al. 2021

J Food Process Engineering

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To scientifically and effectively predict the shelf life of damaged Korla fragrant pears, this paper explores the effects of maturity, storage temperature, and damage degree on their shelf life, which was furthermore predicted employing three neural network modeling methods: Back Propagation Neural Network, General Regression Neural Network (GRNN), and Adaptive Network-based Fuzzy Inference System (ANFIS). A prediction model was then built to achieve the optimal prediction of the shelf life of damaged Korla fragrant pears. The results suggest that maturity, storage temperature, and damage degree exert a significant effect on the shelf life of fragrant pears, and are inversely proportional to the shelf life length; the damaged fragrant pears still have storage value, which can be stored at low temperature according to different damage degree to prolong the shelf life of damaged fragrant pears; the prediction model of maturity, storage temperature, and damage degree on the shelf life of fragrant pears is built based on well-trained neural networks using the GRNN and ANFIS model, and the ANFIS model with gaussmf input membership function yields the best performance in predicting the shelf life of damaged fragrant pears (root mean square error = 3.5190; R 2 = 0.9881). The results offer theoretical guidance for the selection of optimal storage conditions and the effective prediction of the shelf life of fruits and vegetables. Practical applicationsBefore the Korla fragrant pears are stored in the warehouse, the practitioners will directly discard damaged fruits, causing a lot of waste and economic losses. The damaged fragrant pears can be temporarily stored and preserved, and they can be processed and sold in time before the end of the critical shelf life, thereby increasing the economic income of practitioners. Therefore, three neural networks-Back Propagation Neural Network, General Regression Neural Network, and Adaptive Network-based Fuzzy Inference System-are evaluated for their ability to predict the shelf life of damaged fragrant pears.A shelf life model of damaged fragrant pears is then constructed and screened out, and employed to realize the prediction of the shelf life of damaged fragrant pears. This method is also suitable for predicting the shelf life of other damaged fruits. | INTRODUCTIONKorla fragrant pears are considered a Xinjiang pear species, which are deeply favored by a vast number of consumers thanks to their sweet and smooth taste, thin skin, fine meat, and abundant nutrition (Wang, Su, Chen, Mao, & Jiang, 2021). In 2019, the gross output of Xinjiang Korla fragrant pears has been more than 1 million tons, and, after the harvest, more than 85% of the fragrant pears await to be put into cold

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Prediction of bruise volume propagation of pear during the storage using soft computing methods

Cited by 9 publications

References 24 publications

Bruise Damage and Quality Changes in Impact-Bruised, Stored Tomatoes

Bruise Damage and Quality Changes in Impact-Bruised, Stored Tomatoes

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Prediction method of shelf life of damaged Korla fragrant pears

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