Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period

Liu, Yang; Zhao, Jinfei; Tang, Yurong; Jiang, Xin; Liao, Jiean

doi:10.3390/agriculture12091348

Cited by 6 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GRNN is another deformation form of radial basis function network proposed by D. F. Specht in 1991, and it has a similar structure to the RBF network. It consists of four layers: the input layer, the mode layer, the summation layer and the output layer [21,22]. Based on non-parameter regression, GRNN used sample data as posterior conditions, executed Parzen non-parametric estimation, and calculated the network output according to the principle of maximum probability.…”

Section: General Regression Neural Network (Grnn)mentioning

confidence: 99%

Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears

Liu

Niu

et al. 2023

Agronomy

Self Cite

View full text Add to dashboard Cite

For a fast and accurate evaluation of the values of damaged fragrant pears, a prediction method of the damage degree of Korla fragrant pears was proposed. To study variation laws of damages of fragrant pears under different volumes of squeezing deformation, the partial least squares regression (PLSR), the generalised regression neural network (GRNN) and the adaptive neural fuzzy inference system (ANFIS) were chosen to predict the damage degree of fragrant pears and establish the optimal prediction model. The results demonstrated that with the increase of ripeness or deformation value, the damage degree of fragrant pears increases gradually. For performance comparison of prediction models based on PLSR, GRNN and ANFIS, it was found that the trained PLSR, GRNN and ANFIS can all predict the damage degree of Korla fragrant pears. The ANFIS, which inputs the membership function of dsigmf (R2 = 0.9979, RMSE = 46.6) and psigmf (R2 = 0.9979, RMSE = 46.6), achieves the best performance. Research results can provide theoretical references to the evaluation of the commodity value of damaged fragrant pears, quality grading of fragrant pears and design of the picking machine.

show abstract

Section: General Regression Neural Network (Grnn)mentioning

confidence: 99%

Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears

Liu

Niu

et al. 2023

Agronomy

Self Cite

View full text Add to dashboard Cite

show abstract

“…When these values were reached, the storage test was stopped. Therefore, fragrant pears were stored for 0, 5,10,15,20,25,30,35, and 40 days, and their hardness and SSC were measured every five days. A total of 1260 fragrant pears were needed for the storage test.…”

Section: Storage Tests Of Korla Fragrant Pearsmentioning

confidence: 99%

“…Hence, it is urgent to develop a low-cost and highefficiency method to predict the storage quality of Korla fragrant pears. Because of its quick convergence, strong self-learning capacity, and great adaption ability, ANFIS is frequently used to forecast the quality of fruits and vegetables [19,20]. It can offer useful methods for predicting the storage quality of Korla fragrant pears.…”

Section: Introductionmentioning

confidence: 99%

Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage

et al. 2023

Self Cite

View full text Add to dashboard Cite

To increase the commercial value of damaged fragrant pears and improve marketing competitiveness, this study explored the degree of damage degree and effects of storage time on the internal quality of fragrant pears during storage and predicted the internal quality of fragrant pears using an adaptive neural fuzzy inference system (ANFIS). The internal quality prediction models of damaged fragrant pears during storage with eight membership functions were constructed, and the optimal model was chosen, allowing for accurate internal quality prediction of damaged fragrant pears. The research results demonstrated that the hardness and soluble solid content (SSC) of fragrant pears decrease as the storage time increases. Given the same storage time, the hardness and SSC of fragrant pears are negatively correlated to the degree of damage. The ANFIS modelling technique is feasible for predicting the internal quality of fragrant pears during storage. The best prediction performances for the hardness and SSC of fragrant pears, respectively, are displayed by the ANFIS using the input membership function of trimf (RMSE = 0.1362, R2 = 0.9752; RMSE = 0.0315, R2 = 0.9892). The findings of this study can be used to predict the storage quality of fruits.

show abstract

“…Due to strengthened respiration of the damaged fragrant pears during the storage period, the releasing amount of endogenous ethylene increased to accelerate the degradation of chlorophyll. The chlorophyll content in the pericarp declined with the increase in the storage time [29,30], thus resulting in the fading green. Anthocyanin began to accumulate during the violent degradation of chlorophyll, making the fruits turn red [31][32][33].…”

Section: Variation Laws Of A* Valuementioning

confidence: 99%

Construction of Color Prediction Model for Damaged Korla Pears during Storage Period

Zhang

Liu

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

In this work, to scientifically predict the color of damaged Korla fragrant pears during the storage period with lower economic loss and improved added value of the fragrant pears, eight pericarp color prediction models of damaged Korla fragrant pears during the storage period were established. These models had different membership functions, which were based on the adaptive neuro-fuzzy inference system (ANFIS). The optimal model was chosen and verified. Finally, the pericarp color of fragrant pears was accurately predicted through the degree of damage and storage time. According to the acquired test results, the pericarp brightness (L*) decreased, while both the red–green (a*) and yellow–blue (b*) values increased as the storage time prolonged. In addition, the pericarp color of the damaged fragrant pears during the storage period could be well predicted by using the ANFIS model. More specifically, the model with a membership function of trimf showed the optimal prediction effects of L*, a*, and b* (RMSE = 0.1089, R2 = 0.9773; RMSE = 0.5894, R2 = 0.9853; and RMSE = 0.2360, R2 = 0.9772). Our work provides valuable insights for the prediction of the quality of Korla fragrant pears during the storage period.

show abstract

Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period

Cited by 6 publications

References 21 publications

Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears

Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears

Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage

Construction of Color Prediction Model for Damaged Korla Pears during Storage Period

Contact Info

Product

Resources

About