Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage
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 adaptiv...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Horticulturae |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-7524/9/6/666 |
| _version_ | 1797594552378327040 |
|---|---|
| author | Yang Liu Xiyue Niu Yurong Tang Shiyuan Li Haipeng Lan Hao Niu |
| author_facet | Yang Liu Xiyue Niu Yurong Tang Shiyuan Li Haipeng Lan Hao Niu |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | 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, R<sup>2</sup> = 0.9752; RMSE = 0.0315, R<sup>2</sup> = 0.9892). The findings of this study can be used to predict the storage quality of fruits. |
| first_indexed | 2024-03-11T02:23:54Z |
| format | Article |
| id | doaj.art-db0d22b0ebc249c5af4ebaede6ba88dc |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-11T02:23:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-db0d22b0ebc249c5af4ebaede6ba88dc2023-11-18T10:40:56ZengMDPI AGHorticulturae2311-75242023-06-019666610.3390/horticulturae9060666Internal Quality Prediction Method of Damaged Korla Fragrant Pears during StorageYang Liu0Xiyue Niu1Yurong Tang2Shiyuan Li3Haipeng Lan4Hao Niu5College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, ChinaCollege of Food Science and Engineering, Tarim University, Alaer 843300, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alaer 843300, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alaer 843300, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alaer 843300, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alaer 843300, ChinaTo 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, R<sup>2</sup> = 0.9752; RMSE = 0.0315, R<sup>2</sup> = 0.9892). The findings of this study can be used to predict the storage quality of fruits.https://www.mdpi.com/2311-7524/9/6/666Korla fragrant pearsinternal qualitydamagesadaptive neural fuzzy inference systemstorage |
| spellingShingle | Yang Liu Xiyue Niu Yurong Tang Shiyuan Li Haipeng Lan Hao Niu Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage Horticulturae Korla fragrant pears internal quality damages adaptive neural fuzzy inference system storage |
| title | Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage |
| title_full | Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage |
| title_fullStr | Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage |
| title_full_unstemmed | Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage |
| title_short | Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage |
| title_sort | internal quality prediction method of damaged korla fragrant pears during storage |
| topic | Korla fragrant pears internal quality damages adaptive neural fuzzy inference system storage |
| url | https://www.mdpi.com/2311-7524/9/6/666 |
| work_keys_str_mv | AT yangliu internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage AT xiyueniu internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage AT yurongtang internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage AT shiyuanli internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage AT haipenglan internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage AT haoniu internalqualitypredictionmethodofdamagedkorlafragrantpearsduringstorage |