Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms
Acoustic response is non-destructive evaluation technique that replicates the conventional method for determining maturity by tapping the fruit. The physical (dimensions, color, firmness, and specific gravity) chemical (TSS, %TA, and TSS/TA), and acoustic properties of Phulae pineapple were determ...
| Autores principales: | , , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/9027 https://www.sciencedirect.com/science/article/pii/S2772375524002065 |
| _version_ | 1855492609833697280 |
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| author | Arwatchananukul, Sujitra Chaiwong, Saowapa Aunsri, Nattapol Kittiwachana, Sila Luengwilai, Kietsuda Trongsatitkul, Tatiya Mahajan, Pramod Blasco, José Rattapon, Saengrayap |
| author_browse | Arwatchananukul, Sujitra Aunsri, Nattapol Blasco, José Chaiwong, Saowapa Kittiwachana, Sila Luengwilai, Kietsuda Mahajan, Pramod Rattapon, Saengrayap Trongsatitkul, Tatiya |
| author_facet | Arwatchananukul, Sujitra Chaiwong, Saowapa Aunsri, Nattapol Kittiwachana, Sila Luengwilai, Kietsuda Trongsatitkul, Tatiya Mahajan, Pramod Blasco, José Rattapon, Saengrayap |
| author_sort | Arwatchananukul, Sujitra |
| collection | ReDivia |
| description | Acoustic response is non-destructive evaluation technique that replicates the conventional method for determining
maturity by tapping the fruit. The physical (dimensions, color, firmness, and specific gravity) chemical
(TSS, %TA, and TSS/TA), and acoustic properties of Phulae pineapple were determined and used to classify the
maturity and defect, e.g. translucency flesh symptoms. Results showed that all physical parameters of the two
maturity stages were not significantly different (p > 0.05). Translucency flesh (TF) defects were observed in 23.5
% and 27.3 % of pineappls in the green and green-yellow stages, respectively. The dominant resonance frequency
(fn) of Phulae pineapple ranged of 0.057 to 3.010 kHz. All the physical, chemical, and acoustic properties were
used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML).
Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive
parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples.
Random Forest (RF) provided a better classification than other MLs with 99.93 % accuracy of maturity classification,
while TF classification was 99.59 %. Results showed acoustic method integrated with ML was a fast
reliable, and cost effective technique for assessing Phulae pineapple quality |
| format | Artículo |
| id | ReDivia9027 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia90272025-04-25T14:49:48Z Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms Arwatchananukul, Sujitra Chaiwong, Saowapa Aunsri, Nattapol Kittiwachana, Sila Luengwilai, Kietsuda Trongsatitkul, Tatiya Mahajan, Pramod Blasco, José Rattapon, Saengrayap Translucency flesh Resonance frequency N01 Agricultural engineering Factor analysis Maturity Acoustic response is non-destructive evaluation technique that replicates the conventional method for determining maturity by tapping the fruit. The physical (dimensions, color, firmness, and specific gravity) chemical (TSS, %TA, and TSS/TA), and acoustic properties of Phulae pineapple were determined and used to classify the maturity and defect, e.g. translucency flesh symptoms. Results showed that all physical parameters of the two maturity stages were not significantly different (p > 0.05). Translucency flesh (TF) defects were observed in 23.5 % and 27.3 % of pineappls in the green and green-yellow stages, respectively. The dominant resonance frequency (fn) of Phulae pineapple ranged of 0.057 to 3.010 kHz. All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. Random Forest (RF) provided a better classification than other MLs with 99.93 % accuracy of maturity classification, while TF classification was 99.59 %. Results showed acoustic method integrated with ML was a fast reliable, and cost effective technique for assessing Phulae pineapple quality 2025-02-18T12:25:50Z 2025-02-18T12:25:50Z 2024 article publishedVersion Arwatchananukul, S., Chaiwong, S., Aunsri, N., Kittiwachana, S., Luengwilai, K., Trongsatitkul, T., Mahajan, P., Blasco, J. & Saengrayap, R. (2024) Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms. Smart Agricultural Technology, 9, 100601. 2772-3755 https://hdl.handle.net/20.500.11939/9027 10.1016/j.atech.2024.100601 https://www.sciencedirect.com/science/article/pii/S2772375524002065 en The authors would like to thank Mae Fah Luang University for research funding through the MFU Research Fund (grant No.641B04005). We also appreciate the financial support provided by the Reinventing University System Project 2024. Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Elsevier electronico |
| spellingShingle | Translucency flesh Resonance frequency N01 Agricultural engineering Factor analysis Maturity Arwatchananukul, Sujitra Chaiwong, Saowapa Aunsri, Nattapol Kittiwachana, Sila Luengwilai, Kietsuda Trongsatitkul, Tatiya Mahajan, Pramod Blasco, José Rattapon, Saengrayap Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title | Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title_full | Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title_fullStr | Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title_full_unstemmed | Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title_short | Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| title_sort | acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms |
| topic | Translucency flesh Resonance frequency N01 Agricultural engineering Factor analysis Maturity |
| url | https://hdl.handle.net/20.500.11939/9027 https://www.sciencedirect.com/science/article/pii/S2772375524002065 |
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