Comparing quality parameters obtained using destructive and optical methods in grading tomatoes Article Swipe
Thainara Rebelo da Silva
,
Anderson Gomide Costa
,
Juliana Lobo Paes
,
Marcus Vinícius Morais de Oliveira
,
Francisco de Assis de Carvalho Pinto
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5935/1806-6690.20210066
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5935/1806-6690.20210066
Optical methods for analysing fruit quality have various advantages compared to conventional methods, including not destroying the sample and the possibility of automating the quality control process. The aim of this study was to compare artificial neural networks developed from biological activity indices obtained using the biospeckle laser optical technique, and from physico-chemical variables obtained by conventional destructive techniques, through an evaluation of their precision in classifying ripe tomatoes, using as a reference an earlier classification carried out by visual inspection. A total of 150 tomatoes were used in the experiment, divided into three ripening stages. Multivariate principal component analysis was used to evaluate interaction of the variance within the groups of data obtained using the biospeckle laser technique and destructive laboratory methods. Two artificial neural networks were developed, the first generated using biological activity indices as input vectors, and the second using physico-chemical variables. The precision of the two neural networks was compared using the Kappa index and overall accuracy, and was based on a reference classification. The variation in ripening as a function of the biological activity indices was explained by the first principal component. The neural network generated from the biological activity indices showed the best performance in classifying the tomatoes into the three ripening stages, with a significant Kappa index and an overall accuracy of 67.5%.
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- Type
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- Language
- en
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- http://doi.org/10.5935/1806-6690.20210066
- http://ccarevista.ufc.br/seer/index.php/ccarevista/article/download/7475/2027
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All OpenAlex metadata
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Comparing quality parameters obtained using destructive and optical methods in grading tomatoesWork title
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articleOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-01-01Full publication date if available
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Thainara Rebelo da Silva, Anderson Gomide Costa, Juliana Lobo Paes, Marcus Vinícius Morais de Oliveira, Francisco de Assis de Carvalho PintoList of authors in order
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https://doi.org/10.5935/1806-6690.20210066Publisher landing page
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https://ccarevista.ufc.br/seer/index.php/ccarevista/article/download/7475/2027Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://ccarevista.ufc.br/seer/index.php/ccarevista/article/download/7475/2027Direct OA link when available
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Horticulture, Biology, PhysicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.used | 88, 102 |
| abstract_inverted_index.were | 87, 128 |
| abstract_inverted_index.with | 210 |
| abstract_inverted_index.Kappa | 157, 213 |
| abstract_inverted_index.based | 164 |
| abstract_inverted_index.first | 131, 185 |
| abstract_inverted_index.fruit | 5 |
| abstract_inverted_index.index | 158, 214 |
| abstract_inverted_index.input | 138 |
| abstract_inverted_index.laser | 48, 118 |
| abstract_inverted_index.study | 32 |
| abstract_inverted_index.their | 64 |
| abstract_inverted_index.three | 94, 207 |
| abstract_inverted_index.total | 83 |
| abstract_inverted_index.using | 45, 70, 115, 133, 143, 155 |
| abstract_inverted_index.67.5%. | 220 |
| abstract_inverted_index.groups | 111 |
| abstract_inverted_index.neural | 37, 126, 151, 189 |
| abstract_inverted_index.sample | 18 |
| abstract_inverted_index.second | 142 |
| abstract_inverted_index.showed | 197 |
| abstract_inverted_index.visual | 80 |
| abstract_inverted_index.within | 109 |
| abstract_inverted_index.Optical | 1 |
| abstract_inverted_index.carried | 77 |
| abstract_inverted_index.compare | 35 |
| abstract_inverted_index.control | 26 |
| abstract_inverted_index.divided | 92 |
| abstract_inverted_index.earlier | 75 |
| abstract_inverted_index.indices | 43, 136, 180, 196 |
| abstract_inverted_index.methods | 2 |
| abstract_inverted_index.network | 190 |
| abstract_inverted_index.optical | 49 |
| abstract_inverted_index.overall | 160, 217 |
| abstract_inverted_index.quality | 6, 25 |
| abstract_inverted_index.stages, | 209 |
| abstract_inverted_index.stages. | 96 |
| abstract_inverted_index.through | 60 |
| abstract_inverted_index.various | 8 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.accuracy | 218 |
| abstract_inverted_index.activity | 42, 135, 179, 195 |
| abstract_inverted_index.analysis | 100 |
| abstract_inverted_index.compared | 10, 154 |
| abstract_inverted_index.evaluate | 104 |
| abstract_inverted_index.function | 175 |
| abstract_inverted_index.methods, | 13 |
| abstract_inverted_index.methods. | 123 |
| abstract_inverted_index.networks | 38, 127, 152 |
| abstract_inverted_index.obtained | 44, 55, 114 |
| abstract_inverted_index.process. | 27 |
| abstract_inverted_index.ripening | 95, 172, 208 |
| abstract_inverted_index.tomatoes | 86, 204 |
| abstract_inverted_index.variance | 108 |
| abstract_inverted_index.vectors, | 139 |
| abstract_inverted_index.accuracy, | 161 |
| abstract_inverted_index.analysing | 4 |
| abstract_inverted_index.component | 99 |
| abstract_inverted_index.developed | 39 |
| abstract_inverted_index.explained | 182 |
| abstract_inverted_index.generated | 132, 191 |
| abstract_inverted_index.including | 14 |
| abstract_inverted_index.precision | 65, 147 |
| abstract_inverted_index.principal | 98, 186 |
| abstract_inverted_index.reference | 73, 167 |
| abstract_inverted_index.technique | 119 |
| abstract_inverted_index.tomatoes, | 69 |
| abstract_inverted_index.variables | 54 |
| abstract_inverted_index.variation | 170 |
| abstract_inverted_index.advantages | 9 |
| abstract_inverted_index.artificial | 36, 125 |
| abstract_inverted_index.automating | 23 |
| abstract_inverted_index.biological | 41, 134, 178, 194 |
| abstract_inverted_index.biospeckle | 47, 117 |
| abstract_inverted_index.component. | 187 |
| abstract_inverted_index.destroying | 16 |
| abstract_inverted_index.developed, | 129 |
| abstract_inverted_index.evaluation | 62 |
| abstract_inverted_index.laboratory | 122 |
| abstract_inverted_index.technique, | 50 |
| abstract_inverted_index.variables. | 145 |
| abstract_inverted_index.classifying | 67, 202 |
| abstract_inverted_index.destructive | 58, 121 |
| abstract_inverted_index.experiment, | 91 |
| abstract_inverted_index.inspection. | 81 |
| abstract_inverted_index.interaction | 105 |
| abstract_inverted_index.performance | 200 |
| abstract_inverted_index.possibility | 21 |
| abstract_inverted_index.significant | 212 |
| abstract_inverted_index.techniques, | 59 |
| abstract_inverted_index.Multivariate | 97 |
| abstract_inverted_index.conventional | 12, 57 |
| abstract_inverted_index.classification | 76 |
| abstract_inverted_index.classification. | 168 |
| abstract_inverted_index.physico-chemical | 53, 144 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.68831773 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |