The synthetical analysis was carried out from specific gravity, firmness and Hunter color index by neural network to develop the new nondestructive method for evaluating soluble solid content, sugar and organic acid contents. The multiple regression analysis was also carried out to determine the possibility of predicting soluble solid content, sugar and organic acid contents. The results showed that there was no significant difference between experimental and calculated values of soluble solid content, indicating that soluble solid content could be predicted from specific gravity, firmness (calculated from a* value), L*, a* and b* values by using neural network. It was also shown that the organic acid content could be estimated from specific gravity, firmness, hue, L*, a* and b* values. But there was significant difference between experimental and calculated values of sugar, demonstrating that it was impossible to predict the sugar content from specific gravity, firmness, hue, L*, a* and b* values by neural network. For the multiple regression analysis, although soluble solid content of tomato fruits was synthetically affected by many factors, it was greatly influenced by firmness and a* value of tomato fruits. For sugar and organic acid contents, both of them were markedly affected by firmness and specific gravity.
|Number of pages||8|
|Journal||Journal of the Faculty of Agriculture, Kyushu University|
|Publication status||Published - Feb 1 2003|
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science