Determination of Astringent Fruit in ‘Le Lectier’ Pears Using Visible and Near-infrared Spectroscopy and Neural Network

DOI JASI Web Site 29 References
  • TAKIZAWA Kenichi
    Graduate School of Science and Technology, Niigata University
  • NAKANO Kazuhiro
    Graduate School of Science and Technology, Niigata University
  • OHASHI Shintaroh
    Graduate School of Science and Technology, Niigata University
  • HIROI Masaru
    Graduate School of Science and Technology, Niigata University
  • CHINO Shuji
    Graduate School of Science and Technology, Niigata University
  • MATSUMOTO Tatsuya
    Horticultural Research Center, Niigata Agricultural Research Institute
  • YAMAZAWA Yasuhide
    Niigata Prefectural Government Department of Agriculture, Forestry and Fisheries Agricultural Management Promotion Division
  • KOJIMA Kiyohide
    Graduate School of Science and Technology, Niigata University

Bibliographic Information

Other Title
  • 可視・近赤外分光法とニューラルネットワークによるセイヨウナシ ‘ル・レクチェ’ の渋味判定
  • 可視・近赤外分光法とニューラルネットワークによるセイヨウナシ'ル・レクチエ'の渋味判定
  • カシ ・ キンセキガイ ブンコウホウ ト ニューラルネットワーク ニ ヨル セイヨウナシ'ル ・ レクチエ'ノ シブミ ハンテイ

Search this article

Abstract

It is impossible to distinguish the astringent fruit in ‘Le Lectier’ pears by visual inspection. This study aimed to develop nondestructive determination of the astringent fruit and quality assurance of the intact fruit using neural network classification, visible and near-infrared spectroscopy. For this study, 51 pears harvested in Sanjo City and 46 pears harvested in the Tsukigata area of Niigata City, 97 pears in all were collected. The recognition ratio was established by neural network learning and validation repeatedly using leave-one-out cross validation. The average recognition ratio was 81.1 % when the neural network was discussed using 15 hidden layer units and set an error goal to 0.11 and calculated by 10 times cross validation.

Journal

References(29)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top