Scholarlink

<p>Perilla (<i>Perilla frutescens</i> L.) seeds are a rich source of functional fatty acids, including α-linolenic acid, and serve as a major plant-based omega-3 resource; however, to the best of our knowledge, non-destructive prediction of fatty acid contents using hyperspectral imaging (HSI) has not been reported. In this study, a rapid and non-destructive method was developed for predicting fatty acid contents in perilla seeds by combining near-infrared HSI with regression modeling. Hyperspectral images covering the 1,050–1,600&#xa0;nm range were acquired from 236 germplasm accessions, and four spectral preprocessing methods were applied. Effective wavelengths were selected using a genetic algorithm and competitive adaptive reweighted sampling. Four regression models were evaluated, among which support vector regression yielded the optimal performance for all six components. The optimal models achieved R²<i>p</i> = 0.894 with a normalized root mean square error (NRMSE) of 5.650% for total fat and R²<i>p</i> = 0.865 with an NRMSE of 6.845% for α-linolenic acid, and five of the six components exhibited NRMSE values &lt; 8%, with the exception of stearic acid. SHapley Additive exPlanations and principal component analysis identified key spectral regions contributing to fatty acid prediction, and non-destructive prediction at the individual seed level was demonstrated. To our knowledge, this study presents the first near-infrared-HSI-based prediction of fatty acid contents in perilla seeds, and the proposed approach is expected to serve as a high-throughput non-destructive screening tool for high-fatty-acid lines in breeding programs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Rapid non-destructive determination of fatty acids in perilla germplasm using near-infrared hyperspectral imaging and chemometric modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

查看全文
  • Tae-An Kang,
  • Chi-Yong An,
  • Eunsoo Lee,
  • Jung-In Kim,
  • Hoejeong Jeong,
  • Jinki Park,
  • Hyeongjin Bae,
  • Won-Chan Kim,
  • Hongseok Lee

摘要

Perilla (Perilla frutescens L.) seeds are a rich source of functional fatty acids, including α-linolenic acid, and serve as a major plant-based omega-3 resource; however, to the best of our knowledge, non-destructive prediction of fatty acid contents using hyperspectral imaging (HSI) has not been reported. In this study, a rapid and non-destructive method was developed for predicting fatty acid contents in perilla seeds by combining near-infrared HSI with regression modeling. Hyperspectral images covering the 1,050–1,600 nm range were acquired from 236 germplasm accessions, and four spectral preprocessing methods were applied. Effective wavelengths were selected using a genetic algorithm and competitive adaptive reweighted sampling. Four regression models were evaluated, among which support vector regression yielded the optimal performance for all six components. The optimal models achieved R²p = 0.894 with a normalized root mean square error (NRMSE) of 5.650% for total fat and R²p = 0.865 with an NRMSE of 6.845% for α-linolenic acid, and five of the six components exhibited NRMSE values < 8%, with the exception of stearic acid. SHapley Additive exPlanations and principal component analysis identified key spectral regions contributing to fatty acid prediction, and non-destructive prediction at the individual seed level was demonstrated. To our knowledge, this study presents the first near-infrared-HSI-based prediction of fatty acid contents in perilla seeds, and the proposed approach is expected to serve as a high-throughput non-destructive screening tool for high-fatty-acid lines in breeding programs.