Son Seungwoo, Kim Donghwi, Choul Choi Myoung, Lee Joonhee, Kim Byungjoo, Min Choi Chang, Kim Sunghwan
Department of Chemistry, Kyungpook National University, Daegu 41566, Republic of Korea.
Oil and POPs Research Group, Korea Institute of Ocean Science and Technology, Geoje 53201, Republic of Korea.
Food Chem X. 2022 Aug 12;15:100430. doi: 10.1016/j.fochx.2022.100430. eCollection 2022 Oct 30.
Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.
基于人工神经网络（ANN），利用近红外（NIR）光谱数据建立了水稻主要营养成分的预测模型。为了解对营养成分预测有贡献的波数，提出并进行了权重值的科学解释。从110个水稻样本中获取了近红外光谱。预测的碳水化合物和水分含量的决定系数、相对均方根误差、范围误差比和残差预测偏差值分别为0.98、0.11%、44和7.3，以及0.97、0.80%、27和5.8。结果与先前研究报告的结果以及通过传统偏最小二乘法（PLS）-投影变量重要性方法获得的结果非常吻合。本研究表明，近红外光谱和人工神经网络的结合是监测水稻营养成分的强大而准确的工具，并且可以进行权重的科学解释以克服人工神经网络的黑箱性质。
