Joshi Rahul, Sathasivam Ramaraj, Jayapal Praveen Kumar, Patel Ajay Kumar, Nguyen Bao Van, Faqeerzada Mohammad Akbar, Park Sang Un, Lee Seung Hyun, Kim Moon S, Baek Insuck, Cho Byoung-Kwan
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea.
Department of Crop Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
Plants (Basel). 2022 Mar 22;11(7):836. doi: 10.3390/plants11070836.
The increasing interest in plant phenolic compounds in the past few years has become necessary because of their several important physicochemical properties. Thus, their identification through non-destructive methods has become crucial. This study carried out comparative non-destructive measurements of leaf powder sample phenolic compounds using Fourier-transform infrared and near-infrared spectroscopic techniques under six distinct stress conditions. The prediction analysis of 600 leaf powder samples under different stress conditions (LED lights and drought) was performed using PLSR, PCR, and NAS-based HLA/GO regression analysis methods. The results obtained through FT-NIR spectroscopy yielded the highest correlation coefficient (Rp2) value of 0.999, with a minimum error (RMSEP) value of 0.003 mg/g, based on the PLSR model using the MSC preprocessing method, which was slightly better than the correlation coefficient (Rp2) value of 0.980 with an error (RMSEP) value of 0.055 mg/g for FT-IR spectroscopy. Additionally, beta coefficient plots present spectral differences and the identification of important spectral signatures sensitive to the phenolic compounds in the measured powdered samples. Thus, the obtained results demonstrated that FT-NIR spectroscopy combined with partial least squares regression (PLSR) and suitable preprocessing method has a solid potential for non-destructively predicting phenolic compounds in leaf powder samples.
由于植物酚类化合物具有多种重要的物理化学性质，在过去几年中，人们对其兴趣日益浓厚。因此，通过无损方法对其进行鉴定变得至关重要。本研究在六种不同的胁迫条件下，使用傅里叶变换红外光谱和近红外光谱技术对叶片粉末样品中的酚类化合物进行了比较无损测量。使用偏最小二乘回归（PLSR）、主成分回归（PCR）和基于神经网络自适应软测量（NAS）的HLA/GO回归分析方法，对600个不同胁迫条件（LED光照和干旱）下的叶片粉末样品进行了预测分析。基于使用多元散射校正（MSC）预处理方法的PLSR模型，通过傅里叶变换近红外光谱（FT-NIR）获得的结果具有最高的相关系数（Rp2）值0.999，最小误差（RMSEP）值为0.003 mg/g，略优于傅里叶变换红外光谱（FT-IR）的相关系数（Rp2）值0.980，误差（RMSEP）值为0.055 mg/g。此外，β系数图呈现了光谱差异，并识别出了对所测粉末样品中酚类化合物敏感的重要光谱特征。因此，所得结果表明，傅里叶变换近红外光谱结合偏最小二乘回归（PLSR）和合适的预处理方法，在无损预测叶片粉末样品中的酚类化合物方面具有很大潜力。
