Falcioni Renan, Moriwaki Thaise, Gibin Mariana Sversut, Vollmann Alessandra, Pattaro Mariana Carmona, Giacomelli Marina Ellen, Sato Francielle, Nanni Marcos Rafael, Antunes Werner Camargos
Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil.
Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil.
Plants (Basel). 2022 Dec 7;11(24):3413. doi: 10.3390/plants11243413.
Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.
绿色或紫色生菜品种会产生许多次生代谢产物，如叶绿素、类胡萝卜素、花青素、黄酮类化合物和酚类化合物，这是生物分子研究领域的一个新兴探索方向。本研究的主要目的是对基于衰减全反射傅里叶变换红外光谱（ATR-FTIR）的光谱使用多元和机器学习算法，以对化学计量属性进行分类、预测和归类。聚类热图显示，基于色素谱对相似生菜品种进行分组时效率最高。色素之间的关系比绝对含量更为显著。其他结果表明，可以根据与生菜中存在的结构和化学成分相关的ATR-FTIR指纹进行分类，通过使用主成分分析和判别分析（PCA-LDA）相关的线性LDA和支持向量机（SVM）机器学习算法，可获得高精度和高精确度（>97%）。此外，偏最小二乘回归（PLSR）模型能够预测叶绿素a（Chla）、叶绿素b（Chlb）、叶绿素a+b（Chla+b）、类胡萝卜素（Car）、花青素（AnC）、黄酮（Flv）和苯丙素（Phe）的含量，对于类胡萝卜素、花青素和黄酮，其预测决定系数（R2P）和预测偏差百分比（RPDP）值被认为非常好（0.81 - 0.88），对于苯丙素则为优秀（0.91 - 0.93）。根据RPDP指标，对于所有估计变量，模型都被认为是优秀的（>2.10）。因此，本研究表明了机器学习解决方案在ATR-FTIR光谱分析中对生菜中与次生代谢产物相关的生物分子进行分类、估计和表征的潜力。
