Abstract
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.
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil
Subject
Plant Science,Ecology,Ecology, Evolution, Behavior and Systematics
Reference89 articles.
1. Park, C.H., Yeo, H.J., Kim, Y.J., Van Nguyen, B., Park, Y.E., Sathasivam, R., Kim, J.K., and Park, S.U. (2021). Profiles of Secondary Metabolites (Phenolic Acids, Carotenoids, Anthocyanins, and Galantamine) and Primary Metabolites (Carbohydrates, Amino Acids, and Organic Acids) during Flower Development in Lycoris radiata. Biomolecules, 11.
2. High Resolution Leaf Spectral Signature as a Tool for Foliar Pigment Estimation Displaying Potential for Species Differentiation;Falcioni;J. Plant Physiol.,2020
3. Hyperspectral Reflectance Imaging to Classify Lettuce Varieties by Optimum Selected Wavelengths and Linear Discriminant Analysis;Furlanetto;Remote Sens. Appl. Soc. Environ.,2020
4. High-Throughput Phenotyping to Detect Anthocyanins, Chlorophylls, and Carotenoids in Red Lettuce Germplasm;Clemente;Int. J. Appl. Earth Obs. Geoinf.,2021
5. When Are Foliar Anthocyanins Useful to Plants? Re-Evaluation of the Photoprotection Hypothesis Using Arabidopsis thaliana Mutants That Differ in Anthocyanin Accumulation;Gould;Environ. Exp. Bot.,2018