ArticlePlants (Basel, Switzerland)2022
Classification and Prediction by Pigment Content in Lettuce (
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Self-absorption correction in calibration-free laser-induced breakdown spectroscopy for quantitative elemental profiling and chemometric classification ofRSC advances · 2026Article
- KG-FT-Transformer: a knowledge-guided feature tokenizer transformer for hyperspectral prediction of lettuce quality trait and fingerprint analysis.Food chemistry: X · 2026Article
- From leaf to canopy: Inversion of lettuce pigment distribution using hyperspectral imaging technology combined with deep learning algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- A Comprehensive Review on Minimally Destructive Quality and Safety Assessment of Agri-Food Products: Chemometrics-Coupled Mid-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- TiOMolecules (Basel, Switzerland) · 2025Article
- Biological Activities and Phytochemical Profile of Hawm Gra Dang Ngah Rice: Water and Ethanolic Extracts.Foods (Basel, Switzerland) · 2025Article
- Harnessing NMR technology for enhancing field crop improvement: applications, challenges, and future perspectives.Metabolomics : Official journal of the Metabolomic Society · 2025Review
- Influence of sample preparation methods on FTIR spectra for taxonomic identification of tropical trees in the Atlantic forest.Heliyon · 2024Article
- A pilot study on non-invasive in situ detection of phytochemicals and plant endogenous status using fiber optic infrared spectroscopy.Scientific reports · 2023Article
- The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics.Antioxidants (Basel, Switzerland) · 2023Article
- Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
- Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.Plants (Basel, Switzerland) · 2023Article
- Article
- A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach.Sensors (Basel, Switzerland) · 2023Article
- Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.