Assessing Linear FAME-Property Relationships in Heterogeneous Biodiesel Databases: A Sequential Cross-Validated Subset-Selection Approach

Abstract Linear correlations based on fatty acid methyl ester (FAME) composition provide a transparent approach for estimating biodiesel properties, although their applicability to heterogeneous literature databases depends on the existence of a consistent composition-property structure. This work assesses linear relationships for density, kinematic viscosity, flash point, and lower and higher heating values using the normalized shares of palmitic, stearic, oleic, linoleic, and linolenic esters. A sequential nested leave-one-out procedure selects, at each stage, the observation whose exclusion maximizes the cross-validated performance of the remaining subset. The complete procedure is also applied to response-permuted, or Y-randomized, surrogate databases. The archived operating point is the first stage at which the fitted real trajectory reaches Rfit2 ≥ 0.60, while the real-versus-permuted differential Δ(k) compares the fitted linear compatibility of the actual FAME-property pairing with the mean fitted linear compatibility obtained after response randomization. Because sustained removal also raises the surrogate trajectories, the fitted R2 alone is not interpreted as evidence of a composition–property relation; the comparison relies on Δ(k). Exploratory data analysis first characterizes the original databases through target-property distributions, descriptive statistics, and literature-source and feedstock representation. This baseline is then extended by comparing the original and retained databases in terms of literature-source and dominant-FAME retention, target-property distributions, principal-component coverage, and Spearman rank-correlation structure, showing that the retained subsets largely preserve the main compositional characteristics of the original databases. Cook’s distance and Williams plots provide independent influence flags for a substantial, but incomplete, fraction of the selected observations, including response-inconsistent and high-leverage cases. Selected observations are interpreted as operationally incompatible with the five-FAME linear description at their respective selection stages; possible causes include influence, omitted descriptors, or nonlinear behavior. The combined statistical, representational, and physical evidence supports conditional linear correlations for kinematic viscosity, flash point, and higher heating value, in decreasing order of feasibility. In contrast, the selected density and lower-heating-value databases do not provide sufficient evidence for reliable linear correlations using the five-FAME representation. Semisynthetic manufactured databases show above-blind enrichment of planted response mismatches and generally reproducible coefficient recovery, with property-specific exceptions. The proposed framework therefore provides a systematic means of identifying and characterizing linear-compatible regimes within heterogeneous biodiesel property databases.

Authors

Institutions

Publication Details

Journal
ACS Omega
Published
2026-10-26
DOI
https://doi.org/10.1021/acsomega.6c06466
Primary Topic
Biodiesel Production and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing Linear FAME-Property Relationships in Heterogeneous Biodiesel Databases: A Sequential Cross-Validated Subset-Selection Approach

Horacio J. Aguerre, Guillermo O. Artana, Norberto M. Nigro, Alviso Dario
ACS Omega
Biodiesel Production and Applications
article

Assessing Linear FAME-Property Relationships in Heterogeneous Biodiesel Databases: A Sequential Cross-Validated Subset-Selection Approach

Horacio J. Aguerre, Guillermo O. Artana, Norberto M. Nigro, Alviso Dario
article en

Abstract

Abstract Linear correlations based on fatty acid methyl ester (FAME) composition provide a transparent approach for estimating biodiesel properties, although their applicability to heterogeneous literature databases depends on the existence of a consistent composition-property structure. This work assesses linear relationships for density, kinematic viscosity, flash point, and lower and higher heating values using the normalized shares of palmitic, stearic, oleic, linoleic, and linolenic esters. A sequential nested leave-one-out procedure selects, at each stage, the observation whose exclusion maximizes the cross-validated performance of the remaining subset. The complete procedure is also applied to response-permuted, or Y-randomized, surrogate databases. The archived operating point is the first stage at which the fitted real trajectory reaches Rfit2 ≥ 0.60, while the real-versus-permuted differential Δ(k) compares the fitted linear compatibility of the actual FAME-property pairing with the mean fitted linear compatibility obtained after response randomization. Because sustained removal also raises the surrogate trajectories, the fitted R2 alone is not interpreted as evidence of a composition–property relation; the comparison relies on Δ(k). Exploratory data analysis first characterizes the original databases through target-property distributions, descriptive statistics, and literature-source and feedstock representation. This baseline is then extended by comparing the original and retained databases in terms of literature-source and dominant-FAME retention, target-property distributions, principal-component coverage, and Spearman rank-correlation structure, showing that the retained subsets largely preserve the main compositional characteristics of the original databases. Cook’s distance and Williams plots provide independent influence flags for a substantial, but incomplete, fraction of the selected observations, including response-inconsistent and high-leverage cases. Selected observations are interpreted as operationally incompatible with the five-FAME linear description at their respective selection stages; possible causes include influence, omitted descriptors, or nonlinear behavior. The combined statistical, representational, and physical evidence supports conditional linear correlations for kinematic viscosity, flash point, and higher heating value, in decreasing order of feasibility. In contrast, the selected density and lower-heating-value databases do not provide sufficient evidence for reliable linear correlations using the five-FAME representation. Semisynthetic manufactured databases show above-blind enrichment of planted response mismatches and generally reproducible coefficient recovery, with property-specific exceptions. The proposed framework therefore provides a systematic means of identifying and characterizing linear-compatible regimes within heterogeneous biodiesel property databases.

ACS Omega
Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Universidad de Buenos Aires (AR), Universidad Nacional del Litoral (AR), Centro Científico Tecnológico - Santa Fe (AR), Universidad Nacional de Asunción (PY)
Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional del Litoral, Consejo Nacional de Ciencia y Tecnología, Paraguay, Agencia Santafesina de Ciencia, Tecnología e Innovación
Openalex Percentile: Top 25%
Biodiesel Production and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon