Prediction of the heats of combustion for food-related organic compounds. A quantitative structure–property relationship (QSPR) study

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Mario G. Diaz, Frida V. Dimarco Palencia, Matias F. Andrada, Esteban G. Vega-Hissi, Pablo R. Duchowicz, Juan C. Garro Martinez
{"title":"Prediction of the heats of combustion for food-related organic compounds. A quantitative structure–property relationship (QSPR) study","authors":"Mario G. Diaz, Frida V. Dimarco Palencia, Matias F. Andrada, Esteban G. Vega-Hissi, Pablo R. Duchowicz, Juan C. Garro Martinez","doi":"10.1007/s10973-024-13559-w","DOIUrl":null,"url":null,"abstract":"<p>In the field of food research, the determination of the heats of combustion (Δ<sub>c</sub><i>H</i>) of the nutrients is essential to estimate the amount of energy obtained by metabolizing during digestion. Here, we have developed six novel QSPR models to predict this thermodynamic property of different families of organic compounds. The models were developed using the experimental data set of 215 compounds (71 organic acids, 28 amino acids, 37 amines and amides, 31 sulfur compounds and 48 heterocyclic compounds). About 16,000 molecular descriptors were calculated to represent the molecular structure of the compounds. The QSPR models resulted to be simple MLRs with a maximum of three variables, facilitating the interpretation and comparison with existing models in the literature. The statistical parameters exhibited excellent predictive capacity and robustness of the models obtained. The correlation coefficients of the selected models were major to 0.8 and the root means square error minor to 0.1. These results suggested that the models could be utilized for the prediction of the Δ<sub>c</sub><i>H</i> of other compounds that could be present in the foods.</p>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"37 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Thermal Analysis and Calorimetry","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10973-024-13559-w","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

In the field of food research, the determination of the heats of combustion (ΔcH) of the nutrients is essential to estimate the amount of energy obtained by metabolizing during digestion. Here, we have developed six novel QSPR models to predict this thermodynamic property of different families of organic compounds. The models were developed using the experimental data set of 215 compounds (71 organic acids, 28 amino acids, 37 amines and amides, 31 sulfur compounds and 48 heterocyclic compounds). About 16,000 molecular descriptors were calculated to represent the molecular structure of the compounds. The QSPR models resulted to be simple MLRs with a maximum of three variables, facilitating the interpretation and comparison with existing models in the literature. The statistical parameters exhibited excellent predictive capacity and robustness of the models obtained. The correlation coefficients of the selected models were major to 0.8 and the root means square error minor to 0.1. These results suggested that the models could be utilized for the prediction of the ΔcH of other compounds that could be present in the foods.

Abstract Image

预测食品相关有机化合物的燃烧热。定量结构-性能关系(QSPR)研究
在食品研究领域,营养物质燃烧热(ΔcH)的测定对于估算消化过程中通过新陈代谢获得的能量至关重要。在此,我们开发了六种新型 QSPR 模型来预测不同系列有机化合物的这一热力学性质。这些模型是利用 215 种化合物(71 种有机酸、28 种氨基酸、37 种胺和酰胺、31 种硫化合物和 48 种杂环化合物)的实验数据集建立的。计算了约 16,000 个分子描述符来表示化合物的分子结构。得出的 QSPR 模型是简单的 MLR,最多只有三个变量,便于解释和与文献中的现有模型进行比较。统计参数显示了所获模型的出色预测能力和稳健性。所选模型的相关系数最大为 0.8,均方根误差最小为 0.1。这些结果表明,这些模型可用于预测食品中可能存在的其他化合物的ΔcH。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.50
自引率
9.10%
发文量
577
审稿时长
3.8 months
期刊介绍: Journal of Thermal Analysis and Calorimetry is a fully peer reviewed journal publishing high quality papers covering all aspects of thermal analysis, calorimetry, and experimental thermodynamics. The journal publishes regular and special issues in twelve issues every year. The following types of papers are published: Original Research Papers, Short Communications, Reviews, Modern Instruments, Events and Book reviews. The subjects covered are: thermogravimetry, derivative thermogravimetry, differential thermal analysis, thermodilatometry, differential scanning calorimetry of all types, non-scanning calorimetry of all types, thermometry, evolved gas analysis, thermomechanical analysis, emanation thermal analysis, thermal conductivity, multiple techniques, and miscellaneous thermal methods (including the combination of the thermal method with various instrumental techniques), theory and instrumentation for thermal analysis and calorimetry.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信