ReviewMolecules (Basel, Switzerland)2020
Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods.
Review in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Optimization of pressure-assisted enzymatic treatment for fiber solubilization and structural modification of Korean cabbage by-products.Food chemistry: X · 2026Article
- Metabolomics for origin traceability of lamb: An ensemble learning approach based on random forest recursive feature elimination.Food chemistry: X · 2025Article
- Research Progress and Technological Application Prospects of Comprehensive Evaluation Methods for Egg Freshness.Foods (Basel, Switzerland) · 2025Review
- Article
- Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.Foods (Basel, Switzerland) · 2024Article
- Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization.Foods (Basel, Switzerland) · 2024Article
- Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis.RSC advances · 2024Article
- Geographical origin identification of mandarin fruits by analyzing fingerprint signatures based on multielemental composition.Food chemistry: X · 2023Article
- Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry.Foods (Basel, Switzerland) · 2023Article
- Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.Foods (Basel, Switzerland) · 2023Review
- Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers.RSC advances · 2022Article
- Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.
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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.