ArticleSensors (Basel, Switzerland)2020
Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development of a Sensory Lexicon and Predictive ANN Modeling for Black Queen Wine: A Novel Workflow Incorporating Bridge-Linked QDA and Consumer Hedonic Analysis.Foods (Basel, Switzerland) · 2026Article
- The Influence of the Site of Recording and Benchtop and Portable NIRS Equipment on Predicting the Sensory Properties of Iberian Ham.Foods (Basel, Switzerland) · 2026Article
- Antimicrobial Effect of Oregano Essential Oil in Na-Alginate Edible Films for Shelf-Life Extension and Safety of Feta Cheese.Pathogens (Basel, Switzerland) · 2026Article
- Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics.Foods (Basel, Switzerland) · 2024Article
- The Application of Artificial Intelligence and Big Data in the Food Industry.Foods (Basel, Switzerland) · 2023Review
- Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine.Foods (Basel, Switzerland) · 2023Article
- Effect of Packaging and Portioning on the Dynamics of Water-Fat Serum Release from Fresh Pasta Filata Soft Cheese.Foods (Basel, Switzerland) · 2022Article
- Application of Artificial Intelligence in Food Industry-a Guideline.Food engineering reviews · 2022Review
- Determining Sonication Effect onSensors (Basel, Switzerland) · 2021Article
- NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef "Cecina".Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors.
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Abstract
The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical-chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.
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