AIMC Topic: Food Analysis

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A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition.

Molecules (Basel, Switzerland) Feb 1, 2021
Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infr...
Humans Neural Networks, Computer Discriminant Analysis Pattern Recognition, Automated Food Quality Spectroscopy, Near-Infrared Food Analysis
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Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.

Journal of the science of food and agriculture Dec 11, 2020
BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stag...
Blueberry Plants Chlorophyll Pigments, Biological Deep Learning Anthocyanins Fruit Food Analysis
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Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles.

Molecules (Basel, Switzerland) Oct 26, 2020
Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisi...
Cluster Analysis Wine Food Analysis Fuzzy Logic
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Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images.

Sensors (Basel, Switzerland) Oct 18, 2020
In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental c...
Deep Learning Beta vulgaris Sugars Food Analysis Fertilizers Nutrients
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Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning.

Sensors (Basel, Switzerland) Sep 25, 2020
Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain it...
Eggs Light Machine Learning Hyperspectral Imaging Food Analysis
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A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin.

Food chemistry Sep 6, 2020
We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. T...
Spectrophotometry, Infrared Fish Products Parvalbumins Fish Proteins Electrophoresis, Polyacrylamide Gel Food Analysis Mice, Inbred BALB C Fishes Allergens Food Hypersensitivity Enzyme-Linked Immunosorbent Assay Support Vector Machine Animals Mice Neural Networks, Computer Reproducibility of Results
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Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques.

Sensors (Basel, Switzerland) Jul 30, 2020
In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The dev...
Algorithms Animals Support Vector Machine Food Analysis Logistic Models Machine Learning Electronic Nose Discriminant Analysis Milk
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Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network.

Sensors (Basel, Switzerland) Jun 24, 2020
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 produc...
Food Analysis Taste Neural Networks, Computer Spectroscopy, Near-Infrared Smell Cheese
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Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model.

Sensors (Basel, Switzerland) Jun 3, 2020
For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the qual...
Electronic Nose Cluster Analysis Food Quality Machine Learning Algorithms Food Analysis
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Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning.

Sensors (Basel, Switzerland) Mar 25, 2020
Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-co...
Deep Learning Machine Learning Humans Drug Compounding Support Vector Machine Food Analysis Algorithms Neural Networks, Computer Ultrasonics
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