The fishmeal adulteration identification based on microscopic image and deep learning

Geng, Jie; Liu, Jing; Kong, Xianrui; Shen, Bosheng; Niu, Zhiyou

doi:10.1016/j.compag.2022.106974

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…A novel method [18] was created in the research to differentiate and make predictions about genuine and adulterated lemon juice samples using a combination of LC-MS-based metabolomics and machine learning. The model developed in the study [19], utilizes Mobilenetv2 for qualitative identification and YOLOv3-Mobilenetv2 for component recognition. The authors of the paper [20] presented an approach to perform oneclass classification using an autoencoder for identifying food adulteration using spectroscopy.…”

Section: Adulterationmentioning

confidence: 99%

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges

Sneha¹,

S²,

Raj³

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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Food adulteration is common around the globe. To make sure to get high-quality food, and to identify the numerous adulterants in food items. The use of machine learning and deep learning techniques in the detection of food adulteration has gained increasing attention in recent years. This review paper presents an overview of the current state of the art in detecting food adulteration and summarizes various techniques and applications used to detect food adulteration, including traditional analytical techniques and machine learning. The paper also includes a summary of the recent research papers, which includes the objective, techniques, and samples used for adulteration detection in various food items. Additionally, the paper highlights the challenges faced in detecting food adulteration and the rapid evolution of adulteration methods.

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Section: Adulterationmentioning

confidence: 99%

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges

Sneha¹,

S²,

Raj³

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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“…Fishmeal is a nutrient-rich protein source derived from fish, known for its abundant supply of essential fatty acids, minerals, and vitamins. It possesses excellent digestibility and is highly palatable [1]. Due to these qualities, fishmeal is extensively utilized in the formulation of animal feed for livestock, poultry, and aquatic species.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Dynamic Changes of Volatile Organic Components for Fishmeal during Storage by HS-SPME-GC-MS with PLS-DA

Geng,

Cao,

Jiang

et al. 2024

Foods

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Headspace solid-phase microextraction, combined with gas chromatography–mass spectrometry and partial least squares discriminant analysis, was adopted to study the rule of change in volatile organic compounds (VOCs) for domestic and imported fishmeal during storage with different freshness grades. The results showed that 318 kinds of VOCs were detected in domestic fishmeal, while 194 VOCs were detected in imported fishmeal. The total relative content of VOCs increased with storage time, among which acids and nitrogen-containing compounds increased significantly, esters and ketones increased slightly, and phenolic and ether compounds were detected only in domestic fishmeal. Regarding the volatile base nitrogen, acid value, pH value, and mold counts as freshness indexes, the freshness indexes were significantly correlated with nine kinds of VOCs (p < 0.05) through the correlation analysis. Among them, volatile base nitrogen had a significant correlation with VOCs containing nitrogen, acid value with VOCs containing carboxyl group and hydrocarbons, pH value with acids which could be used to adjust pH value, and mold counts with part of acids adjusting pH value and VOCs containing nitrogen. Due to the fact that the value of all freshness indexes increased with freshness degradation during storage, based on volatile base nitrogen and acid value, the fishmeal was divided into three freshness grades, superior freshness, corrupting, and completely corrupted. By using partial least squares discriminant analysis, this study revealed the differences in flavor of the domestic and imported fishmeal during storage with different freshness grades, and it identified four common characteristic VOCs, namely ethoxyquinoline, 6,7,8,9-tetrahydro-3H-benzo[e]indole-1,2-dione, hexadecanoic acid, and heptadecane, produced by the fishmeal samples during storage, as well as the characteristic VOCs of fishmeal at each freshness grade.

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“…Fishmeal is a high-quality animal protein with high protein and amino acid content . Fishmeal is susceptible to the influence of raw material quality, environmental factors, and storage conditions during processing, preparation, transportation, and storage.…”

Section: Introductionmentioning

confidence: 99%

“…Fishmeal is a high-quality animal protein with high protein and amino acid content. 1 Fishmeal is susceptible to the influence of raw material quality, environmental factors, and storage conditions during processing, preparation, transportation, and storage. Proteolysis and oxidative rancidity of fatty acids gradually intensify undesirable odors such as sourness and burnt notes.…”

Section: Introductionmentioning

confidence: 99%

Study on VOCs of Fishmeal during Storage Based on HS-SPME-GC-MS

Geng,

Nie,

Wang

et al. 2024

ACS Omega

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Fishmeal is widely used in the feed industry as the main protein material. The freshness grade directly affects the quality of the fishmeal. During the storage of fishmeal, the odor would change accordingly as the freshness grades decreased. To study the characteristic volatile organic compounds (VOCs) of fishmeal, stored at 25 °C and 80%RH with different freshness grades, headspace solid-phase microextraction combined with gas chromatography−mass spectrometry (HS-SPME-GC-MS) was used to analyze. The single-factor test was chosen for 50/30 μm divinylbenzene/carboxe/polydimethylsiloxane (DVB/CAR/PDMS) fiber. The equilibration time of 24 min, the extraction time of 60 min, the extraction temperature of 87 °C, and the addition of a saturated saline volume of 4 mL were determined by Box-Behnken design. There were 15 common VOCs detected during storage, the relative contents of acids increased significantly, ketones, aldehydes, esters, and nitrogen-containing compounds increased, and aromatic compounds and alcohols decreased. Combined with freshness indexes, volatile base nitrogen (VBN) and acid value (AV), hexadecanoic acid, tetradecanoic acid, methyl (Z)-N-hydroxybenzenecarboximidate, (Z)-hexadec-9-enoic acid, 6-ethoxy-2,2,4trimethyl-3,4-dihydro-1H-quinoline, octadecanal, and [(Z)-octadec-9-enyl] acetate were determined as the characteristic VOCs based on the PLS-DA model. This study may provide data support for the development of fishmeal freshness-detecting instruments.

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The fishmeal adulteration identification based on microscopic image and deep learning

Cited by 5 publications

References 9 publications

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges

Evaluation of Dynamic Changes of Volatile Organic Components for Fishmeal during Storage by HS-SPME-GC-MS with PLS-DA

Study on VOCs of Fishmeal during Storage Based on HS-SPME-GC-MS

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