Research

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12 pages, 4106 KiB

Open AccessArticle

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

by Chunxia Dai, Jun Sun, Xingyi Huang, Xiaorui Zhang, Xiaoyu Tian, Wei Wang, Jingtao Sun and Yu Luan

Foods 2023, 12(15), 2957; https://doi.org/10.3390/foods12152957 - 04 Aug 2023

Cited by 2 | Viewed by 1144

Abstract

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation [...] Read more.

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R²_P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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13 pages, 2703 KiB

Open AccessArticle

Single-Walled Carbon Nanohorn-Based Fluorescence Energy Resonance Transfer Aptasensor Platform for the Detection of Aflatoxin B1

by Yiting Fan, Huanhuan Yang, Jiaxin Li, Khalid Amin, Bo Lyu, Wendan Jing, Sainan Wang, Hongling Fu, Hansong Yu and Zhijun Guo

Foods 2023, 12(15), 2880; https://doi.org/10.3390/foods12152880 - 28 Jul 2023

Cited by 3 | Viewed by 1078

Abstract

Aflatoxin B1 (AFB1) is one of the most contaminated fungal toxins worldwide and is prone to cause serious economic losses, food insecurity, and health hazards to humans. The rapid, on-site, and economical method for AFB1 detection is need of the day. In this [...] Read more.

Aflatoxin B1 (AFB1) is one of the most contaminated fungal toxins worldwide and is prone to cause serious economic losses, food insecurity, and health hazards to humans. The rapid, on-site, and economical method for AFB1 detection is need of the day. In this study, an AFB1 aptamer (AFB1-Apt) sensing platform was established for the detection of AFB1. Fluorescent moiety (FAM)-modified aptamers were used for fluorescence response and quenching, based on the adsorption quenching function of single-walled carbon nanohorns (SWCNHs). Basically, in our constructed sensing platform, the AFB1 specifically binds to AFB1-Apt, making a stable complex. This complex with fluorophore resists to be adsorbed by SWCNHs, thus prevent SWCNHs from quenching of fluorscence, resulting in a fluorescence response. This designed sensing strategy was highly selective with a good linear response in the range of 10–100 ng/mL and a low detection limit of 4.1 ng/mL. The practicality of this sensing strategy was verified by using successful spiking experiments on real samples of soybean oil and comparison with the enzyme-linked immunosorbent assay (ELISA) method. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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11 pages, 4297 KiB

Open AccessArticle

Detection of Specific Volatile Organic Compounds in Tribolium castaneum (Herbst) by Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry

by Shaoyun Han, Ke He, Jing An, Mengmeng Qiao, Runhui Ke, Xiao Wang, Yang Xu and Xiuying Tang

Foods 2023, 12(13), 2484; https://doi.org/10.3390/foods12132484 - 25 Jun 2023

Viewed by 1168

Abstract

The red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), is a major storage pest that could lead to a wide range of damage. Its secretions have a significant impact on the quality of stored grain and food, leading to serious food safety problems [...] Read more.

The red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), is a major storage pest that could lead to a wide range of damage. Its secretions have a significant impact on the quality of stored grain and food, leading to serious food safety problems such as grain spoilage and food carcinogenesis. This study investigates new detection techniques for grain storage pests to improve grain insect detection in China. The primary volatile organic chemicals (VOCs) in these secretions are identified using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The specific VOCs that are unique to T. castaneum are selected as criteria for determining the presence of T. castaneum in the granary. To obtain more specific VOCs, experiments were designed for the analysis of T. castaneum samples under different extraction times, two types of SPME fibers and two GC-MS devices of different manufacturers. The experimental results showed that 12 VOCs were detected at relatively high levels, seven of which were common and which were not detected in other grains and grain insects. The seven compounds are 1-pentadecene, 2-methyl-p-benzoquinone, 2-ethyl-p-benzoquinone, 1-hexadecene, cis-9-tetradecen-1-ol, m-cresol and paeonol. These seven compounds can be used as volatile markers to identify the presence of T. castaneum, which could serve as a research foundation for the creation of new techniques for T. castaneum monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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18 pages, 2597 KiB

Open AccessArticle

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

by Yating Hu, Benxue Ma, Huting Wang, Yujie Li, Yuanjia Zhang and Guowei Yu

Foods 2023, 12(9), 1773; https://doi.org/10.3390/foods12091773 - 25 Apr 2023

Cited by 1 | Viewed by 1568

Abstract

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored [...] Read more.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000–2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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14 pages, 1742 KiB

Open AccessArticle

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon

by Guowei Yu, Huihui Li, Yujie Li, Yating Hu, Gang Wang, Benxue Ma and Huting Wang

Foods 2023, 12(9), 1742; https://doi.org/10.3390/foods12091742 - 22 Apr 2023

Cited by 3 | Viewed by 1272

Abstract

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most [...] Read more.

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380–1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of “5 × 1” and “3 × 1” kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (

R^{2}

) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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13 pages, 44210 KiB

Open AccessArticle

Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition

by Wenjing Shao, Sujuan Hou, Weikuan Jia and Yuanjie Zheng

Foods 2022, 11(21), 3429; https://doi.org/10.3390/foods11213429 - 29 Oct 2022

Cited by 5 | Viewed by 2237

Abstract

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food’s nutrient content. However, existing food nutrient NDDT performs poorly in [...] Read more.

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food’s nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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12 pages, 943 KiB

Open AccessCommunication

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

by Qiulin Li, Xiaohong Wu, Jun Zheng, Bin Wu, Hao Jian, Changzhi Sun and Yibiao Tang

Foods 2022, 11(14), 2101; https://doi.org/10.3390/foods11142101 - 14 Jul 2022

Cited by 5 | Viewed by 1841

Abstract

The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR [...] Read more.

The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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12 pages, 3871 KiB

Open AccessArticle

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage

by Kunshan Yao, Jun Sun, Jiehong Cheng, Min Xu, Chen Chen, Xin Zhou and Chunxia Dai

Foods 2022, 11(14), 2024; https://doi.org/10.3390/foods11142024 - 08 Jul 2022

Cited by 5 | Viewed by 1501

Abstract

S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and [...] Read more.

S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R²_P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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14 pages, 3947 KiB

Open AccessEditor’s ChoiceArticle

Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis

by Zuxuan Qi, Xiaohong Wu, Yangjian Yang, Bin Wu and Haijun Fu

Foods 2022, 11(5), 763; https://doi.org/10.3390/foods11050763 - 07 Mar 2022

Cited by 18 | Viewed by 2244

Abstract

In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows [...] Read more.

In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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13 pages, 4162 KiB

Open AccessCommunication

High-Resolution X-ray Phase-Contrast Imaging and Sensory and Rheometer Tests in Cooked Edamame

by Masafumi Hidaka, Shuhei Miyashita, Naoto Yagi, Masato Hoshino, Yukiya Kogasaka, Tomoyuki Fujii and Yoshinori Kanayama

Foods 2022, 11(5), 730; https://doi.org/10.3390/foods11050730 - 01 Mar 2022

Cited by 3 | Viewed by 2504

Abstract

Although several reports exist on the use of X-ray analysis in vegetables and fruits to examine internal disorders, cavities, and porosity, information on X-ray analysis of qualities, such as texture, is lacking as well as information on X-ray analysis of legumes. Therefore, this [...] Read more.

Although several reports exist on the use of X-ray analysis in vegetables and fruits to examine internal disorders, cavities, and porosity, information on X-ray analysis of qualities, such as texture, is lacking as well as information on X-ray analysis of legumes. Therefore, this study aimed to perform X-ray analysis with sensory and rheometer tests in cooked vegetable soybean (edamame). Edamame is popular worldwide due to its deliciousness and nutritional value. Vascular structures and cracks around them were clearly visualized using X-ray phase-contrast computed tomography (CT) imaging. In addition, we observed the fine structure of the seed coat, which could be important for seed development, germination, and processing. The density in the edamame beans declined as the boiling time increased, promoting a reduction in hardness described in sensory and rheometer tests. The reduction in density proceeded from the gap between cotyledons, the opposite side of the hypocotyl, and the crack. Collectively, the findings show that the high-resolution X-ray phase-contrast CT imaging conducted in a nondestructive manner may help in effectively evaluating the quality of vegetables and in observing the internal structures related to plant development. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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Review

Jump to: Research

23 pages, 8804 KiB

Open AccessReview

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

by Zeyu Xu, Yu Han, Dianbo Zhao, Ke Li, Junguang Li, Junyi Dong, Wenbo Shi, Huijuan Zhao and Yanhong Bai

Foods 2024, 13(3), 469; https://doi.org/10.3390/foods13030469 - 02 Feb 2024

Viewed by 1005

Abstract

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by [...] Read more.

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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21 pages, 1704 KiB

Open AccessReview

Applications of Gas Sensing in Food Quality Detection: A Review

by Minzhen Ma, Xinting Yang, Xiaoguo Ying, Ce Shi, Zhixin Jia and Boce Jia

Foods 2023, 12(21), 3966; https://doi.org/10.3390/foods12213966 - 30 Oct 2023

Cited by 1 | Viewed by 3077

Abstract

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them [...] Read more.

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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30 pages, 432 KiB

Open AccessReview

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

by Xiaohong Wu, Xinyue Liang, Yixuan Wang, Bin Wu and Jun Sun

Foods 2022, 11(22), 3713; https://doi.org/10.3390/foods11223713 - 18 Nov 2022

Cited by 9 | Viewed by 4120

Abstract

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, [...] Read more.

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend. Full article

(This article belongs to the Special Issue Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety)

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