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Special Issue "Recent Advances in Emerging Techniques for Non-destructive Detection of Food Quality and Safety"

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 20 January 2024 | Viewed by 15942

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Special Issue Editor

Prof. Dr. Xiaohong Wu

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Guest Editor

1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
2. High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang, China
Interests: nondestructive detection; hyperspectral imaging technology; spectroscopy; electronic nose; chemometrics; machine learning

Special Issue Information

Currently, the issue of food safety and quality is a great public concern. The non-destructive detection technique (NDDT) has emerged as a powerful analytical tool in the food industries. In order to satisfy the demands of consumers and obtain superior food qualities, NDDT methods are required for quality evaluation. NDDT methods (such as near- and mid-infrared spectroscopy (NIRS), Raman spectroscopy, fluorescence spectroscopy (FS), hyperspectral imaging (HSI), terahertz spectroscopy, X-ray imaging methods and thermal imaging) have provided interesting and promising results in detecting a variety of foods.

NDDT allows the simultaneous measurement of chemical data from food without destruction of the substance. Additionally, NDDT can obtain both quantitative and qualitative data at the same time without separate analyses. This Special Issue aims to collect recent and novel applications of NDDT methods in relation to food quality and safety.

Prof. Dr. Xiaohong Wu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

non-destructive detection technique
near-infrared spectroscopy
mid-infrared spectroscopy
Raman spectroscopy
terahertz spectroscopy
hyperspectral imaging
X-ray imaging
thermal imaging
machine vision
electronic nose

Published Papers (12 papers)

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Research

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Open AccessArticle

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

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

Viewed by 600

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 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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Open AccessArticle

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

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

Cited by 1 | Viewed by 543

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 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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Open AccessArticle

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

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

Viewed by 599

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 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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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

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

Viewed by 927

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 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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Open AccessArticle

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

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

Viewed by 742

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 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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Open AccessCommunication

Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis

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Foods 2023, 12(3), 541; https://doi.org/10.3390/foods12030541 - 26 Jan 2023

Viewed by 1052

Abstract

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade. 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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Open AccessArticle

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

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

Cited by 2 | Viewed by 1499

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 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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Open AccessCommunication

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

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

Cited by 3 | Viewed by 1376

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 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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Open AccessArticle

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

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

Cited by 4 | Viewed by 1143

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 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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Open AccessArticle

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

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

Cited by 10 | Viewed by 1812

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 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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Open AccessCommunication

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

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

Cited by 2 | Viewed by 1999

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 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

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Open AccessReview

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

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

Cited by 5 | Viewed by 2379

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, 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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