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A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Environmental Biosensors and Biosensing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 29620

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Special Issue Editors

Dr. Yonghuan Yun

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Guest Editor

School of Food Science and Engineering, Hainan University, Haikou 570228, China
Interests: metabolomics; foodomics; food quality and safety; chemometrics; nondestructive; near infrared spectroscopy

Dr. Jiangbo Li

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Guest Editor

Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: quality and safety assessment of agricultural products; development of agricultural product grading systems; computer vision and machine learning; hyperspectral and multispectral imaging; near infrared spectroscopy analysis and modeling
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Dear Colleagues,

Rapid nondestructive testing technology is a powerful technical support tool for food analysis, ensuring food quality and safety in an easy, rapid, reliable and nondestructive way. With the help of multivariate data analysis (i.e., chemometrics), particularly artificial intelligence algorithms, analytical-methods-based biosensors ensure rapid nondestructive testing technology used in food analysis is more sensitive, accurate and reliable.

This Special Issue aims to collect papers and reviews regarding the implementation of biosensors such as fluorescence, Raman, near-infrared and Terahertz spectroscopy, electronic nose and electronic tongue, in the field of food microbial contaminants, food process monitoring, food chemistry and toxicology, food quality, food authenticity and food traceability.

Dr. Yonghuan Yun
Dr. Jiangbo Li
Guest Editors

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Keywords

food analysis
biosensors
nondestructive
chemometrics
raman spectroscopy
near infrared spectroscopy
fluorescence spectroscopy
terahertz spectroscopy
electronic nose
electronic tongue
artificial intelligence
machine learning
hyperspectral
multispectral imaging

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Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 187 KiB

Open AccessEditorial

Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis

by Yong-Huan Yun and Jiangbo Li

Biosensors 2023, 13(5), 521; https://doi.org/10.3390/bios13050521 - 6 May 2023

Cited by 2 | Viewed by 1764

Abstract

Food analysis plays a vital role in ensuring the safety and quality of food products [...] Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

Research

Jump to: Editorial

13 pages, 2080 KiB

Open AccessArticle

An Impedance-Based Immunosensor for the Detection of Ovalbumin in White Wine

by Alessia Calabrese, Alessandro Capo, Angela Capaccio, Elettra Agovino, Antonio Varriale, Michelangelo Pascale, Sabato D’Auria and Maria Staiano

Biosensors 2023, 13(7), 669; https://doi.org/10.3390/bios13070669 - 22 Jun 2023

Cited by 3 | Viewed by 1907

Abstract

Food allergies are an exceptional response of the immune system caused by the ingestion of specific foods. The main foods responsible for allergic reactions are milk, eggs, seafood, soy, peanuts, tree nuts, wheat, and their derived products. Chicken egg ovalbumin (OVA), a common allergen molecule, is often used for the clarification process of wine. Traces of OVA remain in the wine during the fining process, and they can cause significant allergic reactions in sensitive consumers. Consequently, the European Food Safety Authority (EFSA) and the American Food and Drug Administration (FDA) have shown the risks for allergic people to assume allergenic foods and food ingredients, including eggs. Commonly, OVA detection requires sophisticated and time-consuming analytical techniques. Intending to develop a faster assay, we designed a proof-of-concept non-Faradaic impedimetric immunosensor for monitoring the presence of OVA in wine. Polyclonal antibodies anti-OVA were covalently immobilised onto an 11-mercaptoundecanoic-acid (11-MUA)-modified gold surface. The developed immunosensor was able to detect OVA in diluted white wine without the need for an external probe or any pre-treatment step with a sensitivity of 0.20 µg/mL, complying with the limit established by the resolution OIV/COMEX 502–2012 for the quantification of allergens in wine. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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16 pages, 8830 KiB

Open AccessArticle

Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling

by Yong Hao, Xiyan Li, Chengxiang Zhang and Zuxiang Lei

Biosensors 2023, 13(2), 203; https://doi.org/10.3390/bios13020203 - 29 Jan 2023

Cited by 18 | Viewed by 2554

Abstract

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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13 pages, 23644 KiB

Open AccessArticle

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors

by Guangxin Ren, Xusheng Zhang, Rui Wu, Lingling Yin, Wenyan Hu and Zhengzhu Zhang

Biosensors 2023, 13(1), 92; https://doi.org/10.3390/bios13010092 - 5 Jan 2023

Cited by 13 | Viewed by 2935

Abstract

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO−SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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11 pages, 2264 KiB

Open AccessArticle

A Competitive “On-Off-Enhanced On” AIE Fluorescence Switch for Detecting Biothiols Based on Hg²⁺ Ions and Gold Nanoclusters

by Shuqi Li, Yuqi Wan, Yu Li, Jinghan Liu, Fuwei Pi and Ling Liu

Biosensors 2023, 13(1), 35; https://doi.org/10.3390/bios13010035 - 27 Dec 2022

Cited by 7 | Viewed by 2385

Abstract

In this study, a novel “on-off-enhanced on” approach to highly sensitive rapid sensing of biothiols was developed, based on competitive modulation of gold nanoclusters (AuNCs) and Hg²⁺ ions. In our approach, the AuNCs were encapsulated into a zeolite imidazole framework (ZIF) for predesigned competitive aggregation-induced luminescence (AIE) emission. To readily operate this approach, the Hg²⁺ ions were selected as mediators to quench the fluorescence of AuNCs. Then, due to the stronger affinities between the interactions of Hg²⁺ ions with -SH groups in comparison to the AuNCs with -SH groups, the quenched probe of AuNCs@ZIF-8/Hg²⁺ displayed enhanced fluorescence after the Hg²⁺ ions were competitively interacted with -SH groups. Based on enhanced fluorescence, the probe for AuNCs@ZIF-8/Hg²⁺ had a sensitive and specific response to trace amounts of biothiols. The developed fluorescence strategy had limit of quantification (LOQ) values of 1.0 μM and 1.5 μM for Cys and GSH molecules in serum, respectively. This competitive AIE strategy provided a new direction for developing biological probes and a promising method for quantifying trace amounts of biothiols in serum. It could promote progress in disease diagnosis. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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17 pages, 4665 KiB

Open AccessArticle

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

by Fujia Dong, Yongzhao Bi, Jie Hao, Sijia Liu, Yu Lv, Jiarui Cui, Songlei Wang, Yafang Han and Argenis Rodas-González

Biosensors 2022, 12(11), 1043; https://doi.org/10.3390/bios12111043 - 18 Nov 2022

Cited by 21 | Viewed by 2218

Abstract

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near–infrared hyperspectral imaging (NIR–HSI) combined with two–dimensional correlation spectroscopy (2D–COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D–COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH₂ in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136–1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D–COS analysis, and provided excellent results (R²_C of 0.8141, R²_P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D–COS combined with NIR–HSI could be used as an effective method to monitor Ala content in beef. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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14 pages, 3249 KiB

Open AccessArticle

A Real-Time Detection Method of Hg²⁺ in Drinking Water via Portable Biosensor: Using a Smartphone as a Low-Cost Micro-Spectrometer to Read the Colorimetric Signals

by Yifan Gu, Leizi Jiao, Fengjing Cao, Xinchao Liu, Yunhai Zhou, Chongshan Yang, Zhen Gao, Mengjie Zhang, Peng Lin, Yuxing Han and Daming Dong

Biosensors 2022, 12(11), 1017; https://doi.org/10.3390/bios12111017 - 14 Nov 2022

Cited by 6 | Viewed by 2674

Abstract

This paper reported a real-time detection strategy for Hg²⁺ inspired by the visible spectrophotometer that used a smartphone as a low-cost micro-spectrometer. In combination with the smartphone’s camera and optical accessories, the phone’s built-in software can process the received light band image and then read out the spectral data in real time. The sensor was also used to detect gold nanoparticles with an LOD of 0.14 μM, which are widely used in colorimetric biosensors. Ultimately, a gold nanoparticles-glutathione (AuNPs-GSH) conjugate was used as a probe to detect Hg²⁺ in water with an LOD of 1.2 nM and was applied successfully to natural mineral water, pure water, tap water, and river water samples. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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12 pages, 4357 KiB

Open AccessArticle

Optimization of Sensors Data Transmission Paths for Pest Monitoring Based on Intelligent Algorithms

by Yuyang Lian, Aqiang Wang, Sihua Peng, Jingjing Jia, Liang Zong, Xiaofeng Yang, Jinlei Li, Rongjiao Zheng, Shuyan Yang, Jianjun Liao and Shihao Zhou

Biosensors 2022, 12(11), 948; https://doi.org/10.3390/bios12110948 - 1 Nov 2022

Cited by 1 | Viewed by 1820

Abstract

The harm of agricultural pests presents a remarkable effect on the quality and safety of edible farm products and the monitoring and identification of agricultural pests based on the Internet of Things (IoT) produce a large amount of data to be transmitted. To achieve efficient and real-time transmission of the sensors’ data for pest monitoring, this paper selects 235 geographic coordinates of agricultural pest monitoring points and uses genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) to optimize the data transmission paths of sensors. The three intelligent algorithms are simulated by MATLAB software. The results show that the optimized path based on PSO can make the shortest time used for transmitting data, and its corresponding minimum time is 4.868012 s. This study can provide a reference for improving the transmission efficiency of agricultural pest monitoring data, provide a guarantee for developing real-time and effective pest control strategies, and further reduce the threat of pest damage to the safety of farm products. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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14 pages, 4002 KiB

Open AccessArticle

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm

by Changtong Zhao, Jie Ma, Wenshen Jia, Huihua Wang, Hui Tian, Jihua Wang and Wei Zhou

Biosensors 2022, 12(9), 692; https://doi.org/10.3390/bios12090692 - 28 Aug 2022

Cited by 9 | Viewed by 2290

Abstract

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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11 pages, 3395 KiB

Open AccessArticle

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

by Xihui Bian, Deyun Wu, Kui Zhang, Peng Liu, Huibing Shi, Xiaoyao Tan and Zhigang Wang

Biosensors 2022, 12(8), 586; https://doi.org/10.3390/bios12080586 - 1 Aug 2022

Cited by 11 | Viewed by 1984

Abstract

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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12 pages, 3736 KiB

Open AccessArticle

RhB@MOF-5 Composite Film as a Fluorescence Sensor for Detection of Chilled Pork Freshness

by Jingyi Li, Ning Zhang, Xin Yang, Xinting Yang, Zengli Wang and Huan Liu

Biosensors 2022, 12(7), 544; https://doi.org/10.3390/bios12070544 - 20 Jul 2022

Cited by 9 | Viewed by 2634

Abstract

This study presents a novel composite thin film based on rhodamine B encapsulated into MOF-5 (Metal Organic Frameworks) as a fluorescence sensor for the real-time detection of the freshness of chilled pork. The composite film can adsorb and respond to the volatile amines produced by the quality deterioration of pork during storage at 4 °C, with the fluorescence intensity of RhB decreasing over time. The quantitative model used for predicting the freshness indicator (total volatile base nitrogen) of pork was built using the fluorescence spectra (excited at 340 nm) of the RhB@MOF-5 composite film combined with the partial least squares (PLS) algorithm, providing R_c² and R_p² values of 0.908 and 0.821 and RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) values of 3.435 mg/100 g and 3.647 mg/100 g, respectively. The qualitative model established by the partial least squares discriminant analysis (PLS-DA) algorithm was able to accurately classify pork samples as fresh, acceptable or spoiled, and the accuracy was 86.67%. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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12 pages, 3091 KiB

Open AccessArticle

Technology for Rapid Detection of Cyromazine Residues in Fruits and Vegetables: Molecularly Imprinted Electrochemical Sensors

by Sihua Peng, Aqiang Wang, Yuyang Lian, Jingjing Jia, Xuncong Ji, Heming Yang, Jinlei Li, Shuyan Yang, Jianjun Liao and Shihao Zhou

Biosensors 2022, 12(6), 414; https://doi.org/10.3390/bios12060414 - 14 Jun 2022

Cited by 11 | Viewed by 2776

Abstract

Cyromazine is an insect growth regulator insecticide with high selectivity and is widely used in the production and cultivation of fruits and vegetables. In recent years, incidents of excessive cyromazine residues in food have occurred frequently, and it is urgent to establish an accurate, fast, and convenient method for the detection of cyromazine residues to ensure the safety of edible agricultural products. To achieve rapid detection of cyromazine residues, we prepared a molecularly imprinted electrochemical sensor for the detection of cyromazine residues in agricultural products. Samples of tomato (Lycopersicon esculentum Miller), cowpea (Vigna unguiculata), and water were tested for the recovery rate of cyromazine. The results showed that the concentration of cyromazine showed a good linear relationship with the peak response current of the sensor developed in this study. The lower limit of detection for cyromazine was 0.5 µmol/L, and the sensor also had good reproducibility and interference resistance. This paper can be used as a basis for the study of methods for the detection of cyromazine residues in edible agricultural products. Full article

(This article belongs to the Special Issue Rapid Nondestructive Testing Technology-Based Biosensors for Food Analysis)

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