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Moulahoum H, Ghorbanizamani F. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chem 2024; 445:138800. [PMID: 38382253 DOI: 10.1016/j.foodchem.2024.138800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.
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Affiliation(s)
- Hichem Moulahoum
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
| | - Faezeh Ghorbanizamani
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
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Zhang J, Wu X, He C, Wu B, Zhang S, Sun J. Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties. Foods 2024; 13:1439. [PMID: 38790739 PMCID: PMC11119828 DOI: 10.3390/foods13101439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
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Affiliation(s)
- Jiawei Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shuyu Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
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Luo S, Yuan X, Liang R, Feng K, Xu H, Zhao J, Wang S, Lan Y, Long Y, Deng H. Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 297:122720. [PMID: 37058840 DOI: 10.1016/j.saa.2023.122720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 05/14/2023]
Abstract
Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with Rp2 of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with Rp2 of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with Rp2 of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.
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Affiliation(s)
- Shuiyang Luo
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Xue Yuan
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
| | - Ruiqing Liang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Kunsheng Feng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haitao Xu
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Jing Zhao
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Shaokui Wang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yubin Lan
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Yongbing Long
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haidong Deng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
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Liu Y, Liu Z, Qiao F, Xu L, Xu Z. Identification of Perna viridis contaminated with diarrhetic shellfish poisoning toxins in vitro using NIRS and a discriminative non-negative representation-based classifier. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 294:122514. [PMID: 36870183 DOI: 10.1016/j.saa.2023.122514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Diarrhetic shellfish poisoning (DSP) toxins are one of the most widespread marine biotoxins that affect aquaculture and human health, and their detection has become crucial. In this study, near-infrared reflectance spectroscopy (NIRS) with non-destructive characteristics was used to identify DSP toxins in Perna viridis. The spectral data of the DSP toxin-contaminated and non-contaminated Perna viridis samples were acquired in the 950-1700 nm range. To solve the discrimination of spectra with crossover and overlapping, a discriminative non-negative representation-based classifier (DNRC) has been proposed. Compared with collaborative and non-negative representation-based classifiers, the DNRC model exhibited better performance in detecting DSP toxins, with a classification accuracy of 99.44 %. For a relatively small-scale sample dataset in practical applications, the performance of the DNRC model was compared with those of classical models. The DNRC model achieved the best results for both identification accuracy and F-measure, and its detection performance did not significantly decrease with decreasing sample size. The experimental results validated that a combination of NIRS and the DNRC model can facilitate rapid, convenient, and non-destructive detection of DSP toxins in Perna viridis.
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Affiliation(s)
- Yao Liu
- School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China.
| | - Zhongyan Liu
- School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang 524048, China
| | - Fu Qiao
- School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang 524048, China; Mangrove Institute, Lingnan Normal University, Zhanjiang 524048, China
| | - Lele Xu
- School of Life Science and Technology, Lingnan Normal University, Zhanjiang 524048, China
| | - Zhen Xu
- Science and Technology Extension Department, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
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Wu X, He F, Wu B, Zeng S, He C. Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis. Foods 2023; 12:foods12030541. [PMID: 36766070 PMCID: PMC9913903 DOI: 10.3390/foods12030541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/12/2023] [Accepted: 01/21/2023] [Indexed: 01/28/2023] Open
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.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Fei He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shupeng Zeng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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Liu H, Zhuang S, Gu Y, Shen Y, Zhang W, Ma L, Xiao G, Wang Q, Zhong Y. Effect of storage time on the volatile compounds and taste quality of Meixian green tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Wei K, Chen B, Li Z, Chen D, Liu G, Lin H, Zhang B. Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7764. [PMID: 36298114 PMCID: PMC9609479 DOI: 10.3390/s22207764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.
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Affiliation(s)
- Kaihua Wei
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Bojian Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zejian Li
- Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyu Liu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hongze Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Digital Economy Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China
- Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China
| | - Baihua Zhang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325000, China
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He F, Wu X, Wu B, Zeng S, Zhu X. Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Fei He
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Bin Wu
- Department of Information Engineering Chuzhou Polytechnic Chuzhou China
| | - Shupeng Zeng
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xingchen Zhu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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