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Zhang L, Zhang M, Mujumdar AS, Wang D. Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness. ACS APPLIED MATERIALS & INTERFACES 2024; 16:37445-37455. [PMID: 38980942 DOI: 10.1021/acsami.4c04223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Intelligent colorimetric freshness indicator is a low-cost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by p-dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit) for the quantitative analysis of indole, which is an indicator of shrimp freshness. As a result of indole simulation, the array strip turned from faint yellow to pink or mulberry color with the increasing indole concentration, like a progress bar. The indicator film exhibited excellent permeability, mechanical and thermal stability, and color responsiveness to indole, which was attributed to the interactions between PDL and Chit/PVA. Furthermore, the colorimetric strip sensor array provided a good relationship between the indole concentration and the color intensity within a range of 50-350 ppb. The pathogens and spoilage bacteria of shrimp possessed the ability to produce indole, which caused the color changes of the strip sensor array. In the shrimp freshness monitoring experiment, the color-changing progress of the strip sensor array was in agreement with the simulation and could distinguish the shrimp freshness levels. The image classification system based on deep learning were developed, the accuracies of four DCNN algorithms are above 90%, with VGG16 achieving the highest accuracy at 97.89%. Consequently, a "progress bar" strip sensor array has the potential to realize nondestructive, more precise, and commercially available food freshness monitoring using simple visual inspection and intelligent equipment identification.
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Affiliation(s)
- Lihui Zhang
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Min Zhang
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal H3A 0G4, Quebec, Canada
| | - Dayuan Wang
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu 214122, China
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Luo JY, Huang ZJ, Zhao M, Li S, Zheng F, Huang X, Liu F, Lin L, Huang ZB, Xie H. One-to-Nine Single Spectroscopic Intelligent Probe for Risk Assessment of Multiple Metals in Drinking Water. Anal Chem 2024; 96:11508-11515. [PMID: 38953489 DOI: 10.1021/acs.analchem.4c02181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
26% of the world's population lacks access to clean drinking water; clean water and sanitation are major global challenges highlighted by the UN Sustainable Development Goals, indicating water security in public water systems is at stake today. Water monitoring using precise instruments by skilled operators is one of the most promising solutions. Despite decades of research, the professionalism-convenience trade-off when monitoring ubiquitous metal ions remains the major challenge for public water safety. Thus, to overcome these disadvantages, an easy-to-use and highly sensitive visual method is desirable. Herein, an innovative strategy for one-to-nine metal detection is proposed, in which a novel thiourea spectroscopic probe with high 9-metal affinity is synthesized, acting as "one", and is detected based on the 9 metal-thiourea complexes within portable spectrometers in the public water field; this is accomplished by nonspecialized personnel as is also required. During the processing of multimetal analysis, issues arise due to signal overlap and reproducibility problems, leading to constrained sensitivity. In this innovative endeavor, machine learning (ML) algorithms were employed to extract key features from the composite spectral signature, addressing multipeak overlap, and completing the detection within 30-300 s, thus achieving a detection limit of 0.01 mg/L and meeting established conventional water quality standards. This method provides a convenient approach for public drinking water safety testing.
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Affiliation(s)
- Jia-Yi Luo
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Zhao-Jing Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ming Zhao
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Shunxing Li
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengying Zheng
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Xuguang Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengjiao Liu
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Luxiu Lin
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
- Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Zheng Bin Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Haijiao Xie
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
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Zhao M, You Z, Chen H, Wang X, Ying Y, Wang Y. Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning. Foods 2024; 13:793. [PMID: 38472906 DOI: 10.3390/foods13050793] [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: 01/28/2024] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography-mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.
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Affiliation(s)
- Mingming Zhao
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Zhiheng You
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Huayun Chen
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Xiao Wang
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Yibin Ying
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
| | - Yixian Wang
- School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
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Kishnani V, Gupta A. Predictive Framework Development for User-Friendly On-Site Glucose Detection. ACS APPLIED BIO MATERIALS 2023; 6:4336-4344. [PMID: 37683114 DOI: 10.1021/acsabm.3c00532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
This study explores a smartphone-based spot detection framework for glucose in a rapid, simple, and affordable paper-based analytical device (PAD), which employs machine-learning algorithms to estimate various glucose concentrations. Herein, two different detection mixtures were chosen with chitosan (C) and without chitosan (WC) for the color change analysis. Being a biopolymer, chitosan improves the analytical performance of PADs when used with a chromogenic agent. Moreover, the influence of the illumination conditions and camera optics on the professed color of glucose strips was observed by choosing various illumination conditions and different smartphones. Hence, this study focuses on developing a framework for smartphone-based simple and user-friendly spot-based glucose detection (with a concentration range of 10-40 mM) at any illumination conditions and in any direction of illumination. Additionally, the combination of color spaces and machine-learning algorithms was applied for the signal enhancement. It was observed that the machine learning classifiers, cubic support vector machine (SVM) and narrow neutral network show higher accuracy for the WC samples, which are 92.7 and 92.3%, respectively. The samples with chitosan show higher accuracy for the linear discriminant and quadratic SVM classifiers, which are 94.1 and 93.9%, respectively. Simultaneously, cubic SVM shows ∼93% accuracy for both cases. In order to assess the performance of the devices, a blind test was also conducted. This study demonstrates the potential of the developed system for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease.
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Affiliation(s)
- Vinay Kishnani
- Department of Mechanical Engineering, Indian Institute of Technology, Jodhpur 342030, Rajasthan, India
| | - Ankur Gupta
- Department of Mechanical Engineering, Indian Institute of Technology, Jodhpur 342030, Rajasthan, India
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Cui F, Zheng S, Wang D, Tan X, Li Q, Li J, Li T. Recent advances in shelf life prediction models for monitoring food quality. Compr Rev Food Sci Food Saf 2023; 22:1257-1284. [PMID: 36710649 DOI: 10.1111/1541-4337.13110] [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: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/31/2023]
Abstract
Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.
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Affiliation(s)
- Fangchao Cui
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Shiwei Zheng
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Dangfeng Wang
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
- College of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Xiqian Tan
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Qiuying Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Jianrong Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Tingting Li
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, College of Life Science, Dalian Minzu University, Dalian, China
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Kishnani V, Kumari S, Gupta A. A Chemometric-Assisted Colorimetric-Based Inexpensive Paper Biosensor for Glucose Detection. BIOSENSORS 2022; 12:bios12111008. [PMID: 36421125 PMCID: PMC9688802 DOI: 10.3390/bios12111008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 05/20/2023]
Abstract
This article reports a simple and inexpensive leak-proof paper pad with an initial selection of a paper substrate on the grounds of surface morphology and fluid absorption time. Herein, a drying method is used for glucose detection on a paper pad through colorimetric analysis, and the spot detection of glucose is analyzed by optimizing the HRP concentration and volume to obtain accurate results. The rapid colorimetric method for the detection of glucose on the paper pad was developed with a limit of detection (LOD) of 2.92 mmol L-1. Furthermore, the effects of the detection conditions were investigated and discussed comprehensively with the help of chemometric methods. Paper pads were developed for glucose detection with a range of 0.5-20 mM (apropos to the normal glucose level in the human body) and 0.1-0.5 M (to test the excessive intake of glucose). The developed concept has huge potential in the healthcare sector, and its extension could be envisioned to develop the reported paper pad as a point-of-care testing device for the initial screening of a variety of diseases.
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Kokulnathan T, Wang TJ, Ahmed F, Kumar S. Deep Eutectic Solvents-Assisted Synthesis of NiFe-LDH/Mo2C Nanocomposites for Electrochemical Determination of Nitrite. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Luo J, Huang Z, Li S, Zheng F, Liu F, Huang Q, Huang X, Xie H. Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation. Anal Chem 2022; 94:14801-14809. [PMID: 36239120 DOI: 10.1021/acs.analchem.2c04014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Via the photodegradation of dissolved iron (dFe) complexes in the euphotic zone, released free Fe(III) is the most important source of bioavailable iron for eukaryotic phytoplankton. There is an urgent need to establish bioavailability-based dissolved iron speciation (BDIS) methods. Herein, an intelligent system with dFe pretreatment and a colorimetric sensor is developed for real-time monitoring of newly generated Fe(III) ions. According to the photodegradation kinetics of dFe, including kinetic constant and photogenerated time of free Fe(III) ions, 3 sources, 6 kinds, and 12 species of dFe are determined by our photocatalytic-assisted colorimetric sensor and deep learning model within 20.0 min. The algal dFe-uptake for 4 days can be predicted by BDIS with correlation coefficient 0.85, which could be explained by the hard and soft acids and bases theory (HSAB) and density functional theory (DFT). These results successfully demonstrate the proof-of-concept for photodegradation kinetics-based speciation and bioavailability assessments of dissolved metals.
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Affiliation(s)
- Jiayi Luo
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Zhaojing Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Shunxing Li
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengying Zheng
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengjiao Liu
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Qianyan Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Xuguang Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Haijiao Xie
- Hangzhou Yanqu Information Technology Company of Limited Liability, Hangzhou 310003, China
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