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Ji Z, Zhu J, Deng J, Jiang H, Chen Q. Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometrics algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 323:124858. [PMID: 39068846 DOI: 10.1016/j.saa.2024.124858] [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: 05/10/2024] [Revised: 07/01/2024] [Accepted: 07/19/2024] [Indexed: 07/30/2024]
Abstract
In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted RP2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 μg∙kg-1.
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Affiliation(s)
- Zhanbo Ji
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jingwen Zhu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jihong Deng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China.
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You J, Li D, Wang Z, Chen Q, Ouyang Q. Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:5486-5494. [PMID: 38349009 DOI: 10.1002/jsfa.13381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/31/2024] [Accepted: 02/10/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared. RESULTS The 1D-CNN model and Z score preprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions. CONCLUSION The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Jie You
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
| | - Dengshan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
| | - Zhen Wang
- National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou, P.R. China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, P.R. China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
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Mazur F, Han Z, Tjandra AD, Chandrawati R. Digitalization of Colorimetric Sensor Technologies for Food Safety. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2404274. [PMID: 38932639 DOI: 10.1002/adma.202404274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 06/06/2024] [Indexed: 06/28/2024]
Abstract
Colorimetric sensors play a crucial role in promoting on-site testing, enabling the detection and/or quantification of various analytes based on changes in color. These sensors offer several advantages, such as simplicity, cost-effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with color models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of color models via digitalization in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis toward determining the color model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.
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Affiliation(s)
- Federico Mazur
- School of Chemical Engineering and Australian Centre for Nanomedicine (ACN), The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Zifei Han
- School of Chemical Engineering and Australian Centre for Nanomedicine (ACN), The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Angie Davina Tjandra
- School of Chemical Engineering and Australian Centre for Nanomedicine (ACN), The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Rona Chandrawati
- School of Chemical Engineering and Australian Centre for Nanomedicine (ACN), The University of New South Wales, Sydney, NSW, 2052, Australia
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Guo Z, Chen X, Zhang Y, Sun C, Jayan H, Majeed U, Watson NJ, Zou X. Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms. Foods 2024; 13:1698. [PMID: 38890926 PMCID: PMC11171995 DOI: 10.3390/foods13111698] [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: 04/23/2024] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/20/2024] Open
Abstract
Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.
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Affiliation(s)
- Zhiming Guo
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
| | - Xuan Chen
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Yiyin Zhang
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Chanjun Sun
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Heera Jayan
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Usman Majeed
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
| | - Nicholas J. Watson
- School of Food Science and Nutrition, University of Leeds, Leeds LS2 9JT, UK;
| | - Xiaobo Zou
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
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Zhang Y, Zareef M, Rong Y, Lin H, Chen Q, Ouyang Q. Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets. Food Chem 2024; 439:138172. [PMID: 38091785 DOI: 10.1016/j.foodchem.2023.138172] [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: 06/27/2023] [Revised: 11/07/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.
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Affiliation(s)
- Yuxin Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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