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Liang J, Wang Y, Shi Y, Huang X, Li Z, Zhang X, Zou X, Shi J. Non-destructive discrimination of homochromatic foreign materials in cut tobacco based on VIS-NIR hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:4545-4552. [PMID: 36840508 DOI: 10.1002/jsfa.12528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND The presence of foreign materials (FM) not only reduces the commercial value of tobacco and the quality of cigarette products, but also affects the aroma and flavor of cigarettes. Existing tobacco deblending equipment has received little study with respect to homochromatic FM. In the present study, visible-near infrared (VIS-NIR) hyperspectral imaging technique combined with chemometrics were used to identify and visualize the homochromatic FM on the surface of thining tobacco. A comparison with conventional vision method was made to analyze the feasibility of the method. The importance of detecting FM in cut tobacco was further demonstrated by first studying the volatile organic compounds produced in cigarette mixed FM smoke and their effects on human health before conducting hyperspectral experiments. RESULTS The results indicated that solid-phase microextraction and gas chromatography mass spectrometry could detect volatile organic compounds in mainstream cigarette smoke that were not cigarette components and affected consumer health. Then, spectral features of the samples were extracted from hyperspectral images for building identification models to distinguish FM from cut tobacco. The visual RGB values of cut tobacco and FM were also used for the analysis of the recognition models. The results showed that the accuracy, precision and recall reached 100.00% using the back propagation artificial neural network classification model based on the principal component analysis raw wavelengths. The visualization results based on the optimal model produced clearer localization than conventional computer vision method. CONCLUSION The present study revealed that the VIS-NIR hyperspectral imaging technology had advantage in the detection and localization of FM on the surface of thinning tobacco, which provided a foundation for improving the quality and safety of cut tobacco production. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Jing Liang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Yueying Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yu Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xiaowei Huang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang, China
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Zou Z, Wu Q, Wang J, Xu L, Zhou M, Lu Z, He Y, Wang Y, Liu B, Zhao Y. Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121785. [PMID: 36058172 DOI: 10.1016/j.saa.2022.121785] [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: 05/04/2022] [Revised: 08/21/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Eating repeatedly used hotpot oil will cause serious harm to human health. In order to realize rapid non-destructive testing of hotpot oil quality, a modeling experiment method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. Five preprocessing algorithms were used to preprocess the original spectral data, which realized data denoising and reduces the influence of baseline drift and tilt. The feature bands extracted from the spectral data showed that the best feature bands for the two-classification model and the six-classification model were concentrated between 469 and 962 nm and 534-809 nm, respectively. Using the PCA algorithm to visualize the spectral data, the results showed the distribution of the six types of samples intuitively, and indicated that the data could be classified. Based on the modeling analysis of the feature bands, the results showed that the best two-classification models and the best six-classification models were MF-RF-RF and MF-XGBoost-LGB models, respectively, and the classification accuracy reached 100 %. Compared with the traditional model, the error was greatly reduced, and the calculation time was also saved. This study confirmed that fluorescence hyperspectral technology combined with machine learning algorithm could effectively realize the detection of reused hotpot oil.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Jian Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Man Zhou
- College of Food Sciences, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Zhiwei Lu
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866, Yuhangtang Road, Hangzhou 310058, PR China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Bi Liu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China.
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Wu S, Wang L, Zhou G, Liu C, Ji Z, Li Z, Li W. Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer. Food Res Int 2023; 163:112192. [PMID: 36596130 DOI: 10.1016/j.foodres.2022.112192] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R2 = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R2 = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.
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Affiliation(s)
- Sijun Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Long Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Guoming Zhou
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Chao Liu
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zhongrui Ji
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
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Quality Characteristics of Novel Pasta Enriched with Non-Extruded and Extruded Blackcurrant Pomace. Molecules 2022; 27:molecules27238616. [PMID: 36500707 PMCID: PMC9737421 DOI: 10.3390/molecules27238616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/29/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022] Open
Abstract
Fruit pomace is a valuable by-product in terms of its chemical composition, which potential might be used through transformation of the pomace into food ingredients. The aim of this work was to assess the effect of partial (5% and 10%) substitution of powdered non-extruded or extruded blackcurrant pomace for semolina in pasta formula on nutritional and technological properties of the final product. The pasta was assessed for chemical composition, DPPH antiradical activity, color, cooking and textural properties. Presence of the by-products in the pasta resulted in increased total dietary fiber content (from 1.89 ± 0.06 up to 10.03 ± 0.15 g/100 g, dwb), fat content (from 1.29 ± 0.01 up to 2.70 ± 0.05 g/100 g, dwb) and DPPH antiradical activity (from 253 ± 15 up to 1037 ± 7 µmol TE/g, dwb), as well as in significantly different color (p < 0.05) as compared to the semolina-only pasta. The optimal cooking time was shortened by 1.0−1.5 min and by 2.0 min in the case of the lower and higher, respectively, level of pasta supplementation. The water absorption decreased by up to 32% in the enriched pasta. In general, the cooking loss remained unchanged. The uncooked product containing the extruded fruit pomace was characterized by significantly higher breaking strength (p < 0.05) as compared to the standard pasta. Presence of the pomace also affected texture of the cooked pasta, increasing its firmness and hardness and, when using the non-extruded pomace, the tensile strength. In our research, we have shown that durum wheat pasta enriched with 5 or 10% of powdered blackcurrant pomace or their extrudates constitute a food product of improved nutritional value and of appropriate textural characteristics, while maintaining culinary properties that meet pasta industry requirements.
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Wu Q, Xu L, Zou Z, Wang J, Zeng Q, Wang Q, Zhen J, Wang Y, Zhao Y, Zhou M. Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models. FRONTIERS IN PLANT SCIENCE 2022; 13:1047479. [PMID: 36438117 PMCID: PMC9685660 DOI: 10.3389/fpls.2022.1047479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
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Affiliation(s)
- Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jian Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qifeng Zeng
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Man Zhou
- College of Food Sciences, Sichuan Agricultural University, Yaan, China
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6
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Near infrared techniques applied to analysis of wheat-based products: Recent advances and future trends. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109115] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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7
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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8
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Coic L, Sacré PY, Dispas A, De Bleye C, Fillet M, Ruckebusch C, Hubert P, Ziemons É. Selection of essential spectra to improve the multivariate curve resolution of minor compounds in complex pharmaceutical formulations. Anal Chim Acta 2022; 1198:339532. [DOI: 10.1016/j.aca.2022.339532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 01/27/2023]
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9
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Liu Y, Gao H, Zeng J, Zhang K, Dai Y. Effect of canna edulis ker flour on quality of quick‐frozen dumpling wrappers. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16516] [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)
- Yufen Liu
- School of Food Science Henan Institute of Science and Technology Xinxiang Henan 453003 China
| | - Haiyan Gao
- School of Food Science Henan Institute of Science and Technology Xinxiang Henan 453003 China
| | - Jie Zeng
- School of Food Science Henan Institute of Science and Technology Xinxiang Henan 453003 China
| | - Keke Zhang
- School of Food Science Henan Institute of Science and Technology Xinxiang Henan 453003 China
| | - Yunfei Dai
- School of Food Science Henan Institute of Science and Technology Xinxiang Henan 453003 China
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10
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Zhang L, Nie Q, Ji H, Wang Y, Wei Y, An D. Hyperspectral imaging combined with generative adversarial network (GAN)-based data augmentation to identify haploid maize kernels. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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11
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Zhang L, Wang Y, Wei Y, An D. Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel. Food Chem 2022; 370:131047. [PMID: 34626928 DOI: 10.1016/j.foodchem.2021.131047] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/29/2021] [Accepted: 08/31/2021] [Indexed: 11/18/2022]
Abstract
Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.
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Affiliation(s)
- Liu Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Yaqian Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Yaoguang Wei
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
| | - Dong An
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
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Gao Q, Wang P, Niu T, He D, Wang M, Yang H, Zhao X. Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging. Food Chem 2022; 370:131013. [PMID: 34509150 DOI: 10.1016/j.foodchem.2021.131013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/08/2021] [Accepted: 08/29/2021] [Indexed: 11/04/2022]
Abstract
Malus micromalus Makino has great commercial and nutritional value. The regression and classification models were investigated by using near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics to improve the efficiency of non-destructive detection. The successive projections algorithm (SPA), interval random frog, and competitive adaptive reweighted sampling were employed to extract effective wavelengths sensitive to changes of soluble solid content (SSC) and firmness index (FI) information. Two types of assessment models based on full spectrum and effective wavelengths, namely partial least squares regression and extreme learning machine, were established to predict SSC and FI. In addition, the classification models based on the support vector machine improved by the grey wolf optimizer (GWO-SVM) and partial least squares discrimination analysis were constructed to differentiate maturity stage. The SPA-ELM and SPA-GWO-SVM models achieved satisfactory performance. The results illustrate that NIR-HSI is feasible for evaluation of the quality of Malus micromalus Makino.
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Affiliation(s)
- Qiang Gao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
| | - Peng Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Tong Niu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Dongjian He
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China.
| | - Meili Wang
- College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China.
| | - Huijun Yang
- College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China
| | - Xiaoqiang Zhao
- School of Communication and Information, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China.
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Dos Santos VJ, Baqueta MR, Março PH, Valderrama P, Visentainer JV. Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics. Food Chem 2022; 368:130675. [PMID: 34419795 DOI: 10.1016/j.foodchem.2021.130675] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/16/2021] [Accepted: 07/20/2021] [Indexed: 01/02/2023]
Abstract
Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.
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Affiliation(s)
| | - Michel Rocha Baqueta
- Universidade Tecnológica Federal do Paraná (UTFPR), 87301-899 Campo Mourão-Paraná, Brazil
| | - Paulo Henrique Março
- Universidade Tecnológica Federal do Paraná (UTFPR), 87301-899 Campo Mourão-Paraná, Brazil
| | - Patrícia Valderrama
- Universidade Tecnológica Federal do Paraná (UTFPR), 87301-899 Campo Mourão-Paraná, Brazil.
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Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott). Processes (Basel) 2021. [DOI: 10.3390/pr9101804] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.
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Current Trends in Enrichment of Wheat Pasta: Quality, Nutritional Value and Antioxidant Properties. Processes (Basel) 2021. [DOI: 10.3390/pr9081280] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Wheat pasta is one of the most important cereal products and is becoming increasingly popular worldwide because of its convenience, simple formulation, long shelf life, and high energetic value. Wheat pasta is usually obtained from refined flour rich in carbohydrates but with low content of phytochemicals, micronutrients, and fibre. The increased demand of consumers for healthy foods has generated interest among both researchers and food producers in developing functional food products. This review showcases the current trends in pasta fortification. Changes in the nutritional value, cooking quality, sensory attributes, and antioxidant properties of durum and common wheat pasta enriched with both plant and animal raw materials are discussed.
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