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Zhu D, Han J, Liu C, Zhang J, Qi Y. Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging. FRONTIERS IN PLANT SCIENCE 2024; 15:1344143. [PMID: 38410736 PMCID: PMC10895056 DOI: 10.3389/fpls.2024.1344143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/24/2024] [Indexed: 02/28/2024]
Abstract
Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the R p 2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.
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Affiliation(s)
- Dongyu Zhu
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Junying Han
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Chengzhong Liu
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Jianping Zhang
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
| | - Yanni Qi
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
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Bai R, Zhou J, Wang S, Zhang Y, Nan T, Yang B, Zhang C, Yang J. Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning. Foods 2024; 13:498. [PMID: 38338633 PMCID: PMC10855119 DOI: 10.3390/foods13030498] [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: 12/18/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.
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Affiliation(s)
- Ruibin Bai
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Junhui Zhou
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Siman Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Yue Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Tiegui Nan
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Bin Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jian Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
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Shiv K, Singh A, Kumar S, Prasad LB, Gupta S, Bharty MK. Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:105-119. [PMID: 38180769 DOI: 10.1080/19440049.2023.2297869] [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: 09/04/2023] [Accepted: 12/10/2023] [Indexed: 01/06/2024]
Abstract
Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.
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Affiliation(s)
- Kunal Shiv
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Anupam Singh
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Sachin Kumar
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Lal Bahadur Prasad
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Seema Gupta
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Manoj Kumar Bharty
- Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi, India
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Pang T, Chen C, Fu R, Wang X, Yu H. An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image. FRONTIERS IN PLANT SCIENCE 2023; 14:1322391. [PMID: 38192695 PMCID: PMC10773811 DOI: 10.3389/fpls.2023.1322391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/24/2023] [Indexed: 01/10/2024]
Abstract
Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.
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Affiliation(s)
- Tiantian Pang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
| | - Chengcheng Chen
- School of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Ronghao Fu
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
| | - Xianchang Wang
- College of Computer Science and Technology, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jlin University, Changchun, China
- Chengdu Kestrel Artificial Intelligence Institute, Chengdu, China
| | - Helong Yu
- College of Information Technology, Jilin Agricultural University, Changchun, China
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Liu Y, Zhao M, Shi J, Yang S, Xue Y. Genome-Wide Identification of AhMDHs and Analysis of Gene Expression under Manganese Toxicity Stress in Arachis hypogaea. Genes (Basel) 2023; 14:2109. [PMID: 38136931 PMCID: PMC10743186 DOI: 10.3390/genes14122109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023] Open
Abstract
Malate dehydrogenase (MDH) is one kind of oxidation-reduction enzyme that catalyzes the reversible conversion of oxaloacetic acid to malic acid. It has vital functions in plant development, photosynthesis, abiotic stress responses, and so on. However, there are no reports on the genome-wide identification and gene expression of the MDH gene family in Arachis hypogaea. In this study, the MDH gene family of A. hypogaea was comprehensively analyzed for the first time, and 15 AhMDH sequences were identified. According to the phylogenetic tree analysis, AhMDHs are mainly separated into three subfamilies with similar gene structures. Based on previously reported transcriptome sequencing results, the AhMDH expression quantity of roots and leaves exposed to manganese (Mn) toxicity were explored in A. hypogaea. Results revealed that many AhMDHs were upregulated when exposed to Mn toxicity, suggesting that those AhMDHs might play an important regulatory role in A. hypogaea's response to Mn toxicity stress. This study lays foundations for the functional study of AhMDHs and further reveals the mechanism of the A. hypogaea signaling pathway responding to high Mn stress.
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Affiliation(s)
- Ying Liu
- Department of Biotechnology, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China; (Y.L.); (J.S.)
| | - Min Zhao
- Department of Biotechnology, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China; (Y.L.); (J.S.)
| | - Jianning Shi
- Department of Biotechnology, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China; (Y.L.); (J.S.)
| | - Shaoxia Yang
- Department of Biotechnology, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China; (Y.L.); (J.S.)
| | - Yingbin Xue
- Department of Agronomy, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China
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Hu H, Wang T, Wei Y, Xu Z, Cao S, Fu L, Xu H, Mao X, Huang L. Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix. FRONTIERS IN PLANT SCIENCE 2023; 14:1271320. [PMID: 37954990 PMCID: PMC10634472 DOI: 10.3389/fpls.2023.1271320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.
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Affiliation(s)
- Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yunpeng Wei
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiyu Cao
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ling Fu
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Luqi Huang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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