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Meyenberg C, Braun V, Longin CFH, Thorwarth P. Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:188. [PMID: 39037501 PMCID: PMC11263437 DOI: 10.1007/s00122-024-04695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/10/2024] [Indexed: 07/23/2024]
Abstract
KEY MESSAGE Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky-Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02-0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment ( G × E ) interaction variance.
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Affiliation(s)
- Carina Meyenberg
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Vincent Braun
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | | | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany.
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Shewry PR, Prins A, Kosik O, Lovegrove A. Challenges to Increasing Dietary Fiber in White Flour and Bread. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:13513-13522. [PMID: 38834187 PMCID: PMC11191685 DOI: 10.1021/acs.jafc.4c02056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 06/06/2024]
Abstract
Increasing the intake of dietary fiber from staple foods is a key strategy to improve the health of consumers. White bread is an attractive vehicle to deliver increased fiber as it is widely consumed and available to all socio-economic groups. However, fiber only accounts for about 4% of the dry weight of white flour and bread compared to 10-15% in whole grain bread and flour. We therefore discuss the challenges and barriers to developing and exploiting new types of wheat with high fiber content in white flour. These include defining and quantifying individual fiber components and understanding how they are affected by genetic and environmental factors. Rapid high throughput assays suitable for determining fiber content during plant breeding and in grain-utilizing industries are urgently required, while the impact of fiber amount and composition on flour processing quality needs to be understood. Overcoming these challenges should have significant effects on human health.
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Affiliation(s)
| | - Anneke Prins
- Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, U.K.
| | - Ondrej Kosik
- Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, U.K.
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Aeindartehran L, Sadri Z, Rahimi F, Alinejad T. Fluorescence in depth: integration of spectroscopy and imaging with Raman, IR, and CD for advanced research. Methods Appl Fluoresc 2024; 12:032002. [PMID: 38697201 DOI: 10.1088/2050-6120/ad46e6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Fluorescence spectroscopy serves as a vital technique for studying the interaction between light and fluorescent molecules. It encompasses a range of methods, each presenting unique advantages and applications. This technique finds utility in various chemical studies. This review discusses Fluorescence spectroscopy, its branches such as Time-Resolved Fluorescence Spectroscopy (TRFS) and Fluorescence Lifetime Imaging Microscopy (FLIM), and their integration with other spectroscopic methods, including Raman, Infrared (IR), and Circular Dichroism (CD) spectroscopies. By delving into these methods, we aim to provide a comprehensive understanding of the capabilities and significance of fluorescence spectroscopy in scientific research, highlighting its diverse applications and the enhanced understanding it brings when combined with other spectroscopic methods. This review looks at each technique's unique features and applications. It discusses the prospects of their combined use in advancing scientific understanding and applications across various domains.
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Affiliation(s)
- Lida Aeindartehran
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Zahra Sadri
- Department of Biological Science, Southern Methodist University, Dallas, Texas 75205, United States of America
| | - Fateme Rahimi
- Department of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Tahereh Alinejad
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou 325015, Zhejiang, People's Republic of China
- Institute of Cell Growth Factor, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health), Wenzhou Medical University, Wenzhou 325000, People's Republic of China
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Novikov A, Perevoschikov S, Usenov I, Sakharova T, Artyushenko V, Bogomolov A. Multimodal fiber probe for simultaneous mid-infrared and Raman spectroscopy. Sci Rep 2024; 14:7430. [PMID: 38548800 PMCID: PMC10978856 DOI: 10.1038/s41598-024-57539-4] [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: 08/03/2023] [Accepted: 03/19/2024] [Indexed: 04/01/2024] Open
Abstract
A fiber probe has been developed that enables simultaneous acquisition of mid-infrared (MIR) and Raman spectra in the region of 3100-2600 cm-1. Multimodal measurement is based on a proposed ZrO2 crystal design at the tip of an attenuated total reflection (ATR) probe. Mid-infrared ATR spectra are obtained through a pair of chalcogenide infrared (CIR) fibers mounted at the base of the crystal. The probe enables both excitation and acquisition of a weak Raman signal from a portion of the sample in front of the crystal using an additional pair of silica fibers located in a plane perpendicular to the CIR fibers. The advantages of combining MIR and Raman spectra in a single probe have been discussed.
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Affiliation(s)
- Alexander Novikov
- Art Photonics GmbH, Rudower Chaussee 46, 12489, Berlin, Germany.
- Technische Universität Berlin, Straße Des 17. Juni 135, 10623, Berlin, Germany.
| | - Stanislav Perevoschikov
- Art Photonics GmbH, Rudower Chaussee 46, 12489, Berlin, Germany
- Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205, Moscow, Russia
| | - Iskander Usenov
- Art Photonics GmbH, Rudower Chaussee 46, 12489, Berlin, Germany
- Technische Universität Berlin, Straße Des 17. Juni 135, 10623, Berlin, Germany
| | | | | | - Andrey Bogomolov
- Art Photonics GmbH, Rudower Chaussee 46, 12489, Berlin, Germany
- Samara State Technical University, Molodogvardeyskaya Str. 244, 443100, Samara, Russia
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Bakhshipour A. A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques. Food Sci Nutr 2023; 11:6116-6132. [PMID: 37823103 PMCID: PMC10563735 DOI: 10.1002/fsn3.3548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 10/13/2023] Open
Abstract
A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.
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Affiliation(s)
- Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural SciencesUniversity of GuilanRashtIran
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An H, Zhai C, Zhang F, Ma Q, Sun J, Tang Y, Wang W. Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion. Food Chem 2022; 405:134821. [DOI: 10.1016/j.foodchem.2022.134821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/26/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
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