Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Qiu, Mengqing; Zheng, Shouguo; Tang, Le; Hu, Xujin; Xu, Qingshan; Zheng, Ling; Weng, Shizhuang

doi:10.3390/foods11040578

Cited by 15 publications

(7 citation statements)

References 45 publications

(47 reference statements)

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“…132 RS combined with deep learning networks were recently used for the detection of Fusarium head blight (FHB)-infected wheat kernels. 133…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

“…Raman spectroscopy was adopted to detect rot disease of maize caused by Colletotrichum graminicola , 130 rose rosette infection 131 and to investigate wheat and sorghum grains infected with ergot, black tip or mold 132 . RS combined with deep learning networks were recently used for the detection of Fusarium head blight (FHB)‐infected wheat kernels 133 …”

Section: Newly Developed Techniquesmentioning

confidence: 99%

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Next‐generation methods for early disease detection in crops

Trippa,

Scalenghe,

Basso

et al. 2023

Pest Management Science

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BackgroundPlant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. Herein, in this review were presented and discussed the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations.Resultstraditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme‐linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high‐throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non‐cultivable pathogens in the laboratory, nucleic acid‐based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in the field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last‐generation portable thermocyclers may provide rapid detection results in situ.ConclusionThe advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially can revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens.This article is protected by copyright. All rights reserved.

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“…132 RS combined with deep learning networks were recently used for the detection of Fusarium head blight (FHB)-infected wheat kernels. 133…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Section: Newly Developed Techniquesmentioning

confidence: 99%

Next‐generation methods for early disease detection in crops

Trippa,

Scalenghe,

Basso

et al. 2023

Pest Management Science

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“…While the NIR spectrometer consistently yielded the best results, the Raman spectrometer was nearly as accurate. However, using a 1064 nm laser instead of a 785 nm laser, as in most Raman and wheat‐related publications (Czaja et al, 2020; Qiu et al, 2022), could reduce the baseline and enhance prediction accuracy. The fluorescence spectrometer used for flour samples was unsuitable for long‐term measurements due to nonlinear changes in the light source spectrum, necessitating its replacement during the study.…”

Section: Resultsmentioning

confidence: 99%

Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications

Nagel‐Held,

El Hassouni,

Longin

et al. 2023

Cereal Chem

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Abstract:Background and ObjectivesQuality assessment of bread wheat is time‐consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near‐infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations and years. The prediction error is corrected for the measurement error of the reference methods.FindingsSuccessful prediction, robustness testing and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 ml/100 g flour and a R² of 0.86. However, model robustness was limited due to data distribution, environmental factors and temporal influences.ConclusionsThe proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations.Significance and NoveltyWith up to 1200 wheat samples, this is the largest study on predicting complex characteristics which comprise agronomic traits, dough rheological parameters measured by Extensograph, micro‐doughLAB and GlutoPeak, baking trial parameters like loaf volume and specific ingredients such as grain protein content, sugars and minerals.This article is protected by copyright. All rights reserved.

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“…This technique is primarily based on the relative concentrations of metabolites present within a plant, namely Raman active molecules phytochemicals such as terpenes, phenolics, and alkaloids. For example, a study in 2022 showed that RS could detect Fusarium head blight in wheat kernels due to the spectral changes in bands associated with lignin, carotenoids, pectin, cellulose, protein, and starch ( Qiu et al., 2022 ). It was also recently demonstrated that RS could be used to diagnose nutritional deficiencies, salinity stress, and aluminum and iron toxicities with high accuracy in rice crops ( Sanchez et al., 2020 ; Higgins et al., 2022a ).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy

Juárez,

Dou,

Biswas

et al. 2024

Front. Plant Sci.

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Rice (Oryza sativa) is the primary crop for nearly half of the world’s population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS’s potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.

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Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Cited by 15 publications

References 45 publications

Next‐generation methods for early disease detection in crops

Next‐generation methods for early disease detection in crops

Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications

Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy

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