Use of near-infrared spectroscopy for the rapid evaluation of soybean [Glycine max (L.) Merri.] water soluble protein content

Xu, Ruixin; Hu, Wei; Zhou, Yanchen; Zhang, Xianyi; Xu, Shu Ying; Guo, Qingyuan; Qi, Ping; Chen, Lingling; Yang, Xiao; Zhang, Fan; Liu, Li-Ke; Qiu, Lijuan; Wang, Jun

doi:10.1016/j.saa.2019.117400

Cited by 26 publications

(17 citation statements)

References 33 publications

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“…The measured versus predicted oil content by PLS is shown in Figure 2. Similar results for soybean oil prediction were reported by Ferreira et al (2013) and Xu et al (2020).…”

Section: Chemometrics: Oil Predictionsupporting

confidence: 89%

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Leite

Corrêa

Júnior

et al. 2020

Journal of Food Composition and Analysis

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In soybean (Glycine max L.) breeding programs, segregation is normally observed, and it is not possible to have replicates of individuals because each genotype is a unique copy. Therefore, near-infrared spectroscopy (NIRS) was used as a nondestructive tool to classify soybeans by genotypes and to predict oil content. A total of 260 soybean genotypes were divided into five classes, which were composed of 32, 52, 82, 46, and 49 samples of the BV, BVV, EB, JAB, and L class, respectively. NIR spectra were obtained using oven-dried samples (80 g) in a reflectance mode. A successive projection algorithm and genetic algorithm with linear discriminant analysis discriminated genotypes of the low (L class) from the high (EB class) for oil content (88.89% accuracy). The partial least square regression models for oil content were considered good (root mean square error of prediction of 0.96%). Therefore, NIRS can be used as a non-destructive tool in soybean breeding programs, but further investigation is necessary to improve the robustness of the models. It is important to note that to use the models, it is necessary to collect NIR spectra from dry soybean samples.

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“…The measured versus predicted oil content by PLS is shown in Figure 2. Similar results for soybean oil prediction were reported by Ferreira et al (2013) and Xu et al (2020).…”

Section: Chemometrics: Oil Predictionsupporting

confidence: 89%

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Leite

Corrêa

Júnior

et al. 2020

Journal of Food Composition and Analysis

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“…Near-infrared reflectance spectroscopy (NIRS) has been widely used for quick, accurate, non-destructive, a highly repeatable assay of multiple quality traits in many crops ( Delwiche et al., 1996 ; Windham et al., 1997 ; Tarr et al., 2012 ; Han et al., 2017 ; Xu et al., 2019 ). In wheat, it has been proved that reliable prediction of wheat composition is possible using NIR directly on the whole kernels, and especially for estimating essential grain components such as protein content and wet gluten content ( Kuchel et al., 2006 ; Kristensen et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…With the improvement on measurement throughout, speed and accuracy by Near-infrared reflectance spectroscopy (NIRS) analyzer, NIRS has been widely used in the non-destructive analysis of grain components, such as grain protein content, oil, fatty acids, dietary fiber, moisture, seed physical traits ( Cen and He, 2007 ). Compared with traditional laboratory methods, the NIRS methods have the great advantages of non-destructive, less time, low cost, and fast speed, and have been used for rapid phenotyping of quality traits in maize, soybean, rice, barley, oat and triticale ( Delwiche et al., 1996 ; Windham et al., 1997 ; Fox et al., 2011 ; Tarr et al., 2012 ; Han et al., 2017 ; Xu et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

et al. 2020

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In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in highquality breeding of bread wheat.

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“…These proteins have insecticidal properties [20]. In the near-infrared region, the absorption of protein is quite different from that of starch [21][22][23][24]. The absorption peaks associated with the first overtone of C-H stretching of the starch are around 1131-1155 nm, and the protein and combination bands are around 1950-2250 nm [25].…”

Section: Rice Samplesmentioning

confidence: 99%

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Hao

Geng

et al. 2019

Molecules

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Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris-Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

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Use of near-infrared spectroscopy for the rapid evaluation of soybean [Glycine max (L.) Merri.] water soluble protein content

Cited by 26 publications

References 33 publications

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

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