Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques

Porto, Nathália de Aguiar; Roque, Jussara V.; Wartha, Cleiton Antônio; Cardoso, Wilson; Peternelli, Luíz Alexandre; Barbosa, Márcio Henrique Pereira; Teófilo, Reinaldo F.

doi:10.1016/j.saa.2019.03.114

Cited by 17 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, HTP technologies can replace standard less effective phenotyping protocols, thus saving much time and resources. For instance, near-infrared (NIR) spectroscopy technology has been successfully applied in agriculture to screen biological sample compositions and also for breeding purposes [9,16,17].…”

Section: Introductionmentioning

confidence: 99%

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Gonçalves

Morota

Costa

et al. 2020

Preprint

View full text Add to dashboard Cite

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics s and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms (SNPs) and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted genotypic values. Our results showed that models fitted using BayesB were most predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genotypic value of sugarcane clones.

show abstract

Section: Introductionmentioning

confidence: 99%

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Gonçalves

Morota

Costa

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The present study is in agreement with Skoog et al [18], who infer that organic compounds have functional groups with specific characteristics in the range of radiation absorption, being carbonyl in the range (186 -280 nm), amine (214 nm). The presence of the band between 300 -460 and at 720 nm may be related to both color and the presence of chlorophyll, and may also be responsible for the turbidity of the sample [19,20]. In the subsequent hours of fermentation, there was a decrease in the intensity of the peaks, this fact can be related to the metabolism of the yeast, since it absorbs nutrients present in the substrate, to maintain its fermentative capacity.…”

Section: Resultsmentioning

confidence: 98%

“…In this study, bands between 300 -460 and up to 720 nm occurred, indicating the presence of chlorophyll. For Samide and Tutunaru [19] and Porto et al [20], in their studies related the presence of chlorophyll in the spectrum range from 430 to 710 nm.…”

Section: Resultsmentioning

confidence: 98%

The Use of UV-vis Spectroscopy Technique for Investigation of Organic Compounds in Saccharine Substrates

Santos¹,

Batistote²,

Cardoso³

2022

Orbital: Electron. J. Chem.

View full text Add to dashboard Cite

Sugar substrates are rich in organic compounds. However, its concentration may vary depending on the culture. Thus, the objective of this work was to evaluate the composition of sugarcane and sorghum juices using the UV-vis technique. An evaluation of the composition of the substrates was carried out and a pre-inoculum with YPSAC 5% medium, where 0.10 grams of FT858 yeast was inoculated and kept incubated at 30°C for 24h at 250rpm. The cells were recovered by centrifugation, resulting in a concentration of 10 mg mL-1 of wet mass that was inoculated into 50 mL of the previously prepared fermentation medium with a concentration of 22°Brix and pH 5.0. Aliquots were taken for evaluation of molecular absorption spectra using a spectrophotometer (Global Trade Technology). These substrates are composed of water and fermentable sugars. The evaluation of the molecular absorption spectra showed a difference in the profile of the substrates as well as in the subsequent hours of fermentation, with a decrease in the intensity of the peaks being observed that may be related to the absorption of nutrients by the yeast. vis shows promise for this type of analysis.

show abstract

“…The predictive power estimation for discrimination against infected grains (positive result) and healthy grains (negative result) using the above approaches was carried out based on such commonly used criteria as sensitivity, specificity, positive predictive value, negative predictive value and accuracy, which were defined as follows [ 31 , 34 , 35 , 36 ]: Sensitivity = T P /(T P + F N ), Specificity = T N /(T N + F P ), Positive predictive value = T P /(T P + F P ), Negative predictive value = T N /(T N + F N ), Accuracy = N corr /N tot . where T P is the number of true positive results, T N is the number of true negative results, F P is the number of false positive results, F N is the number of false negative results, N corr is the number of correctly classified samples, N tot is the total number of samples.…”

Section: Methodsmentioning

confidence: 99%

Section: Model Evaluationmentioning

confidence: 99%

Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains

Pankin,

Povolotckaia,

Borisov

et al. 2023

Foods

View full text Add to dashboard Cite

Wheat has played an important role in human agriculture since ancient times. Increasing rates of processed wheat product fabrication require more and more laboratory studies of product quality. This, in turn, requires the use, in production and in field conditions, of sufficiently accurate, fast and relatively low-cost quality control methods, including the detection of fungal diseases. One of the most widespread fungal diseases of wheat in the world is ergot caused by the fungi genus Claviceps. Optical methods are promising for this disease identification due to the relative ease of implementation and the possibility of performing fast analyses in large volumes. However, for application in practice, it is necessary to identify and substantiate characteristic spectral markers that make it possible to judge the sample contamination. In this regard, within the framework of this study, the methods of IR absorption spectroscopy in the MIR region and reflection spectroscopy in the UV-vis-NIR ranges, as well as luminescence spectroscopy, were used to study ergot-infected grains of winter wheat of the “Moskovskaya 56” cultivar. To justify the choice of the most specific spectral ranges, the methods of chemometric analysis with supervised classification, namely PCA-LDA and PCA-SVM, were applied. The possibility of separating infected grains according to the IR absorption, reflection spectra in the UV-vis-NIR ranges and visible luminescence spectra was tested.

show abstract

Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques

Cited by 17 publications

References 25 publications

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

The Use of UV-vis Spectroscopy Technique for Investigation of Organic Compounds in Saccharine Substrates

Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains

Contact Info

Product

Resources

About