Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy

Carmo, Cátia Dias do; Sousa, Massáine Bandeira e; Pereira, Jocilene dos Santos; Ceballos, Hernán; Oliveira, E. J. de

doi:10.1002/csc2.20102

Cited by 5 publications

(5 citation statements)

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“…In the present study, seeds from segregating populations of cassava for waxy starch were used as sample material for the identification/classification of waxy and non-waxy genotypes by near-infrared spectroscopy (NIRS). A previous study using spectral data collected from leaf tissue allowed the early and accurate identification of waxy genotypes ( Carmo et al., 2019 ). The NIRS technique allows capturing differences in the chemical constitution of plants because of the expression of different genes.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, NIRS can serve as an important predictor of these compounds in organic substances. Carmo et al (2019) evaluated Fourier-transform near-infrared spectroscopy (FT-NIRS) for indirect, early identification of waxy starch cassava genotypes by screening samples of dried, macerated leaves. In this study, the distribution between the classes of waxy and non-waxy genotypes was similar, and the results showed high accuracy, deeming it a potential technique for the classification of waxy genotypes.…”

Section: Introductionmentioning

confidence: 99%

“… Carmo et al. (2019) evaluated Fourier-transform near-infrared spectroscopy (FT-NIRS) for indirect, early identification of waxy starch cassava genotypes by screening samples of dried, macerated leaves.…”

Section: Introductionmentioning

confidence: 99%

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Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis

et al. 2023

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Cassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88−0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96−100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis

et al. 2023

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“…Machine learning models are an efficient tool and have been widely used in different agriculture approaches to unravel, quantify, and understand data-intensive processes [ 47 ]. However, the predictive ability depends on the dataset under analysis; for this reason, it is interesting to evaluate different approaches in order to explore differences in algorithms that may benefit the predictions [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction

et al. 2022

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Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.

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“…They achieved a confidence interval of 95% for the two models. Carmo et al reported the phenotyping of waxy starch cassava using Fourier Transform Infrared (FTIR) analysis on the leaves rather than the root [121]. This method has the potential to make analysis much easier for breeders, as the leaves are formed before the root.…”

Section: Cassava and Wheatmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy

Cited by 5 publications

References 60 publications

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

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