Achieving robustness across different ages and cultivars for an NIRS-PLSR model of fresh cassava root starch and dry matter content

Maraphum, Kanvisit; Saengprachatanarug, Khwantri; Wongpichet, Seree; Phuphuphud, Arthit; Posom, Jetsada

doi:10.1016/j.compag.2022.106872

Cited by 19 publications

(10 citation statements)

References 55 publications

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“…NIR spectrometry has demonstrated a high potential in predicting key traits such as carotenoids, starch, and dry matter content in cassava ( Ikeogu et al., 2017 ; Bantadjan et al., 2020 ; Maraphum et al., 2022 ). The correlation coefficient of prediction was 0.83 for starch content ( Bantadjan et al., 2020 ), 0.88 for carotenoids, and 0.80 for dry matter content ( Ikeogu et al., 2017 ), which ensures a sufficient predictive accuracy of new phenotypes to be generated and evaluated by the cassava breeding programs.…”

Section: Discussionmentioning

confidence: 99%

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%

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

et al. 2023

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“…12 Equations to calculate RMSEP and RPD are provided in the references. 12,15 Principal components analysis (PCA) was applied on hyperspectral images of transversal slices of cassava after 30 min boiling for the visualization of the variations in water content among cassava genotypes. This variation can be linked to cooking behavior, as evaluated by WAB.…”

Section: Multivariate Data Analysesmentioning

confidence: 99%

“…[5][6][7][8][9] Currently, the non-destructive techniques of near infrared (NIR) and visible-NIR (VNIR) spectroscopy are used in cassava breeding programs as high-throughput phenotyping methods. [10][11][12][13][14][15][16][17][18] However, those classical spectral techniques ignore the spatial variations of biochemical and physical properties within a root. Therefore, in recent years, many researchers became interested in the spatial distribution of quality traits in food.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava

et al. 2023

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BACKGROUNDThe purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality.RESULTSHyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination , root‐mean‐square error of prediction RMSEP = 0.96 g/100 g, and ratio of the standard deviation values RPD = 3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters.CONCLUSIONSThis study showed that hyperspectral imaging could be used as a high‐throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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“…The spectra of 82 wood powder samples were collected by the above method on two types of instruments under the same environmental conditions, respectively. The collected spectral data were preprocessed using Standard Normal Variate Transformation (SNV) (Kang et al 2022;Maraphum et al 2022) to eliminate the interference of surface scattering and light range variation of wood samples on the NIR diffuse reflectance spectra for the subsequent wavelength screening and modeling process.…”

Section: Instrumentation and Spectral Acquisitionmentioning

confidence: 99%

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

Wang

Xiong

et al. 2022

BioRes

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Model transfer techniques in near infrared spectroscopy are important for avoiding duplicate modeling, sharing samples and data resources, and reducing the human and material consumption required for modeling. Use of the slope/bias correction algorithm (S/B) based on screening wavelengths with consistent and stable signals (SWCSS) for model transfer is a new strategy. To enable sharing of near infrared analysis models of pulp holocellulose and lignin content in two different types of spectroscopic instruments, a combined SWCSS-S/B algorithm was proposed. The stable and consistent wavelengths between the spectroscopic instruments screened by the SWCSS method reduced the differences between the instruments, thereby improving the universality and transmission accuracy of the S/B method. The SWCSS-S/B based model transfer method reduced the predicted standard deviation RMSEP of holocellulose and lignin contents of the samples measured on the target spectrometer of the from 5.4686 and 7.6823 to 1.2133 and 1.3494, respectively. This result showed a significant improvement in the transfer effect compared to the SWCSS and S/B correction results alone, and the prediction of holocellulose was better than that of the prediction effect of lignin. The method has fewer wavelength variables involved in model transfer, fast transfer speed, and high prediction accuracy, which provides a new solution for the wide application of NIR analytical models.

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Achieving robustness across different ages and cultivars for an NIRS-PLSR model of fresh cassava root starch and dry matter content

Cited by 19 publications

References 55 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

Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

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