Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots

Abincha, Wilfred; Ikeogu, Ugochukwu N.; Kawuki, R.; Egesi, Chiedozie; Rabbi, Ismail; Parkes, Elizabeth; Kulakow, Peter; Edema, Richard; Gibson, Paul; Owor, Betty-Elizabeth

doi:10.3390/app11041714

Cited by 7 publications

(9 citation statements)

References 33 publications

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“…The observed variation in algorithm performance between studies could be attributed to differences in the trait investigated, data distribution, and sample variability ( Frizzarin et al., 2021 ). Consistent with previous studies, SVM and PLSR outperformed the RF algorithm in this study ( Mendez et al., 2019 ; Abincha et al., 2021 ; Hershberger et al., 2022 ). PLSR may remain the go-to model for trait prediction with NIRS due to its sensitivity and computational efficiency.…”

Section: Assessment Of Model Predictionsupporting

confidence: 92%

“…Recent studies have reported the value of NIRS for predicting key cassava traits such as dry matter, carotenoids, cyanogenic glucosides, and starch content in fresh cassava roots ( Sánchez et al., 2014 ; Ikeogu et al., 2019 ; Bantadjan et al., 2020a ; Bantadjan et al., 2020b ; Abincha et al., 2021 ; Hershberger et al., 2022 ). NIR sensors, particularly miniaturized devices, will be helpful in cassava breeding programs where thousands of samples are processed, and data turnaround is critical.…”

Section: The Routine Use Of Near-infrared Spectroscopy For Trait Pred...mentioning

confidence: 99%

“…Several high-throughput, non-invasive phenotyping technologies, such as image analysis ( Baek et al., 2020 ), satellite imaging, and remote sensing with unoccupied aerial vehicles ( Chawade et al., 2019 ), have recently been developed, opening up new opportunities in breeding. In cassava, spectroscopy-based approaches that use near-infrared (NIR) regions of the electromagnetic spectrum have shown promise for the rapid estimation of key traits ( Sánchez et al., 2014 ; Abincha et al., 2021 ; Hershberger et al., 2022 ). Near-infrared technology could replace the laborious and time-consuming approach currently used for root starch content quantification.…”

Section: Introductionmentioning

confidence: 99%

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Predicting starch content in cassava fresh roots using near-infrared spectroscopy

et al. 2022

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The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.

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Section: Assessment Of Model Predictionsupporting

confidence: 92%

Section: The Routine Use Of Near-infrared Spectroscopy For Trait Pred...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting starch content in cassava fresh roots using near-infrared spectroscopy

et al. 2022

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“…Nearinfrared reflectance spectroscopy (NIRS) is the most widespread reflectance spectroscopy currently in use. NIRS was successfully deployed for assessment in maize [65], cassava [66], and sweet potato [67]. While the non-destructive assessment of carotenoids is still a relatively new approach, it has not been introduced in cowpea, suggesting there is an avenue for technology development in cowpea research.…”

Section: Non-destructive Analysis Of Carotenoid Content In Cowpeamentioning

confidence: 99%

An Integrated Approach for Biofortification of Carotenoids in Cowpea for Human Nutrition and Health

Sodedji,

Assogbadjo,

Lee

et al. 2024

Plants

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Stress-resilient and highly nutritious legume crops can alleviate the burden of malnutrition and food security globally. Here, we focused on cowpea, a legume grain widely grown and consumed in regions at a high risk of micronutrient deficiencies, and we discussed the past and present research on carotenoid biosynthesis, highlighting different knowledge gaps and prospects for increasing this micronutrient in various edible parts of the crop. The literature survey revealed that, although carotenoids are important micronutrients for human health and nutrition, like in many other pulses, the potential of carotenoid biofortification in cowpea is still underexploited. We found that there is, to some extent, progress in the quantification of this micronutrient in cowpea; however, the diversity in content in the edible parts of the crop, namely, grains, pods, sprouts, and leaves, among the existing cowpea genetic resources was uncovered. Based on the description of the different factors that can influence carotenoid biosynthesis and accumulation in cowpea, we anticipated that an integrated use of omics in breeding coupled with mutagenesis and genetic engineering in a plant factory system would help to achieve a timely and efficient increase in carotenoid content in cowpea for use in the food systems in sub-Saharan Africa and South Asia.

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“…For the quick estimate of important cassava traits, near infrared spectroscopy (NIRS) has shown promise (Ikegu et al,2017;Rittiron et al,2020;Abincha et al, 2021;Hershberger et al, 2022;Nkouaya Mbanjo et al, 2022). In addition, pasting properties of rice were predicted with su cient accuracy by NIRS based on our spectra (Bao et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of Low Cost Near-Infrared Spectroscopy, to Predict Pasting Properties of High Quality Cassava Flour

Abubakar,

Wasswa,

Masumba

et al. 2023

Preprint

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Mobile near infrared spectroscopy (SCiO™) can offer quick, in-field phenotyping of cassava roots for pasting properties. However, validation is necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated to approve one or two best models to move on with calibration and optimization. Based on calibration statistics (R2, RMSE and MAE), the best model was identified and further optimized. We found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

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Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots

Cited by 7 publications

References 33 publications

Predicting starch content in cassava fresh roots using near-infrared spectroscopy

Predicting starch content in cassava fresh roots using near-infrared spectroscopy

An Integrated Approach for Biofortification of Carotenoids in Cowpea for Human Nutrition and Health

Use of Low Cost Near-Infrared Spectroscopy, to Predict Pasting Properties of High Quality Cassava Flour

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