Establishment of an Accurate Starch Content Analysis System for Fresh Cassava Roots Using Short-Wavelength Near Infrared Spectroscopy

Bantadjan, Yuranan; Rittiron, Ronnarit; Malithong, Kritsanun; Narongwongwattana, Sureeporn

doi:10.1021/acsomega.0c01598

Cited by 12 publications

(12 citation statements)

References 25 publications

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“…In Figure 5(b), some significant peaks could be observed at 1090 nm of O-H bands [23], 1390 nm of C-H combination, 1450 and 1540 nm of O-H stretch overtone, and 1765 nm of C-H stretch first overtone, which might be assigned to starch, similar to previously reported findings [24] at 1040-1200 nm of C-H stretching. Several peaks in developing the amylose calibration model were detected at 1170, 1470, and 1750 nm, which corresponded to C-H second overtone, N-H stretch first overtone, and C-H stretch first overtone, as shown in Figure 5(c).…”

Section: Plsr Analysissupporting

confidence: 88%

“…Figure5(a) shows several distinct peaks with high beta regression coefficients at 960 nm related to O-H, and at 1470, 1520, 1570, 1660, 1720, and 1780 nm assigned to N-H stretch first overtone and C-H stretch first overtone, which might be related to proteins, similar to previously reported findings[21],[22]. Table1shows variations of protein contents of all flour samples in which wheat is the highest and is the lowest.In Figure5(b), some significant peaks could be observed at 1090 nm of O-H bands[23], 1390 nm of C-H combination, 1450 and 1540 nm of O-H stretch overtone, and 1765 nm of C-H stretch first overtone, which might be assigned to starch, similar to previously reported findings[24] at 1040-1200 nm of C-H stretching. Several peaks in developing the amylose calibration model were detected at 1170, 1470, and 1750 nm, which corresponded to C-H second overtone, N-H stretch first overtone, and C-H stretch first overtone, as shown in Figure5(c).…”

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confidence: 88%

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Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour

Masithoh¹,

Kandpal²,

Lohumi³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Understanding the physicochemical properties of flour in food preparation is important to determine the appropriate food utilization and processing. Some crops are processed into flour to improve their shelf life and extend their applications in food preparation. This study employed shortwave infrared hyperspectral imaging (SWIR HSI) coupled with multivariate analyses to determine wheat's protein, starch, amylose, glucose, and moisture compositions and several tuber flours. Tubers used were arrowroot, Canna edulis, modified cassava flour, taro, and sweet potato (purple, yellow, and white color). Hyperspectral images of all flour samples were captured using the SWIR HSI system at the wavelength range of 895-2504 nm in reflectance mode. The extracted spectral data were then processed and analyzed using partial least square regression (PLSR). Normalization (mean, max, and range), multiple scatter correction, standard normal variate, first and second Savitzky-Golay derivatives, and smoothing spectral pre-processing were applied to reduce scattering noise resulting from the HSI system. The PLSR models predicted the chemical concentrations of all samples with coefficients of determination of 0.85-0.97, 0.83-0.96, and 0.85-0.96 for calibration, validation, and prediction, respectively. Moreover, the models resulted in standard errors of 0.61-27.26, 0.63-27.71, and 0.63-29.14 for calibration, validation, and prediction. The concentration and distribution of protein, starch, amylose, glucose, and moisture in the flour samples were visualized by chemical imaging. This paper confirmed the potential of HSI for the rapid interpretation of chemical contents in different flour samples.

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Section: Plsr Analysissupporting

confidence: 88%

supporting

confidence: 88%

Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour

Masithoh¹,

Kandpal²,

Lohumi³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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“…The variable importance analysis identified the relative contribution of wavelengths in predicting root starch content. The most informative wavelengths for both PLSR and RF models were between 815 and 980 nm, corresponding to a) the third overtone of C-H and C-H 2 stretching related to the presence of carbohydrates and b) the second overtone for O-H bands, the most prominent signal for water ( Bantadjan et al., 2020a ; Bantadjan et al., 2020b ; Farhadi et al., 2020 ) ( Table 2 ; Supplementary Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

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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“…Meanwhile, SSC related peaks were discovered at 636 nm, and this was the amylose waveband ( Bantadjan et al., 2020b ). The RC peaks for water content appeared at 759 nm (third overtone of the O–H band) and 975 nm (second overtone of the O–H band), corresponding to water molecules ( Phetpan et al., 2018 ; Posom et al., 2020 ; Bantadjan et al., 2020a ). This confirmed the SSC prediction was influenced by the C–H band, while the water content determination referred to the bond vibration of O–H and C–H.…”

Section: Resultsmentioning

confidence: 99%

Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy

Kusumiyati

Hadiwijaya

Putri

et al. 2021

Heliyon

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Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R 2 c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R 2 p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.

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Establishment of an Accurate Starch Content Analysis System for Fresh Cassava Roots Using Short-Wavelength Near Infrared Spectroscopy

Cited by 12 publications

References 25 publications

Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour

Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour

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

Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy

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