Study on starch content detection and visualization of potato based on hyperspectral imaging

Wang, Fuxiang; Wang, Chunguang; Song, Shiyong; Xie, Shengshi; Kang, Feilong

doi:10.1002/fsn3.2415

Cited by 31 publications

(22 citation statements)

References 36 publications

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“…Meanwhile, in this study, the significant differences in the prediction results of different screening methods were found, and especially showed the suitability of IVSO and MASS methods and inefficiency of SPA selection for effective wavelengths in prediction of three chemical indexes of HJ samples. Interestingly, the former reports also showed that starch content in potato was well predicted based on effective wavelengths selected via IVSO method, with R 2 values > 0.88 in both calibration and prediction sets, and RPD values > 2.0 in almost all groups when combined with regression models (Wang et al, 2021a). Similar to our results, total phenolics, flavonoids, and anthocyanins contents in dry black goji berries (Lycium ruthenicum Murr.)…”

Section: Comparison and Analysis Of Three Methods On Selection E Ecti...supporting

confidence: 91%

“…Many previous similar reports have shown that effective HSI wavelengths often have the same or better prediction results for chemical indexes compared with those from the full wavelength group. The examples can be studies on starch and water contents prediction in potato (Xiao et al, 2020;Wang et al, 2021a), total caffeine and theanine contents prediction from tea (Wang et al, 2020(Wang et al, , 2021c, amylose and amylopectin contents prediction in sorghum (Huang et al, 2021b), and total anthocyanin content and antioxidant activity prediction in mulberry fruit (Huang et al, 2017). The results of this research showed that the prediction effect of selection wavelengths groups was the same as or even better than that from the full wavelengths groups.…”

Section: Comparison and Analysis Of Three Methods On Selection E Ecti...mentioning

confidence: 99%

“…The latent variables of PLSR model can increase the accuracy of prediction by considering the covariance between independent variables and target variables. In this analysis, the leave-one-out method was used for cross validation, and the optimal number of latent variables ranging from 6 to 8 was determined in the different model groups by the minimum root mean square error (RMSE) value in the cross validation set (Wang et al, 2021a).…”

Section: Three Regression Models For Prediction Of Chemical Indicatorsmentioning

confidence: 99%

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Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

Wang

Yang

et al. 2022

Front. Sustain. Food Syst.

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Fruits of Zanthoxylum bungeanum Maxim (Red “Huajiao,” RHJ) and Z. schinifolium Sieb. et Zucc. (Green “Huajiao,” GHJ) are famous spices around the world. Antioxidant capability (AOC), total alkylamides content (TALC) and volatile oil content (VOC) in HJ are three important quality indicators and lack rapid and effective methods for detection. Non-destructive, time-saving, and effective technology of hyperspectral imaging (HSI) combined with chemometrics was adopted to improve the indicators prediction in this study. Results showed that the three chemical indexes exhibited significant differences between different regions and varieties (P < 0.05). Specifically, the mass percentages of TALC were 11–22% in RHJ group and 21–36% in GHJ group. The mass percentages of VOC content were 23–31% and 16–24% in RHJ and GHJ groups, respectively. More importantly, these indicators could be well predicted based on the full or effective HSI wavelengths via model adaptive space shrinkage (MASS) and iteratively variable subset optimization (IVSO) selections combined with wavelet transform (WT) method for noise reduction. The best prediction results of AOC, TALC, and VOC indicators were achieved with the highest residual predictive deviation (RPD) values of 7.43, 7.82, and 3.73 for RHJ, respectively, and 6.82, 2.66, and 4.64 for GHJ, respectively. The above results highlight the great potential of HSI assisted with chemometrics in the rapid and effective prediction of chemical indicators of Zanthoxylum spices.

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Section: Comparison and Analysis Of Three Methods On Selection E Ecti...supporting

confidence: 91%

Section: Comparison and Analysis Of Three Methods On Selection E Ecti...mentioning

confidence: 99%

Section: Three Regression Models For Prediction Of Chemical Indicatorsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

Wang

Yang

et al. 2022

Front. Sustain. Food Syst.

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“…This is consistent with the findings of Mendez et al (2019) and Hershberger et al (2022). They reported a marginal improvement in predictive ability for SVM over PLS, contradicting Ludwig et al (2019) and Wang et al (2021) who found SVM superior to PLSR. 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).…”

Section: Assessment Of Model Predictionsupporting

confidence: 89%

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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“…Then, the starch content (SC) of different potato varieties was determined based on linear PLSR and nonlinear support vector regression (SVR) models. Based on selected feature wavelengths, the CARS-SVR model achieved the highest accuracy with the R P of 0.93 [ 74 ]. Besides the whole potato, the SC of fresh-cut potato slices was also measured.…”

Section: Applications For Tuber Quality Assessmentmentioning

confidence: 99%

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Xue

2021

Foods

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Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

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Study on starch content detection and visualization of potato based on hyperspectral imaging

Cited by 31 publications

References 36 publications

Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics

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

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

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