Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Aulia, Rizkiana; Kim, Yena; Amanah, Hanim Zuhrotul; Andi, Arief Muhammad Akbar; Kim, Haeun; Kim, Hangi; Lee, Wang‐Hee; Kim, Kyung-Hwan; Baek, Ji-Hye; Cho, Byoung‐Kwan

doi:10.1016/j.infrared.2022.104365

Cited by 19 publications

(9 citation statements)

References 48 publications

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“…used HSI and a partial least squares (PLS) algorithm to determine the protein content of wheat; the coefficient of determination R 2 of their validation set was 0.79 and the root mean square error (RMSE) value was 0.94%. Aulia et al 8 . combined HSI and partial least squares regression (PLSR) algorithms to construct a protein content prediction model for the analysis of soybean seeds; the R 2 and RMSE of their test set were 0.92 and 1.08%, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Caporaso et al 5 used HSI and a partial least squares (PLS) algorithm to determine the protein content of wheat; the coefficient of determination R 2 of their validation set was 0.79 and the root mean square error (RMSE) value was 0.94%. Aulia et al 8 combined HSI and partial least squares regression (PLSR) algorithms to construct a protein content prediction model for the analysis of soybean seeds; the R 2 and RMSE of their test set were 0.92 and 1.08%, respectively. Cheng et al 9 used HSI and PLSR models to determine the contents of oil and protein in peanuts; the R 2 and RMSE of the test set for the prediction of the peanut oil content were 0.945 and 0.196, respectively, and the R 2 and RMSE of the test set for the prediction of the protein content were 0.901 and 0.441, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods have strict technical requirements for operators and require long analysis times with lag, which cannot meet the requirements for the rapid analysis of the saccharification power and protein content of large quantities of wheat. Instead, hyperspectral imaging (HSI) has been extensively used for conducting rapid and non‐destructive composition analysis of grains and other raw food materials 5,8 …”

Section: Introductionmentioning

confidence: 99%

“…Instead, hyperspectral imaging (HSI) has been extensively used for conducting rapid and non-destructive composition analysis of grains and other raw food materials. 5,8 HSI combines spectroscopy and digital imaging to obtain spatial and spectral information to determine a sample's chemical and physical properties. 5 At present, HSI is often combined with a single machine learning (ML) model to determine the chemical composition of grains.…”

Section: Introductionmentioning

confidence: 99%

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Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

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BACKGROUNDWheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non‐destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).RESULTSThis study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full‐band spectral data and the characteristic wavelengths. The findings indicate that the MSC–competitive adaptive reweighted sampling–SEL model demonstrated the highest prediction accuracy, with Rp2 (test set‐determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg−1, respectively.CONCLUSIONThe predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non‐destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

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“…Partial least squares regression (PLSR) can be used to effectively analyze datasets that contain many noisy and strongly collinear predictor variables, while simultaneously modeling multiple response variables (Wold et al, 2001). PLSR based on hyperspectral information is considered an effective tool for predicting food ingredients and has widely been used in various food‐related applications (Aulia et al, 2023; Saleh et al, 2022; Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessment of drying characteristics for whole and sliced red ginseng using short‐wave infrared spectroscopy and mathematical model

Park,

Cho,

Park

et al. 2023

J Food Process Engineering

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Monitoring the moisture content is crucial for red ginseng quality control. In this study, we established a moisture content prediction model using partial least squares regression (PLSR) based on short‐wave infrared (SWIR) data of different ginseng types (whole and sliced) at various drying temperatures and times. The model parameters, including the R2 values, ratio of prediction deviation, and range error ratio, showed that the developed model was robust. Sliced ginseng showed a constant rate period, followed by a falling rate period, whereas whole ginseng showed only a falling rate period. The falling‐rate period coincided with the formation of a dry zone on the sample surface in the moisture distribution map, indicating that monitoring the moisture distribution map can predict the onset of the falling‐rate period. These results suggest that the SWIR technique is a promising nondestructive method for evaluating the drying process of red ginseng.Practical applicationsMonitoring the moisture content of red ginseng is essential to prevent microbial spoilage during transportation. Traditional monitoring of water content is destructive and time‐consuming. Therefore, using hyperspectral imaging, we aimed to develop an efficient moisture prediction model for the drying process of red ginseng. We believe that our study makes a significant contribution to non‐destructive and rapid quality evaluation in red ginseng production.

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Biochar Mitigates the Harmful Effects of Drought in Soybean Through Changes in Leaf Development, Stomatal Regulation, and Gas Exchange

Gonçalves,

da Silva,

Nobre

et al. 2024

J Soil Sci Plant Nutr

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Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Cited by 19 publications

References 48 publications

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Assessment of drying characteristics for whole and sliced red ginseng using short‐wave infrared spectroscopy and mathematical model

Biochar Mitigates the Harmful Effects of Drought in Soybean Through Changes in Leaf Development, Stomatal Regulation, and Gas Exchange

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