Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy

Amanah, Hanim Zuhrotul; Joshi, Rahul; Masithoh, Rudiati Evi; Choung, Myoung-Gun; Kim, Kyung-Hwan; Kim, Geonwoo; Cho, Byoung‐Kwan

doi:10.1016/j.infrared.2020.103477

Cited by 35 publications

(23 citation statements)

References 32 publications

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“…PLS is a multivariate analysis method that extracts new latent variables or factors from raw spectra. These factors indicate the maximum covariance between the reference and spectral information [13]. Thus, the PLS method can resolve the multi-collinearity problem using the latent variables of the independent and dependent variables.…”

Section: Model Delvelopmentmentioning

confidence: 99%

“…Hyperspectral imaging (HIS) can simultaneously acquire spatial and spectral data from a target by combining image processing and spectroscopy techniques, and is a powerful tool for agricultural analyses [13][14][15]. Rapid improvements in HSI technology have facilitated nondestructive evaluation of soil nutrition, fertilizer quality, plant productivity, and so on.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

et al. 2021

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Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.

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Section: Model Delvelopmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

et al. 2021

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“…Kovalenko et al [ 29 ] evaluated amino acids content in soybean based on NIR spectral data of the seeds. Moreover, three different sample types (single-bean, whole bean, and powder) have been investigated to determine their effects on the success of predicting protein and amino acids in soybeans [ 30 ], while FT-NIR and FT-IR (in MIR region) spectra from intact seed have also been successfully used to predict anthocyanin in soybean [ 31 ]. The reports reveal that using a single bean to obtain NIR and MIR spectral data is a promising strategy for nondestructive chemical component evaluation of soybeans.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Amanah

Tunny

Masithoh

et al. 2022

Foods

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The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.

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“…Near-infrared and mid-infrared spectroscopy methods have been widely used to detect anthocyanins in agricultural products such as soybeans [18], flowering tea [19], and sweet cherries [20]. Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) can also be applied to predict anthocyanins content in black soybean based on the seed's spectra [21]. Many researchers have favored the hyperspectral imaging (HSI) system, which integrates the spectroscopic and imaging techniques due to the capability in acquiring spectral and spatial information simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

et al. 2021

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Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.

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Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy

Cited by 35 publications

References 32 publications

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

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