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( Hanim Z. Amanah ),( Byoung-kwan Cho ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Pigmented soybean is one of the potential sources of flavonoid such as anthocyanin and isoflavone which have benefit effect on human health. Determination of anthocyanin and isoflavone content in soybean is usually carried out by conventional chemical analytical methods which is destructive, time-consuming and costly. FT-NIR and FT-IR spectroscopy have been proven to be a rapid, simple, non-destructive and chemical free analytical tool to measure chemical compounds in food and agricultural products. In this research, the feasibility of FT-NIR and FT-IR to measure total anthocyanin and isoflavone contents in pigmented soybean was investigated. Partial least squares (PLS) analysis was used to develop calibration and validation models for quantitative analysis of total content of the substances. The result showed that FT-NIR and FT-IR have significant potential in non-destructive determination of total anthocyanin and isoflavone contents in pigmented soybean
( Hanim Z. Amanah ),( Collins Wakholi ),( Hoonsoo Lee ),( Wang-hee Lee ),( Byoung-kwan Cho ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.1
Protein and lipid are essential nutrients in soybean, aimed at the soybean cultivar, and the content was affected by the mother seed. Thus, the seed selection based on the chemical content on soybean seed is one of the critical successes on the soybean breeding and production. Near-infrared hyperspectral imaging is a promising instrument that can give information about the chemical composition as well as provide its spatial distribution. This study aims to evaluate the feasibility of the NIR-hyperspectral imaging system (NIR-HSI) to predict protein and lipid contents in soybean seed to open up the possibility of developing a seed sorting machine based on the chemical component. HSI in the NIR spectral region of 900 - 1800 nm was operated in reflectance mode to capture an image of 20 seeds of 120 varieties of soybean obtained from Rural Development and Administration (RDA) South Korea. Extracted spectral data from the images were then synchronized with the reference values from analytical analysis to develop a partial least square (PLS) regression model for protein and lipid prediction of intact single bean. The calibration models revealed the performance (R2) of 0.88 and 0.85 with standard error (SEC) of 0.73 and 0.75% for protein and lipid, respectively. The validation results demonstrated a good performance of the developed model with a standard error less than 1% for both targeted components. The developed models were used to generate chemical images to visualize the protein and lipid content distribution on every single kernel of soybean. This study showed the good potential of NIR-HSI to be applied for the chemical-based seed sorting machine.
( Rizkiana Aulia ),( Hanim Z. Amanah ),( Wang-hee Lee ),( Byoung-kwan Cho ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2
Chemical composition-based seed inspection on a real-time, non-destructive, and precise basis is important for establishing industries that maximize product cost and quality. Among the chemical compositons of soyean, anthocyanin has many benefits for health such as antioxidants and anti-inflammatory. The objective of this study was to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of anthocyanin content in soybeans. The NIR-HSI system with a wavelength of 895-2504 nm was used to acquire images of eighty-two samples of soybean seeds. The extracted spectral data was synchronized with the soybean anthocyanin reference value, which was determined using high-performance liquid chromatography (HPLC). The calibration models revealed R2 of 0.81 with standard error (SEC) of 0.14 for the prediction of anthocyanin concentrations. This study showed the potential of NIR-HSI to be applied for the prediction of anthocyanin content in soybean seeds.
Raman spectroscopic analysis to detect olive oil mixtures in argan oil
Rahul Joshi,조병관,Ritu Joshi,Santosh Lohumi,Mohammad Akbar Faqeerzada,Hanim Z Amanah,이재영,모창연,이훈수 충남대학교 농업과학연구소 2019 Korean Journal of Agricultural Science Vol.46 No.1
Adulteration of argan oil with some other cheaper oils with similar chemical compositions has resulted in increasing demands for authenticity assurance and quality control. Fast and simple analytical techniques are thus needed for authenticity analysis of high-priced argan oil. Raman spectroscopy is a potent technique and has been extensively used for quality control and safety determination for food products In this study, Raman spectroscopy in combination with a net analyte signal (NAS)-based methodology, i.e., hybrid linear analysis method developed by Goicoechea and Olivieri in 1999 (HLA/GO), was used to predict the different concentrations of olive oil (0 - 20%) added to argan oil. Raman spectra of 90 samples were collected in a spectral range of 400 - 1400 cm-1, and calibration and validation sets were designed to evaluate the performance of the multivariate method. The results revealed a high coefficient of determination (R2) value of 0.98 and a low root-mean-square error (RMSE) value of 0.41% for the calibration set, and an R2 of 0.97 and RMSE of 0.36% for the validation set. Additionally, the figures of merit such as sensitivity, selectivity, limit of detection, and limit of quantification were used for further validation. The high R2 and low RMSE values validate the detection ability and accuracy of the developed method and demonstrate its potential for quantitative determination of oil adulteration.