Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents

Masithoh, Rudiati Evi; Lohumi, Santosh; Yoon, Won-Seob; Amanah, Hanim Zuhrotul; Cho, Byoung‐Kwan

doi:10.1016/j.heliyon.2020.e05099

Cited by 27 publications

(20 citation statements)

References 30 publications

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“…Other less evident absorbance peaks are caused by overtones and combination modes of C–H, N–H, and O–H. The O–H and C–H vibrations are caused by compounds such as polysaccharides, triterpenoids and proteins [ 34 , 44 ]. As shown in Figure 2 , the high overlap of NIR spectra makes it difficult to directly utilize the spectral information of target components.…”

Section: Resultsmentioning

confidence: 99%

Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics

Xie

Huang

Ren

et al. 2022

Foods

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Poria cocos (PC) is an important fungus with high medicinal and nutritional values. However, the quality of PC is heavily dependent on multiple factors in the cultivation regions. Traditional methods are not able to perform quality evaluation for this fungus in a short time, and a new method is needed for rapid quality assessment. Here, we used near-infrared (NIR) spectroscopy combined with chemometric method to identify the cultivation regions and determine PC chemical compositions. In our study, 138 batches of samples were collected and their cultivation regions were distinguished by combining NIR spectroscopy and random forest method (RFM) with an accuracy as high as 92.59%. In the meantime, we used partial least square regression (PLSR) to build quantitative models and measure the content of water-soluble extract (WSE), ethanol-soluble extract (ASE), polysaccharides (PSC) and the sum of five triterpenoids (SFT). The performance of these models were verified with correlation coefficients (R2cal and R2pre) above 0.9 for the four quality parameters and the relative errors (RE) of PSC, WSE, ASE and SFT at 4.055%, 3.821%, 4.344% and 3.744%, respectively. Overall, a new approach was developed and validated which is able to distinguish PC production regions, quantify its chemical contents, and effectively evaluate PC quality.

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Section: Resultsmentioning

confidence: 99%

Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics

Xie

Huang

Ren

et al. 2022

Foods

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“…Satisfactory correlation between spectra and moisture content reference data was indicated by an R 2 c of 0.97 and RPD of 6. Masithoh et al. (2020) also conducted multi-product modeling with a large dataset, to determine the polysaccharide content.…”

Section: Resultsmentioning

confidence: 99%

“…Several researches have been conducted on the development of global multivariate models for rapid quality evaluation. In one study, an investigation was conducted to determine water content in freeze-dried drug products using NIR ( Clavaud et al., 2017 ), while another study utilized NIR spectroscopy was utilized to build global models for the assessment of amylose, cellulose, and starch content in various tuber and root products ( Masithoh et al., 2020 ). However, no research has been conducted on multi-product prediction based on Vis/NIR, in the Cucurbitaceae family.…”

Section: Introductionmentioning

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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“…Statistical methods, i.e., coefficient determination ( R 2 ) and standard error (S.E.) for both calibration and validation datasets, were used to select the best-obtained model [ 38 ]. The R 2 is the proportion of the variance in the dependent variable that is predictable from the independent variables and can be found with Equation (4).…”

Section: Methodsmentioning

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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Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents

Cited by 27 publications

References 30 publications

Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics

Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics

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

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

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