دورية أكاديمية
Detection of encapsulant addition in butterfly-pea (Clitoria ternatea L.) extract powder using visible–near-infrared spectroscopy and chemometrics analysis
| العنوان: | Detection of encapsulant addition in butterfly-pea (Clitoria ternatea L.) extract powder using visible–near-infrared spectroscopy and chemometrics analysis |
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| المؤلفون: | Rahmawati Laila, Pahlawan Muhammad Fahri Reza, Hariadi Hari, Masithoh Rudiati Evi |
| المصدر: | Open Agriculture, Vol 7, Iss 1, Pp 711-723 (2022) |
| بيانات النشر: | De Gruyter, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Agriculture LCC:Agriculture (General) |
| مصطلحات موضوعية: | butterfly peas extract, vis–nir, pca, pca-da, plsr, pls-da, Agriculture, Agriculture (General), S1-972 |
| الوصف: | Butterfly-pea (Clitoria ternatea L.) extract powder is a functional product with numerous benefits obtained by extraction followed by the drying process. During drying, encapsulations can be added to protect the color and antioxidants of the samples. Using visible-near-infrared (Vis–NIR) spectroscopy, this research aimed to detect maltodextrin and soybean protein isolate (SPI) added as encapsulants to butterfly-pea extract powder. Butterfly-pea extract powder were added with 10, 20, 30, 40, and 50% concentrations of maltodextrin and SPI. Spectral data were acquired using a Vis–NIR fiber optic spectrometer at 350–1,000 nm. The chemometric methods used were principal component analysis (PCA), PCA-discriminant analysis (PCA–DA), partial least square regression (PLSR), and partial least square discriminant analysis (PLS-DA). The results showed that PCA can discriminate pure and maltodextrin- and SPI-added samples using low principal components. PCA-DA determined the accuracy levels of 88% for maltodextrin and 94.67% for SPIs. The PLSR models predicted the addition of maltodextrin with the following variables: coefficient of determination of calibration (R c 2), 0.98; coefficient of determination of prediction (R p 2), 0.98; root mean square error of calibration (RMSEC), 2.1%; and root mean square error of prediction (RMSEP), 4.02%. The values for the addition of SPI were R c 2 of 0.97, R p 2 of 0.97, RMSEC of 2.72%, and RMSEP of 2.83%. The PLS-DA models resulted in an accuracy of 98 and 91% for the identification of maltodextrin and SPI, respectively. In conclusion, this research showed the potency of Vis–NIR spectroscopy combined with a proper chemometric analysis to detect additives in butterfly-pea extract powders. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2391-9531 |
| Relation: | https://doaj.org/toc/2391-9531 |
| DOI: | 10.1515/opag-2022-0135 |
| URL الوصول: | https://doaj.org/article/230e925188bb438597bfc7663a875943 |
| رقم الأكسشن: | edsdoj.230e925188bb438597bfc7663a875943 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23919531 |
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| DOI: | 10.1515/opag-2022-0135 |