Visible-Near-Infrared Spectroscopy and Chemometrics for Authentication Detection of Organic Soybean Flour

Abstract

Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and non-organic soybean flours were captured using a Vis-NIR spectrometer at 350–1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA–linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability.

Evaluation of Indonesian Local Soybean Based on Chemical Characteristics and Visible - Near Infrared Spectra With Chemometrics

“…Before reflectance spectra measurement, instrument calibration procedures were performed by measuring the white reflectance spectra standard, followed by black reference spectra. This calibration was performed on every 10 samples, which were individually measured in triplicate, as described by Masithoh et al (2023). Subsequently, color and chemical measurements were carried out after spectra measurement.…”

“…Vis-NIR spectrometer has been extensively investigated, emphasizing its suitability for field applications (Walsh et al 2020) due to simplicity, speed, and cost-effectiveness for quality evaluation (Cortés et al 2019) and classification (Monago-Maraña et al 2021). Specifically, fiber optic-based Vis-NIR spectroscopy has been explored for soybean characterization (Abadi et al 2022), showing good performance for classification (Pahlawan et al 2022), water evaluation (Pahlawan et al 2023), and authentication (Masithoh et al 2023). The results showed the potential of this instrument to support the production process for soybean characterization in field applications.…”