Vis-NIR spectroscopic and chemometric models for detecting contamination of premium green banana flour with wheat by quantifying resistant starch content

Green banana resistant starch: A promising potential as functional ingredient against certain maladies

This review covers the significance of green banana resistant starch (RS), a substantial polysaccharide. The food industry has taken an interest in green banana flour due to its 30% availability of resistant starch and its approximately 70% starch content on a dry basis, making its use suitable for food formulations where starch serves as the base. A variety of processing techniques, such as heat-moisture, autoclaving, microwaving, high hydrostatic pressure, extrusion, ultrasound, acid hydrolysis, and enzymatic debranching treatments, have made significant advancements in the preparation of resistant starch. These advancements aim to change the structure, techno-functionality, and subsequently the physiological functions of the resistant starch. Green bananas make up the highest RS as compared to other foods and cereals. Many food processing industries and cuisines now have a positive awareness due to the functional characteristics of green bananas, such as their pasting, thermal, gelatinization, foaming, and textural characteristics. It is also found useful for controlling the rates of cancer, obesity, and diabetic disorders. Moreover, the use of GBRS as prebiotics and probiotics might be significantly proved good for gut health. This study aimed at the awareness of the composition, extraction and application of the green banana resistant starch in the future food products.

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

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.

Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R²p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R²p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

Authomatic in-situ monitoring and characterization of photopolymerization.