The demand for gluten-free banana flour has led manufactures to enforce strict measures for quality control. A need has arisen for the development of more sensitive and reliable methods to test the quality of green banana flour (GBF). The objective of this study was to develop rapid visible to near-infrared (Vis-NIR) based spectroscopic models to detect gluten concentration, as a biomarker to detect wheat flour adulteration in green banana flour (GBF). Spectroscopic data were acquired using a desktop (FOSS®) Vis-NIR spectroscopy ranging from 400 to 2500 nm of the electromagnetic spectrum. The spectral and reference data were submitted to principal component analysis (PCA) and partial least squares regression (PLSR) for the development of gluten adulteration detection models. Calibration models were constructed based on a full cross-validation approach, consisting of 51 samples for the calibration set and 21 samples for the test set. PCA scores plot discriminated gluten adulterated and unadulterated GBF samples with 100% accuracy for the first two principal components (PCs). The optimal prediction model was obtained after a combination of baseline (offset and baseline linear correlation) and standard normal variate (SNV) pre-processing technique. This model showed a 94% coefficient of determination of cross-validation (R2cv) and prediction (R2p); root mean square error of cross-validation (RMSECV) of 3.7 mg/kg, root mean square error of prediction (RMSEP) of 3.9 mg/kg; and RPD value of 4. This work has demonstrated that Vis-NIRS method is a robust and feasible technology that may be used to ensure the safety of banana flour and that this product stays gluten-free by providing good and reliable gluten detection and quantification prediction models.
Keywords: Consumer protection; Gluten prediction; Non-destructive technology; Partial least square regression; Product safety.
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