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Unripe banana flour is a premium nutritious product with a potential to curb degenerative diseases through resistant starch and gluten free traits, however, with scant techniques to monitor adulteration practices. The objective of the present study was to determine the efficacy of visible-near infrared spectroscopy (Vis-NIR) spectroscopy (Vis-NIRS) in the detection and quantification of unripe banana flour adulteration with wheat flour. To do this, simulated adulteration of a composite banana flour was performed with different levels of wheat flour, in intervals of 20 g kg -1 , ranging from 0 to 800 g kg -1 . Each level was acquired in duplicate giving a total of 82 samples. Vis-NIR spectral data was acquired using a portable F-750 spectrometer in the range 447-1005 nm. Spectral data was analysed chemometrically using principle components analysis and partial least squares regression (PLSR), with 41 samples used as a calibration set and 41 for validation. The first two principal components accounted for 95% of spectral data variation, revealing five distinct clusters related to 0 g kg -1 , 20-200 g kg -1 , 220-400 g kg -1 , 420-600 g kg -1 and 620-800 g kg -1 adulterated samples. The best PLSR model to predict wheat flour adulteration degrees in unripe banana flour was obtained using 2nd derivative Savitzky-Golay (19-point smoothing, 2nd order polynomial), showing the highest R c 2 (0.991); R p 2 (0.993); RPD (12.021) and the lowest RMSEC (2.226 g kg -1 ) and RMSEP (1.993 g kg -1 ) values. The obtained Vis-NIRS PLSR models therefore demonstrated the technology novelty in monitoring unripe banana flour quality by the processing industries and in retail markets during product verification. Keywords Rapid detection Á Unripe banana flour Á Nondestructive technology Á Chemometrics Á Principal component analysis (PCA) Á Partial least squares regression (PLSR)

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