Determination of semolina adulteration by NIR spectroscopy

Abstract

T he objective was to implement a semolina percentage recognition system using near-infrared spectroscopy (NIR) and multivariate data analysis. For this purpose, 6 samples were an aly zed with different percentages of semolina (20, 4 0, 6 0, 80 an d 100 %) . Samples were repeated 20 times. T he observed NI R sp ect r um was absorbance in the range of 1100 and 2500 nm. In order to reduce the data, the analysis of main components was used by testing 24 classification models, from which the one that reached the highest level of precision was the Linear Support Vector Machine (SVM) algorithm, reaching 98.8%, achieving fairly satisfactory discriminatio n with values of PC1 (99.7%), PC2 (0.3%) and PC3 (0.1%), reachin g a total cumulative variation of the contribution of th e f ir st 3 P Cs of 99.9%. Partial Least Regression (PLS) models applied to NIR-spectra showed R2 between 0.9388. T hese values demonstrated that NIR spectroscopy can be used for the identification and quantif ication of fiber added to semolina.

Non-destructive evaluation of dry matter in ‘Edward’ mango by reflectance spectroscopy

“…Currently, new technologies based on nondestructive methods are used to help predict mango quality and maturity, such as nuclear magnetic resonance (NMR) [13], impact response [14], electronic nose [15], hyperspectral analysis [16]- [18] and near infrared (NIR) spectroscopy [6], [7], [10], [19]- [21]. Spectroscopy has been used in various applications such as the detection of explosives [22], adulteration of food products [23] and the detection of substances [24].…”