Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging.

Based on a new approach for wavelength selection, a multispectral real-time imaging system was proposed for the staple food industry to determine the fidelity of organic spelt flour (OSF) from three categories of adulterants including rye flour (RF), organic wheat flour (OWF) and spelt flour (SF). Calibration models were first built by partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR) with spectral pretreatment for multivariate analysis of hyperspectral image in the spectral range of 900-1700nm. Instead of qualifying certain groups of characteristic wavelengths for RF, OWF, SF and OSF separately, a set of mutual wavelengths (1145, 1192, 1222, 1349, 1359, 1396, 1541, and 1567nm) was chosen by first-derivative and mean centring iteration algorithm (FMCIA) for all investigated flour samples. Then these selected feature wavelengths were utilized in PLSDA, PLSR and multiple linear regression (MLR) models to devise multispectral imaging system. Better performances for both qualitative discrimination of OSF and quantitative measure of adulterants were emerged in simplified PLSDA and PLSR models, with mean determination coefficients in cross validation (R(2)CV) of 0.958 and in prediction (R(2)P) of 0.957, respectively. To visualize the adulterants in OSF samples, the distribution maps were drawn by computing the spectral response of each pixel on corresponding spectral images at specific frequencies using a quantitative identification function. The results reveal that spectral imaging integrated with multivariate analysis has good potential for rapidly evaluating the purity of organic spelt flour.