In this study, white mushrooms ( Agaricus bisporus) were subjected to physical perturbation by mechanical vibration. Hyperspectral images were obtained after perturbation using a pushbroom line-scanning instrument operating in the wavelength range of 1000–1700 nm (7nm spectral resolution). Changes in sample spectra arising from perturbation were examined by observation of difference spectra and partial least squares regression (PLSR) coefficients. Different spectral pre-treatments [multiplicative scatter correction (MSC), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV)] were employed in order to decrease spectral variability caused by scattering and differences in the optical path length due to physical changes in the mushrooms induced by the perturbation. Candidate water matrix co-ordinates were proposed at 950 nm, 1174 nm, 1398 nm, 1433 nm, 1454 nm, 1496 nm and 1510 nm. Mechanical vibration increased the concentration of weakly hydrogen-bonded water and decreased that of strongly hydrogen-bonded water in the mushrooms without causing changes in the bulk moisture content.

The increasingly common application of the near-infrared (NIR) hyperspectral imaging technique to the analysis of food powders has led to the need for optical characterization of samples. This study was aimed at exploring the feasibility of quantifying penetration depth of NIR hyperspectral imaging light for milk powder. Hyperspectral NIR reflectance images were collected for eight different milk powder products that included five brands of non-fat milk powder and three brands of whole milk powder. For each milk powder, five different powder depths ranging from 1 mm–5 mm were prepared on the top of a base layer of melamine, to test spectral-based detection of the melamine through the milk. A relationship was established between the NIR reflectance spectra (937.5–1653.7 nm) and the penetration depth was investigated by means of the partial least squares-discriminant analysis (PLS-DA) technique to classify pixels as being milk-only or a mixture of milk and melamine. With increasing milk depth, classification model accuracy was gradually decreased. The results from the 1-mm, 2-mm and 3-mm models showed that the average classification accuracy of the validation set for milk-melamine samples was reduced from 99.86% down to 94.93% as the milk depth increased from 1 mm–3 mm. As the milk depth increased to 4 mm and 5 mm, model performance deteriorated further to accuracies as low as 81.83% and 58.26%, respectively. The results suggest that a 2-mm sample depth is recommended for the screening/evaluation of milk powders using an online NIR hyperspectral imaging system similar to that used in this study.