a Selection of the number of latent variables (LVs) to include in a partial least squares (PLS) model is an important step in the data analysis. Inclusion of too few or too many LVs may lead to, respectively, under or over-fitting of the data and subsequently result in poor future model performance. One well-known sign of over-fitting is the appearance of noise in regression coefficients; this often takes the form of a reduction in apparent structure and the presence of sharp peaks with a high degree of directional oscillation, features which are usually estimated subjectively. In this work, a simple method for quantifying the shape and size of a regression coefficient is presented. This measure can be combined with an indicator of model bias (e.g. root mean square error) to aid in estimation of the appropriate number of LVs to include in a PLS model. The performance of the proposed method is evaluated on simulated and and real NIR spectroscopy datasets sets and compared with several existing methods.

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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 (7 nm 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 coordinates were proposed at 950 nm,

Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.