Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms

Abstract

Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka–Munck (K–M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K–M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40log10 CFU per gram as well as 0.94 and 0.50log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications.