NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen–thawed porcine longissimus dorsi muscles

Identification of fresh and frozen–thawed meat is an important authenticity issue, although a challenging task. The potential application of a pushbroom hyperspectral imaging system in the near-infrared (NIR) range (900–1700 nm) was explored as a rapid and non-destructive technique for the investigation of meat freshness. The effect of standard freeze–thaw routines was studied in pork samples from the longissimus dorsi muscle. Partial least squares discriminant analysis (PLS-DA) models were used to distinguish between fresh and frozen–thawed samples. Optimal wavelengths were selected and used for sample discrimination with reduced spectral data and image processing. Classification models with reduced spectral data achieved an overall correct classification of 100% for an independent set of samples. An image processing algorithm was also developed for visualizing the classification results. The best classification model obtained was successfully applied to the images to produce classification maps with high overall accuracy. Results confirmed the possibility of developing a fast and reliable system for discrimination between fresh and frozen–thawed pork based on reflectance in the NIR wavelength range.Industrial relevanceThe application of NIR hyperspectral analysis could enable the development of a rapid, reliable and non-destructive method for authentication of fresh meat samples for the benefit of the retail sector and the consumer.

► Spectral data from hyperspectral images were used to identify frozen–thawed pork.
► Fresh and frozen–thawed pork was discriminated by using PLS-DA models.
► Most important wavelengths were identified for faster data analysis.
► Good discrimination was achieved using few selected wavelengths as predictors.
► Selected wavelengths were used for building classification images.