Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require timeconsuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and nondestructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 degrees C and 20 degrees C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a R2cv in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in mi-crobial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.