Maize samples (n ¼ 309) containing 3090 single intact kernels from a broad variety of breeding materials were scanned by Near Infrared Reflectance Spectroscopy (NIRS) to develop non-destructive determination calibrations for crude protein, starch and oil content in kernels. Calibration equations of single kernels were developed by partial least square regression (PLS). Regression parameters between the chemical values, determined by reference methods, and the predicted values, determined by Near Infrared Reflectance Spectroscopy, were verified by cross validation and external validation. It was found that embryo position had a significant influence on the effect of calibrations. Reliable calibrations were developed for the prediction of protein and starch contents with the embryo upwards, whereas the oil content required the embryo to be downwards. The cross validation and external validation coefficients for protein were 0.91 and 0.94, for starch 0.90 and 0.89 and for oil 0.94 and 0.95, respectively. The data suggested that NIRS could be successfully used in breeding programmes, as an accurate and non-destructive tool to predict protein, starch and oil contents at the level of single kernels.

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

No abstract

No abstract