Evaluation of a vat wall-mounted image capture system using image processing techniques to monitor curd moisture during syneresis with temperature treatments

Characterization of curd grain size and shape by 2-dimensional image analysis during the cheesemaking process in artisanal sheep dairies

The size and shape of curd grains are the most important parameters used by cheesemakers to decide when to end the cutting or stirring processes during cheesemaking. Thus, 2-dimensional image analysis was used to measure the characteristics of curd grains in commercial cheese productions carried out by artisanal sheep dairies. Dairies used different technical settings for cutting and stirring steps, causing differences in the size and shape of curd grains. A linear relationship between total revolutions used for cutting and stirring and curd particle size was established. However, particle size distributions after curd cutting and stirring were highly heterogeneous. Actual cheese yield was correlated with particle size and cutting revolutions, whereas curd grain shape and fat loss were associated with stirring conditions by a multivariate approach. Image analysis of the size and shape of curd grains gives useful information for determining characteristics related to cheese yield and quality and may contribute to improving and controlling the cheesemaking process in small artisanal dairies.

Rapid and non-invasive quantification of intramuscular fat content of intact pork cuts

Having acquired near infrared (NIR) hyperspectral images of intact pork loin samples through an NIR hyperspectral imaging system, the efficiency of a variety of image processing techniques including texture pattern analysis techniques were applied to process hyperspectral images so as to determine the intramuscular fat (IMF) content non-destructively. After the segmentation of region of interest (ROI), the raw spectral, texture-based spectral and textural characteristics of pork images were extracted by spectral averaging and pattern recognition techniques namely Gabor filter and improved gray level co-occurrence matrix (GLCM), respectively. First derivatives of the non-filtered and the Gabor filtered spectra were also investigated. Full waveband partial least squares regression (PLSR) was employed to determine the optimal parameters of Gabor filter and GLCM, and to select optimal wavelengths for IMF prediction. A stepwise procedure was applied to the optimal wavelengths to further optimize them to key wavelengths. Multiple linear regression (MLR) models were built based on the key wavelengths. Mean spectra and the Gabor filtered spectra outperformed GLCM. The best result, represented by correlation coefficients of calibration (Rc), cross validation (Rcv) and prediction (Rp) of 0.89, 0.89, and 0.86, respectively, was achieved using the first derivative of Gabor filtered spectra at 1193 and 1217 nm. To visualize the IMF content in pork, the distribution maps of IMF content in pork were drawn using a mean spectra-based MLR model. These promising results highlight the great potential of NIR hyperspectral imaging for non-destructive prediction of IMF content of intact pork.