Kunjie Chen

Predicting beef tenderness using color and multispectral image texture features

The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner-Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender.

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat.

Potential Use of NIR Spectroscopy for the Estimation of Milled Rice Yield

Milled rice yield is one of the important indicators used to define the quality of milled rice. Near-infrared reflectance (NIR) spectroscopy was evaluated as a tool to estimate milled rice yield. Rough rice was dehusked and then milled to various milled rice yields. A total of 198 samples with milled rice yields of 85.72% to 96.91% were scanned over the NIR spectral wavelength ranging from 833 to 2500 nm with a Fourier transform near-infrared reflectance spectroscopy (FT-NIR) system. After finding the optimal spectral region (1638.8 to 2354.9 nm) according to the coefficients of determination, eleven mathematical pretreatments were performed on the selected spectra. The optimal calibration and prediction were obtained in the selected optimal spectral region using partial least square regression (PLS) on the spectra pretreated by a combination of first-derivation and multiplicative scattering correction. The best calibration model yielded a coefficient of determination (r2) of 0.994, a root mean square error of prediction (RMSEP) of 0.174%, and a bias of -0.021%, showing good predictability. Thus, the NIR spectroscopy technology has potential for predicting milled rice yield.