Hongzhe Jiang

Effect of stachyose supplementation on growth performance, nutrient digestibility and caecal fermentation characteristics in broilers.

1. The objective of this study was to evaluate whether the oligosaccharide stachyose enhances gastrointestinal tract health by fermentation and proliferation of desirable bacteria species and thus affects growth performance and nutrient digestibility in broilers. 2. A total of 432 1-d-old male Arbor Acres (AA) broilers were randomly allocated to one of 6 treatments, with 12 replicate pens per treatment and 6 birds per pen. Chicks were fed a maize–hamlet protein 300 (HP300) basal diet with 0, 4·0, 8·0, 12·0 or 16·0 g/kg stachyose. A sixth diet contained no HP300 but soybean meal (SBM) and provided 8·7 g/kg stachyose and 3·1 g/kg raffinose. The duration of the study was 42 d. 3. Stachyose contents above 12·0 g/kg depressed group body weights, average daily gain and feed/gain but not feed intake during the whole experimental period. Broiler growth decreased linearly and quadratically with increasing stachyose content. No differences were detected between diets supplemented with 12·0 g/kg stachyose and SBM. 4. Nutrient digestibility tended to decrease but not significantly with increasing stachyose. 5. Stachyose content had no significant positive effects on caecal pH, microflora population and the resulting short-chain fatty acid (SCFA) metabolites during the 42 d experiment, with only butyrate differing significantly in the initial period.

Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging

The aim of this study was to classify and visualize tenderness of intact fresh broiler breast fillets using hyperspectral imaging (HSI) technique. A total of 75 chicken fillets were scanned by HSI system of 400-1000nm in reflectance mode. Warner-Bratzler shear force (WBSF) value was used as reference tenderness indicator and fillets were grouped into least, moderately and very tender categories accordingly. To extract additional image textural features, principal component analysis (PCA) transform of images were conducted and gray level co-occurrence matrix (GLCM) analysis was implemented in region of interests (ROIs) on first three PC score images. Partial least square discriminant analysis (PLS-DA) or radial basis function-support vector machine (RBF-SVM) was developed for predicting tenderness based on full wavelengths (CCR=0.92), selected wavelengths (CCR=0.84), textural or combined data (CCR=0.88). Classification maps were created by pixels prediction in images and breast fillet tenderness was readily discernible. Overall, HSI technique is a promising methodology for predicting tenderness of intact fresh broiler breast meat.

Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy

In this study visible/near-infrared spectroscopy (Vis/NIRS) was evaluated to rapidly classify intact chicken breast fillets. Five principal components (PC) were extracted from reference quality traits (L∗, pH, drip loss, expressible fluid, and salt-induced water gain). A quality grades classification method by PC1 score was proposed. With this method, 150 chicken fillets were properly classified into three quality grades, i.e., DFD (dark, firm and dry), normal, and PSE (pale, soft and exudative). Furthermore, PC1 score could be predicted using partial least squares regression (PLSR) model based on Vis/NIRS (R2p = 0.78, RPD = 1.9), without the measurement of any quality traits. Thresholds of PC1 classification method were applied to classify the predicted PC1 score values of each fillet into three quality grades. The classification accuracy of calibration and prediction set were 85% and 80%, respectively. Results revealed that PC1 score classification method is feasible, and with Vis/NIRS, this method could be rapidly implemented.

Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University

Near infrared spectroscopy and hyperspectral imaging are fast-growing, rapid, powerful, and non-destructive optical technologies that can be used especially in quality and safety control of agro-food products. The Non-destructive Detecting Laboratory for Agricultural and Food Products in the College of Engineering, China Agricultural University in Beijing, China, has engaged in research on sensing and characterizing agro-food quality and safety attributes with the latest optical methods including near infrared spectroscopy and hyperspectral imaging for over five years. In this report, some of our latest research and developments through multidisciplinary international collaborations will be highlighted to demonstrate our contributions to this near infrared spectroscopy and hyperspectral imaging sensing area to improve non-destructive diagnosis and quality control of agricultural and food products.

Evaluation of factors in development of Vis/NIR spectroscopy models for discriminating PSE, DFD and normal broiler breast meat

ABSTRACT 1. To evaluate the performance of visible and near-infrared (Vis/NIR) spectroscopic models for discriminating true pale, soft and exudative (PSE), normal and dark, firm and dry (DFD) broiler breast meat in different conditions of preprocessing methods, spectral ranges, characteristic wavelength selection and water-holding capacity (WHC) indexes were assessed. 2. Quality attributes of 214 intact chicken fillets (pectoralis major), such as lightness (L*), pH and WHC indicators including drip loss (DL), water gain and expressible fluid were measured. Fillets were grouped into PSE, normal and DFD categories based on combination of L*, pH and WHC threshold criteria. Classification models were developed using support vector machine based methods on characteristic wavelengths selected from the unprocessed or 2nd-derivative spectra, respectively, in three spectral subsets of 400–2500, 400–1100 and 1100–2500 nm. 3. Better classification of three meat groups was obtained based on unprocessed spectra (72–94%) than 2nd-derivative spectra (55–72%). The classification based on 400–2500 nm (91% average) and 400–1100 nm (89% average) performed better than that on 1100–2500 nm (78% average). In terms of the three different WHC indicators, the combination of L*, pH and DL produced better results than the other two groups, with recognition accuracy of 94.4% using 400–2500-nm range. 4. These analytical results suggest that for a better classification of true PSE, normal and DFD broiler breast meat with Vis/NIR spectra, unprocessed spectra wavelengths should be used, ranges of 400–1000 nm should be included in the data collection, and DL as an indicator of WHC might provide a better prediction model.