Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Traditional marbling meat evaluation is a tedious, repetitive, costly and time-consuming task performed by panellists. Alternatively, we have Computer Vision Systems (CVS) to mitigate these problems. However, most of CVS are restricted to specific environments, configurations or muscle types, and marbling scores are settled for a particular marbling meat standard. In this context, we developed a CVS for meat marbling grading, which is flexible to different muscle colour contrasts and grading standards. Essentially, the proposed method segments an image pre-processed by illumination normalisation and contrast enhancement, analyses visible intramuscular fat pixels and attributes a score based on a desired meat standard defined in the learning step. Learning approach is an instance-based system making use of k-Nearest Neighbours algorithm (k-NN) to attribute a score from segmentation results. The algorithm classifies the new samples based on scores assigned by panellists. We investigated the optimal number of samples for modelling, focusing on the smallest number leading to acceptable accuracy, and considering two different animal species: bovine and 1 swine. The CVS led to accuracy values equal to 81.59% (bovine) and to 76.14% (swine), using only three samples for each marbling score.

ResumoObjetivou-se com o estudo avaliar os efeitos da imunocastração sobre o ganho de peso, características de carcaça e qualidade da carne de novilhos Nelore. Foram utilizados oitenta bovinos Nelores, com peso inicial médio de 357±8,63 kg, que foram alimentados em confinamento e distribuídos em dois tratamentos com 40 bovinos inteiros e 40 imunocastrados (Bopriva ® , Pfizer Saúde Animal). Os imunocastrados receberam duas doses da vacina, sendo a primeira aplicação 30 dias antes da entrada dos animais em confinamento e a segunda na entrada do confinamento. Ao final do período experimental de 67 dias, calculou-se o ganho diário de peso vivo em kg/dia dos 80 animais e selecionou-se 20 de cada grupo para o abate e avaliações de carcaça, e posteriormente dez de cada tratamento para as análises de carne. Os dados foram submetidos à análise de variância. Animais imunocastrados apresentaram menor ganho diário de peso vivo, peso de carcaça quente, rendimento de carcaça, pH final, espessura de perna, profundidade de músculo, área de olho de lombo, porcentagem de músculo, força de cisalhamento e umidade. Entretanto, obtiveram maior concentração sanguínea de lactato e cortisol, profundidade torácica, espessura de gordura subcutânea, grau de cobertura da carcaça, a*, b* e c*, perda de líquido no descongelamento, índice de fragmentação miofibrilar e extrato etéreo da carne em relação aos inteiros. A imunocastração (Bopriva ® ) é uma alternativa para melhorar a qualidade da carne, através da maior deposição de gordura na carcaça e redução da força de cisalhamento da carne em relação aos animais inteiros. Palavras-chave: Grau de acabamento, hormônio liberador de gonadotrofinas, imunização, maciez, novilhos AbstractThe objective of this study was evaluate the effects of immunocastration on body weight gain, carcass characteristics and meat quality of Nellore beef cattle. Eighty Nellore beef cattle, with initial body weight of 357±8.63 kg, were placed in feedlots and distributed in two treatments (40 animals per treatments) as follow: one -non-vaccinated bulls and two -immunocastrated bulls (Bopriva ® , Pfizer Animal Health). The animals placed on treatment two were vaccinated in two doses, first application 30 days before they arrive on the feedlots and second on the day they arrive on feedlots. After 67 days

Objective The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

The potential of near-infrared spectroscopy (NORS) to predict the physicochemical characteristics of the porcine longissimus dorsi (LD) muscle was evaluated in comparison to the standard methods of pH and color for meat quality analysis compared to the pH results with Colorimeter and pH meter. Spectral information from each sample (n = 77) was obtained as the average of 32 successive scans acquired over a spectral range from 400 -2498 nm with a 2 -nm gap for calibration and validation models. Partial least squares (PLS) regression was used for each individual model. An R 2 and a residual predictive deviation (RPD) of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a*, and b*, respectively. Final pH had an R 2 of 0.67 and a RPD of 1.6. NORS showed great potential to predict color parameter a* of porcine LD muscle. Further studies with larger samples should help improve model quality.Keywords: carcasses; color analysis; longissimus dorsi; partial least squares regression; pH analysis.Practical Application: Development of prediction curves for evaluation of pork meat quality.