ABSTRACT
BACKGROUND: Fraudulent milk adulteration is a dangerous practice in the dairy industry that is harmful to consumers since milk is one of the most consumed food products. Milk quality can be assessed by Fourier Transformed Infrared Spectroscopy (FTIR), a simple and fast method for obtaining its compositional information. The spectral data produced by this technique can be explored using machine learning methods, such as neural networks and decision trees, in order to create models that represent the characteristics of pure and adulterated milk samples. RESULTS: Thousands of milk samples were collected, some of them were manually adulterated with five different substances and subjected to infrared spectroscopy. This technique produced spectral data from the milk samples composition, which were used for training different machine learning algorithms, such as deep and ensemble decision tree learners. The proposed method is used to predict the presence of adulterants in a binary classification problem and also the specific assessment of which of five adulterants was found through multiclass classification. In deep learning, we propose a Convolutional Neural Network architecture that needs no preprocessing on spectral data. Classifiers evaluated show promising results, with classification accuracies up to 98.76%, outperforming commonly used classical learning methods. CONCLUSIONS: The proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry.
ABSTRACT
BACKGROUND: Mammary tumors are among the most frequent neoplasms in female dogs, but the strategies employed in animal treatment are limited. In human medicine, hormone manipulation is used in cancer therapy. Tamoxifen citrate is a selective inhibitor of oestrogen receptors and exerts a potent anti-oestrogen effect on the mammary gland. The aim of this study was to evaluate the adverse effects when exposing healthy female dogs to tamoxifen. METHODS: Tamoxifen was administered for 120 days at a dose of 0.5 or 0.8 mg/kg/day to either intact or spayed female dogs. The effects were assessed through clinical examination, haematology, serum biochemistry, ophthalmology and bone marrow aspirate examination. Ovariohysterectomy was performed and the uterus examined by histopathology. RESULTS: Vulva oedema and purulent vaginal discharge developed with 10 days of tamoxifen exposure in all groups. Pyometra was diagnosed after around 90 days of exposure in intact females with frequencies increasing during the following 30 days of exposure. Up to 50% of dogs within the groups developed retinitis but none of the dogs had signs of reduced visual acuity. The prevalence of retinitis in each group was similar after 120 days of exposure. Haematological, biochemical and bone marrow changes were not observed. Due to the high risk of developing pyometra after prolonged exposure to tamoxifen, only spayed animals should be given this medication. CONCLUSIONS: A dose of 0.8 mg tamoxifen/kg body weight/day is recommended when treating tamoxifen-responsive canine mammary tumors. Due to the high risk of developing pyometra, ovariohysterectomy is recommended.
