Your search
Results 2 resources
-
Food spoilage is mainly caused by microorganisms, such as bacteria. In this study, we measure the autofluorescence in meat samples longitudinally over a week in an attempt to develop a method to rapidly detect meat spoilage using fluorescence spectroscopy. Meat food is a biological tissue, which contains intrinsic fluorophores, such as tryptophan, collagen, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) etc. As meat spoils, it undergoes various morphological and chemical changes. The concentrations of the native fluorophores present in a sample may change. In particular, the changes in NADH and FAD are associated with microbial metabolism, which is the most important process of the bacteria in food spoilage. Such changes may be revealed by fluorescence spectroscopy and used to indicate the status of meat spoilage. Therefore, such native fluorophores may be unique, reliable and non-subjective indicators for detection of spoiled meat. The results of the study show that the relative concentrations of all above fluorophores change as the meat samples kept in room temperature (~19°C) spoil. The changes become more rapidly after about two days. For the meat samples kept in a freezer (~ -12°C), the changes are much less or even unnoticeable over a-week-long storage.
-
Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.
Explore
Resource type
- Conference Paper (1)
- Journal Article (1)
Publication year
Resource language
- English (2)