Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis - Wu, Binlin; Gao, Xin; Smith, Jason; Bailin, Jacob (Therapeutics and Diagnostics in Urology: Lasers, Robotics, Minimally Invasive, and Advanced Biomedical Devices, 2017)
Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/116-092-737-895-249, pop00138 tex.type: [object Object]
Keywords: Artificial intelligence; Biopsy; Cross validation; Diseases; Factorization; Fluorescence; Fluorescence spectroscopy; Fluorophores; Learning algorithms; Learning systems; Matrix algebra; Multivariant analysis; Nonnegative matrix factorization; Nucleotides; Optical biopsies; Prostate cancers; Redox ratio; Robotics; Support vector machines; Tissue; Urology; cross validation; dept.phy; fluorescence spectroscopy; machine learning; nonnegative matrix factorization; optical biopsy; prostate cancer; redox ratio; support vector machine

Rapid measurement of meat spoilage using fluorescence spectroscopy - Wu, Binlin; Dahlberg, Kevin; Gao, Xin; Smith, Jason; Bailin, Jacob (Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, 2017)
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 nonsubjective 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.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/026-929-135-501-547, pop00136 tex.type: [object Object]
Keywords: Amino acids; Bacteria; Biological tissues; Biomolecules; Collagen; Flavin adenine dinucleotide; Fluorescence; Fluorescence spectroscopy; Fluorophores; Food microbiology; Histology; Meat food; Meats; Metabolism; Microbial metabolism; Molecular biology; NADH; Nicotinamide adenine dinucleotides; Nucleotides; Physiology; Rapid measurement; Relative concentration; Spectrum analysis; Spoilage; Tissue; Tryptophan; dept.phy

A Rapid Method Based on Fluorescence Spectroscopy for Meat Spoilage Detection - Wu, Binlin; Dahlberg, Kevin; Gao, Xin; Smith, Jason; Bailin, Jacob (International Journal of High Speed Electronics and Systems, 2018)
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.
4 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/014-150-261-026-674, pop00221 tex.type: [object Object]
Keywords: Amino acids; Autofluorescence; Bacteria; Biological tissues; Collagen; Flavin adenine dinucleotide; Fluorescence; Fluorescence spectroscopy; Fluorophores; Food microbiology; Meats; Metabolism; Microbial metabolism; NADH; Nicotinamide adenine dinucleotides; Nucleotides; Relative concentration; Spoilage; bacteria; collagen; dept.phy; fluorescence spectroscopy; meat food; microbial metabolism; peer-reviewed; tryptophan

Statistical analysis and machine learning algorithms for optical biopsy - Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
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.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/030-448-994-607-54X, pop00047
Keywords: Artificial neural network; Biological basis; Biopsy; Cancer diagnosis; Cancerous tissues; Factorization; Learning algorithms; Learning systems; Least squares approximations; Machine learning; Matrix algebra; Neural networks; Nonnegative matrix factorization; Optical biopsies; Optical biopsy; Partial least square (PLS); Partial least squares; Principal component analysis; Resonance Raman spectroscopy; Spatial signature; Support vector machine; Support vector machines; Tissue; Tissue engineering; dept.phy