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Gredell DA, Schroeder AR, Belk KE, Broeckling CD, Heuberger AL, Kim SY, King DA, Shackelford SD, Sharp JL, Wheeler TL, Woerner DR, Prenni JE. Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data. Sci Rep 2019; 9:5721. [PMID: 30952873 PMCID: PMC6450883 DOI: 10.1038/s41598-019-40927-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 02/26/2019] [Indexed: 11/26/2022] Open
Abstract
Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef.
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Affiliation(s)
- Devin A Gredell
- Center for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Amelia R Schroeder
- Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN, 37614, USA
| | - Keith E Belk
- Center for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, 80523, USA
| | - Adam L Heuberger
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, 80523, USA
| | - Soo-Young Kim
- Department of Statistics, Colorado State University, Fort Collins, CO, 80523, USA
| | - D Andy King
- USDA-ARS U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | | | - Julia L Sharp
- Department of Statistics, Colorado State University, Fort Collins, CO, 80523, USA
| | - Tommy L Wheeler
- USDA-ARS U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Dale R Woerner
- Center for Meat Safety and Quality, Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Jessica E Prenni
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, 80523, USA.
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Stelle L, Wellington J, Liang W, Buras R, Tafra L. Local-Regional Evaluation and Therapy: Maximizing Margin-Negative Breast Cancer Resection Rates on the First Try. CURRENT BREAST CANCER REPORTS 2018. [DOI: 10.1007/s12609-018-0273-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Grootendorst MR, Fitzgerald AJ, Brouwer de Koning SG, Santaolalla A, Portieri A, Van Hemelrijck M, Young MR, Owen J, Cariati M, Pepper M, Wallace VP, Pinder SE, Purushotham A. Use of a handheld terahertz pulsed imaging device to differentiate benign and malignant breast tissue. BIOMEDICAL OPTICS EXPRESS 2017; 8:2932-2945. [PMID: 28663917 PMCID: PMC5480440 DOI: 10.1364/boe.8.002932] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 05/02/2017] [Indexed: 05/19/2023]
Abstract
Since nearly 20% of breast-conserving surgeries (BCS) require re-operation, there is a clear need for developing new techniques to more accurately assess tumor resection margins intraoperatively. This study evaluates the diagnostic accuracy of a handheld terahertz pulsed imaging (TPI) system to discriminate benign from malignant breast tissue ex vivo. Forty six freshly excised breast cancer samples were scanned with a TPI handheld probe system, and histology was obtained for comparison. The image pixels on TPI were classified using (1) parameters in combination with support vector machine (SVM) and (2) Gaussian wavelet deconvolution in combination with Bayesian classification. The results were an accuracy, sensitivity, specificity of 75%, 86%, 66% for method 1, and 69%, 87%, 54% for method 2 respectively. This demonstrates the probe can discriminate invasive breast cancer from benign breast tissue with an encouraging degree of accuracy, warranting further study.
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Affiliation(s)
- Maarten R Grootendorst
- King's College London, Division of Cancer Studies, London, UK
- Department of Breast Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Contributed equally
| | - Anthony J Fitzgerald
- School of Physics, University of Western Australia, Perth, Australia
- Contributed equally
| | - Susan G Brouwer de Koning
- King's College London, Division of Cancer Studies, London, UK
- Department of Breast Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | | | | | - Matthew R Young
- Department of Breast Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Julie Owen
- King's College London, Division of Cancer Studies, King's Health Partners Cancer Biobank and Breast Pathology Research Group, London, UK
| | - Massi Cariati
- King's College London, Division of Cancer Studies, London, UK
- Department of Breast Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Michael Pepper
- Teraview Ltd., Cambridge, UK
- London Centre for Nanotechnology, University College London, UK
| | - Vincent P Wallace
- School of Physics, University of Western Australia, Perth, Australia
| | - Sarah E Pinder
- King's College London, Division of Cancer Studies, King's Health Partners Cancer Biobank and Breast Pathology Research Group, London, UK
| | - Arnie Purushotham
- King's College London, Division of Cancer Studies, London, UK
- Department of Breast Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
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