Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Lung Samples
2.2. Near-Infrared Spectroscopic Analysis
2.3. NIR Data Processing
- Standard normal variate (SNV): useful to mitigate the impact of light scattering and reduce baseline shifts/drifts;
- Fourth-order derivative (4Der): useful to increase the resolution among overlapping peaks and highlight spectral differences.
2.4. Multivariate Statistics
- A 3-class model (i.e., “Model 1”), created with the objective of distinguishing between N, C, and P lung tissues;
- A 3-class model (i.e., “Model 2”), including only spectra from pathological tissues and designed to discriminate among CBP, FPP, and IP lung lesions.
3. Results
3.1. NIR Spectral Profiles of Lung Tissues
3.2. Discriminant Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Opriessnig, T.; Giménez-Lirola, L.G.; Halbur, P.G. Polymicrobial Respiratory Disease in Pigs. Anim. Health Res. Rev. 2011, 12, 133–148. [Google Scholar] [CrossRef] [PubMed]
- Fablet, C.; Marois-Créhan, C.; Simon, G.; Grasland, B.; Jestin, A.; Kobisch, M.; Madec, F.; Rose, N. Infectious Agents Associated with Respiratory Diseases in 125 Farrow-to-Finish Pig Herds: A Cross-Sectional Study. Vet. Microbiol. 2012, 157, 152–163. [Google Scholar] [CrossRef] [PubMed]
- Petri, F.A.M.; Ferreira, G.C.; Arruda, L.P.; Malcher, C.S.; Storino, G.Y.; Almeida, H.M.d.S.; Sonalio, K.; da Silva, D.G.; de Oliveira, L.G. Associations between Pleurisy and the Main Bacterial Pathogens of the Porcine Respiratory Diseases Complex (PRDC). Animals 2023, 13, 1493. [Google Scholar] [CrossRef] [PubMed]
- Sarli, G.; D’Annunzio, G.; Gobbo, F.; Benazzi, C.; Ostanello, F. The Role of Pathology in the Diagnosis of Swine Respiratory Disease. Vet. Sci. 2021, 8, 256. [Google Scholar] [CrossRef] [PubMed]
- Michael, J.Y.; William, G.V.A. Respiratory System. In Diseases of Swine, 11th ed.; Zimmerman, J.J., Karriker, L.A., Ramirez, A., Schwartz, K.J., Stevenson, G.W., Zhang, J., Eds.; State University Press: Ames, IA, USA, 2019; pp. 393–407. [Google Scholar]
- Ruggeri, J.; Salogni, C.; Giovannini, S.; Vitale, N.; Boniotti, M.B.; Corradi, A.; Pozzi, P.; Pasquali, P.; Alborali, G.L. Association Between Infectious Agents and Lesions in Post-Weaned Piglets and Fattening Heavy Pigs With Porcine Respiratory Disease Complex (PRDC). Front. Vet. Sci. 2020, 7, 636. [Google Scholar] [CrossRef] [PubMed]
- Pieters, M.; Maes, D. Mycoplasmosis. In Diseases of Swine, 11th ed.; Zimmerman, J.J., Karriker, L.A., Ramirez, A., Schwartz, K.J., Stevenson, G.W., Zhang, J., Eds.; State University Press: Ames, IA, USA, 2019; pp. 863–883. [Google Scholar]
- Gottschalk, M.; Broes, A. Actinobacillosis. In Diseases of Swine, 11th ed.; Zimmerman, J.J., Karriker, L.A., Ramirez, A., Schwartz, K.J., Stevenson, G.W., Zhang, J., Eds.; State University Press: Ames, IA, USA, 2019; pp. 749–766. [Google Scholar]
- Paz-Sánchez, Y.; Herráez, P.; Quesada-Canales, Ó.; Poveda, C.G.; Díaz-Delgado, J.; Quintana-Montesdeoca, M.D.P.; Stefanova, E.P.; Andrada, M. Assessment of Lung Disease in Finishing Pigs at Slaughter: Pulmonary Lesions and Implications on Productivity Parameters. Animals 2021, 11, 3604. [Google Scholar] [CrossRef] [PubMed]
- García-Díez, J.; Saraiva, S.; Moura, D.; Grispoldi, L.; Cenci-Goga, B.T.; Saraiva, C. The Importance of the Slaughterhouse in Surveilling Animal and Public Health: A Systematic Review. Vet. Sci. 2023, 10, 167. [Google Scholar] [CrossRef]
- Scollo, A.; Gottardo, F.; Contiero, B.; Mazzoni, C.; Leneveu, P.; Edwards, S.A. Benchmarking of Pluck Lesions at Slaughter as a Health Monitoring Tool for Pigs Slaughtered at 170 Kg (Heavy Pigs). Prev. Vet. Med. 2017, 144, 20–28. [Google Scholar] [CrossRef]
- De Luca, S.; Zanardi, E.; Alborali, G.L.; Ianieri, A.; Ghidini, S. Abattoir-Based Measures to Assess Swine Welfare: Analysis of the Methods Adopted in European Slaughterhouses. Animals 2021, 11, 226. [Google Scholar] [CrossRef]
- Garcia-Morante, B.; Segalés, J.; Fraile, L.; Pérez de Rozas, A.; Maiti, H.; Coll, T.; Sibila, M. Assessment of Mycoplasma Hyopneumoniae-Induced Pneumonia Using Different Lung Lesion Scoring Systems: A Comparative Review. J. Comp. Pathol. 2016, 154, 125–134. [Google Scholar] [CrossRef]
- Ghidini, S.; De Luca, S.; Rinaldi, E.; Zanardi, E.; Ianieri, A.; Guadagno, F.; Alborali, G.L.; Meemken, D.; Conter, M.; Varrà, M.O. Comparing Visual-Only and Visual-Palpation Post-Mortem Lung Scoring Systems in Slaughtering Pigs. Animals 2023, 13, 2419. [Google Scholar] [CrossRef] [PubMed]
- Maes, D.; Sibila, M.; Pieters, M.; Haesebrouck, F.; Segalés, J.; de Oliveira, L.G. Review on the Methodology to Assess Respiratory Tract Lesions in Pigs and Their Production Impact. Vet. Res. 2023, 54, 8. [Google Scholar] [CrossRef] [PubMed]
- Regulation (EU) 2017/625; European Parliament Council of 15 March 2017 on Official Controls and Other Official Activities Performed to Ensure the Application of Food and Feed Law, Rules on Animal Health and Welfare, Plant Health and Plant Protection Products. 2017. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0625 (accessed on 11 February 2024).
- Blagojevic, B.; Nesbakken, T.; Alvseike, O.; Vågsholm, I.; Antic, D.; Johler, S.; Houf, K.; Meemken, D.; Nastasijevic, I.; Vieira Pinto, M.; et al. Drivers, Opportunities, and Challenges of the European Risk-Based Meat Safety Assurance System. Food Control 2021, 124, 107870. [Google Scholar] [CrossRef]
- Antunović, B.; Blagojević, B.; Johler, S.; Guldimann, C.; Vieira-Pinto, M.; Vågsholm, I.; Meemken, D.; Alvseike, O.; Georgiev, M.; Alban, L. Challenges and Opportunities in the Implementation of New Meat Inspection Systems in Europe. Trends Food Sci. Technol. 2021, 116, 460–467. [Google Scholar] [CrossRef]
- Pessoa, J.; Rodrigues da Costa, M.; García Manzanilla, E.; Norton, T.; McAloon, C.; Boyle, L. Managing respiratory disease in finisher pigs: Combining quantitative assessments of clinical signs and the prevalence of lung lesions at slaughter. Prev. Vet. Med. 2021, 186, 105208. [Google Scholar] [CrossRef] [PubMed]
- Pasquini, C. Near Infrared Spectroscopy: A Mature Analytical Technique with New Perspectives—A Review. Anal. Chim. Acta. 2018, 1026, 8–36. [Google Scholar] [CrossRef] [PubMed]
- Sakudo, A. Near-Infrared Spectroscopy for Medical Applications: Current Status and Future Perspectives. Clin. Chim. Acta 2016, 455, 181–188. [Google Scholar] [CrossRef] [PubMed]
- Delrue, C.; De Bruyne, S.; Speeckaert, M.M. The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases. Int. J. Mol. Sci. 2023, 24, 6740. [Google Scholar] [CrossRef]
- Sharma, V.J.; Adegoke, J.A.; Fasulakis, M.; Green, A.; Goh, S.K.; Peng, X.; Liu, Y.; Jackett, L.; Vago, A.; Poon, E.K.W.; et al. Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning. Health Sci. Rep. 2023, 6, e1652. [Google Scholar] [CrossRef]
- Rienda, I.; Moro, E.; Pérez-Rubio, Á.; Trullenque-Juan, R.; Pérez-Guaita, D.; Lendl, B.; Kuligowski, J.; Castell, J.V.; Pérez-Rojas, J.; Pareja, E.; et al. Comparing the Direct Assessment of Steatosis in Liver Explants with Mid- and near-Infrared Vibrational Spectroscopy, Prior to Organ Transplantation. Analyst 2023, 148, 3986–3991. [Google Scholar] [CrossRef]
- de Carvalho, L.M.; Madruga, M.S.; Estévez, M.; Badaró, A.T.; Barbin, D.F. Occurrence of Wooden Breast and White Striping in Brazilian Slaughtering Plants and Use of Near-Infrared Spectroscopy and Multivariate Analysis to Identify Affected Chicken Breasts. J. Food Sci. 2020, 85, 3102–3112. [Google Scholar] [CrossRef]
- Geronimo, B.C.; Mastelini, S.M.; Carvalho, R.H.; Barbon Júnior, S.; Barbin, D.F.; Shimokomaki, M.; Ida, E.I. Computer Vision System and Near-Infrared Spectroscopy for Identification and Classification of Chicken with Wooden Breast, and Physicochemical and Technological Characterization. Infrared Phys. Technol. 2019, 96, 303–310. [Google Scholar] [CrossRef]
- Wold, J.P.; Løvland, A.; Wold, J.P.; Løvland, A. NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry. MMB 2020, 4, 1–8. [Google Scholar] [CrossRef]
- Park, B.; Chen, Y.R.; Huffman, R.W. Integration of Visible/NIR Spectroscopy and Multispectral Imaging for Poultry Carcass Inspection. J. Food Eng. 1996, 30, 197–207. [Google Scholar] [CrossRef]
- Cugmas, B.; Bürmen, M.; Jemec, J.; Pernuš, F.; Likar, B. Towards Automated Detection of Milk Spot Livers by Diffuse Reflectance Spectroscopy. J. Food Eng. 2014, 124, 128–132. [Google Scholar] [CrossRef]
- Coombs, C.E.O.; Allman, B.E.; Morton, E.J.; Gimeno, M.; Horadagoda, N.; Tarr, G.; González, L.A. A Preliminary Investigation into the Automatic Detection of Diseased Sheep Organs Using Hyperspectral Imaging Sensors. Smart Agric. Technol. 2023, 3, 100122. [Google Scholar] [CrossRef]
- Fawcett, T. An Introduction to ROC Analysis. Pattern Recognit. Lett. 2006, 27, 861–874. [Google Scholar] [CrossRef]
- Andersen, C.M.; Bro, R. Variable Selection in Regression—A Tutorial. J. Chemom. 2010, 24, 728–737. [Google Scholar] [CrossRef]
- Workman, J.; Weyer, L. Practical Guide to Interpretive Near-Infrared Spectroscopy, 1st ed.; CRC Press: Boca Raton, FL, USA, 2007; pp. 1–333. [Google Scholar] [CrossRef]
- Scanziani, E.; Mandelli, G.; Radaelli, E. Apparato Respiratorio. In Trattato di Anatomia Patologica Veterinaria, 4th ed.; Guarda, F., Mandelli, G., Biolatti, B., Scanziani, E., Eds.; UTET scienze mediche: Torino, Italy, 2013; pp. 297–344. [Google Scholar]
- Caswell, J.L.; Williams, K.J. Respiratory System. In Jubb, Kennedy, and Palmer’s Pathology of Domestic Animals, 6th ed.; Maxie, M.G., Ed.; Elsevier: Amsterdam, The Netherlands, 2016; Volume 2, pp. 465–591. [Google Scholar]
- Ackermann, M.R. Inflammation and Healing. In Pathologic Basis of Veterinary Disease, 6th ed.; Zachary, J.F., Ed.; Elsevier: St. Louis, MO, USA, 2017; pp. 73–131. [Google Scholar]
- Kombolo-Ngah, M.; Goi, A.; Santinello, M.; Rampado, N.; Atanassova, S.; Liu, J.; Faure, P.; Thoumy, L.; Neveu, A.; Andueza, D.; et al. Across Countries Implementation of Handheld Near-Infrared Spectrometer for the on-Line Prediction of Beef Marbling in Slaughterhouse. Meat Sci. 2023, 200, 109169. [Google Scholar] [CrossRef]
- Regulation (EU) 2019/627; Laying Down Uniform Practical Arrangements for the Performance of Official Controls on Products of Animal Origin Intended for Human Consumption in Accordance with Regulation (EU) 2017/625 of the European Parliament and of the Council and Amending Commission Regulation (EC) No 2074/2005 as Regards Official Controls. 2019. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32019R0627 (accessed on 11 February 2024).
- Ghidini, S.; Zanardi, E.; Di Ciccio, P.A.; Borrello, S.; Belluzi, G.; Guizzardi, S.; Ianieri, A. Development and Test of a Visual-Only Meat Inspection System for Heavy Pigs in Northern Italy. BMC Vet. Res. 2018, 14, 6. [Google Scholar] [CrossRef]
Lung Tissue Class | No. of Collected NIR Spectra |
---|---|
N | 419 |
C | 291 |
CBP | 451 |
FPP | 113 |
IP | 24 |
Model | Components (Predictive + Orthogonal) | R2Xcum | R2Ycum | Q2cum |
---|---|---|---|---|
Model 1 (N vs. C vs. P) | 2 + 7 | 0.936 | 0.547 | 0.532 |
Model 2 (CBP vs. FPP. vs. IP) | 2 + 7 | 0.904 | 0.647 | 0.615 |
Model 1 (N vs. C vs. P) | Model 2 (CBP vs. FPP vs. IP) | ||||
---|---|---|---|---|---|
NIR Wavelength (nm) | VIP Value | Assignment | NIR Wavelength (nm) | VIP Value | Assignment |
1515 | 1.28 | Amide/protein | 1580 | 2.24 | Alcohol/water |
976 | 1.27 | Water | 1453 | 2.16 | Water |
970 | 1.24 | - | 1471 | 2.06 | Amide/protein |
964 | 1.23 | Alkyl alcohol | 1205 | 2.05 | Water |
1472 | 1.22 | Aromatic amine | 1463 | 2.03 | Amide/protein |
1521 | 1.22 | Amide/protein | 1212 | 1.95 | Aliphatic hydrocarbons |
1391 | 1.21 | Aliphatic hydrocarbon | 1023 | 1.95 | Aromatic amines |
1428 | 1.21 | Primary amides | 1218 | 1.62 | Aliphatic hydrocarbons |
1422 | 1.20 | Aromatic hydrocarbon | 1042 | 1.48 | Aliphatic hydrocarbons |
1397 | 1.20 | Aliphatic hydrocarbon | 1174 | 1.44 | Alkenes |
Model 1 | Model 2 | ||||||||
---|---|---|---|---|---|---|---|---|---|
Lung Tissue Class | Actual Members | N | C | P | Lung Tissue Class | Actual Members | CBP | FPP | IP |
N | 104 | 88 | 6 | 10 | CBP | 112 | 107 | 5 | 0 |
C | 73 | 13 | 51 | 9 | FPP | 29 | 9 | 20 | 0 |
P | 147 | 4 | 10 | 133 | IP | 6 | 0 | 0 | 6 |
Total | 324 | 105 | 67 | 152 | Total | 147 | 116 | 23 | 6 |
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Varrà, M.O.; Conter, M.; Recchia, M.; Alborali, G.L.; Maisano, A.M.; Ghidini, S.; Zanardi, E. Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions. Vet. Sci. 2024, 11, 181. https://doi.org/10.3390/vetsci11040181
Varrà MO, Conter M, Recchia M, Alborali GL, Maisano AM, Ghidini S, Zanardi E. Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions. Veterinary Sciences. 2024; 11(4):181. https://doi.org/10.3390/vetsci11040181
Chicago/Turabian StyleVarrà, Maria Olga, Mauro Conter, Matteo Recchia, Giovanni Loris Alborali, Antonio Marco Maisano, Sergio Ghidini, and Emanuela Zanardi. 2024. "Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions" Veterinary Sciences 11, no. 4: 181. https://doi.org/10.3390/vetsci11040181