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Rodríguez-Hernández P, Rodríguez-Estévez V, Burguillo-Martín C, Núñez-Sánchez N. Regression Models for In Vivo Discrimination of the Iberian Pig Feeding Regime after Near Infrared Spectroscopy Analysis of Faeces. Animals (Basel) 2024; 14:1548. [PMID: 38891595 PMCID: PMC11171303 DOI: 10.3390/ani14111548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
The Iberian pig is a native breed of the Iberian Peninsula, which holds an international reputation due to the superior quality and the added value of its products. Different rearing practices and feeding regimes are regulated, resulting in different labelling schemes. However, there is no official analytical methodology that is standardised for certification purposes in the sector. Near Infrared Spectroscopy (NIRS) is a technology that provides information about the physicochemical composition of a sample, with several advantages that have enabled its implementation in different fields. Although it has already been successfully used for the analysis of Iberian pig's final products, samples evaluated with NIRS technology are characterised by a postmortem collection. The goal of this study was to evaluate the potential of NIRS analysis of faeces for in vivo discrimination of the Iberian pig feeding regime, using the spectral information per se for the development of modified partial least squares regressions. Faecal samples were used due to their easy collection, especially in extensive systems where pig handling is difficult. A total of 166 individual samples were collected from 12 farms, where the three different feeding regimes available in the sector were ensured. Although slight differences were detected depending on the chemometric approach, the best models obtained a classification success and a prediction accuracy of over 94% for feeding regime discrimination. The results are considered very satisfactory and suggest NIRS analysis of faeces as a promising approach for the in vivo discrimination of the Iberian pigs' diet, and its implementation during field inspections, a significative achievement for the sector.
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Affiliation(s)
| | - Vicente Rodríguez-Estévez
- Departamento de Producción Animal, Facultad de Veterinaria, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, Spain; (P.R.-H.); (C.B.-M.); (N.N.-S.)
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Vasconcelos L, Dias LG, Leite A, Ferreira I, Pereira E, Bona E, Mateo J, Rodrigues S, Teixeira A. Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin? Foods 2023; 12:4335. [PMID: 38231830 DOI: 10.3390/foods12234335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 01/19/2024] Open
Abstract
This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616-0.9955) and low RMSE values (0.0400-0.1031). The prediction set's relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.
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Affiliation(s)
- Lia Vasconcelos
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Department of Food Hygiene and Technology, University of Veterinary Medicine, Campus Vegazana S/N, 24007 León, Spain
| | - Luís G Dias
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ana Leite
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Iasmin Ferreira
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Department of Food Hygiene and Technology, University of Veterinary Medicine, Campus Vegazana S/N, 24007 León, Spain
| | - Etelvina Pereira
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Paraná 80230-901, Brazil
- Post-Graduation Program of Chemistry (PPGQ), Federal University of Technology Paraná (UTFPR), Paraná 80230-901, Brazil
| | - Javier Mateo
- Department of Food Hygiene and Technology, University of Veterinary Medicine, Campus Vegazana S/N, 24007 León, Spain
| | - Sandra Rodrigues
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- School of Agriculture, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Alfredo Teixeira
- Mountain Research Center (CIMO), Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- School of Agriculture, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
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Parrini S, Sirtori F, Čandek-Potokar M, Charneca R, Crovetti A, Kušec ID, Sanchez EG, Cebrian MMI, Garcia AH, Karolyi D, Lebret B, Ortiz A, Panella-Riera N, Petig M, Jesus da Costa Pires P, Tejerina D, Razmaite V, Aquilani C, Bozzi R. Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy. Sci Rep 2023; 13:7874. [PMID: 37188692 DOI: 10.1038/s41598-023-34996-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/11/2023] [Indexed: 05/17/2023] Open
Abstract
The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.
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Affiliation(s)
- Silvia Parrini
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Francesco Sirtori
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy.
| | | | - Rui Charneca
- MED - Mediterranean Institute for Agriculture, Environment and Development and CHANGE - Global Change and Sustainability Institute, Departamento de Zootecnia, Escola de Ciências e Tecnologia, Universidade de Évora, Pólo da Mitra, Ap. 94, 7006-554, Évora, Portugal
| | - Alessandro Crovetti
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Ivona Djurkin Kušec
- Department for Animal Production and Biotechnology, Faculty of Agrobiotechnical Sciences Osijek, Vladimira Preloga 1, Osijek, Croatia
| | - Elena González Sanchez
- Department of Animal Production and Food Science, School of Agricultural Engineering, University of Extremadura, Avda. Adolfo Suarez, s/n, 06007, Badajoz, Spain
| | | | - Ana Haro Garcia
- Department of Nutrition and Sustainable Animal Production, Estacion Experimental del Zaidin, Spanish National Research Council, CSIC, Profesor Albareda 1, 18008, Granada, Spain
| | - Danijel Karolyi
- Department of Animal Science, University of Zagreb Faculty of Agriculture, Svetosimunska cesta 25, 10000, Zagreb, Croatia
| | | | - Alberto Ortiz
- Centre of Scientific and Technological Research of Extremadura, CICYTEX, Badajoz, Spain
| | | | | | - Preciosa Jesus da Costa Pires
- Center for Research and Development in Agri-Food Systems and Sustainability (CISAS), Polytechnic Institute of Viana do Castelo. Praça General Barbosa, 4900-347, Viana do Castelo, Portugal
| | - David Tejerina
- Centre of Scientific and Technological Research of Extremadura, CICYTEX, Badajoz, Spain
| | - Violeta Razmaite
- Animal Science Institute, Lithuanian University of Health Sciences, 82317, Baisogala, Lithuania
| | - Chiara Aquilani
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Riccardo Bozzi
- Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy
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Application of NIR spectroscopy coupled with DD-SIMCA class modelling for the authentication of pork meat. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Vasconcelos L, Dias LG, Leite A, Ferreira I, Pereira E, Silva S, Rodrigues S, Teixeira A. SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology. Foods 2023; 12:foods12030470. [PMID: 36766001 PMCID: PMC9914495 DOI: 10.3390/foods12030470] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm-1 with a resolution of 4 cm-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R2 ≥ 0.95) except for the RT variable (RMSE of 0.891% and R2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R2 of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.
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Affiliation(s)
- Lia Vasconcelos
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Luís G. Dias
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ana Leite
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Iasmin Ferreira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Etelvina Pereira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Severiano Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
| | - Sandra Rodrigues
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Alfredo Teixeira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Correspondence:
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Rady A, Watson N. Detection and quantification of peanut contamination in garlic powder using NIR sensors and machine learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Xu K, Yi Y, Deng J, Wang Y, Zhao B, Sun Q, Gong C, Yang Z, Wan H, He R, Wu X, Yao B, Zhang M, Tang Y. Evaluation of the freshness of rainbow trout ( Oncorhynchus mykiss) fillets by the NIR, E-nose and SPME-GC-MS. RSC Adv 2022; 12:11591-11603. [PMID: 35425088 PMCID: PMC9006240 DOI: 10.1039/d2ra00038e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/30/2022] [Indexed: 11/21/2022] Open
Abstract
A comparison study on the freshness of rainbow trout (Oncorhynchus mykiss) fillets in the course of their sale was performed using near-infrared spectroscopy (NIRS), solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), and the electronic nose (E-nose) technique. Quantitative analysis of the volatile salt nitrogen (TVB-N) of rainbow trout fillets with different freshness using NIR combined with the partial least squares (PLS) method revealed that the predicted values of TVB-N of the samples were significantly correlated with the true values (P < 0.01). SPME-GC-MS combined with E-nose analysis demonstrated that there were significant differences in the volatile flavor components of rainbow trout fillets at different freshness, and E-nose combined with principal component analysis (PCA) and linear discriminant analysis (LDA) could achieve rapid and non-destructive freshness ranking of rainbow trout fillets based on volatile flavor characteristics. Consequently, the NIRS and E-nose non-destructive testing techniques are capable of acting as rapid screening tools for detecting the freshness of rainbow trout fillets during their sale.
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Affiliation(s)
- Kunli Xu
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Yuwen Yi
- Cuisine Science Key Laboratory of Sichuan Province, University of Sichuan Tourism Chengdu Sichuan 610100 China
| | - Jing Deng
- Cuisine Science Key Laboratory of Sichuan Province, University of Sichuan Tourism Chengdu Sichuan 610100 China
| | - Yuanhui Wang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Bo Zhao
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Qianran Sun
- Chengdu Esun Modern Technology Co., Ltd. Chengdu Sichuan 610041 China
| | - Chenhui Gong
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Zepeng Yang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Hailun Wan
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Ruiyan He
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Xinyu Wu
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Bo Yao
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Meichao Zhang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
| | - Yong Tang
- School of Food and Biological Engineering, University of Xihua Chengdu Sichuan 610039 China
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