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Cozzolino D, Zhang S, Khole A, Yang Z, Ingle P, Beya M, van Jaarsveld PF, Bureš D, Hoffman LC. Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2024; 61:950-957. [PMID: 38487278 PMCID: PMC10933230 DOI: 10.1007/s13197-023-05890-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/13/2023] [Accepted: 10/30/2023] [Indexed: 03/17/2024]
Abstract
Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.
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Affiliation(s)
- D. Cozzolino
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - S. Zhang
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - A. Khole
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - Z. Yang
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - P. Ingle
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - M. Beya
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - P. F. van Jaarsveld
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
| | - D. Bureš
- Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic
- Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
| | - L. C. Hoffman
- Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia
- The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
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2
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Hoffman L, Ingle P, Hemant Khole A, Zhang S, Yang Z, Beya M, Bureš D, Cozzolino D. Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 294:122506. [PMID: 36868023 DOI: 10.1016/j.saa.2023.122506] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95 % %w/w, 90 % %w/w, 50 % %w/w, 10 % %w/w and 5 % %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028 nm and 1224 nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R2) obtained for the determination of the per cent of species in a binary mixture was above 90 % with a standard error in cross validation (SECV) ranging between 12.6 and 15 %w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.
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Affiliation(s)
- L Hoffman
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - P Ingle
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - A Hemant Khole
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - S Zhang
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - Z Yang
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia; The University of Queensland, School of Agriculture and Food Sciences, Brisbane, Queensland 4072, Australia
| | - M Beya
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia
| | - D Bureš
- Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic; Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague, 165 00 Prague, Czech Republic
| | - D Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences (CNAFS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, Queensland 4072, Australia.
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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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Li Z, Song J, Ma Y, Yu Y, He X, Guo Y, Dou J, Dong H. Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables. Food Chem X 2022; 17:100539. [PMID: 36845513 PMCID: PMC9943763 DOI: 10.1016/j.fochx.2022.100539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/10/2022] [Accepted: 12/03/2022] [Indexed: 12/13/2022] Open
Abstract
The long-term storage of rice will inevitably be involved in the deterioration of edible quality, and aged rice poses a great threat to food safety and human health. The acid value can be employed as a sensitive index for the determination of rice quality and freshness. In this study, near-infrared spectra of three kinds of rice (Chinese Daohuaxiang, southern japonica rice, and late japonica rice) mixed with different proportions of aged rice were collected. The partial least squares regression (PLSR) model with different preprocessing was constructed to identify the aged rice adulteration. Meanwhile, a competitive adaptive reweighted sampling (CARS) algorithm was used to extract the optimization model of characteristic variables. The constructed CARS-PLSR model method could not only reduce greatly the number of characteristic variables required by the spectrum but also improve the identification accuracy of three kinds of aged-rice adulteration. As above, this study proposed a rapid, simple, and accurate detection method for aged-rice adulteration, providing new clues and alternatives for the quality control of commercial rice.
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Affiliation(s)
- Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Jiahui Song
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yinxing Ma
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China,Corresponding authors.
| | - Xueming He
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
| | - Yuanxin Guo
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Jinxin Dou
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,Corresponding authors.
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Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:3713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies. Foods 2022; 11:foods11081156. [PMID: 35454743 PMCID: PMC9030905 DOI: 10.3390/foods11081156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 02/04/2023] Open
Abstract
Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango.
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Cruz-López SO, Álvarez-Cisneros YM, Domínguez-Soberanes J, Escalona-Buendía HB, Sánchez CN. Physicochemical and Sensory Characteristics of Sausages Made with Grasshopper (Sphenarium purpurascens) Flour. Foods 2022; 11:foods11050704. [PMID: 35267337 PMCID: PMC8909260 DOI: 10.3390/foods11050704] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/19/2022] [Accepted: 02/23/2022] [Indexed: 02/04/2023] Open
Abstract
Insects are currently of interest due to their high nutritional value, in particular for the high concentration of quality protein. Moreover, it can also be used as an extender or binder in meat products. The objective was to evaluate grasshopper flour (GF) as a partial or total replacement for potato starch to increase the protein content of sausages and achieve good acceptability by consumers. GF has 48% moisture, 6.7% fat and 45% total protein. Sausages were analyzed by NIR and formulations with GF in all concentrations (10, 7, 5 and 3%) combined with starch (3, 5 and 7%) increased protein content. Results obtained for the sausages formulations with grasshoppers showed an increase in hardness, springiness, gumminess and chewiness through a Texture-Profile-Analysis. Moreover, a* and b* are similar to the control, but L* decreased. The check-all-that-apply test showed the attributes highlighted for sausages with GF possessed herbal flavor, brown color, and granular texture. The liking-product-landscape map showed that the incorporation of 7 and 10% of GF had an overall liking of 3.2 and 3.3, respectively, considered as “do not like much”. GF can be used as a binder in meat products up to 10% substitution. However, it is important to improve the overall liking of the sausage.
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Affiliation(s)
- Salvador O. Cruz-López
- Departamento de Biotecnología, Universidad Autónoma Metropolitana, Iztapalapa, Av. Ferrocarril de San Rafael Atlixco 186, Col. Leyes de Reforma 1a. Sección, Alcaldía Iztapalapa, Ciudad de México 09310, Mexico; (S.O.C.-L.); (H.B.E.-B.)
| | - Yenizey M. Álvarez-Cisneros
- Departamento de Biotecnología, Universidad Autónoma Metropolitana, Iztapalapa, Av. Ferrocarril de San Rafael Atlixco 186, Col. Leyes de Reforma 1a. Sección, Alcaldía Iztapalapa, Ciudad de México 09310, Mexico; (S.O.C.-L.); (H.B.E.-B.)
- Correspondence:
| | - Julieta Domínguez-Soberanes
- Escuela de Dirección de Negocios Alimentarios, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes 20296, Mexico;
| | - Héctor B. Escalona-Buendía
- Departamento de Biotecnología, Universidad Autónoma Metropolitana, Iztapalapa, Av. Ferrocarril de San Rafael Atlixco 186, Col. Leyes de Reforma 1a. Sección, Alcaldía Iztapalapa, Ciudad de México 09310, Mexico; (S.O.C.-L.); (H.B.E.-B.)
| | - Claudia N. Sánchez
- Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes 20296, Mexico;
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