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Spina AA, Ceniti C, De Fazio R, Oppedisano F, Palma E, Gugliandolo E, Crupi R, Raza SHA, Britti D, Piras C, Morittu VM. Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds. Animals (Basel) 2024; 14:1271. [PMID: 38731274 PMCID: PMC11083570 DOI: 10.3390/ani14091271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.
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Affiliation(s)
- Anna Antonella Spina
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Carlotta Ceniti
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Rosario De Fazio
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Francesca Oppedisano
- Department of Health Sciences, Institute of Research for Food Safety & Health (IRC-FSH), “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Ernesto Palma
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Department of Health Sciences, Institute of Research for Food Safety & Health (IRC-FSH), “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Nutramed S.c.a.r.l., Complesso Ninì Barbieri, Roccelletta di Borgia, 88021 Catanzaro, Italy
| | - Enrico Gugliandolo
- Department of Veterinary Science, University of Messina, 98166 Messina, Italy; (E.G.); (R.C.)
| | - Rosalia Crupi
- Department of Veterinary Science, University of Messina, 98166 Messina, Italy; (E.G.); (R.C.)
| | - Sayed Haidar Abbas Raza
- Guangdong Provincial Key Laboratory of Food Quality and Safety, Nation-Local Joint Engineering Research Center for Machining and Safety of Livestock and Poultry Products, South China Agricultural University, Guangzhou 510642, China;
| | - Domenico Britti
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Cristian Piras
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Valeria Maria Morittu
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Department of Medical and Surgical Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy
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Neves MM, Guerra RF, de Lima IL, Arrais TS, Guevara-Vega M, Ferreira FB, Rosa RB, Vieira MS, Fonseca BB, Sabino da Silva R, da Silva MV. Perspectives of FTIR as Promising Tool for Pathogen Diagnosis, Sanitary and Welfare Monitoring in Animal Experimentation Models: A Review Based on Pertinent Literature. Microorganisms 2024; 12:833. [PMID: 38674777 PMCID: PMC11052489 DOI: 10.3390/microorganisms12040833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Currently, there is a wide application in the literature of the use of the Fourier Transform Infrared Spectroscopy (FTIR) technique. This basic tool has also proven to be efficient for detecting molecules associated with hosts and pathogens in infections, as well as other molecules present in humans and animals' biological samples. However, there is a crisis in science data reproducibility. This crisis can also be observed in data from experimental animal models (EAMs). When it comes to rodents, a major challenge is to carry out sanitary monitoring, which is currently expensive and requires a large volume of biological samples, generating ethical, legal, and psychological conflicts for professionals and researchers. We carried out a survey of data from the relevant literature on the use of this technique in different diagnostic protocols and combined the data with the aim of presenting the technique as a promising tool for use in EAM. Since FTIR can detect molecules associated with different diseases and has advantages such as the low volume of samples required, low cost, sustainability, and provides diagnostic tests with high specificity and sensitivity, we believe that the technique is highly promising for the sanitary and stress and the detection of molecules of interest of infectious or non-infectious origin.
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Affiliation(s)
- Matheus Morais Neves
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Renan Faria Guerra
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
| | - Isabela Lemos de Lima
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Thomas Santos Arrais
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Marco Guevara-Vega
- Innovation Center in Salivary Diagnostic and Nanotheranostics, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlândia 38408-100, MG, Brazil; (M.G.-V.); (R.S.d.S.)
| | - Flávia Batista Ferreira
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Rafael Borges Rosa
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
| | - Mylla Spirandelli Vieira
- Faculty of Medicine, Maria Ranulfa Institute, Av. Vasconselos Costa 321, Uberlândia 38400-448, MG, Brazil;
| | | | - Robinson Sabino da Silva
- Innovation Center in Salivary Diagnostic and Nanotheranostics, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlândia 38408-100, MG, Brazil; (M.G.-V.); (R.S.d.S.)
| | - Murilo Vieira da Silva
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
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Shi Z, Tan X, Li Y, Sheng Y, Zhang Q, Xu J, Yang Y. A novel fungal-algal coupling system for slaughterhouse wastewater treatment and lipid production. BIORESOURCE TECHNOLOGY 2023; 387:129585. [PMID: 37517707 DOI: 10.1016/j.biortech.2023.129585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023]
Abstract
In this study, a novel fungal-algal coupling system was established for slaughterhouse wastewater treatment with Chlorella sp. DT025 and a new fungus, Penicillium sp. AHP141. With the optimization of cultivation conditions for the fungal-algal coupling system, the harvest efficiency of Chlorella sp. DT025 reached 99.79%. The mechanism of microalgae harvest of the fungal-algal system was revealed to be related to the morphological characteristics, surface charge, and the secretion of humic acid-like compounds and tryptophan on the surface of the fungi cells. For the original slaughterhouse wastewater treatment, the fungal-algal coupling system had a better removal efficiency of COD, TN, and TP than both monoculture systems. In the high-concentration artificial slaughterhouse wastewater, COD removal of the fungal-algal system reached more than 5350 mg/L. The lipid production of the fungal-algal coupling system in the high-concentration artificial slaughterhouse wastewater treatment was improved by 343.33% to 1.33 g/L compared to the microalgae monoculture treatment.
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Affiliation(s)
- Zhengsheng Shi
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Xin Tan
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China; Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Yanbin Li
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China; Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, Anhui Polytechnic University, Wuhu, Anhui 241000, China.
| | - Yequan Sheng
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China; Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Qin Zhang
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China; Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Jialu Xu
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Yong Yang
- College of Biological and Food Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China
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Nguyen M, Thomas JB, Farup I. Measuring the Optical Properties of Highly Diffuse Materials. SENSORS (BASEL, SWITZERLAND) 2023; 23:6853. [PMID: 37571636 PMCID: PMC10422425 DOI: 10.3390/s23156853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/28/2023] [Accepted: 07/30/2023] [Indexed: 08/13/2023]
Abstract
Measuring the optical properties of highly diffuse materials is a challenge as it could be related to the white colour or an oversaturation of pixels in the acquisition system. We used a spatially resolved method and adapted a nonlinear trust-region algorithm to the fit Farrell diffusion theory model. We established an inversion method to estimate two optical properties of a material through a single reflectance measurement: the absorption and the reduced scattering coefficient. We demonstrate the validity of our method by comparing results obtained on milk samples, with a good fitting and a retrieval of linear correlations with the fat content, given by R2 scores over 0.94 with low p-values. The values of absorption coefficients retrieved vary between 1 × 10-3 and 8 × 10-3 mm-1, whilst the values of the scattering coefficients obtained from our method are between 3 and 8 mm-1 depending on the percentage of fat in the milk sample, and under the assumption of the anisotropy factor g>0.8. We also measured and analyzed the results on white paint and paper, although the paper results were difficult to relate to indicators. Thus, the method designed works for highly diffuse isotropic materials.
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Affiliation(s)
- Mathieu Nguyen
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
| | - Jean-Baptiste Thomas
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
- Imagerie et Vision Artificielle (ImVIA) Laboratory, Department IEM (Informatique, Électronique, Mécanique), Université de Bourgogne, 21000 Dijon, France
| | - Ivar Farup
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
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Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands. Mikrochim Acta 2022; 189:301. [PMID: 35906496 PMCID: PMC9338147 DOI: 10.1007/s00604-022-05393-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/20/2022] [Indexed: 11/22/2022]
Abstract
Milk is one of the most important multicomponent superfoods owing to its rich macronutrient composition. It requires quality control at all the production stages from the farm to the finished products. A localized surface plasmon resonance optical sensor based on a citrate-capped silver nanoparticle (Cit-AgNP)–coated glass substrate was developed. The fabrication of such sensors involved a single-step synthesis of Cit-AgNPs followed by surface modification of glass slides to be coated with the nanoparticles. The scanning electron microscope micrographs demonstrated that the nanoparticles formed monolayer islands on glass slides. The developed surface-enhanced infrared absorption spectroscopy (SEIRA) sensor was coupled to artificial neural networking (ANN) for the qualitative differentiation between cow, camel, goat, buffalo, and infants’ formula powdered milk types. Moreover, it can be used for the quantitative determination of the main milk components such as fat, casein, urea, and lactose in each milk type. The qualitative results showed that the obtained FTIR spectra of cow and buffalo milk have high similarity, whereas camel milk resembled infant formula powdered milk. The most difference in FTIR characteristics was evidenced in the case of goat milk. The developed sensor adds several advantages over the traditional techniques of milk analysis using MilkoScan™ such as less generated waste, elimination of pre-treatment steps, minimal sample volume, low operation time, and on-site analysis.
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Application of Optical Quality Control Technologies in the Dairy Industry: An Overview. PHOTONICS 2021. [DOI: 10.3390/photonics8120551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.
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