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de Andrade JC, de Oliveira AT, Amazonas MGFM, Galvan D, Tessaro L, Conte-Junior CA. Fingerprinting based on spectral reflectance and chemometrics - An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon. Food Chem 2024; 436:137637. [PMID: 37832414 DOI: 10.1016/j.foodchem.2023.137637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/04/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.
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Affiliation(s)
- Jelmir Craveiro de Andrade
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil.
| | - Adriano Teixeira de Oliveira
- Animal Morphophysiology Laboratory, Academic Department of Teacher Training (DAEF), Federal Institute of Education, Science and Technology of Amazonas (IFAM), Manaus Centro Campus (CMC), Manaus 69020-120, AM, Brazil; Graduate Program in Animal Science and Fisheries Resources (PPGCARP), Faculty of Agricultural Sciences (FCA), Federal University of Amazonas (UFAM), University Campus, Manaus 69077-000, AM, Brazil
| | - Maria Glauciney Fernandes Macedo Amazonas
- Animal Morphophysiology Laboratory, Academic Department of Teacher Training (DAEF), Federal Institute of Education, Science and Technology of Amazonas (IFAM), Manaus Centro Campus (CMC), Manaus 69020-120, AM, Brazil; Graduate Program in Animal Science and Fisheries Resources (PPGCARP), Faculty of Agricultural Sciences (FCA), Federal University of Amazonas (UFAM), University Campus, Manaus 69077-000, AM, Brazil
| | - Diego Galvan
- Chemistry Department, Federal University of Santa Catarina (UFSC), Florianópolis, SC 88.040-900, Brazil
| | - Letícia Tessaro
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil
| | - Carlos Adam Conte-Junior
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil
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Falcioni R, Moriwaki T, Gibin MS, Vollmann A, Pattaro MC, Giacomelli ME, Sato F, Nanni MR, Antunes WC. Classification and Prediction by Pigment Content in Lettuce ( Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11243413. [PMID: 36559526 PMCID: PMC9783279 DOI: 10.3390/plants11243413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 05/14/2023]
Abstract
Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.
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Affiliation(s)
- Renan Falcioni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
- Correspondence: ; Tel.: +55-44-3011-8940
| | - Thaise Moriwaki
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Sversut Gibin
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Alessandra Vollmann
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Carmona Pattaro
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marina Ellen Giacomelli
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Francielle Sato
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marcos Rafael Nanni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Werner Camargos Antunes
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
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Falcioni R, Moriwaki T, Pattaro M, Herrig Furlanetto R, Nanni MR, Camargos Antunes W. High resolution leaf spectral signature as a tool for foliar pigment estimation displaying potential for species differentiation. JOURNAL OF PLANT PHYSIOLOGY 2020; 249:153161. [PMID: 32353607 DOI: 10.1016/j.jplph.2020.153161] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/19/2020] [Accepted: 03/19/2020] [Indexed: 05/27/2023]
Abstract
Optical leaf profiles depend on foliar pigment type and content, as well as anatomical aspects and cellular ultrastructure, whose effects are shown in several species. Monocotyledon and Dicotyledon plants presenting natural pigment content variations and anatomical alterations were analyzed. Each plant species displays its own spectral signatures, which are, in turn, influenced by foliar pigment class (composition) and concentration, as well as anatomical and ultrastructural plant cell characteristics. Plants with no anthocyanin displayed increased reflectance and transmittance in the green spectral region (501-565 nm), while values decreased in the presence of anthocyanin. At wavelengths below 500 nm (350-500 nm), strong overlapping signatures of phenolics, carotenoids, chlorophylls, flavonoids and anthocyanins were observed. Using a partial least squares regression applied to 350-700 nm spectral data allowed for accurate estimations of different foliar pigment levels. In addition, a PCA and discriminant analysis were able to efficiently discriminate different species displaying spectra overlapping. The use of absorbance spectra only was able to discriminate species with 100 % confidence. Finally, a discussion on how different wavelengths are absorbed and on anatomical interference of light interaction in leaf profiles is presented.
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Affiliation(s)
- Renan Falcioni
- Plant Ecophysiology Laboratory, Department of Biology, Brazil; Biochemistry of Plants Laboratory, Department of Biochemistry, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil
| | - Thaise Moriwaki
- Plant Ecophysiology Laboratory, Department of Biology, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil
| | - Mariana Pattaro
- Plant Ecophysiology Laboratory, Department of Biology, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil
| | - Renato Herrig Furlanetto
- Group Applied to Soil Survey and Spatialization, Department of Agronomy, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil
| | - Marcos Rafael Nanni
- Group Applied to Soil Survey and Spatialization, Department of Agronomy, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil
| | - Werner Camargos Antunes
- Plant Ecophysiology Laboratory, Department of Biology, Brazil; State University of Maringá, Av. Colombo, 5790, Jd. Universitário, 87020-900, Maringá, Paraná, Brazil.
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Borraz-Martínez S, Boqué R, Simó J, Mestre M, Gras A. Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy. Talanta 2019; 204:320-328. [PMID: 31357300 DOI: 10.1016/j.talanta.2019.05.105] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 05/21/2019] [Accepted: 05/27/2019] [Indexed: 10/26/2022]
Abstract
Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.
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Affiliation(s)
- Sergio Borraz-Martínez
- Universitat Politècnica de Catalunya, Department of Agri-Food Engineering and Biotechnology, Esteve Terrades 8, 08860, Castelldefels, Spain; Agromillora Iberia S.L.U, Center of Initial Materials, Ctra. BV-2247 km. 3, 08770, Sant Sadurní d'Anoia, Spain.
| | - Ricard Boqué
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Spain
| | - Joan Simó
- Universitat Politècnica de Catalunya, Department of Agri-Food Engineering and Biotechnology, Esteve Terrades 8, 08860, Castelldefels, Spain; Fundació Miquel Agustí, Esteve Terrades 8, 08860, Castelldefels, Spain
| | - Mariàngela Mestre
- Agromillora Iberia S.L.U, Center of Initial Materials, Ctra. BV-2247 km. 3, 08770, Sant Sadurní d'Anoia, Spain
| | - Anna Gras
- Universitat Politècnica de Catalunya, Department of Agri-Food Engineering and Biotechnology, Esteve Terrades 8, 08860, Castelldefels, Spain
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Steidle Neto AJ, Moura LDO, Lopes DDC, Carlos LDA, Martins LM, Ferraz LDCL. Non-destructive prediction of pigment content in lettuce based on visible-NIR spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2017; 97:2015-2022. [PMID: 27553517 DOI: 10.1002/jsfa.8002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 08/15/2016] [Accepted: 08/18/2016] [Indexed: 05/27/2023]
Abstract
BACKGROUND Lettuce (Lactuca sativa L.) is one of the most important salad vegetables in the world, with a number of head shapes, leaf types and colors. The lettuce pigments play important physiological functions, such as photosynthetic processes and light stress defense, but they also benefit human health because of their antioxidant action and anticarcinogenic properties. In this study three lettuce cultivars were grown under different farming systems, and partial least squares models were built to predict the leaf chlorophyll, carotenoid and anthocyanin content. RESULTS The three proposed models resulted in high coefficients of determination and variable importance for the projection values, as well as low estimative errors for calibration and external validation datasets. These results confirmed that it is possible to accurately predict chlorophyll, carotenoid and anthocyanin content of green and red lettuces, grown in different farming systems, based on the spectral reflectance from 500 to 1000 nm. CONCLUSION The proposed models were adequate for estimating lettuce pigments in a quick and non-destructive way, representing an alternative to conventional measurement methods. Prediction accuracies were improved by using the detrending, smoothing and first derivative pretreatments to the original spectral signatures prior to estimating lettuce chlorophyll, carotenoid and anthocyanin content, respectively. © 2016 Society of Chemical Industry.
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Affiliation(s)
- Antonio José Steidle Neto
- Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
| | - Lorena de Oliveira Moura
- Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
| | - Daniela de Carvalho Lopes
- Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
| | - Lanamar de Almeida Carlos
- Department of Food Engineering, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
| | - Luma Moreira Martins
- Department of Food Engineering, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
| | - Leila de Castro Louback Ferraz
- Department of Agrarian Sciences, Sete Lagoas Campus, Federal University of São João del-Rei, Minas Gerais, 35701-970, Brazil
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