Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics
Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities...
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11534 |
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| author | Milton Martínez-Julca Renny Nazario-Naveda Moises Gallozzo-Cárdenas Segundo Rojas-Flores Hector Chinchay-Espino Amilu Alvarez-Escobedo Emzon Murga-Torres |
| author_facet | Milton Martínez-Julca Renny Nazario-Naveda Moises Gallozzo-Cárdenas Segundo Rojas-Flores Hector Chinchay-Espino Amilu Alvarez-Escobedo Emzon Murga-Torres |
| author_sort | Milton Martínez-Julca |
| collection | DOAJ |
| description | Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm<sup>−1</sup> associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined. |
| first_indexed | 2024-03-10T21:28:33Z |
| format | Article |
| id | doaj.art-969e1d6d1bee40b59918ac27980b1429 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:33Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-969e1d6d1bee40b59918ac27980b14292023-11-19T15:33:31ZengMDPI AGApplied Sciences2076-34172023-10-0113201153410.3390/app132011534Classification of Peruvian Flours via NIR Spectroscopy Combined with ChemometricsMilton Martínez-Julca0Renny Nazario-Naveda1Moises Gallozzo-Cárdenas2Segundo Rojas-Flores3Hector Chinchay-Espino4Amilu Alvarez-Escobedo5Emzon Murga-Torres6Departamento de Ciencias Virtual Campus, Universidad Privada del Norte, Trujillo 13007, PeruVicerrectorado de Investigación, Universidad Autónoma del Perú, Lima 15842, PeruFacultad de Ciencias de la Salud, Universidad César Vallejo, Trujillo 13001, PeruInstituto de Investigación en Ciencia y Tecnología de la Universidad César Vallejo, Trujillo 13001, PeruDepartamento de Ciencias, Universidad Privada del Norte, Chorrillos 15054, PeruDepartamento de Ciencias, Universidad Privada del Norte, Chorrillos 15054, PeruLaboratorio de Investigación Multidisciplinario, Universidad Privada Antenor Orrego, Trujillo 13008, PeruNowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm<sup>−1</sup> associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined.https://www.mdpi.com/2076-3417/13/20/11534PCANIR spectroscopyPeruvian flourschemometricsmaca |
| spellingShingle | Milton Martínez-Julca Renny Nazario-Naveda Moises Gallozzo-Cárdenas Segundo Rojas-Flores Hector Chinchay-Espino Amilu Alvarez-Escobedo Emzon Murga-Torres Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics Applied Sciences PCA NIR spectroscopy Peruvian flours chemometrics maca |
| title | Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics |
| title_full | Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics |
| title_fullStr | Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics |
| title_full_unstemmed | Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics |
| title_short | Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics |
| title_sort | classification of peruvian flours via nir spectroscopy combined with chemometrics |
| topic | PCA NIR spectroscopy Peruvian flours chemometrics maca |
| url | https://www.mdpi.com/2076-3417/13/20/11534 |
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