Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials
Abstract Background Accuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals (Triticum...
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CABI
2024-04-01
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| Series: | CABI Agriculture and Bioscience |
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| Online Access: | https://doi.org/10.1186/s43170-024-00244-z |
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| author | Cosimo Ricci Agata Gadaleta Annamaria Gerardino Angelo Didonna Giuseppe Ferrara Francesca Romana Bertani |
| author_facet | Cosimo Ricci Agata Gadaleta Annamaria Gerardino Angelo Didonna Giuseppe Ferrara Francesca Romana Bertani |
| author_sort | Cosimo Ricci |
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| description | Abstract Background Accuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals (Triticum turgidum var. durum and Tritordeum genotypes) and ripening pomegranate fruits (Wonderful cultivar). Methods Two species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum, and Tritordeum (durum wheat × wild barley) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor. Results Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. A Root Mean Square Error (RMSE) of 1.25 was obtained for the determination of total soluble solids (TSS) over a 2-year period for pomegranate fresh fruits of Wonderful cultivar, which is commonly harvested with TSS values of 16–17. Conclusions The application of portable sensors using NIR spectroscopy can be a valid and rapid alternative to the use of destructive laboratory techniques for the assessment of protein content in intact wheat seeds and ripeness grade (TSS) in intact pomegranates. |
| format | Article |
| id | doaj-art-ce395c75c4004687997e2c6adc02327a |
| institution | Kabale University |
| issn | 2662-4044 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | CABI |
| record_format | Article |
| series | CABI Agriculture and Bioscience |
| spelling | doaj-art-ce395c75c4004687997e2c6adc02327a2025-02-03T04:07:38ZengCABICABI Agriculture and Bioscience2662-40442024-04-015111210.1186/s43170-024-00244-zPortable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materialsCosimo Ricci0Agata Gadaleta1Annamaria Gerardino2Angelo Didonna3Giuseppe Ferrara4Francesca Romana Bertani5CNR – IFN Institute of Photonics and NanotechnologiesDepartment of Soil, Plant and Food Science (Di.S.S.P.A.), University of Bari Aldo MoroCNR – IFN Institute of Photonics and NanotechnologiesDepartment of Soil, Plant and Food Science (Di.S.S.P.A.), University of Bari Aldo MoroDepartment of Soil, Plant and Food Science (Di.S.S.P.A.), University of Bari Aldo MoroCNR – IFN Institute of Photonics and NanotechnologiesAbstract Background Accuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals (Triticum turgidum var. durum and Tritordeum genotypes) and ripening pomegranate fruits (Wonderful cultivar). Methods Two species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum, and Tritordeum (durum wheat × wild barley) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor. Results Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. A Root Mean Square Error (RMSE) of 1.25 was obtained for the determination of total soluble solids (TSS) over a 2-year period for pomegranate fresh fruits of Wonderful cultivar, which is commonly harvested with TSS values of 16–17. Conclusions The application of portable sensors using NIR spectroscopy can be a valid and rapid alternative to the use of destructive laboratory techniques for the assessment of protein content in intact wheat seeds and ripeness grade (TSS) in intact pomegranates.https://doi.org/10.1186/s43170-024-00244-zPortable spectroscopyNIR spectroscopyChemometricsWheatPomegranate |
| spellingShingle | Cosimo Ricci Agata Gadaleta Annamaria Gerardino Angelo Didonna Giuseppe Ferrara Francesca Romana Bertani Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials CABI Agriculture and Bioscience Portable spectroscopy NIR spectroscopy Chemometrics Wheat Pomegranate |
| title | Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| title_full | Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| title_fullStr | Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| title_full_unstemmed | Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| title_short | Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| title_sort | portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials |
| topic | Portable spectroscopy NIR spectroscopy Chemometrics Wheat Pomegranate |
| url | https://doi.org/10.1186/s43170-024-00244-z |
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