Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques
The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Te...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Applied Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502224000817 |
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| author | Francis Padi Lamptey Charles Lloyd Yeboah Amuah Vida Gyimah Boadu Ernest Ekow Abano Ernest Teye |
| author_facet | Francis Padi Lamptey Charles Lloyd Yeboah Amuah Vida Gyimah Boadu Ernest Ekow Abano Ernest Teye |
| author_sort | Francis Padi Lamptey |
| collection | DOAJ |
| description | The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry. |
| format | Article |
| id | doaj-art-23df7a70c678494ea5f8e6a71b5fcba9 |
| institution | OA Journals |
| issn | 2772-5022 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Food Research |
| spelling | doaj-art-23df7a70c678494ea5f8e6a71b5fcba92025-08-20T02:36:59ZengElsevierApplied Food Research2772-50222024-12-014210047110.1016/j.afres.2024.100471Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniquesFrancis Padi Lamptey0Charles Lloyd Yeboah Amuah1Vida Gyimah Boadu2Ernest Ekow Abano3Ernest Teye4Department of Agricultural Engineering, College of Agriculture and Natural Sciences, School of Agriculture, University of Cape Coast, Cape Coast, Ghana; Department of Food Science and Postharvest Technology, School of Applied Sciences and Technology, Cape Coast Technical University, Cape Coast, Ghana; Africa Centre of Excellence for Food Fraud and Safety, AfriFoodinTegrity Centre, University of Cape Coast, Cape Coast, GhanaDepartment of Physics, College of Agriculture and Natural Sciences, School of Physical Sciences, Laser and Fibre Optics Centre, University of Cape Coast, Cape Coast, GhanaDepartment of Agricultural Engineering, College of Agriculture and Natural Sciences, School of Agriculture, University of Cape Coast, Cape Coast, Ghana; Department of Hospitality and Tourism Education, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Kumasi, Ghana; Africa Centre of Excellence for Food Fraud and Safety, AfriFoodinTegrity Centre, University of Cape Coast, Cape Coast, GhanaDepartment of Agricultural Engineering, College of Agriculture and Natural Sciences, School of Agriculture, University of Cape Coast, Cape Coast, GhanaDepartment of Agricultural Engineering, College of Agriculture and Natural Sciences, School of Agriculture, University of Cape Coast, Cape Coast, Ghana; Africa Centre of Excellence for Food Fraud and Safety, AfriFoodinTegrity Centre, University of Cape Coast, Cape Coast, Ghana; Corresponding author at: Department of Agricultural Engineering, College of Agriculture and Natural Sciences, School of Agriculture, University of Cape Coast, Cape Coast, Ghana.The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry.http://www.sciencedirect.com/science/article/pii/S2772502224000817Consumer preferencesJuice differentiationNIR spectroscopyNondestructivePartial Least Squares-Discriminant Analysis |
| spellingShingle | Francis Padi Lamptey Charles Lloyd Yeboah Amuah Vida Gyimah Boadu Ernest Ekow Abano Ernest Teye Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques Applied Food Research Consumer preferences Juice differentiation NIR spectroscopy Nondestructive Partial Least Squares-Discriminant Analysis |
| title | Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques |
| title_full | Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques |
| title_fullStr | Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques |
| title_full_unstemmed | Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques |
| title_short | Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques |
| title_sort | smart classification of organic and inorganic pineapple juice using dual nir spectrometers combined with chemometric techniques |
| topic | Consumer preferences Juice differentiation NIR spectroscopy Nondestructive Partial Least Squares-Discriminant Analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772502224000817 |
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