Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>)
With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (<i>Ananas comosus</i>) is on the rise,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/17/3243 |
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| author | Kaveh Mollazade Norhashila Hashim Manuela Zude-Sasse |
| author_facet | Kaveh Mollazade Norhashila Hashim Manuela Zude-Sasse |
| author_sort | Kaveh Mollazade |
| collection | DOAJ |
| description | With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (<i>Ananas comosus</i>) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380–1690 nm) and reference measurements in 10 regions of interest of 60 fruit (<i>n</i> = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce. |
| first_indexed | 2024-03-10T23:23:55Z |
| format | Article |
| id | doaj.art-a55a8d8bb0c3425d903897ce9ba422a8 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T23:23:55Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-a55a8d8bb0c3425d903897ce9ba422a82023-11-19T08:08:52ZengMDPI AGFoods2304-81582023-08-011217324310.3390/foods12173243Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>)Kaveh Mollazade0Norhashila Hashim1Manuela Zude-Sasse2Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 6617715175, IranDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, GermanyWith increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (<i>Ananas comosus</i>) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380–1690 nm) and reference measurements in 10 regions of interest of 60 fruit (<i>n</i> = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.https://www.mdpi.com/2304-8158/12/17/3243dimensionality reductionhypercubequality evaluationwavelength selection |
| spellingShingle | Kaveh Mollazade Norhashila Hashim Manuela Zude-Sasse Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) Foods dimensionality reduction hypercube quality evaluation wavelength selection |
| title | Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) |
| title_full | Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) |
| title_fullStr | Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) |
| title_full_unstemmed | Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) |
| title_short | Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (<i>Ananas comosus</i>) |
| title_sort | towards a multispectral imaging system for spatial mapping of chemical composition in fresh cut pineapple i ananas comosus i |
| topic | dimensionality reduction hypercube quality evaluation wavelength selection |
| url | https://www.mdpi.com/2304-8158/12/17/3243 |
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