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Vega-Castellote M, Sánchez MT, Torres-Rodríguez I, Entrenas JA, Pérez-Marín D. NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review. Foods 2024; 13:1612. [PMID: 38890841 PMCID: PMC11172355 DOI: 10.3390/foods13111612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.
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Affiliation(s)
- Miguel Vega-Castellote
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - María-Teresa Sánchez
- Department of Bromatology and Food Technology, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain;
| | - Irina Torres-Rodríguez
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - José-Antonio Entrenas
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
| | - Dolores Pérez-Marín
- Department of Animal Production, University of Cordoba, Rabanales Campus, 14071 Córdoba, Spain; (I.T.-R.); (J.-A.E.)
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An efficient method for the rapid detection of industrial paraffin contamination levels in rice based on hyperspectral imaging. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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3
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Mid-infrared and near-infrared spectroscopies to classify improper fermentation of pineapple wine. CHEMICAL PAPERS 2022. [DOI: 10.1007/s11696-022-02472-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Jang KE, Kim G, Shin MH, Cho JG, Jeong JH, Lee SK, Kang D, Kim JG. Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in ‘Madoka’ Peaches. PLANTS 2022; 11:plants11172327. [PMID: 36079708 PMCID: PMC9460469 DOI: 10.3390/plants11172327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.
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Affiliation(s)
- Kyeong Eun Jang
- Division of Applied Life Science, Graduate School of Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Geonwoo Kim
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Mi Hee Shin
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jung Gun Cho
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Jae Hoon Jeong
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Seul Ki Lee
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Dongyoung Kang
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jin Gook Kim
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Department of Horticulture, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Correspondence:
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Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.
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A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters. HORTICULTURAE 2022. [DOI: 10.3390/horticulturae8050431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.
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Reddy P, Guthridge KM, Panozzo J, Ludlow EJ, Spangenberg GC, Rochfort SJ. Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview. SENSORS 2022; 22:s22051981. [PMID: 35271127 PMCID: PMC8914962 DOI: 10.3390/s22051981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 11/30/2022]
Abstract
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.
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Affiliation(s)
- Priyanka Reddy
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Kathryn M. Guthridge
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Joe Panozzo
- Agriculture Victoria Research, 110 Natimuk Road, Horsham, VIC 3400, Australia;
- Centre for Agriculture Innovation, University of Melbourne, Parkville, VIC 3010, Australia
| | - Emma J. Ludlow
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - German C. Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Simone J. Rochfort
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence:
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Screening Method for the Detection of Other Allergenic Nuts in Cashew Nuts Using Chemometrics and a Portable Near-Infrared Spectrophotometer. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-021-02184-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Zhang L, Sun H, Li H, Rao Z, Ji H. Identification of rice-weevil (Sitophilus oryzae L.) damaged wheat kernels using multi-angle NIR hyperspectral data. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13050930] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.
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