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Liu X, Fan K, Lu Y, Zhao H, Rao Q, Geng H, Chen Y, Rogers KM, Song W. Assessing Seasonal Effects on Identification of Cultivation Methods of Short-Growth Cycle Brassica chinensis L. Using IRMS and NIRS. Foods 2024; 13:1165. [PMID: 38672838 PMCID: PMC11049375 DOI: 10.3390/foods13081165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Seasonal (temporal) variations can influence the δ13C, δ2H, δ18O, and δ15N values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that δ15N values had significant differences among the three cultivation methods and that δ13C, δ2H, and δ18O values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences.
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Affiliation(s)
- Xing Liu
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Kai Fan
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Yangyang Lu
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Hong Zhao
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Qinxiong Rao
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Hao Geng
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Yijiao Chen
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
| | - Karyne Maree Rogers
- National Isotope Centre, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New Zealand
- Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Weiguo Song
- Institute for Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China; (X.L.); (K.F.); (Y.L.); (H.Z.); (Q.R.); (H.G.); (Y.C.)
- Shanghai Service Platform of Agro-Products Quality and Safety Evaluation Technology, Shanghai 201403, China
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Zhang S, Yin Y, Liu C, Li J, Sun X, Wu J. Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123050. [PMID: 37379715 DOI: 10.1016/j.saa.2023.123050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/14/2023] [Accepted: 06/18/2023] [Indexed: 06/30/2023]
Abstract
Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.
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Affiliation(s)
- Shanzhe Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Yingqian Yin
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Jiacong Li
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Xiaorong Sun
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Jingzhu Wu
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
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Sentellas S, Saurina J. Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features. Foods 2023; 12:3120. [PMID: 37628119 PMCID: PMC10453789 DOI: 10.3390/foods12163120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.
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Affiliation(s)
- Sonia Sentellas
- Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain;
- Research Institute in Food Nutrition and Food Safety, Universitat de Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), 08921 Santa Coloma de Gramenet, Spain
- Serra Húnter Fellow Programme, Generalitat de Catalunya, Via Laietana 2, 08003 Barcelona, Spain
| | - Javier Saurina
- Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain;
- Research Institute in Food Nutrition and Food Safety, Universitat de Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), 08921 Santa Coloma de Gramenet, Spain
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Saha D, Senthilkumar T, Singh CB, Manickavasagan A. Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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Sahachairungrueng W, Thompson AK, Terdwongworakul A, Teerachaichayut S. Non-Destructive Classification of Organic and Conventional Hens' Eggs Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2519. [PMID: 37444257 DOI: 10.3390/foods12132519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares-discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.
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Affiliation(s)
- Woranitta Sahachairungrueng
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK43 0AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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Bu Y, Jiang X, Tian J, Hu X, Han L, Huang D, Luo H. Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3970-3983. [PMID: 36397181 DOI: 10.1002/jsfa.12344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/24/2022] [Accepted: 11/18/2022] [Indexed: 05/03/2023]
Abstract
BACKGROUND The purity of sorghum varieties is an important indicator of the quality of raw materials used in the distillation of liquors. Different varieties of sorghum may be mixed during the acquisition process, which will affect the flavor and quality of liquor. To facilitate the rapid identification of sorghum varieties, this study proposes a sorghum variety identification model using hyperspectral imaging (HSI) technology combined with convolutional neural network (AlexNet). RESULTS First, the watershed algorithm, which was modified with the extended-maxim transform, was used to segment the hyperspectral images of a single sorghum grain. The isolated forest algorithm was used to eliminate abnormal spectral data from the complete spectral data. Secondly, the AlexNet model of sorghum variety identification was established based on the two-dimensional gray image data of sorghum grain in group 1. The effects of different preprocessing methods and different convolution kernel sizes on the performance of the AlexNet model were discussed. The eigenvalues of the last layer of the AlexNet model were visualized using the t-distributed random neighborhood embedding method, which is used to evaluate the separability of features extracted by the AlexNet model. The performance differences between the optimal AlexNet model and traditional machine learning models for sorghum variety identification were compared. Finally, the varieties of sorghum grains in groups 2 and 3 were identified based on the optimal AlexNet model, and the average accuracy values of the test set reached 95.62% and 95.91% respectively. CONCLUSION The results in this study demonstrated that HSI combined with the AlexNet model could provide a feasible technical approach for the detection of sorghum varieties. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Youhua Bu
- College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xinna Jiang
- College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Jianping Tian
- College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Xinjun Hu
- College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
- Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China
| | - Lipeng Han
- College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China
| | - Dan Huang
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, China
- Sichuan Engineering Technology Research Center for Liquor-Making Grains, Yibin, China
| | - Huibo Luo
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin, China
- Sichuan Engineering Technology Research Center for Liquor-Making Grains, Yibin, China
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Wang J, Sun L, Xing W, Feng G, Yang J, Li J, Li W. Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion. APPLIED SPECTROSCOPY 2023:37028231171908. [PMID: 37246428 DOI: 10.1177/00037028231171908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Germination rate is important for seed selection and planting and quality. In this study, hyperspectral image technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sugarbeet seeds. In this study, we proposed a nondestructive prediction method for sugarbeet seed germination. Sugarbeet seed was studied, and hyperspectral imaging (HIS) performed by binarization, morphology, and contour extraction was applied as a nondestructive and accurate technique to achieve single seed image segmentation. Comparative analysis of nine spectral pretreatment methods, SNV + 1D was used to process the average spectrum of sugarbeet seeds. Fourteen characteristic wavelengths were obtained by the Kullback-Leibler (KL) divergence, as the spectral characteristics of sugarbeet seeds. Principal component analysis (PCA) and material properties verified the validity of the extracted characteristic wavelengths. It was extracted of six image features of the hyperspectral image of a single seed obtained based on the gray-level co-occurrence matrix (GLCM). The spectral features, image features, and fusion features were used to establish partial least squares discriminant analysis (PLS-DA), CatBoost, and support vector machine radial-basis function (SVM-RBF) models respectively to predict the germination. The results showed that the prediction effect of fusion features was better than spectral features and image features. By comparing other models, the prediction results of the CatBoost model accuracy were up to 93.52%. The results indicated that, based on HSI and fusion features, the prediction of germinating sugarbeet seeds was more accurate and nondestructive.
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Affiliation(s)
- Jiaying Wang
- Key Laboratory of Electronic Engineering, Heilongjiang University, Harbin, China
| | - Laijun Sun
- Key Laboratory of Electronic Engineering, Heilongjiang University, Harbin, China
| | - Wang Xing
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin, China
| | - Guojun Feng
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin, China
| | - Jun Yang
- Key Laboratory of Electronic Engineering, Heilongjiang University, Harbin, China
| | - Jiajia Li
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin, China
| | - Wangsheng Li
- Key Laboratory of Sugarbeet Genetics and Breeding, Heilongjiang University, Harbin, China
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Sun D, Zhou C, Hu J, Li L, Ye H. Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning. Food Chem 2023; 408:135166. [PMID: 36521293 DOI: 10.1016/j.foodchem.2022.135166] [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: 08/31/2022] [Revised: 11/24/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022]
Abstract
Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possibility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off-flavor-related compounds. Classification models, including the partial least squares discriminant analysis, least-squares support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were constructed using the full wavelengths and selected spectral features for the identification of off-flavor salmonids. The 1DCNN achieved the highest discrimination accuracy with full and selected wavelengths (i.e., 91.11 and 86.39 %, respectively). Furthermore, the prediction and visualization of off-flavor-related compounds were achieved with acceptable performances (R2 > 0.6) for practical applications. These results indicate the potential of NIR-HSI for the off-flavor profiling of salmonid muscle samples for producers and researchers.
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Affiliation(s)
- Dawei Sun
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
| | - Chengquan Zhou
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
| | - Jun Hu
- Food Science Institute, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, PR China.
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, PR China.
| | - Hongbao Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310000, PR China.
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Wu D, Liu X, Bai B, Li J, Wang R, Zhang Y, Deng Q, Huang H, Wu J. Determining farming methods and geographical origin of chinese rice using NIR combined with chemometrics methods. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01901-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Cruz-Tirado JP, Lima Brasil Y, Freitas Lima A, Alva Pretel H, Teixeira Godoy H, Barbin D, Siche R. Rapid and non-destructive cinnamon authentication by NIR-hyperspectral imaging and classification chemometrics tools. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122226. [PMID: 36512964 DOI: 10.1016/j.saa.2022.122226] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication.
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Affiliation(s)
- J P Cruz-Tirado
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Yasmin Lima Brasil
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Adriano Freitas Lima
- Department of Food Science, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Heiler Alva Pretel
- Escuela de Ingeniería Agroindustrial, Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Av. Juan Pablo II s/n, Trujillo, Peru
| | - Helena Teixeira Godoy
- Department of Food Science, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Douglas Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
| | - Raúl Siche
- Escuela de Ingeniería Agroindustrial, Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Av. Juan Pablo II s/n, Trujillo, Peru.
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Long W, Zhang Q, Wang SR, Suo Y, Chen H, Bai X, Yang X, Zhou YP, Yang J, Fu H. Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121786. [PMID: 36087403 DOI: 10.1016/j.saa.2022.121786] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/14/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are affected by geographical origin. Fast and accurate identification of the geographical origin of Hangbaiju is very significant for producers, consumers and market regulators. In this work, hyperspectral imaging combined with chemometrics, was used, for the first time, to explore and implement the geographical origin classification of Hangbaiju. The hyperspectral images in the spectral range of 410-2500 nm for 75 samples of five different origins were collected. As a versatile chemometrics tool, bagging classification tree-radial basis function (BAGCT-RBFN), compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins. The results showed that BAGCT-RBFN based on optimal wavelengths yielded superior classification performances to CT and RBFN with full wavelengths. The recognition rates (RR) of the training and prediction sets by BAGCT-RBFN were 96.0 % and 92.0 %, respectively. Hyperspectral imaging combined with chemometric can be considered as a powerful, feasible and convenient tool for the classification of Hangbaiju samples from different origins. It promises to be a potential way for origin discriminant analysis and quality monitor in food fields.
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Affiliation(s)
- Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Qi Zhang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, PR China
| | - Si-Rui Wang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, PR China
| | - Yixin Suo
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Xiuyun Bai
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Xiaolong Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Yan-Ping Zhou
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, PR China.
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
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12
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Storage of wafer cookies: Assessment by destructive techniques, and non-destructive spectral detection methods. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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Rapid detection of fumonisin B1 and B2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics. Food Chem 2022; 384:132487. [DOI: 10.1016/j.foodchem.2022.132487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
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15
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Geographical Discrimination of Ground Amazon Cocoa by Near-Infrared Spectroscopy: Influence of Sample Preparation. J FOOD QUALITY 2022. [DOI: 10.1155/2022/8126810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This work presents the application of the NIR technique associated with exploratory analysis of spectral data by main principal components for the discrimination of Amazon cocoa ground seeds. Cocoa samples from different geographic regions of the state of Pará, Brazil (Medicilândia, Tucumã, and Tomé-Açu), were evaluated. The samples collected from each region were divided into four groups distinguished by the treatment applied to the samples, which were fermented (1-with fat and 2-fat-free) and unfermented (3-with moisture and 4-dried). Each set of samples was analyzed separately to identify the influence of moisture, fermentation, and fat on the geographical differentiation of the three regions. From the results obtained, it can be observed that it was not possible to differentiate the samples of seeds not fermented by geographic origin. However, fermentation was crucial for efficient discrimination, providing more defined clusters for each geographic region. The presence of fat in the seeds was a determinant to obtain the best model of geographic discrimination.
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Liu X, Bai B, Rogers KM, Wu D, Qian Q, Qi F, Zhou J, Yao C, Song W. Determining the geographical origin and cultivation methods of Shanghai special rice using NIR and IRMS. Food Chem 2022; 394:133425. [DOI: 10.1016/j.foodchem.2022.133425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/15/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
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17
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Huang H, Hu X, Tian J, Peng X, Luo H, Huang D, Zheng J, Wang H. Rapid and nondestructive determination of sorghum purity combined with deep forest and near-infrared hyperspectral imaging. Food Chem 2022; 377:131981. [PMID: 34979401 DOI: 10.1016/j.foodchem.2021.131981] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/26/2021] [Accepted: 12/28/2021] [Indexed: 11/04/2022]
Abstract
This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity.
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Affiliation(s)
- Haoping Huang
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Xinjun Hu
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Jianping Tian
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
| | - Xinghui Peng
- School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Huibo Luo
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Dan Huang
- College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Jia Zheng
- Wuliangye Co., Ltd., Yibin 644000, China
| | - Hong Wang
- Wuliangye Co., Ltd., Yibin 644000, China
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18
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Li F, Zhang J, Wang Y. Vibrational Spectroscopy Combined with Chemometrics in Authentication of Functional Foods. Crit Rev Anal Chem 2022; 54:333-354. [PMID: 35533108 DOI: 10.1080/10408347.2022.2073433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Many foods have both edible and medical importance and are appreciated as functional foods, preventing diseases. However, due to unscrupulous vendors and imperfect market supervision mechanisms, curative foods are prone to adulteration or some other events that harm the interests of consumers. However, traditional analytical methods are unsuitable and expensive for a broad and complex application. Therefore, people urgently need a fast, efficient, and accurate detection method to protect self-interests. Recently, the study of target samples by vibration spectrum shows strong qualitative and quantitative ability. The model established by platform technology combined with the stoichiometric analysis method can obtain better parameters, which it has good robustness and can detect functional food efficiently, quickly and nondestructive. The review compared and prospect five different vibrational spectroscopic techniques (near-infrared, Fourier transform infrared, Raman, hyperspectral imaging spectroscopy and Terahertz spectroscopy). In order to better solve some of the actual situations faced by certification, we explore and through relevant research and investigation to appropriately highlight the applicability and importance of technology combined with chemometrics in functional food authentication. There are four categories of authentication discussed: functional food authenticated in source, processing method, fraud and ingredient ratio. This paper provides an innovative process for the authentication of functional food, which has a meaningful reference value for future review or scientific research of relevant departments.
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Affiliation(s)
- Fengjiao Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- School of Agriculture, Yunnan University, Kunming, China
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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19
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Detection of nutshells in cumin powder using NIR hyperspectral imaging and chemometrics tools. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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21
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Castro W, De-la-Torre M, Avila-George H, Torres-Jimenez J, Guivin A, Acevedo-Juárez B. Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120815. [PMID: 34990919 DOI: 10.1016/j.saa.2021.120815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/29/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F1-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.
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Affiliation(s)
- Wilson Castro
- Facultad de Ingeniería de Industrias Alimentarias, Universidad Nacional de Frontera, Sullana 20100, Peru
| | - Miguel De-la-Torre
- Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca 46600, Jalisco, Mexico
| | - Himer Avila-George
- Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca 46600, Jalisco, Mexico
| | | | - Alex Guivin
- Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Chachapoyas 01001, Peru
| | - Brenda Acevedo-Juárez
- Departamento de Ciencias Naturales y Exactas, Universidad de Guadalajara, Ameca 46600, Jalisco, Mexico.
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22
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da Silva Medeiros ML, Cruz-Tirado J, Lima AF, de Souza Netto JM, Ribeiro APB, Bassegio D, Godoy HT, Barbin DF. Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104403] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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23
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Panda BK, Mishra G, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110889] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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24
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Zhang L, Nie Q, Ji H, Wang Y, Wei Y, An D. Hyperspectral imaging combined with generative adversarial network (GAN)-based data augmentation to identify haploid maize kernels. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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25
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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26
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Development of real-time PCR methods for cocoa authentication in processed cocoa-derived products. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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27
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Rocha PD, Medeiros EP, Silva CS, da Silva Simões S. Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:5065-5074. [PMID: 34651617 DOI: 10.1039/d1ay01076j] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.
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Affiliation(s)
- Priscilla Dantas Rocha
- State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil.
| | - Everaldo Paulo Medeiros
- Brazilian Agricultural Research Corporation, Embrapa Cotton, Rua Osvaldo Cruz, 1143, Bairro Centenário, Campina Grande, Paraiba, 58428-095, Brazil
| | - Carolina Santos Silva
- Department of Chemistry Engineering, Federal University of Pernambuco, Av. da Arquitetura, Cidade Universitária, Recife, Pernambuco, 50740-540, Brazil.
- Department of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Simone da Silva Simões
- State University of Paraiba, Bairro Universitário, Rua Baraúnas, 351 Campina Grande, Paraiba, 58429-500, Brazil.
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28
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Saeidan A, Khojastehpour M, Golzarian MR, Mooenfard M, Khan HA. Detection of foreign materials in cocoa beans by hyperspectral imaging technology. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108242] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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29
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Lastras C, Revilla I, González-Martín M, Vivar-Quintana A. Prediction of fatty acid and mineral composition of lentils using near infrared spectroscopy. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Yan Z, Liu H, Li J, Wang Y. Application of Identification and Evaluation Techniques for Edible Mushrooms: A Review. Crit Rev Anal Chem 2021; 53:634-654. [PMID: 34435928 DOI: 10.1080/10408347.2021.1969886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Edible mushrooms are healthy food with high nutritional value, which is popular with consumers. With the increase of the problem of mushrooms being confused with the real and pollution in the market, people pay more and more attention to food safety. More than 167 articles of edible mushroom published in the past 20 years were reviewed in this paper. The analysis tools and data analysis methods of identification and quality evaluation of edible mushroom species, origin, mineral elements were reviewed. Five techniques for identification and evaluation of edible mushrooms were introduced and summarized. The macroscopic, microscopic and molecular identification techniques can be used to identify species. Chromatography, spectroscopy technology combined with chemometrics can be used for qualitative and quantitative study of mushroom and evaluation of mushroom quality. In addition, multiple supervised pattern-recognition techniques have good classification ability. Deep learning is more and more widely used in edible mushroom, which shows its advantages in image recognition and prediction. These techniques and analytical methods can provide strong support and guarantee for the identification and evaluation of mushroom, which is of great significance to the development and utilization of edible mushroom.
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Affiliation(s)
- Ziyun Yan
- College of Resources and Environmental, Yunnan Agricultural University, Kunming, China
| | | | - Jieqing Li
- College of Resources and Environmental, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
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31
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Hernandez CE, Granados L. Quality differentiation of cocoa beans: implications for geographical indications. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3993-4002. [PMID: 33421139 DOI: 10.1002/jsfa.11077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 01/03/2021] [Accepted: 01/09/2021] [Indexed: 06/12/2023]
Abstract
Geographical indications may stimulate collective actions of governance for quality control, trade and marketing as well as innovation based on the use of local resources and regional biodiversity. Cocoa production, however, dominated by small family agriculture in tropical regions, has rarely made use of such strategies. This review is aimed at understanding major research interests and emerging technologies helpful for the origin differentiation of cocoa quality. Results from literature search and cited references of publications on cocoa research were imported into VOSviewer for data analysis, which aided in visualizing major research hotpots. Co-occurrence analysis yielded major research clusters which guided the discussion of this review. Observed was a consensus recognizing cocoa quality resulting from the interaction of genotype, fermentation variables and geographical origin. A classic view of cocoa genetics based on the dichotomy of 'fine versus bulk' has been reexamined by a broader perspective of human selection and cocoa genotype evolution. This new approach to cocoa genetic diversity, together with the understanding of complex microbiome interactions through fermentation, as well as quality reproducibility challenged by geographical conditions, have demonstrated the importance of terroir in the production of special attributes. Cocoa growing communities around the tropics have been clearly enabled by new omics and chemometrics to systematize producing conditions and practices in the designation of specifications for the differentiation of origin quality. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Carlos Eduardo Hernandez
- Laboratory of Food Quality Innovation, School of Agricultural Sciences, National University (UNA), Heredia, Costa Rica
| | - Leonardo Granados
- Center for the Development of Denominations of Origin and Agrifood Quality (CADENAGRO), School of Agricultural Sciences, National University (UNA), Heredia, Costa Rica
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Caporaso N, Whitworth MB, Fisk ID. Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging. Food Chem 2020; 344:128663. [PMID: 33277124 PMCID: PMC7814379 DOI: 10.1016/j.foodchem.2020.128663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/09/2020] [Accepted: 11/14/2020] [Indexed: 11/30/2022]
Abstract
Quantitative calibrations were built from shelled and in-shell single cocoa beans by HSI. The fat content of commercial batches of cocoa beans varies by up to 15% within batches. HSI prediction of the total lipid content was successful for shelled and unshelled beans. Segregation using HSI fat calibration enhanced cocoa bean fat content by 6%.
This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI). 170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions. HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material.
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Affiliation(s)
- Nicola Caporaso
- Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK
| | | | - Ian D Fisk
- Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK.
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