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Hwang J, Choi KO, Jeong S, Lee S. Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images. Curr Res Food Sci 2024; 8:100742. [PMID: 38708100 PMCID: PMC11066601 DOI: 10.1016/j.crfs.2024.100742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R2 > 0.992 and RMSE < 2.75).
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Affiliation(s)
- Jeongin Hwang
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
| | - Kyeong-Ok Choi
- Department of Food Science and Technology, Chungnam National University, Daejeon, 34134, South Korea
| | - Sungmin Jeong
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
| | - Suyong Lee
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
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Chen S, Du K, Shan B, Xu Q, Zhang F. A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1021-1033. [PMID: 38312025 DOI: 10.1039/d3ay01942j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
In this paper, a novel hybrid variable selection method for model building by near-infrared (NIR) spectroscopy is proposed for composition measurement in industrial processes. A double-layer structure is designed for variable selection by combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS). The Fisher classifier combined with model population analysis is used to select the variable interval wherein the useful variables are roughly located even when strong multicollinearity exists among spectral variables. Opposition-based learning (OBL) and jumping genes (JG) are introduced to improve the binary cuckoo search algorithm for the fine selection of key variables, thus avoiding the loss of excellent solutions due to randomness and the local optimum. Different variable selection methods were used to select variables for beer, corn, and diesel fuel datasets, and the partial least squares (PLS) algorithms were used to build calibration models to predict the original extract concentration of beer, the protein and starch content of corn, and the boiling point of diesel fuel, respectively. The results showed that the proposed PLS modeling method based on FCPA-IBCS has higher fitting accuracy and smaller prediction errors.
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Affiliation(s)
- Shuobo Chen
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Kang Du
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Baoming Shan
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Qilei Xu
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Fangkun Zhang
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
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Ning H, Wang J, Jiang H, Chen Q. Quantitative detection of zearalenone in wheat grains based on near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121545. [PMID: 35767904 DOI: 10.1016/j.saa.2022.121545] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (RP) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.
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Affiliation(s)
- Hongwei Ning
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiawei Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Zhou L, Wang X, Zhang C, Zhao N, Taha MF, He Y, Qiu Z. Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02866-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Dai Y, Dai Z, Guo G, Wang B. Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2101060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Yuanfeng Dai
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Zuoxiao Dai
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Guangzhi Guo
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Boran Wang
- School of Microelectronics, Fudan University, Shanghai, China
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Martín-Torres S, Ruiz-Castro L, Jiménez-Carvelo AM, Cuadros-Rodríguez L. Applications of multivariate data analysis in shelf life studies of edible vegetal oils – A review of the few past years. Food Packag Shelf Life 2022. [DOI: 10.1016/j.fpsl.2021.100790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Currò S, Fasolato L, Serva L, Boffo L, Ferlito JC, Novelli E, Balzan S. Use of a portable near-infrared tool for rapid on-site inspection of freezing and hydrogen peroxide treatment of cuttlefish (Sepia officinalis). Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108524] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Wang J, Jiang H, Chen Q. High-precision recognition of wheat mildew degree based on colorimetric sensor technique combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Su N, Pan F, Wang L, Weng S. Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods. BIOSENSORS-BASEL 2021; 11:bios11080261. [PMID: 34436063 PMCID: PMC8395004 DOI: 10.3390/bios11080261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 12/27/2022]
Abstract
The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.
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Affiliation(s)
- Ning Su
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
| | - Liusan Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
- Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
- Correspondence: (L.W.); (S.W.)
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei 230601, China;
- Correspondence: (L.W.); (S.W.)
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Jiang H, He Y, Chen Q. Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3328-3335. [PMID: 33222172 DOI: 10.1002/jsfa.10962] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. CONCLUSION The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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