1
|
Wang Z, Wang R, Chu Y, Chen G, Lin T, Jiang R, Wang J. A method to assess industrial paraffin contamination levels in rice and its transferability analysis based on transfer component analysis. Food Chem 2024; 436:137682. [PMID: 37837682 DOI: 10.1016/j.foodchem.2023.137682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/19/2023] [Accepted: 10/04/2023] [Indexed: 10/16/2023]
Abstract
Accurate assessment of industrial paraffin contamination levels (IPCLs) in rice is critical for food safety. However, time-consuming and labor-intensive experiments to produce labels for targeted adulterated rice have hindered the development of IPCL estimation methods. In this paper, a transfer learning method (TCA-LSSVR) has been developed. The algorithm integrates transfer component analysis (TCA) with domain adaptive capabilities to produce accurate estimates. Rice from 7 different regions and 3 industrial paraffins were used to generate 4,680 samples from 9 datasets for benchmarking. The test results showed that the established algorithm achieved good estimation performance in various modelling strategies, and only 20 % of off-site samples were needed to supplement the source dataset, the average determination coefficient R2 reached 0.7045, the average RMSE reached 0.140 %, and the average RPD reached 2.023. This work highlights the prospect of rapidly developing a new generation of adulteration detection algorithms using only previous trial data.
Collapse
Affiliation(s)
- Zhentao Wang
- College of Engineering, Northeast Agricultural University, Harbin 150030, China; Heilongjiang Provincial Key Laboratory of Modern Agricultural Equipment Technology in Northern Cold Regions, Harbin 150030, China
| | - Ruidong Wang
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| | - Yuhang Chu
- College of Engineering, Northeast Agricultural University, Harbin 150030, China; Heilongjiang Provincial Key Laboratory of Modern Agricultural Equipment Technology in Northern Cold Regions, Harbin 150030, China
| | - Guoqing Chen
- College of Engineering, Northeast Agricultural University, Harbin 150030, China; Heilongjiang Provincial Key Laboratory of Modern Agricultural Equipment Technology in Northern Cold Regions, Harbin 150030, China
| | - Tenghui Lin
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| | - Rui Jiang
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| | - Jinfeng Wang
- College of Engineering, Northeast Agricultural University, Harbin 150030, China; Heilongjiang Provincial Key Laboratory of Modern Agricultural Equipment Technology in Northern Cold Regions, Harbin 150030, China.
| |
Collapse
|
2
|
Li S, Li T, Cai Y, Yao Z, He M. Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123382. [PMID: 37725883 DOI: 10.1016/j.saa.2023.123382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/05/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.
Collapse
Affiliation(s)
- Shiwen Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Tian Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Yaoyi Cai
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Zekai Yao
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Miaolei He
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China.
| |
Collapse
|
3
|
Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
Collapse
Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| |
Collapse
|
4
|
Aslam R, Sharma SR, Kaur J, Panayampadan AS, Dar OI. A systematic account of food adulteration and recent trends in the non-destructive analysis of food fraud detection. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01846-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
|
5
|
Liu Y, Zhao S, Gao X, Fu S, Chao Song, Dou Y, Shaozhong Song, Qi C, Lin J. Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin. RSC Adv 2022; 12:34520-34530. [PMID: 36545607 PMCID: PMC9710531 DOI: 10.1039/d2ra06892c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/02/2022] Open
Abstract
With the events of fake and inferior rice and food products occurring frequently, how to establish a rapid and high accuracy monitoring method for rice food identification becomes an urgent problem. In this work, we investigate using combined laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) with machine learning algorithms to identify the place of origin of rice production. Six geographical origin rice samples grown in different parts of China are selected and pretreated, and measured by the atomic emission spectra of LIBS and the reflection spectra of HSI, respectively. The principal component analysis (PCA) is utilized to realize data dimensionality and extract the data feat of LIBS, HSI and fusion data, and based on this, three models employing the partial least squares discriminant analysis (PLS-DA), the support vector machine (SVM) and the extreme learning machine (ELM) are used to identify the rice geographical origin. The results show that the accuracy of LIBS and HSI analysis with the SVM machine learning algorithm can reach 93.06% and 88.07%, respectively, and the accuracy of combined LIBS and HSI data fusion recognition can reach 99.85%. Besides, the classification accuracy of the three models measured after pretreatment is basically all above 95%, and up to 99.85%. This study proves the effectiveness of using the combined LIBS and HSI with the machine learning algorithm in rice geographical origin identification, which can achieve rapid and accurate rice quality and identity detection.
Collapse
Affiliation(s)
- Yuanyuan Liu
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shangyong Zhao
- Department of Energy and Power Engineering, Tsinghua UniversityBeijing100084China
| | - Xun Gao
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shaoyan Fu
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Chao Song
- School of Chemistry and Environmental Engineering, Changchun University of Science and TechnologyJilin130022China
| | - Yinping Dou
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shaozhong Song
- School of Data Science and Artificial Intelligence, Jilin Engineering Normal UniversityJilin130052China
| | - Chunyan Qi
- Jilin Academy of Agricultural SciencesJilin130033China
| | - Jingquan Lin
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| |
Collapse
|
6
|
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]
|
7
|
Near-infrared spectroscopy and machine learning for classification of food powders under moving conditions. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
8
|
Wadood SA, Nie J, Li C, Rogers KM, Khan A, Khan WA, Qamar A, Zhang Y, Yuwei Y. Rice authentication: An overview of different analytical techniques combined with multivariate analysis. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
9
|
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
| |
Collapse
|
10
|
Rapid identification of the variety and geographical origin of Wuyou No.4 rice by fourier transform near-infrared spectroscopy coupled with chemometrics. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
11
|
Śliwińska-Bartel M, Burns DT, Elliott C. Rice fraud a global problem: A review of analytical tools to detect species, country of origin and adulterations. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.06.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
12
|
Low requirement imaging enables sensitive and robust rice adulteration quantification via transfer learning. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
13
|
Kniese J, Race AM, Schmidt H. Classification of cereal flour species using Raman spectroscopy in combination with spectra quality control and multivariate statistical analysis. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
14
|
Pandiselvam R, Sruthi NU, Kumar A, Kothakota A, Thirumdas R, Ramesh S, Cozzolino D. Recent Applications of Vibrational Spectroscopic Techniques in the Grain Industry. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1904253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- R. Pandiselvam
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - N. U. Sruthi
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Ankit Kumar
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science & Technology, Telangana, India
| | - S.V. Ramesh
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), the University of Queensland, Brisbane, Australia
| |
Collapse
|
15
|
Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging. J FOOD QUALITY 2021. [DOI: 10.1155/2021/6642220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.
Collapse
|
16
|
Song H, Lu B, Ye C, Li J, Zhu Z, Zheng L. Fraud vulnerability quantitative assessment of Wuchang rice industrial chain in China based on AHP-EWM and ANN methods. Food Res Int 2020; 140:109805. [PMID: 33648162 DOI: 10.1016/j.foodres.2020.109805] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/05/2020] [Accepted: 10/13/2020] [Indexed: 12/20/2022]
Abstract
Vulnerability assessment has been used in the food fraud mitigation based on the subjective judgement of industry participants and simple calculation. To have a more objective result, an improved vulnerability quantitative assessment method was proposed. The overall fraud vulnerability was described by the vulnerability of fraud factors and the health and economic impact of fraud incidents. The fraud factors were related to opportunity, motivation and control measure. Analytic hierarchy process combined with entropy weighting method (AHP-EWM) and artificial neural networking (ANN) to improve judgment accuracy. In the application in Wuchang rice industrial chain, 51 fraud factors were used in the assessment and 10 experts, 36 farmers, 15 suppliers and 15 supervisors were interviewed. Results showed that Wuchang rice industrial chain was highly vulnerable to fraud. The opportunity for fraud was high, the motivation to commit it was moderate, and controls to prevent it needed reinforcing. Fraud vulnerability differed between farmers and suppliers. To reduce the fraud vulnerability, improved regulations and policies and stiffer penalties were strongly recommended.
Collapse
Affiliation(s)
- Huaxin Song
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture and Rural Affairs, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou 310058, China; Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
| | - Baiyi Lu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture and Rural Affairs, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou 310058, China; Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Ningbo Research Institute, Zhejiang University, Ningbo 315100, China.
| | - Chunhui Ye
- China Academy for Rural Development (CARD), Zhejiang University, Hangzhou 310058, China
| | - Jie Li
- Charles H. Dyson School of Applied Economics and Management, Cornell University, 403 Warren Hall, Ithaca, NY 14853, United States
| | - Zhiwei Zhu
- China National Rice Research Institute, Laboratory of Quality & Safety Risk Assessment for Rice (Hangzhou), Ministry of Agriculture, China
| | - Lufei Zheng
- Institute of Agricultural Quality Standards and Testing Technology, Chinese Academy of Agricultural Sciences, Ministry of Rural Products Agricultural Products Quality Standards Research Center, Beijing 100081, China
| |
Collapse
|
17
|
|
18
|
Zhu L, Bi S, Wu G, Zhang H, Wang L, Qian H, Qi X, Jiang H. Comparative analysis of the texture and physicochemical properties of cooked rice based on adjustable rice cooker. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
19
|
Liu Y, Li Y, Peng Y, Yang Y, Wang Q. Detection of fraud in high-quality rice by near-infrared spectroscopy. J Food Sci 2020; 85:2773-2782. [PMID: 32713030 DOI: 10.1111/1750-3841.15314] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 05/03/2020] [Accepted: 05/04/2020] [Indexed: 11/29/2022]
Abstract
A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.
Collapse
Affiliation(s)
- Yachao Liu
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Yanming Yang
- College of Engineering, China Agricultural University, Beijing, 100083, China
| | - Qi Wang
- College of Engineering, China Agricultural University, Beijing, 100083, China
| |
Collapse
|
20
|
Rocha WFDC, do Prado CB, Blonder N. Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods. Molecules 2020; 25:E3025. [PMID: 32630676 PMCID: PMC7411792 DOI: 10.3390/molecules25133025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/25/2020] [Accepted: 06/29/2020] [Indexed: 11/16/2022] Open
Abstract
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.
Collapse
Affiliation(s)
- Werickson Fortunato de Carvalho Rocha
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
| | - Charles Bezerra do Prado
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
| | - Niksa Blonder
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
| |
Collapse
|
21
|
Yue X, Tan Y, Fan W, Song S, Ji H, Li B. Raman spectroscopic analysis of paddy rice infected by three pests and diseases common in Northeast Asia. ACTA ACUST UNITED AC 2019. [DOI: 10.1088/1742-6596/1324/1/012050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
22
|
Evaluation of sample pretreatment method for geographic authentication of rice using Raman spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00087-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
23
|
Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, Cuadros-Rodríguez L. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. Food Res Int 2019; 122:25-39. [PMID: 31229078 DOI: 10.1016/j.foodres.2019.03.063] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
Collapse
Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain.
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - M Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| |
Collapse
|
24
|
Impact of binding interaction characteristics on physicochemical, structural, and rheological properties of waxy rice flour. Food Chem 2018; 266:551-556. [DOI: 10.1016/j.foodchem.2018.05.010] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/26/2018] [Accepted: 05/02/2018] [Indexed: 11/19/2022]
|
25
|
Chen H, Lin Z, Tan C. Untargeted Identification of Black Rice by Near-Infrared Spectroscopy and One-Class Models. ANAL LETT 2018. [DOI: 10.1080/00032719.2018.1429458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Hui Chen
- Key Laboratory of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan, China
- Hospital, Yibin University, Yibin, Sichuan, China
| | - Zan Lin
- Key Laboratory of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan, China
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chao Tan
- Key Laboratory of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan, China
| |
Collapse
|
26
|
Zhu L, Sun J, Wu G, Wang Y, Zhang H, Wang L, Qian H, Qi X. Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy. J Cereal Sci 2018. [DOI: 10.1016/j.jcs.2018.06.010] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
27
|
Using RVA-full pattern fitting to develop rice viscosity fingerprints and improve type classification. J Cereal Sci 2018. [DOI: 10.1016/j.jcs.2018.02.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
28
|
Maione C, Barbosa RM. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Crit Rev Food Sci Nutr 2018; 59:1868-1879. [DOI: 10.1080/10408398.2018.1431763] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Camila Maione
- Instituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brazil
| | | |
Collapse
|
29
|
González-Cruz L, Montañez-Soto JL, Conde-Barajas E, Negrete-Rodríguez MDLLX, Flores-Morales A, Bernardino-Nicanor A. Spectroscopic, calorimetric and structural analyses of the effects of hydrothermal treatment of rice beans and the extraction solvent on starch characteristics. Int J Biol Macromol 2018; 107:965-972. [DOI: 10.1016/j.ijbiomac.2017.09.074] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 08/30/2017] [Accepted: 09/18/2017] [Indexed: 11/28/2022]
|
30
|
Huo Y, Kamal GM, Wang J, Liu H, Zhang G, Hu Z, Anwar F, Du H. 1 H NMR-based metabolomics for discrimination of rice from different geographical origins of China. J Cereal Sci 2017. [DOI: 10.1016/j.jcs.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
31
|
Bernardino-Nicanor A, Acosta-García G, Güemes-Vera N, Montañez-Soto JL, de los Ángeles Vivar-Vera M, González-Cruz L. Fourier transform infrared and Raman spectroscopic study of the effect of the thermal treatment and extraction methods on the characteristics of ayocote bean starches. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2017; 54:933-943. [PMID: 28303044 PMCID: PMC5336448 DOI: 10.1007/s13197-016-2370-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/12/2016] [Accepted: 10/07/2016] [Indexed: 10/20/2022]
Abstract
Starches isolated from four ayocote bean varieties were modified by thermal treatment to determinate the effect of the treatment on the structural changes of ayocote bean starch. Scanning electron microscopy indicates that the starch granules have oval and round shapes, with heterogeneous sizes and fractures when the extraction method is used. The presence of new bands at 2850 and 1560 cm-1 in the FT-IR spectra showed that the thermal treatment of ayocote beans induced an interaction between the protein or lipid and the amylose or amylopectin, while the sharpest band at 3400 cm-1 indicated a dehydration process in the starch granule in addition to the presence of the band at 1260 cm-1, indicating the product of the retrogradation process. The thermal treatment reduced the crystallinity as well as short-range order. Raman spectroscopy revealed that acute changes occurred in the polysaccharide bonds after thermal treatment. This study showed that the thermal treatment affected the structural properties of ayocote bean starches, the interactions of the lipids and proteins with starch molecules and the retrogradation process of starch.
Collapse
Affiliation(s)
- Aurea Bernardino-Nicanor
- Instituto Tecnológico de Celaya-Tecnológico Nacional de México, Antonio García Cubas Pte #600 esq., Av. Tecnológico, A. P. 57, 38010 Celaya, Gto Mexico
| | - Gerardo Acosta-García
- Instituto Tecnológico de Celaya-Tecnológico Nacional de México, Antonio García Cubas Pte #600 esq., Av. Tecnológico, A. P. 57, 38010 Celaya, Gto Mexico
| | - Norma Güemes-Vera
- Centro de Investigacion en Cienciay Tecnologia de Alimentos, Instituto de Ciencias Agropecuarias, Universidad Autonoma del Estado de Hidalgo, Av. Universidad Km 1, 43000 Tulancingo, Mexico
| | - José Luis Montañez-Soto
- Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional del Instituto Politécnico Nacional, Unidad Michoacán, Justo Sierra No. 28, 59510 Jiquilpan, Mich Mexico
| | - María de los Ángeles Vivar-Vera
- Instituto Tecnológico de Tuxtepec-Tecnológico Nacional de México, Av. Dr. Victor Bravo Ahuja S/N Col. 5 de Mayo, Tuxtepec, Oaxaca México
| | - Leopoldo González-Cruz
- Instituto Tecnológico de Celaya-Tecnológico Nacional de México, Antonio García Cubas Pte #600 esq., Av. Tecnológico, A. P. 57, 38010 Celaya, Gto Mexico
| |
Collapse
|
32
|
Su WH, He HJ, Sun DW. Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review. Crit Rev Food Sci Nutr 2016; 57:1039-1051. [DOI: 10.1080/10408398.2015.1082966] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin, Ireland
| | - Hong-Ju He
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin, Ireland
| |
Collapse
|
33
|
Márquez C, López MI, Ruisánchez I, Callao MP. FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud. Talanta 2016; 161:80-86. [DOI: 10.1016/j.talanta.2016.08.003] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 07/28/2016] [Accepted: 08/01/2016] [Indexed: 11/15/2022]
|
34
|
Sandwich Immunoassays of Multicomponent Subtrace Pathogenic DNA Based on Magnetic Fluorescent Encoded Nanoparticles. BIOMED RESEARCH INTERNATIONAL 2016; 2016:7324384. [PMID: 26881227 PMCID: PMC4737017 DOI: 10.1155/2016/7324384] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 01/06/2015] [Indexed: 12/27/2022]
Abstract
A novel magnetic fluorescent encoded nanoimmunoassay system for multicomponent detection and separation of the subtrace pathogenic DNA (hepatitis B virus surface gene, HBV; hepatitis A virus poly the protein gene, HAV) was established based on new type of magnetic fluorescent encoded nanoparticles and sandwich immunoassay principle. This method combines multifunctional nanoparticles, immunoassay technique, fluorescence labeling, and magnetic separation of multicomponent technology. It has many advantages such as high sensitivity, low detection limit, easy operation, and great potential for development. The results of this work show that, based on nanoimmunoassay system, it could quantitatively detect the multicomponent trace pathogenic HAV and HBV DNA, as well as detection limit up to 0.1 pM and 0.12 pM. Furthermore, with the improvement of the performances of magnetic fluorescent encoded nanoparticles, the sensitivity will be further improved. In this experiment, a new nanoimmunoassay system based on magnetic fluorescent encoded nanoparticles was established, which will provide a new way for the immunoassay and separation of multicomponent biomolecules.
Collapse
|
35
|
Wang M, He X, Xiong Q, Jing R, Zhang Y, Wen Z, Kuang Q, Pu X, Li M, Xu T. A facile strategy applied to simultaneous qualitative-detection on multiple components of mixture samples: a joint study of infrared spectroscopy and multi-label algorithms on PBX explosives. RSC Adv 2016. [DOI: 10.1039/c5ra20685e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
We combined infrared spectroscopy with multi-label algorithms to propose a facile yet efficient strategy to realize simultaneous qualitative-detection on multiple components of mixture explosives without pre-separation.
Collapse
Affiliation(s)
- Minqi Wang
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Xuan He
- Institute of Chemical Materials
- Chinese Academy of Engineering Physics
- Mianyang
- People's Republic of China
| | - Qing Xiong
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Runyu Jing
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Yuxiang Zhang
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Zhining Wen
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Qifan Kuang
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Xuemei Pu
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Menglong Li
- College of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Tao Xu
- Institute of Chemical Materials
- Chinese Academy of Engineering Physics
- Mianyang
- People's Republic of China
| |
Collapse
|
36
|
A Feasibility Study on the Evaluation of Quality Properties of Chinese Rice Wine Using Raman Spectroscopy. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0295-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
37
|
Boyaci IH, Temiz HT, Geniş HE, Acar Soykut E, Yazgan NN, Güven B, Uysal RS, Bozkurt AG, İlaslan K, Torun O, Dudak Şeker FC. Dispersive and FT-Raman spectroscopic methods in food analysis. RSC Adv 2015. [DOI: 10.1039/c4ra12463d] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Raman spectroscopy is a powerful technique for molecular analysis of food samples.
Collapse
Affiliation(s)
- Ismail Hakki Boyaci
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Havva Tümay Temiz
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Hüseyin Efe Geniş
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | | | - Nazife Nur Yazgan
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Burcu Güven
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Reyhan Selin Uysal
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Akif Göktuğ Bozkurt
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Kerem İlaslan
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Ozlem Torun
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | | |
Collapse
|