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Yu Y, Chai Y, Li Z, Li Z, Ren Z, Dong H, Chen L. Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics. Food Chem 2025; 462:141033. [PMID: 39217750 DOI: 10.1016/j.foodchem.2024.141033] [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: 06/10/2024] [Revised: 08/21/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.
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Affiliation(s)
- Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yinghui Chai
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Zhoutao Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
| | - Zhongyang Ren
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China.
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Lin Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
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2
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Li X, Zhong Y, Li J, Lin Z, Pei Y, Dai S, Sun F. Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124437. [PMID: 38772180 DOI: 10.1016/j.saa.2024.124437] [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: 12/18/2023] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/23/2024]
Abstract
The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.
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Affiliation(s)
- Xiaolong Li
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yongqi Zhong
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Jiaqi Li
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Zhaozhou Lin
- Beijing Zhongyan Tongrentang Medicine R&D Co. Ltd, Beijing, China
| | - Yanling Pei
- Hebei Xinminhe Pharmaceutical Technology Development Co., Ltd, Hebei, China
| | - Shengyun Dai
- National Institutes for Food and Drug Control, Beijing, China.
| | - Fei Sun
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China.
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3
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Bai L, Zhang ZT, Guan H, Liu W, Chen L, Yuan D, Chen P, Xue M, Yan G. Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network. Talanta 2024; 275:126098. [PMID: 38640523 DOI: 10.1016/j.talanta.2024.126098] [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: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.
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Affiliation(s)
- Lei Bai
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Zhi-Tong Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Huanhuan Guan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Wenjian Liu
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Li Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Dongping Yuan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Pan Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Mei Xue
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing 210023, China.
| | - Guojun Yan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China.
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Bian X, Zhao Z, Liu J, Liu P, Shi H, Tan X. Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5190-5198. [PMID: 37779476 DOI: 10.1039/d3ay01636f] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
The blood cholesterol level is strongly associated with cardiovascular disease. It is necessary to develop a rapid method to determine the cholesterol concentration of blood. In this study, a discretized butterfly optimization algorithm-partial least squares (BOA-PLS) method combined with near-infrared (NIR) spectroscopy is firstly proposed for rapid determination of the cholesterol concentration in blood. In discretized BOA, the butterfly vector is described by 1 or 0, which represents whether the variable is selected or not, respectively. In the optimization process, four transfer functions, i.e., arctangent, V-shaped, improved arctangent (I-atan) and improved V-shaped (I-V), are introduced and compared for discretization of the butterfly position. The partial least squares (PLS) model is established between the selected NIR variables and cholesterol concentrations. The iteration number, transfer functions and the performance of butterflies are investigated. The proposed method is compared with full-spectrum PLS, multiplicative scatter correction-PLS (MSC-PLS), max-min scaling-PLS (MMS-PLS), MSC-MMS-PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Results show that the I-V function is the best transfer function for discretization. Both preprocessing and variable selection can improve the prediction performance of PLS. Variable selection methods based on BOA are better than those based on statistics. Furthermore, I-V-BOA-PLS has the highest predictive accuracy among the seven variable selection methods. MSC-MMS can further improve the prediction ability of I-V-BOA-PLS. Therefore, BOA-PLS combined with NIR spectroscopy is promising for the rapid determination of cholesterol concentration in blood.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co. Ltd., Binzhou 256500, China
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan, 250012, China
| | - Zizhen Zhao
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Jianwen Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Huibing Shi
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co. Ltd., Binzhou 256500, China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
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Chai Y, Yu Y, Zhu H, Li Z, Dong H, Yang H. Identification of common buckwheat ( Fagopyrum esculentum Moench) adulterated in Tartary buckwheat ( Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics. Curr Res Food Sci 2023; 7:100573. [PMID: 37650007 PMCID: PMC10463190 DOI: 10.1016/j.crfs.2023.100573] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/17/2023] [Accepted: 08/20/2023] [Indexed: 09/01/2023] Open
Abstract
Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.
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Affiliation(s)
- Yinghui Chai
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Hui Zhu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
- Liyang Tianmu Lake Agricultural Development Co., Ltd., Liyang, 213333, China
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
| | - Hongshun Yang
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Zhejiang, 312000, China
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6
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Gao L, Zhong L, Wei Y, Li L, Wu A, Nie L, Yue J, Wang D, Zhang H, Dong Q, Zang H. A new perspective in understanding the processing mechanisms of traditional Chinese medicine by near-infrared spectroscopy with Aquaphotomics. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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7
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Wang Q, Li H, You J, Yan B, Jin W, Shen M, Sheng Y, He B, Wang X, Meng X, Qin L. An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba. ANAL SCI 2023:10.1007/s44211-023-00334-4. [PMID: 37037970 DOI: 10.1007/s44211-023-00334-4] [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: 01/25/2023] [Accepted: 03/30/2023] [Indexed: 04/12/2023]
Abstract
The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R2 > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.
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Affiliation(s)
- Qi Wang
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Huaqiang Li
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Jinling You
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Binjun Yan
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Weifeng Jin
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Menglan Shen
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Yunjie Sheng
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Bingqian He
- Academy of Chinese Medical Science, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District310053, Hangzhou, Zhejiang Province, People's Republic of China
| | - Xinrui Wang
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China
| | - Xiongyu Meng
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China.
| | - Luping Qin
- School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China.
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Wu Q, Oliveira MM, Achata EM, Kamruzzaman M. Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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9
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Wu Q, Mousa MA, Al-qurashi AD, Ibrahim OH, Abo-Elyousr KA, Rausch K, Abdel Aal AM, Kamruzzaman M. Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Curr Res Food Sci 2023; 6:100483. [PMID: 37033735 PMCID: PMC10073987 DOI: 10.1016/j.crfs.2023.100483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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11
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Yu DX, Guo S, Zhang X, Yan H, Zhang ZY, Chen X, Chen JY, Jin SJ, Yang J, Duan JA. Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics. Food Chem X 2022; 15:100450. [PMID: 36211746 PMCID: PMC9532869 DOI: 10.1016/j.fochx.2022.100450] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/04/2022] [Accepted: 09/16/2022] [Indexed: 11/25/2022] Open
Abstract
Rapid detection of adulteration in GP was realized by NIR spectroscopy. PCA and PLS-DA models were successfully explored to identify adulterants in GP. Three algorithms achieved satisfactory results for discrimination of adulteration. Adulteration levels in GP can be predicted by PLSR model. The optimal pretreatment methods were compared and selected for modeling.
Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.
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12
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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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13
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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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Zhang H, Hu X, Liu L, Wei J, Bian X. Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120841. [PMID: 35033805 DOI: 10.1016/j.saa.2021.120841] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R2P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.
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Affiliation(s)
- Huan Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Xiaoyun Hu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China
| | - Limei Liu
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Junfu Wei
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Environment Science and Engineering, Tiangong University, Tianjin 300387, China; School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, 644000, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
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Wang K, Bian X, Zheng M, Liu P, Lin L, Tan X. Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 263:120138. [PMID: 34304011 DOI: 10.1016/j.saa.2021.120138] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/23/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy.
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Affiliation(s)
- Kaiyi Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, PR China; Tianjin Key Laboratory of Green Chemical Process Engineering, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, PR China; Tianjin Key Laboratory of Green Chemical Process Engineering, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, 644000, PR China.
| | - Meng Zheng
- Tianjin Key Laboratory of Green Chemical Process Engineering, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, PR China; Tianjin Key Laboratory of Green Chemical Process Engineering, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Ligang Lin
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, PR China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, PR China; Tianjin Key Laboratory of Green Chemical Process Engineering, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
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