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Li X, Xu Z, Tang L, Zhao G, Wu Y, Zhang P, Wang Q. An effective moisture interference correction method for maize powder NIR spectra analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124033. [PMID: 38382222 DOI: 10.1016/j.saa.2024.124033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
The detection of maize starch content is of great significance for maize processing industry and near-infrared spectroscopy (NIRS) is an ideal rapid detection technology. However, the interference of moisture in maize is a bottleneck problem that affects the accuracy of NIRS quantitative analysis. In this study, we proposed methods based on external parameter orthogonalization (EPO) combined with wavelength selection algorithms to bring more accurate analytical results. Two groups of maize starch samples with different moisture content distributions were investigated to compare the predictive performance of NIRS models. The results showed that the model built using EPO combined with the synergy interval partial least squares (EPO-siPLS) algorithm exhibited the superior prediction accuracy, whose RMSEP/RMSEPck is improved by 9.7 % compared with that of siPLS model, 25.3 % compared with that of EPO-PLS, and 45.8 % compared with that of the PLS model. This study provides a more accurate and robust new method for rapid detection of maize starch and offers new insights for its application.
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Affiliation(s)
- Xiaohong Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China
| | - Zhuopin Xu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Liwen Tang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Guangxia Zhao
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China
| | - Yuejin Wu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Pengfei Zhang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Qi Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
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Ji Q, Li C, Fu X, Liao J, Hong X, Yu X, Ye Z, Zhang M, Qiu Y. Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods. Molecules 2023; 28:molecules28062803. [PMID: 36985775 PMCID: PMC10057985 DOI: 10.3390/molecules28062803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas ('non-Zhejiang'). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.
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Affiliation(s)
- Qingge Ji
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Chaofeng Li
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Xianshu Fu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Jinyan Liao
- Business and Trade Branch, Zhejiang Yuying College of Vocational Technology, Hangzhou 310018, China
| | - Xuezhen Hong
- College of Quality & Safety Engineering, China Jiliang University, Hangzhou 310018, China
| | - Xiaoping Yu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Zihong Ye
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Mingzhou Zhang
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
| | - Yulou Qiu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou 310018, China
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Geng Y, Shen H, Ni H, Tian Y, Zhao Z, Chen Y, Liu X. Non-destructive determination of total sugar content in tobacco filament based on calibration transfer with parameter free adjustment. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Dalmolin Ciarnoschi L, Claudio de Oliveira L, Lucia Ferreira Simeone M, dos Santos Panero F, dos Santos Panero P, Ruiz Rodriguez A, Alves Filho EG, Gervasio Pereira M, Manoel da Silva L. Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum (Benth) organs by Near-Infrared spectrophotometry. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107621] [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]
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Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106839] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Amirvaresi A, Parastar H. External parameter orthogonalization-support vector machine for processing of attenuated total reflectance-mid-infrared spectra: A solution for saffron authenticity problem. Anal Chim Acta 2021; 1154:338308. [PMID: 33736807 DOI: 10.1016/j.aca.2021.338308] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 11/18/2022]
Abstract
In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is compared with other common classification methods. The obtained results showed that the EPO-SVM approach has a much better classification accuracy (>95%) than other methods (accuracy<89.2%). Finally, two different sample sets including mixture of saffron and four plant adulterants and commercial saffron samples are used for validation of the developed EPO-SVM model. In this regard, classification figures of merit in terms of sensitivity, specificity and accuracy were respectively 96.6%, 97.1%, and 96.8% which showed good classification performance. It is concluded that the proposed EPO-PCA and EPO-SVM approaches can be considered as reliable tools for authentication and adulteration detection in saffron samples.
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Affiliation(s)
- Arian Amirvaresi
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - Hadi Parastar
- Department of Chemistry, Sharif University of Technology, Tehran, Iran.
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Corrêdo LDP, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. SENSORS 2021; 21:s21062195. [PMID: 33801058 PMCID: PMC8003973 DOI: 10.3390/s21062195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/10/2021] [Accepted: 03/19/2021] [Indexed: 12/02/2022]
Abstract
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.
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Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis. SENSORS 2020; 20:s20174822. [PMID: 32858981 PMCID: PMC7506570 DOI: 10.3390/s20174822] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/16/2020] [Accepted: 08/20/2020] [Indexed: 11/29/2022]
Abstract
This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.
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From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review. SUSTAINABILITY 2020. [DOI: 10.3390/su12020443] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.
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