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Ssali Nantongo J, Serunkuma E, Burgos G, Nakitto M, Davrieux F, Ssali R. Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124406. [PMID: 38759574 DOI: 10.1016/j.saa.2024.124406] [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: 09/26/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/19/2024]
Abstract
It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 - 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 - 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance.
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Affiliation(s)
| | - Edwin Serunkuma
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda
| | | | - Mariam Nakitto
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda
| | | | - Reuben Ssali
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda.
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Wang Y, Li Z, Wang W, Liu P, Tan X, Bian X. Rapid quantification of single component oil in perilla oil blends by ultraviolet-visible spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124710. [PMID: 38936207 DOI: 10.1016/j.saa.2024.124710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/23/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
As a unconventional oil, perilla oil is much more expensive than conventional oils since it has the highest content of α-linolenic acid among vegetable oils. Thus the adulteration of perilla oil is serious, which needs to be solved. In this study, the single component oil in perilla oil blends were first quantitatively analyzed by ultraviolet-visible (UV-vis) spectroscopy combined with chemometric methods. Soybean oil and palm oil were added into perilla oil to form binary and ternary perilla oil blends. Partial least squares (PLS), back propagation-artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) were compared and the best model was selected for calibration. In order to improve the prediction performance of the calibration model, ten preprocessing methods and five variable selection methods were investigated. Results show that PLS was the best calibration method for binary and ternary perilla oil blends. For binary perilla oil blends, the correlation coefficients of prediction (Rp) obtained by PLS were both above 0.99, which does not need preprocessing and variable selection. For ternary perilla oil blends, after the best continuous wavelet transform (CWT) preprocessing and discretized whale optimization algorithm (WOA) variable selection, the Rp values obtained by the best model CWT-WOA-PLS were all above 0.97. This research provides a common framework for calibration of perilla oil blends, which maybe a promising method for quality control of perilla oil in industry.
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Affiliation(s)
- Yao Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Zihan Li
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Wenqiang Wang
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Peng Liu
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Xiaoyao Tan
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China
| | - Xihui Bian
- School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China; NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan, 250012, China.
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Zou Z, Guo B, Guo Y, Ma X, Luo S, Feng L, Pan Z, Deng L, Pan S, Wei J, Su Z. A comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis to systematically evaluate spice quality: Cinnamomum cassia as an example. Food Chem 2024; 439:138142. [PMID: 38081096 DOI: 10.1016/j.foodchem.2023.138142] [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: 09/14/2023] [Revised: 11/22/2023] [Accepted: 12/02/2023] [Indexed: 01/10/2024]
Abstract
Spices have long been popular worldwide. Besides serving as aromatic and flavorful food and cooking ingredients, many spices exhibit notable bioactivity. Quality evaluation methods are essential for ensuring the quality and flavor of spices. However, existing methods typically focus on the content of particular components or certain aspects of bioactivity. For a systematic evaluation of spice quality, we herein propose a comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis. Cinnamomum cassia was used as a representative example to illustrate this approach. Near-infrared spectroscopy and chemometric methods were combined to predict the geographical origin, cinnamaldehyde content, ash content, antioxidant activity, and integrated membership function value. All the optimal prediction models displayed good predictive ability (correlation coefficient of prediction > 0.9, residual predictive deviation > 2.1). The proposed approach can provide a valuable reference for the rapid and comprehensive quality evaluation of spices.
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Affiliation(s)
- Ziwei Zou
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Bingjian Guo
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Yue Guo
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Traditional Chinese Medicine Quality Standards, Guangxi Institute of Traditional Medical and Pharmaceutical Sciences, Nanning 530022, China; College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xiaolong Ma
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Sanshan Luo
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Linlin Feng
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Ziping Pan
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Lijun Deng
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China
| | - Shihan Pan
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China; Guangxi Beibu Gulf Marine Biomedicine Precision Development and High-value Utilization Engineering Research Center, Nanning 530021, China
| | - Jinbin Wei
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China; Guangxi Beibu Gulf Marine Biomedicine Precision Development and High-value Utilization Engineering Research Center, Nanning 530021, China
| | - Zhiheng Su
- Pharmaceutical College, Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Bioactive Molecules Research and Evaluation, Nanning 530021, China; Guangxi Beibu Gulf Marine Biomedicine Precision Development and High-value Utilization Engineering Research Center, Nanning 530021, China; Guangxi Health Commission Key Laboratory of Basic Research on Antigeriatric Drugs, Nanning 530021, China.
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Tian M, Han Y, Ma X, Liang W, Meng Z, Cao G, Luo Y, Zang H. Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case. PHYTOCHEMICAL ANALYSIS : PCA 2024. [PMID: 38649268 DOI: 10.1002/pca.3358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/15/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price. OBJECTIVES Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established. METHODS We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed. RESULTS The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model. CONCLUSION The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.
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Affiliation(s)
- Mengyin Tian
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Ying Han
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Xiaobo Ma
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Wenyan Liang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
| | - Zhaoqing Meng
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Guiyun Cao
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Yi Luo
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, Shandong, China
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Bian X, Liu Y, Zhang R, Sun H, Liu P, Tan X. Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:124016. [PMID: 38354676 DOI: 10.1016/j.saa.2024.124016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/04/2024] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm-1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.
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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, PR China; NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan 250012, PR China.
| | - Yuxia Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Rongling Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Hao Sun
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, PR China
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6
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Li K, Zhang X, Zhang J, Du B, Song X, Wang G, Li Q, Zhang Y, Liu F, Zhang Z. Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy. ACS OMEGA 2024; 9:16138-16146. [PMID: 38617685 PMCID: PMC11007685 DOI: 10.1021/acsomega.3c09994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/08/2024] [Accepted: 03/18/2024] [Indexed: 04/16/2024]
Abstract
Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.
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Affiliation(s)
- Ke Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Xin Zhang
- College
of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Jing Zhang
- College
of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Biao Du
- Beijing
Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101301, China
| | - Xiaoping Song
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Guixuan Wang
- Beijing
Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101301, China
| | - Qi Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Yinglan Zhang
- Leibniz
Institut für Polymerforschung Dresden e.V., Hohe Straße 6, Dresden 01069, Germany
- Institut
für Werkstoffwissenschaft, Technische
Universität Dresden, Dresden 01062, Germany
| | - Fan Liu
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
| | - Zhengdong Zhang
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, China
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Srivastava Y, Singh B, Kaur B, Ubaid M, Semwal AD. Kinetic study of thermal degradation of flaxseed oil and moringa oil blends with physico-chemical, oxidative stability index (OSI) and shelf-life prediction. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2024; 61:675-687. [PMID: 38410269 PMCID: PMC10894186 DOI: 10.1007/s13197-023-05868-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/20/2023] [Accepted: 10/09/2023] [Indexed: 02/28/2024]
Abstract
The thermal degradation kinetics of flaxseed oil (FSO) and moringa oil (MO) blends with soyabean oil (SOY; 80%), rice bran oil (RBO; 80%), cotton seed oil (CSO; 80%) and sunflower oil (SFO; 80%) with Rancimat equipment. There was no significant (p ≤ 0.05) difference observed in the specific gravity (SG), density (D), and refractive index (RI) values of the MO and FSO blends, while the rancidity parameters showed the opposite variations. The FTIR spectra showed absorption bands at 966 cm-1, 1097 cm-1, 1160 cm-1, 1217 cm-1, 1377 cm-1, 1464 cm-1, 1743 cm-1, 2945 cm-1, 2852 cm-1 and 3008 cm-1. Oil blends' kinetic degradation (Ea, ΔH, ΔS, A) is represented by the semilogarithmic relationship between the oxidative stability index (OSI) and temperature. The activation energy (Ea) ranged from 77.1 ± 0.21 to 106.9 ± 0.03 kJ/mol and 73.2 ± 0.01 to 104.4 ± 0.02 kJ/mol for flaxseed oil (FSO) and moringa oil (MO) blends, respectively. The enthalpy (ΔH) and entropy (ΔS) ranged from 67.3 to 121.6 kJ/mol, and - 60.2 to - 8.4 J/mol, and 63.55 to 95.59 kJ/mol and - 20.66 to - 4.11 J/mol for FSO blends and MO blends, respectively. Graphical Abstract
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Affiliation(s)
- Yashi Srivastava
- Department of Applied Agriculture, Central University of Punjab, Village Ghudda, Bathinda, Punjab 151401 India
| | - Barinderjit Singh
- Department of Food Science and Technology, I.K. Gujral Punjab Technical University, Kapurthala, Punjab 144603 India
| | | | | | - Anil Dutt Semwal
- Defence Food Research Laboratory, Siddhartha Nagar, Mysore, India
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Teng Y, Chen Y, Chen X, Zuo S, Li X, Pan Z, Shao K, Du J, Li Z. Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry. Food Chem 2024; 436:137694. [PMID: 37844509 DOI: 10.1016/j.foodchem.2023.137694] [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: 06/28/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023]
Abstract
Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.
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Affiliation(s)
- Yuanjie Teng
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Yingxin Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiangou Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shaohua Zuo
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China; Engineering Research Center of Nanoelectronic Integration and Advanced Equipment, Ministry of Education, China.
| | - Xin Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zaifa Pan
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Kang Shao
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jinglin Du
- Grain and Oil Products Quality Inspection Center of Zhejiang Province, Hangzhou 310012, China
| | - Zuguang Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
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9
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Johnson NAN, Adade SYSS, Haruna SA, Ekumah JN, Ma Y. Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123623. [PMID: 37989004 DOI: 10.1016/j.saa.2023.123623] [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: 06/12/2023] [Revised: 09/17/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023]
Abstract
The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.
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Affiliation(s)
- Nana Adwoa Nkuma Johnson
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China
| | - Selorm Yao-Say Solomon Adade
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Dietetics, Ho Teaching Hospital, Ho, Ghana.
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Food Science and Technology, Kano University of Science andTechnology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Food Science, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Ghana
| | - Yongkun Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China.
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León AY, Núñez-Méndez KS, Salas-Chia LM, Orozco-Agamez JC, Peña-Ballesteros DY, Martínez-Vertel JJ, León PA, Molina-Velasco DR. Prediction of some physicochemical properties in Colombian crude oils upgraded by catalytic aquathermolysis using UV-VIS spectroscopy associated with chemometric methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123965. [PMID: 38295596 DOI: 10.1016/j.saa.2024.123965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/02/2024]
Abstract
The simulated distillation curve (ASTM/D-7169) is a quantitative method to determine fractions of crude oils by boiling point temperature ranges (36-720 °C). In this work, 45 samples of typical Colombian crudes were selected, and the samples were produced under conventional process. Also 8 upgraded crude oils under catalytic aquathermolysis conditions at laboratory scale were added. The tests were developed at 270 °C and 800psi (@25 °C) during 66 h of reaction. In addition, 30 samples were selected for density tests, according to the pycnometer method. Subsequently, the crude oil samples under study were diluted in chloroform and analyzed by UV-VIS Spectroscopy. The UV-VIS spectra were correlated with selected properties by using PCA-MLR and PLS models. The distillation curves of the crude oils were modelled using the Riazi probability function. The prediction models of parameters To, A, and B from the Riazi probability function exhibited R2 correlation coefficients, higher than 0.94. The correlation model for the crude oil density showed a much better coefficient, higher than 0.99 and Root-Mean-Squared-Error (RMSE) close to 0.004. Additionally, even more important is the contribution of the use of UV-VIS spectroscopy as a useful tool to quickly evaluate the quality of crude oil.
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Affiliation(s)
- Adan Y León
- Grupo de Investigación en Corrosión (GIC), Universidad Industrial de Santander, Bucaramanga, 680002, Colombia; Grupo de Investigación Recobro Mejorado (GRM), Universidad Industrial de Santander, 680002, Colombia.
| | - Keyner S Núñez-Méndez
- Grupo de Investigación Recobro Mejorado (GRM), Universidad Industrial de Santander, 680002, Colombia
| | - Luis M Salas-Chia
- Grupo de Investigación Recobro Mejorado (GRM), Universidad Industrial de Santander, 680002, Colombia
| | - Juan C Orozco-Agamez
- Grupo de Investigación en Corrosión (GIC), Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
| | - Darío Y Peña-Ballesteros
- Grupo de Investigación en Corrosión (GIC), Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
| | - Jaime J Martínez-Vertel
- Grupo de Investigación Recobro Mejorado (GRM), Universidad Industrial de Santander, 680002, Colombia
| | - Paola A León
- Grupo de Investigación Recobro Mejorado (GRM), Universidad Industrial de Santander, 680002, Colombia
| | - Daniel R Molina-Velasco
- Laboratorio de Resonancia Magnética Nuclear (LEAM), Universidad Industrial de Santander, 680002, Colombia
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11
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Wu X, Zhang X, Du Z, Yang D, Xu B, Ma R, Luo H, Liu H, Zhang Y. Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil. Food Chem 2024; 431:137109. [PMID: 37582325 DOI: 10.1016/j.foodchem.2023.137109] [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: 04/17/2023] [Revised: 07/25/2023] [Accepted: 08/04/2023] [Indexed: 08/17/2023]
Abstract
Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil. The results demonstrate that all three deep learning models exhibited superior predictive ability compared to traditional chemometric methods. Specifically, the CNN-LSTM model achieved a coefficient of determination (R2p) of over 0.995 for each oil in the quantitative analysis of three-component blended oils, with a mean square error of prediction (RMSEP) of less than 2%. This study presents a novel approach for the simultaneous quantitative analysis of multi-component blended oils, providing a rapid and accurate method for the identification of falsely labeled blended oils.
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Affiliation(s)
- Xijun Wu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Xin Zhang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
| | - Zherui Du
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Daolin Yang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Baoran Xu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Renqi Ma
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Hao Luo
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Hailong Liu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yungang Zhang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
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12
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Zhang S, Yin Y, Liu C, Li J, Sun X, Wu J. Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123050. [PMID: 37379715 DOI: 10.1016/j.saa.2023.123050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/14/2023] [Accepted: 06/18/2023] [Indexed: 06/30/2023]
Abstract
Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.
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Affiliation(s)
- Shanzhe Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Yingqian Yin
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Jiacong Li
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Xiaorong Sun
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Jingzhu Wu
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
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13
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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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14
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Liu Q, Gong Z, Li D, Wen T, Guan J, Zheng W. Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods. Molecules 2023; 28:5943. [PMID: 37630193 PMCID: PMC10458121 DOI: 10.3390/molecules28165943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.
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Affiliation(s)
| | | | - Dapeng Li
- School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China; (Q.L.); (Z.G.); (T.W.); (J.G.); (W.Z.)
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15
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Yuan L, Meng X, Xin K, Ju Y, Zhang Y, Yin C, Hu L. A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 288:122120. [PMID: 36473296 DOI: 10.1016/j.saa.2022.122120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.
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Affiliation(s)
- Libo Yuan
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Xiangru Meng
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Kehui Xin
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Ying Ju
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Yan Zhang
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Chunling Yin
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Leqian Hu
- School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China.
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16
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Guo M, Li M, Fu H, Zhang Y, Chen T, Tang H, Zhang T, Li H. Quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in water by surface-enhanced Raman spectroscopy (SERS) combined with Random Forest. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122057. [PMID: 36332395 DOI: 10.1016/j.saa.2022.122057] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/20/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity, teratogenicity, mutagenicity and other adverse effects on human beings. They are one of the most dangerous pollutants, which have attracted great attention in the past decades. In this work, aiming at the actual problems that water environment is polluted and human health is threatened by PAHs, surface enhanced Raman spectroscopy (SERS) combined with Random Forest (RF) calibration models were used to quantitative analysis of phenanthrene and fluoranthene in water. Firstly, the SERS data was collected after samples mixed with Ag NPs, after 31 PAHs samples were prepared. Secondly, it was discussed how spectral preprocessing integration strategies affect on the prediction performance of the RF calibration models. And then, the effect of mutual information (MI) variable selection method on the performance of RF calibration models was explored. Finally, the RF calibration models were established for phenanthrene and fluoranthene. For the prediction set, a lowest mean relative error (MRE) and a largest determination coefficient (R2) were obtained. For quantitative analysis of phenanthrene, the final prediction performance results show that R2p is 0.9780, and MREp is 0.0369 based on the D1st-WT-RF calibration model. For fluoranthene, WT-D1st-MI-RF is a better calibration model, and corresponding to R2p and MREp are 0.9770 and 0.0694, respectively. Hence, a rapid and accurate quantitative method of PAHs is established for the real-time detection of water environmental pollution, which is intended to provide new ideas and methods for the quantitative analysis of PAHs in water.
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Affiliation(s)
- Mengjun Guo
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China
| | - Maogang Li
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China
| | - Han Fu
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China
| | - Yi Zhang
- Xi'an Wanlong Pharmaceutical Co., Ltd., Xi'an 710119, China
| | - Tingting Chen
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China
| | - Hongsheng Tang
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China.
| | - Hua Li
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710127, China; College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, China.
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17
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Bian X, Zhang R, Liu P, Xiang Y, Wang S, Tan X. Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121788. [PMID: 36058170 DOI: 10.1016/j.saa.2022.121788] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.
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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; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Sichuan 644000, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
| | - Rongling Zhang
- 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
| | - Yang Xiang
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
| | - Shuyu Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, 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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18
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Johnson JB, Thani PR, Mani JS, Cozzolino D, Naiker M. Mid-infrared spectroscopy for the rapid quantification of eucalyptus oil adulteration in Australian tea tree oil (Melaleuca alternifolia). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 283:121766. [PMID: 35988468 DOI: 10.1016/j.saa.2022.121766] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/06/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Essential oil distilled from Melaleuca alternifolialeaves, commonly known as tea tree oil, is well known for its biological activity, principally its antimicrobial properties. However, many samples are adulterated with other, cheaper essential oils such as eucalyptus oil. Current methods of detecting such adulteration are costly and time-consuming, making them unsuitable for rapid authentication screening. This study investigated the use of mid-infrared (MIR) spectroscopy for detecting and quantifying the level of eucalyptus oil adulteration in spiked samples of pure Australian tea tree oil. To confirm the authenticity of the tea tree oil samples, GC-MS analysis was used to profile 37 of the main volatile constituents present, demonstrating that the samples conformed to ISO specifications. Three chemometric regression techniques (PLSR, PCR and SVR) were trialled on the MIR spectra, along with a variety of pre-processing techniques. The best-performing full-wavelength PLSR model showed excellent prediction of eucalyptus oil content, with an R2CV of 0.999 and RMSECV of 1.08 % v/v. The RMSECV could be further improved to 0.82 % v/v through a moving window wavenumber optimisation process. The results suggest that MIR spectroscopy combined with PLSR can be used to predict eucalyptus oil adulteration in Australian tea tree oil samples with a high level of accuracy.
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Affiliation(s)
- Joel B Johnson
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, Qld 4701, Australia.
| | - Parbat Raj Thani
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, Qld 4701, Australia
| | - Janice S Mani
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, Qld 4701, Australia
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Mani Naiker
- School of Health, Medical & Applied Sciences, Central Queensland University, Bruce Hwy, North Rockhampton, Qld 4701, Australia
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19
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Quality assessment of liquorice combined with quantum fingerprint profiles and electrochemical activity. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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20
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Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27165141. [PMID: 36014381 PMCID: PMC9793756 DOI: 10.3390/molecules27165141] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/10/2022] [Indexed: 12/30/2022]
Abstract
A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R2) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.
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21
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Haruna SA, Li H, Wei W, Geng W, Yao-Say Solomon Adade S, Zareef M, Ivane NMA, Chen Q. Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:2989-2999. [PMID: 35916118 DOI: 10.1039/d2ay00875k] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (Rp = 0.937, RPD = 3.38) and CP (Rp = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P. M. B 3244, Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | | | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- College of Food and Biological Engineering, Jimei University, Xiamen, 361021, P. R. China
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Bao C, Zeng C, Liu J, Zhang D. Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection. APPLIED OPTICS 2022; 61:5790-5798. [PMID: 36255814 DOI: 10.1364/ao.463443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/15/2022] [Indexed: 06/16/2023]
Abstract
Excessive illegal addition of talc in flour has always been a serious food safety issue. To achieve rapid detection of the talc content in flour (TCF) by near-infrared spectroscopy (NIRS), this study used a Fourier transform near-infrared spectrometer technique. The identification of efficient spectral feature wavelength selection (FWS), such as backward interval partial-least-square (BiPLS), competitive adaptive reweighted sampling (CARS), hybrid genetic algorithm (HGA), and BiPLS combined with CARS; BiPLS combined with HGA; and CARS combined with HGA, was also discussed in this paper, and the corresponding partial-least-square regression models were established. Comparing with whole spectrum modeling, the accuracy and efficiency of regressive models were effectively improved using feature wavelengths of TCF selected by the above algorithms. The BiPLS, combined with HGA, had the best modeling performance; the determination coefficient, root-mean-squared error (RMSE), and residual predictive deviation of the validation set were 0.929, 1.097, and 3.795, respectively. BiPLS combined with CARS had the best dimensionality reduction effect. Through the FWS by BiPLS combined with CARS, the number of modeling wavelengths decreased to 72 from 1845, and the RMSE of the validation set was reduced by 11.6% compared with the whole spectra model. The results showed that the FWS method proposed in this paper could effectively improve detection accuracy and reduce modeling wavelength variables of quantitative analysis of TCF by NIRS. This provides theoretical support for TCF rapid detection research and development in real-time.
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Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection. Molecules 2022; 27:molecules27113373. [PMID: 35684314 PMCID: PMC9182057 DOI: 10.3390/molecules27113373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.
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