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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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Ma C, Zhai L, Ding J, Liu Y, Hu S, Zhang T, Tang H, Li H. Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123953. [PMID: 38290282 DOI: 10.1016/j.saa.2024.123953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/19/2023] [Accepted: 01/21/2024] [Indexed: 02/01/2024]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid-liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.
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Affiliation(s)
- Changfei Ma
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China
| | - Lulu Zhai
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China
| | - Jianming Ding
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China
| | - Yanli Liu
- HBIS Materials Technology Research Institute, Shijiazhuang, Hebei 050000, China
| | - Shunfan Hu
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China
| | - Hongsheng Tang
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China.
| | - Hua Li
- Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material 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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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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Wen Y, Lin S, Li X, Zhang J, Zhao Y, Ma D, Li M, Ren X, Zhang W. Relationship between wheat flour’s quality characteristics and color of fresh wet noodles. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2023. [DOI: 10.1080/10942912.2022.2161565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Yaqing Wen
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Shunshun Lin
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Xuejie Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Jian Zhang
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Yang Zhao
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Dongyun Ma
- Agronomy College, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Mengqin Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Xiujuan Ren
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
| | - Weifeng Zhang
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, Henan Province, PR China
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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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