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Zheng C, Li J, Liu H, Wang Y. Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes. Food Chem X 2024; 23:101661. [PMID: 39113735 PMCID: PMC11304868 DOI: 10.1016/j.fochx.2024.101661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 08/10/2024] Open
Abstract
The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.
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Affiliation(s)
- Chuanmao Zheng
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, China
- Medicinal Plants Research Institute, Yunnan, Academy of Agricultural Sciences, Kunming 650200, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, China
| | - Honggao Liu
- Yunnan Key Laboratory of Gastrodia and Fungi Symbiotic Biology, Zhaotong University, Zhaotong 657000, Yunnan, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan, Academy of Agricultural Sciences, Kunming 650200, China
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Bai L, Zhang ZT, Guan H, Liu W, Chen L, Yuan D, Chen P, Xue M, Yan G. Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network. Talanta 2024; 275:126098. [PMID: 38640523 DOI: 10.1016/j.talanta.2024.126098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.
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Affiliation(s)
- Lei Bai
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Zhi-Tong Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Huanhuan Guan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Wenjian Liu
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Li Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Dongping Yuan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Pan Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Mei Xue
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing 210023, China.
| | - Guojun Yan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China.
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Li Y, Chen B, Ye S, Wu Q, Zhu L, Ding Y. Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:587-600. [PMID: 38648105 DOI: 10.1080/19440049.2024.2341104] [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: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.
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Affiliation(s)
- Yaxin Li
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Baoguo Chen
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Shuhong Ye
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Qi Wu
- China National Institute of Standardization, Beijing, China
| | - Lin Zhu
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Yan Ding
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
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Naskar S, Sing D, Banerjee S, Shcherbakova A, Bandyopadhyay A, Kar A, Haldar PK, Sharma N, Mukherjee PK, Bandyopadhyay R. Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting. PHYTOCHEMICAL ANALYSIS : PCA 2024. [PMID: 38802067 DOI: 10.1002/pca.3397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/11/2024] [Accepted: 05/11/2024] [Indexed: 05/29/2024]
Abstract
INTRODUCTION Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. OBJECTIVE The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. METHODS Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. RESULTS The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. CONCLUSION The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.
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Affiliation(s)
- Sirsha Naskar
- School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Dilip Sing
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, West Bengal, India
- MetaspeQ Division, Ayudyog Pvt. Ltd., Kolkata, India
| | - Subhadip Banerjee
- School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
- MetaspeQ Division, Ayudyog Pvt. Ltd., Kolkata, India
| | - Anastasiia Shcherbakova
- Medical Clinic III, AG Synergy Research and Experimental Medicine, University Hospital Bonn (UKB), Bonn, Germany
| | - Amitabha Bandyopadhyay
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Amit Kar
- Institute of Bioresources and Sustainable Development, Imphal, Manipur, India
| | - Pallab Kanti Haldar
- School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Nanaocha Sharma
- Institute of Bioresources and Sustainable Development, Imphal, Manipur, India
| | - Pulok Kumar Mukherjee
- School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
- Institute of Bioresources and Sustainable Development, Imphal, Manipur, India
| | - Rajib Bandyopadhyay
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, West Bengal, India
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Jongyingcharoen JS, Howimanporn S, Sitorus A, Phanomsophon T, Posom J, Salubsi T, Kongwaree A, Lim CH, Phetpan K, Sirisomboon P, Tsuchikawa S. Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data. Polymers (Basel) 2024; 16:184. [PMID: 38256982 PMCID: PMC10818871 DOI: 10.3390/polym16020184] [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: 10/30/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80-88% TSI, and more than 88% TSI. The 80-88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.
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Affiliation(s)
- Jiraporn Sripinyowanich Jongyingcharoen
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; (J.S.J.); (S.H.); (T.P.); (P.S.)
| | - Suppakit Howimanporn
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; (J.S.J.); (S.H.); (T.P.); (P.S.)
| | - Agustami Sitorus
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; (J.S.J.); (S.H.); (T.P.); (P.S.)
- National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
| | - Thitima Phanomsophon
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; (J.S.J.); (S.H.); (T.P.); (P.S.)
| | - Jetsada Posom
- Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Thanapol Salubsi
- W. A. Rubber Mate Co., Ltd., Bangkok 10240, Thailand; (T.S.); (A.K.)
| | - Adisak Kongwaree
- W. A. Rubber Mate Co., Ltd., Bangkok 10240, Thailand; (T.S.); (A.K.)
| | - Chin Hock Lim
- Thai Rubber Latex Group Public Co., Ltd., Chonburi 20190, Thailand;
| | - Kittisak Phetpan
- Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand;
| | - Panmanas Sirisomboon
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; (J.S.J.); (S.H.); (T.P.); (P.S.)
| | - Satoru Tsuchikawa
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan;
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Yu DX, Guo S, Zhang X, Yan H, Mao SW, Wang JM, Zhou JQ, Yang J, Yuan YW, Duan JA. Combining stable isotope, multielement and untargeted metabolomics with chemometrics to discriminate the geographical origins of ginger (Zingiber officinale Roscoe). Food Chem 2023; 426:136577. [PMID: 37301043 DOI: 10.1016/j.foodchem.2023.136577] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/14/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
Ginger (Zingiber officinale Roscoe) is a high-value food and herb worldwide. The quality of ginger is often related to its production regions. In this study, stable isotopes, multiple elements, and metabolites were investigated together to realize ginger origin traceability. Chemometrics showed that ginger samples could be preliminarily separated, and 4 isotopes (δ13C, δ2H, δ18O, and δ34S), 12 mineral elements (Rb, Mn, V, Na, Sm, K, Ga, Cd, Al, Ti, Mg, and Li), 1 bioelement (%C), and 143 metabolites were the most important variables for discrimination. Furthermore, three algorithms were introduced, and the fused dataset based on VIP features led to the highest accuracies for origin classification, with predictive rates of 98% for K-nearest neighbor and 100% for support vector machine and random forest. The results demonstrated that isotopic, elemental, and metabolic fingerprints were useful indicators for the geographical origins of Chinese ginger.
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Affiliation(s)
- Dai-Xin Yu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Sheng Guo
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Xia Zhang
- College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Hui Yan
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Su-Wan Mao
- College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jie-Mei Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jia-Qi Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jian Yang
- State Key Laboratory of Dao-di Herbs Breeding Base, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yu-Wei Yuan
- Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou 310021, China
| | - Jin-Ao Duan
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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He G, Yang SB, Wang YZ. An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum. Anal Chim Acta 2023; 1280:341869. [PMID: 37858569 DOI: 10.1016/j.aca.2023.341869] [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: 06/26/2023] [Revised: 08/31/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed. RESULTS Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples. SIGNIFICANCE This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.
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Affiliation(s)
- Gang He
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China; College of Food Science and Technology, Yunnan Agricultural University, Kunming, 650201, China
| | - Shao-Bing Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China.
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China.
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Jiang Z, Lv A, Zhong L, Yang J, Xu X, Li Y, Liu Y, Fan Q, Shao Q, Zhang A. Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques. Foods 2023; 12:2904. [PMID: 37569173 PMCID: PMC10417609 DOI: 10.3390/foods12152904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.
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Affiliation(s)
- Zhiwei Jiang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Aimin Lv
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Lingjiao Zhong
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Jingjing Yang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Xiaowei Xu
- Wenzhou Forestry Technology Promotion and Wildlife Protection Management Station, Wenzhou 325027, China
| | - Yuchan Li
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
| | - Yuchen Liu
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
| | - Qiuju Fan
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
| | - Qingsong Shao
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Ailian Zhang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China (Y.L.); (Q.S.)
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
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9
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An T, Wang Z, Li G, Fan S, Huang W, Duan D, Zhao C, Tian X, Dong C. Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level. Food Chem X 2023; 18:100718. [PMID: 37397207 PMCID: PMC10314168 DOI: 10.1016/j.fochx.2023.100718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/14/2023] [Accepted: 05/18/2023] [Indexed: 07/04/2023] Open
Abstract
Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.
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Affiliation(s)
- Ting An
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Guanglin Li
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Dandan Duan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunjiang Zhao
- College of Engineering and Technology, Southwest University, Chongqing 400715, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250033, China
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Rapid Analysis of Raw Meal Composition Content Based on NIR Spectroscopy for Cement Raw Material Proportioning Control Process. Processes (Basel) 2022. [DOI: 10.3390/pr10122494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Due to fast analysis speed, analyzing composition content of cement raw meal utilizing near infrared (NIR) spectroscopy, combined with partial least squares regression (PLS), is a reliable alternative method for the cement industry to obtain qualified cement products. However, it has hardly been studied. The raw materials employed in different cement plants differ, and the spectral absorption intensity in the NIR range of the raw meal component is weaker than organic substances, although there are obvious absorption peaks, which place high demands on the generality of modeling and accuracy of the analytical model. An effective modeling procedure is proposed, which optimizes the quantitative analytical model from several modeling stages, and two groups of samples with different raw material types and origins are collected to validate it. For the samples in the prediction set from Qufu, the root mean square error of prediction (RMSEP) of CaO, SiO2, Al2O3, and Fe2O3 were 0.1910, 0.2307, 0.0921, and 0.0429, respectively; the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.171%, 0.193%, 0.069%, and 0.032%, respectively; for the samples in the prediction set from Linyi, the RMSEP of CaO, SiO2, Al2O3, and Fe2O3 were 0.1995, 0.1267, 0.0336 and 0.0242, respectively, the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.154%, 0.100%, 0.022%, and 0.018%, respectively. The standard methods for chemical analysis of cement require that the mean measurement error for CaO, SiO2, Al2O3, and Fe2O3 should be within 0.40%, 0.30%, 0.20%, and 0.15%, respectively. It is obvious that the results of both groups of samples fully satisfied the requirements of raw material proportioning control of the production line, demonstrating that the modeling procedure has excellent generality, the models established have high prediction accuracy, and the NIR spectroscopy combined with the proposed modeling procedure is a rapid and accurate alternative approach for the analysis of cement raw meal composition content.
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