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Hao N, Ping J, Wang X, Sha X, Wang Y, Miao P, Liu C, Li W. Data fusion of near-infrared and mid-infrared spectroscopy for rapid origin identification and quality evaluation of Lonicerae japonicae flos. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124590. [PMID: 38850827 DOI: 10.1016/j.saa.2024.124590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/09/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
A data fusion strategy based on near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were developed for rapid origin identification and quality evaluation of Lonicerae japonicae flos (LJF). A high-level data fusion for origin identification was formed using the soft voting method. This data fusion model achieved accuracy, log-loss value and Kappa value of 95.5%, 0.347 and 0.910 on the prediction set. The spectral data were converted to liquid chromatography data using a data fusion model constructed by the weighted average algorithm. The Euclidean distance and adjusted cosine similarity were used to evaluate the similarity between the converted and the real chromatographic data, with results of 247.990 and 0.996, respectively. The data fusion models all performed better than the models constructed using single data. This indicates that multispectral data fusion techniques have a wide range of application prospects and practical value in the quality control of natural products such as LJF.
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Affiliation(s)
- Nan Hao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Jiacong Ping
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Xi Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Xin Sha
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yanshuai Wang
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Peiqi Miao
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Changqing Liu
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China.
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2
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Sitorus A, Lapcharoensuk R. Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration. Food Chem 2024; 457:140108. [PMID: 38905832 DOI: 10.1016/j.foodchem.2024.140108] [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: 12/01/2023] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/23/2024]
Abstract
This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%-50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP). The proposed performance strategy effectively addressed and produced satisfactory outcomes for classification and regression challenges in coconut milk adulteration. Finally, the results demonstrated that the proposed approach can more accurately determine the best model, particularly for NIR spectroscopy of coconut milk adulteration.
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Affiliation(s)
- Agustami Sitorus
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand; National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
| | - Ravipat Lapcharoensuk
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
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3
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Lu J, Jiang Y, Jin B, Sun C, Wang L. Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content in Ginkgo biloba Leaves. Int J Mol Sci 2024; 25:9584. [PMID: 39273532 PMCID: PMC11395087 DOI: 10.3390/ijms25179584] [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: 06/26/2024] [Revised: 08/05/2024] [Accepted: 08/31/2024] [Indexed: 09/15/2024] Open
Abstract
Ginkgo biloba is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (RTest2 = 0.87; RMSETest = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.
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Affiliation(s)
- Jinkai Lu
- College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China
| | - Yanbing Jiang
- College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China
| | - Biao Jin
- College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China
| | - Chengming Sun
- Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Li Wang
- College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China
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4
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Ping J, Ying Z, Hao N, Miao P, Ye C, Liu C, Li W. Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies. Food Res Int 2024; 192:114758. [PMID: 39147491 DOI: 10.1016/j.foodres.2024.114758] [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: 04/18/2024] [Revised: 07/06/2024] [Accepted: 07/10/2024] [Indexed: 08/17/2024]
Abstract
The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model's performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.
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Affiliation(s)
- Jiacong Ping
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Zehua Ying
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Nan Hao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Peiqi Miao
- Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China; National Innovation Center for Modern Chinese Medicine, Tianjin 300392, China
| | - Cheng Ye
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Changqing Liu
- Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China; National Innovation Center for Modern Chinese Medicine, Tianjin 300392, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China.
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5
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Yu S, Jia P, Xing K, Yao L, Chen M, Ding L, Huang J, Cheng Y, Xu Z. Novel Immunosensor Based on Metal Single-Atom Nanozymes with Enhanced Oxidase-Like Activity for Capsaicin Analysis in Spicy Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:12832-12841. [PMID: 38785419 DOI: 10.1021/acs.jafc.4c01118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Capsaicin (CAP) is a primary indicator for assessing the level of pungency. Herein, iron-based single-atom nanozymes (SAzymes) (Fe/NC) with exceptional oxidase-like activity were used to construct an immunosensor for CAP analysis. Fe/NC could imitate oxidase actions by transforming O2 to •O2- radicals in the absence of hydrogen peroxide (H2O2), which could avoid complex operations and unstable results. By regulating the Fe atom loads, an optimal Fe0.7/NC atom usage rate could improve the catalytic activity (Michaelis-Menten constant (Km) = 0.09 mM). Fe0.7/NC was integrated with goat antimouse IgG by facile mix incubation to develop a competitive enzyme-linked immunosorbent assay (ELISA). Our Fe0.7/NC immunosensing platform is anticipated to outperform the conventional ELISA in terms of stability and shelf life. The proposed immunosensor provided color responses across 0.01-1000 ng/mL CAP concentrations, with a detection limit of 0.046 ng/mL. Fe/NC may have potential as nanozymes for CAP detection in spicy foods, with promising applications in food biosensing.
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Affiliation(s)
- Shaoyi Yu
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Pei Jia
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Keyu Xing
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Li Yao
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Maolong Chen
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Li Ding
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Jin Huang
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Yunhui Cheng
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
- School of Food Science and Engineering, Qilu University of Technology, Jinan 250353, China
| | - Zhou Xu
- Hunan Provincial Key Laboratory of Cytochemistry, School of Food Science and Bioengineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha 410004, Hunan, China
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6
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Chen R, Li S, Cao H, Xu T, Bai Y, Li Z, Leng X, Huang Y. Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics. Food Chem 2024; 438:137931. [PMID: 37989021 DOI: 10.1016/j.foodchem.2023.137931] [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: 08/21/2023] [Revised: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/23/2023]
Abstract
Ginger powder is an important spice that is susceptible to improper sales such as adulteration or geographical fraud. In this study, a portable near infrared spectroscopy was used to quantitatively predict the 6-gingerol content, an important quality index of ginger, as well as to identify the gingers from three origins in China. Specifically, the optimal preprocessing method was first investigated by comparing the predictions of models. Then three feature variable selection methods including PCA, CARS, and RFrog, on the quantitative analysis of 6-gingerol were also compared, respectively. After comparison, the PLS model established on the S-G combined with SNV preprocessing outperformed the others. The PLS regression of 6-gingerol with variables selected by RFrog possessed the Rc2 of 0.9463, Rp2 of 0.9497, and the RPD of 4.2257, respectively. Moreover, the results further verified that the LDA model by SPA variables extraction successfully identify gingers from different origins with 100 % accuracy.
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Affiliation(s)
- Rui Chen
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China; Institute of Healthy Food Industry, China Agricultural University, Jiangsu 225721, China
| | - Shaoqun Li
- Institute of Healthy Food Industry, China Agricultural University, Jiangsu 225721, China; Food Detection and Supervision Center, Xinghua, Jiangsu 225721, China
| | - Huijuan Cao
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China; Institute of Healthy Food Industry, China Agricultural University, Jiangsu 225721, China
| | - Tongguang Xu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China; Beijing R&D Center, Shanghai Tobacco Group, Beijing 101121, China
| | - Yanchang Bai
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China; Institute of Healthy Food Industry, China Agricultural University, Jiangsu 225721, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Jiangsu 212004, China
| | - Xiaojing Leng
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China
| | - Yue Huang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Sanya Institute of China Agricultural University, Hainan 572025, China; Institute of Healthy Food Industry, China Agricultural University, Jiangsu 225721, China; School of Grain Science and Technology, Jiangsu University of Science and Technology, Jiangsu 212004, China.
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7
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Zhang Y, Wang Y. Recent trends of machine learning applied to multi-source data of medicinal plants. J Pharm Anal 2023; 13:1388-1407. [PMID: 38223450 PMCID: PMC10785154 DOI: 10.1016/j.jpha.2023.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 01/16/2024] Open
Abstract
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
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8
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Park JJ, Cho JS, Lee G, Yun DY, Park SK, Park KJ, Lim JH. Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging. Foods 2023; 12:3471. [PMID: 37761180 PMCID: PMC10528317 DOI: 10.3390/foods12183471] [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: 08/14/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000-1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.
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Affiliation(s)
- Jong-Jin Park
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Jeong-Seok Cho
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Gyuseok Lee
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Dae-Yong Yun
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Seul-Ki Park
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Kee-Jai Park
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
| | - Jeong-Ho Lim
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju-gun 55365, Republic of Korea
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9
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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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10
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Zhong L, Huang R, Gao L, Yue J, Zhao B, Nie L, Li L, Wu A, Zhang K, Meng Z, Cao G, Zhang H, Zang H. A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration. Molecules 2023; 28:5672. [PMID: 37570642 PMCID: PMC10419756 DOI: 10.3390/molecules28155672] [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: 05/11/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.
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Affiliation(s)
- Liang Zhong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Ruiqi Huang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Jianan Yue
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Bing Zhao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Aoli Wu
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Kefan Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Zhaoqing Meng
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China; (Z.M.); (G.C.)
| | - Guiyun Cao
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China; (Z.M.); (G.C.)
| | - Hui Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
- National Glycoengineering Research Center, Shandong University, Jinan 250012, 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 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
- National Glycoengineering Research Center, Shandong University, Jinan 250012, China
- Key Laboratory of Chemical Biology, Ministry of Education, Shandong University, Jinan 250012, China
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Islam K, Rawoof A, Kumar A, Momo J, Ahmed I, Dubey M, Ramchiary N. Genetic Regulation, Environmental Cues, and Extraction Methods for Higher Yield of Secondary Metabolites in Capsicum. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37289974 DOI: 10.1021/acs.jafc.3c01901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Capsicum (chili pepper) is a widely popular and highly consumed fruit crop with beneficial secondary metabolites such as capsaicinoids, carotenoids, flavonoids, and polyphenols, among others. Interestingly, the secondary metabolite profile is a dynamic function of biosynthetic enzymes, regulatory transcription factors, developmental stage, abiotic and biotic environment, and extraction methods. We propose active manipulable genetic, environmental, and extraction controls for the modulation of quality and quantity of desired secondary metabolites in Capsicum species. Specific biosynthetic genes such as Pun (AT3) and AMT in the capsaicinoids pathway and PSY, LCY, and CCS in the carotenoid pathway can be genetically engineered for enhanced production of capsaicinoids and carotenoids, respectively. Generally, secondary metabolites increase with the ripening of the fruit; however, transcriptional regulators such as MYB, bHLH, and ERF control the extent of accumulation in specific tissues. The precise tuning of biotic and abiotic factors such as light, temperature, and chemical elicitors can maximize the accumulation and retention of secondary metabolites in pre- and postharvest settings. Finally, optimized extraction methods such as ultrasonication and supercritical fluid method can lead to a higher yield of secondary metabolites. Together, the integrated understanding of the genetic regulation of biosynthesis, elicitation treatments, and optimization of extraction methods can maximize the industrial production of secondary metabolites in Capsicum.
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Affiliation(s)
- Khushbu Islam
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Abdul Rawoof
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Ajay Kumar
- Department of Plant Sciences, School of Biological Sciences, Central University of Kerala, Kasaragod 671316, Kerala, India
| | - John Momo
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Ilyas Ahmed
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Meenakshi Dubey
- Department of Biotechnology, Delhi Technological University, New Delhi 110042, India
| | - Nirala Ramchiary
- School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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