Foods
·
2024
·Vol. 13
·期 24
·4145
·
IF 4.7
Yunpeng Wei,Huiqiang Hu,Minghua Yuan,Huaxing Xu,Xiaobo Mao,Yuping Zhao,Luqi Huang
The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R2scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral–spatial features achieved the optimal model accuracy on LightGBM. The R2scores reached 0.9541 and 0.9137, increasing by 0.0308–0.1404 and 0.0181–0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry.
研究领域:
食品科学
·
植物化学
·
分析化学
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产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2024
·
IF 4.3
Yingjie Feng,Yu lv,Fujia Dong,Yue Chen,Hui Li,Argenis Rodas-González,Songlei Wang
Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R 2 p ) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.
研究领域:
食品科学
·
分析化学
·
生物医学工程
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产品关联
DOI
PubMed
高影响因子
TALANTA
·
2024
·
IF 5.6
Xiangdong Qing,Guiying Lu,Xiaohua Zhang,Qingling Chen,Xiaohong Zhou,Wei He,Ling Xu,Jin Zhang
Classifying big data in hyperspectral imaging (HSI) can be challenging when minor (low-concentrated) compounds are present in actual samples, as for chemical additives and adulterants in food matrix. Herein, we propose a new strategy to classify HSI data for the identification of adulterants in food material for the first time. This strategy is based on the selection of essential spectral pixels of full HSI data followed by the feature space construction using uniform manifold approximation and projection as well as the data clustering utilizing hierarchical clustering analysis on the reduced data (named ESPs-UMAP-HCA). We apply our approach to analyze two real NIR datasets and four new Raman datasets. Compared with non-ESPs UMAP-HCA and t-distributed stochastic neighbor embedding combined with ESPs and HCA (ESPs-t-SNE-HCA), the developed strategy provides well-separated clusters for major and minor compounds in food matrix. Finally, the adulterants as minor compounds are accurately identified, which is confirmed by the fact that the extracted spectra of them perfectly match with their pure spectra. In addition, their locations are found in the contribution map even though they are present in a few pixels. What’s more, the proposed strategy does not need any a priori knowledge of the data structure and the class memberships and therefore reduced the studied difficulty and confirmation bias in the analysis of big HSI datasets. Overall, the proposed ESPs-UMAP-HCA method could be a potential approach for food adulteration detection.
研究领域:
食品安全
·
数据分析
·
光谱学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MICROELECTRONICS RELIABILITY
·
2024
·
IF 1.6
Wei Chen,Shuo Feng,Xu Liu,Dong Hu,Xu Liu,Xi Zhu,Qi Yao,Xuejun Fan,Guoqi Zhang,Jiajie Fan
Sintered nanocopper (nanoCu) paste, exhibiting excellent electrical, thermal, and mechanical performances, offers promise for interconnections in wide bandgap (WBG) semiconductors operating at higher temperatures. However, sintered nanoCu is prone to severe corrosion in environments containing H 2 S, with on-site characterization methods for the composition of corrosion products currently lacking. In this study, a novel method was proposed for the rapid characterization of corrosion products during the corrosion process based on hyperspectral imaging (HSI) technology. Sintered nanoCu samples were subjected to 336 h H 2 S gas corrosion tests with bulk Cu as the reference, followed by correlating the corrosion element content with hyperspectral characteristic parameters. Then, the morphology and composition of corrosion products were researched using focused ion beam scanning electron microscope (FIB-SEM) and transmission electron microscope (TEM) analysis. The results showed that (1) during the corrosion process, a linear relationship was established between the Cu, O elemental atomic contents on the sample surfaces and their hyperspectral characteristic parameters. (2) The elemental atomic content of S exhibited an exponential relationship with the characteristic parameter. (3) The change rate in the spectral characteristic parameters during the corrosion process reflected the severity of corrosion, which was confirmed by comparing the thickness of the corrosion products of the sintered nanoCu and bulk Cu. This study offers a foundation for the further investigation of rapid on-site characterization of sintered nanoCu corrosion involving H 2 S.
研究领域:
材料科学
·
电子封装技术
·
表面分析技术
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产品关联
DOI
高影响因子
FOOD RESEARCH INTERNATIONAL
·
2024
·
IF 7
Yan Hu,Huahao Yu,Xinbei Song,Wei Chen,Lejia Ding,Jiayi Chen,Zhiyuan Liu,Yihang Guo,Dongyun Xu,Xuesong Zhu,Chuangchuang Zhou,Jingfei Zhang,Binhui Liao,Jihong Zhou,Xiaoli Li,Yuefei Wang,Yong He
Matcha, made from different tea leaves as raw material, exhibits diverse aromas and flavors. Therefore, there is an urgent need for a rapid, non-destructive method to assess the quality of matcha to ensure that these different characteristics are accurately assessed without compromising the integrity of the product. In this study, hyperspectral imaging technology (HSI) combined with machine learning methods enabled the first visual in situ assessment of matcha quality. The physicochemical contents of matcha were determined chemically. Qualitative and quantitative detection models for different types and grades were developed using HSI (containing Vis-NIR and NIR band). The results showed that hyperspectral data in the Vis-NIR were better than in the NIR band. The accuracy of XGBoost in modelling the classification of matcha grades reached 98.10 %. After feature selection using the random forest (RF) method, partial least squares regression (PLSR) was built to predicted the quality of matcha, which showed high prediction accuracy (test set R p 2 > 0.95). The model uses HSI to visually visualize spatial variations in constitutions (catechins, free amino acids, caffeine, soluble proteins, and soluble sugars) to show compositional differences between different types of matcha, providing a rapid non-destructive method for comprehensive assessment of matcha quality.
研究领域:
食品科学
·
光谱学
·
人工智能
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产品关联
DOI
PubMed
高影响因子
MEAT SCIENCE
·
2022
·
IF 7.077
Yu Shi,Yueying Wang,Xuetao Hu,Zhihua Li,Xiaowei Huang,Jing Liang,Xinai Zhang,Di Zhang,Xiaobo Zou,Jiyong Shi
The quality of marinated meat could be assessed by the uniformity of marinade distribution within it. In this study, the diffusion behavior of sucrose inside the marinated beef was quantitatively characterized from 2D plane and 3D space using hyperspectral imaging (HSI) and finite element analysis (FEA), respectively. Visualization of sucrose content based on HSI showed that the distribution state of sucrose on marinated beef was not uniform. Subsequently, the diffusion behavior of sucrose in the 3D beef geometric model was simulated using FEA. Results revealed that the diffusion rate of sucrose on muscle tissues is significantly higher than other tissues, and the difference in diffusion behavior is the main reason for the inhomogeneous distribution of sucrose. The quantitative characterization of marinade diffusion behavior makes it possible to predict the marinade's transient distribution state, thereby determining the optimal marinade conditions for the meat. Therefore, the method proposed has practical significance for evaluating and regulating the quality of marinated meat products.
研究领域:
食品科学
·
生物物理学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Food Analytical Methods
·
2015
·Vol. 9
·期 5
·1180-1187
·
IF 1.956
Ma Fei,Qin Hao,Zhou Cunliu,Wang Xia,Chen Conggui,Zheng Lei
The feasibility of using multispectral imaging (MSI) technique (405–970 nm) for predicting iron porphyrin (IP) content in pork was assessed. Based on nine feature wavelengths identified by successive projections algorithm (SPA), a partial least squares regression (PLSR) calibration model was established, leading to a determination coefficient ( R
2 ) of 0.996 for the prediction set. Visualization of IP content in each pixel of the multispectral images using the prediction model offers an effective way to monitor the distribution and real-time evolution of IP content during the manufacturing process. The results revealed the potentiality of MSI technique combined with PLSR calibration analysis as a rapid and non-destructive evaluation method of IP content for the agri-food industry.
研究领域:
食品科学
·
生物化学
·
农业工程
✓ 已验证
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2018
·
IF 2.88
Guiyan Yang,Qingyan Wang,Chen Liu,Xiaobin Wang,Shuxiang Fan,Wenqian Huang
Rapid and visual detection of the chemical compositions of plant seeds is important but difficult for a traditional seed quality analysis system. In this study, a custom-designed line-scan Raman hyperspectral imaging system was applied for detecting and displaying the main chemical compositions in a heterogeneous maize seed. Raman hyperspectral images collected from the endosperm and embryo of maize seed were acquired and preprocessed by Savitzky-Golay (SG) filter and adaptive iteratively reweighted Penalized Least Squares (airPLS). Three varieties of maize seeds were analyzed, and the characteristics of the spectral and spatial information were extracted from each hyperspectral image. The Raman characteristic peaks, identified at 477, 1443, 1522, 1596 and 1654 cm −1 from 380 to 1800 cm −1 Raman spectra , were related to corn starch, mixture of oil and starch, zeaxanthin , lignin and oil in maize seeds, respectively. Each single-band image corresponding to the characteristic band characterized the spatial distribution of the chemical composition in a seed successfully. The embryo was distinguished from the endosperm by band operation of the single-band images at 477, 1443, and 1596 cm −1 for each variety. Results showed that Raman hyperspectral imaging system could be used for on-line quality control of maize seeds based on the rapid and visual detection of the chemical compositions in maize seeds.
研究领域:
植物学
·
食品科学
·
分析化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MOLECULES
·
2022
·Vol. 27
·期 6
·2017
·
IF 4.927
Zhuoyi Chen,Shijie Ren,Ruimiao Qin,Pengcheng Nie
Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH4-N), sodium nitrate (nitrate nitrogen, NO3-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH4-N, R2p= 0.92, RMSEP= 0.77% and RPD = 3.63; for NO3-N, R2p= 0.92, RMSEP= 0.74% and RPD = 4.17; for urea-N, R2p= 0.96, RMSEP= 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.
研究领域:
农学
·
土壤科学
·
光谱学
✓ 已验证
DOI
高影响因子
SENSORS AND ACTUATORS A-PHYSICAL
·
2023
·
IF 4.6
Ge Gao,Qiang Li,Hongjie Luo,Xiao Huang
Environmentally friendly humidity-sensitive color-changing materials have important applications in humidity indication. Two humidity-sensitive color-changing materials are prepared by utilizing bromocresol purple (BCP)/bromothymol blue (BTB) as humidity color-changing agents, boric acid as chromogenic agent and porous ceramic as substrate. To verify the repeatability and color-changing behavior of humidity-sensitive color-changing materials, a novel quantitative model of humidity is developed using hyperspectral imaging (HSI) and the K-nearest Neighbor (KNN) model. The two samples based on the KNN model can be achieved to match the measured values of ten humidity levels and show good repeatability after 35 cycles. The reflectance of two samples rapidly changed and stabilized within four minutes at a fixed humidity, which reflects their high humidity sensitivity . The predicted values of BCP sample based on KNN model are closer to the measured values of humidity levels in the range of 85–100% RH, while the predicted values of BTB sample based on KNN model are closer to the measured values of humidity levels in 7%− 23% and 85%− 100% RH range . Visualized assessment of humidity levels at each pixel can be obtained to provide a new approach to real-time monitoring of humidity changes , which shows potential applications in many areas.
研究领域:
材料科学
·
环境科学
✓ 已验证
DOI
高影响因子
Foods
·
2024
·Vol. 13
·期 6
·897
·
IF 4.7
Chengzhi Wang,Xiaping Fu,Ying Zhou,Feng Fu
Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.
研究领域:
食品安全
·
光谱学
·
机器学习
✓ 已验证
DOI
PubMed
高影响因子
Journal of Biobased Materials and Bioenergy
·
2021
·Vol. 15
·期 5
·656-662
·
IF 0.708
Wang Xiaobin
Raman hyperspectral imaging can obtain both the internal Raman signals and the external image information of the sample simultaneously. This study investigated the quantitatively analysis of multiple food additives in wheat flour by using this technology. Raman hyperspectral images
of wheat flour containing the three additives, L-ascorbate acid (LAA), azodicarbonamide (ADC) and benzoyl peroxide (BPO), were collected. Raman signals in Raman hyperspectral images were preprocessed by smoothing and baseline correction methods to obtain the corrected image. Chemical images
were created to visually identify additive pixels by selecting single-band image corresponding to Raman characteristic peaks of each additive from the corrected image and combining with the threshold segmentation method. The results showed that the chemical image can identify the above three
additives in wheat flour. The identified additive pixels have a significant linear relationship with their concentration, and the coefficients of determination of LAA, ADC and BPO in the quantitative model were 0.9858, 0.9868 and 0.9830, respectively. This study indicated that the Raman characteristic
peaks and threshold segmentation provide a non-destructive method for quantitative analysis of multiple wheat flour additives in Raman hyperspectral images.
研究领域:
食品安全
·
光谱学
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）