ANALYTICA CHIMICA ACTA
·
2020
·
IF 5.977
Qiannan Duan,Zhaoyi Xu,Shourong Zheng,Jiayuan Chen,Yunjin Feng,Luo Run,Jianchao Lee
Determination of complex pollutants often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their amazing successes in image recognition, drug designing, disease detection, natural language understanding, etc. ML-driven samples testing will inevitably promote the development of related subjects and fields, but the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this work, we present a full strategy for rapid detecting mixed pollutants through the synergistic application of holographic spectrum and convolutional neural network (CNN). The results have shown that a well-trained CNN model could realize quantitative analysis of the mixed pollutants by extracting spectral information of matters, suggesting the strategy’s value in facilitating the study of complex chemical systems.
研究领域:
环境科学
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化学
·
人工智能
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DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
·
2025
·
IF 5.1
Xujin Yang,Meifang Jiang,Yuling Deng,Jun Wang,Yi Tao
Fluid-bed granulation (FBG) is a critical unit in the production of Ginkgo ester , a widely recognized therapeutic agent in cardiovascular medicine. However, persistent challenges such as moisture-induced bed collapse and heterogeneous binder distribution necessitate the need for advanced real-time monitoring solutions. This study presents a novel integration of hyperspectral imaging (HSI) with convolutional neural networks (CNNs) to establish a smart process analytical technology (PAT) platform for multi-objective quality assessment during Ginkgo ester granulation. A comprehensive dataset was obtained through analysis of 99 samples from ten batches of granules, where seven active pharmaceutical ingredients were quantitatively determined using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-TQ-MS). Critical quality attributes, including particle size distribution, content uniformity, specific surface area, moisture content, Hausner ratio, and angle of repose, were systematically characterized. Through comparative evaluation of seven CNN architectures and a Competitive Adaptive Reweighted Sampling-Partial Least Squares Regression (CARS-PLSR) model, DenseNet121 emerged as the optimal architecture, demonstrating superior predictive performance across 15 quality attributes ( R p 2 : 0.9557–0.9933; RMSEP: 0.0149–0.0284). The proposed HSI-CNN framework represents a significant advancement in real-time process monitoring capabilities, offering enhanced predictive accuracy and addressing critical limitations in current FBG operations. This research establishes a robust technological foundation for implementing advanced process analytical technologies in pharmaceutical manufacturing.
研究领域:
制药工程
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生物医学工程
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产品关联
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2024
·
IF 4.3
Zhaoyi Zhang,Hefu Li,Lili Huang,Hongjun Wang,Huijuan Niu,Zhenshan Yang,Minghong Wang
Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues on fish scales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10 -6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R 2 ) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.
研究领域:
食品安全
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分析化学
·
人工智能
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2021
·
IF 7.514
Shizhuang Weng,Zhaojie Chu,Manqin Wang,Kaixuan Han,Gongqin Zhu,Cunchuan Liu,Xinhua Li,Linsheng Huang
A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic acid in edible oils. With application of OAPC, CNN performed well with coefficient of determination of correction (R 2 cor ) of 0.8843 and root mean square error of correction (RMSE cor ) of 0.1182, outperforming partial least squares regression, support vector regression and random forest with OAPC, as well as the cases without OAPC. Based on 16 effective wavelengths selected by combination of bootstrapping soft shrinkage, random frog and Pearson’s correlation, CNN and OAPC exhibited excellent performance with R 2 cor of 0.9560 and RMSE cor of 0.0730. Meanwhile, only 5% correction samples were selected by Kennard–Stone for OAPC. Overall, the proposed method could alleviate the impact of operator differences on spectral analysis, thereby providing potential to correct differences from measurement instruments or environments.
研究领域:
食品科学
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化学计量学
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人工智能
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产品关联
DOI
PubMed
高影响因子
Scientific Programming
·
2021
·
IF 1.025
Tao Li,Yuanyuan Xu,Jiliang Luo,Jianan He,Shiming Lin
In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.
研究领域:
生物信息学
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光谱学
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人工智能
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产品关联
DOI
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
·
2024
·
IF 6
Huiqiang Hu,Yunlong Mei,Yunpeng Wei,Zhenyu Xu,Yuping Zhao,Huaxing Xu,Xiaobo Mao,Luqi Huang
The bioactive components of goji berries ( Lycium barbarum ) are crucial determinants of their nutritional and commercial value. In this study, we combined hyperspectral imaging technology (HSI) with a one-dimensional convolutional neural network (1DCNN) to predict the content of chemical compositions in goji. To enhance the model's ability to focus on relevant information, we introduced the channel attention module (CAM), spectral attention module (SAM) and fused them together, which can focus on output features of the convolution kernels differently and adaptively emphasize more effective spectral bands, respectively. Moreover, considering the limitations of traditional single-task prediction methods and the inherent correlations among different constituents, we employed a multi-task CNN for the simultaneous prediction of various goji constituents. The results indicate that the attention-enhanced 1DCNN model outperforms both the partial least squares regression (PLSR) model and the vanilla 1DCNN. With multi-task learning, the model achieves optimal performance, achieving an average R 2 of 0.9435 for the prediction of the three components. Our research develops an efficient and accurate method for predicting the constituents of goji berries, providing a valuable, convenient, and effective tool for assessing and detecting the quality of fruit products in the food industry.
研究领域:
食品科学
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植物化学
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人工智能
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产品关联
DOI
高影响因子
FOOD CHEMISTRY
·
2020
·
IF 6.306
Chu Zhang,Wenyan Wu,Lei Zhou,Huan Cheng,Xingqian Ye,Yong He
Black goji berry ( Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI.
研究领域:
食品科学
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植物学
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分析化学
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2024
·
IF 8.5
Meng Wang,Xiaoqi Zhao,Xiaoyun Yang,Xueming He
The fluorescence characteristics of aflatoxin B 1 (AFB 1 ) have enabled the development of an effective and non-destructive screening method for AFB 1 in agro-products using fluorescence spectroscopy. However, the complex and varied matrices present in most foodstuffs can significantly distort the intrinsic fluorescence of AFB 1 . In this study, the absorption and scattering properties of vegetable oils were obtained using double integrating spheres (DIS), and fluorescence intensity was obtained using laser-induced fluorescence (LIF) technique. A six-parameter analytical model has been developed to recover AFB 1 intrinsic fluorescence based on the absorption and scattering features at excitation (375 nm) and emission (424 nm) wavelengths employing a one-dimensional convolutional neural network (1D-CNN). Prediction models for AFB 1 concentration in vegetable oils with gradient variations of matrices were calibrated using the disturbed and recovered intrinsic fluorescence intensity, respectively. The models were validated and compared to demonstrate the feasibility and superiority of the proposed method.
研究领域:
食品安全
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光谱学
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人工智能
✓ 已验证
DOI
PubMed
高影响因子
OPTICS COMMUNICATIONS
·
2024
·
IF 2.2
Ganshang Si,Yanchun Wang,Xu Liu,Changwei Sun,Junfang Miao,Zhengang Li
Rapid and accurate analysis of powder samples is crucial for public safety and food security. Raman spectroscopy, noted for its many advantages, traditionally faces issues of low sensitivity and limited detection information when analyzing powder samples. This paper explores a detection method for powder samples using ultraviolet (UV) Raman-fluorescence spectroscopy. By leveraging the principles of Raman and fluorescence spectroscopy, a combined detection optical path was designed. Utilizing a 266 nm laser as the system light source, a UV Raman-fluorescence spectroscopy detection setup was constructed and seven representative powder samples were analyzed. The experimental results demonstrate that the setup effectively detects the Raman spectral features of inorganic powders like baking soda and extracts the fluorescence spectral features of organic powders such as 1,2,4,5-tetramethyltoluene. Additionally, it accurately analyzes the Raman and fluorescence spectral features of amino acids and glyphosate preparation powders. For complex powder samples with issues such as “substances with the same spectrum” and “complex and similar spectra”, UV Raman-fluorescence spectroscopy enhances spectral information dimensions, compensating for the limitations of traditional Raman spectroscopy. Furthermore, a convolutional neural network algorithm was employed to analyze the measured data of the seven powder samples increased the average recognition accuracy (AVG) from 62.53% to 98.41%. This improvement further validates the effectiveness of the proposed method in recognizing the properties of powder samples.
研究领域:
分析化学
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公共安全
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食品科学
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产品关联
DOI
高影响因子
Foods
·
2024
·Vol. 13
·期 5
·793
·
IF 4.7
Mingming Zhao,Zhiheng You,Huayun Chen,Xiao Wang,Yibin Ying,Yixian Wang
Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.
研究领域:
食品科学
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人工智能
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植物生理学
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产品关联
DOI
PubMed
高影响因子
Foods
·
2025
·Vol. 14
·期 10
·1670
·
IF 5.1
Xi Wang,Siyi Wang,Shibing Zhang,Jiping Yin,Qi Zhao
As one kind of ‘probable human carcinogen’ (Group 2B) compound classified by the International Agency for Research on Cancer, 3-MCPD is mainly formed during the thermal processing of food. Tedious pretreatment techniques are needed for the existing analytical methods to quantify 3-MCPD. Hence, a nondestructive sensing technique that offers low noise interference and high quantitative precision must be developed to address this problem. Following this, Fourier transform infrared spectroscopy association with an convolutional neural network (CNN) model was employed in this investigation for the nondestructive quantitative measurement of 3-MCPD in oil samples. Before building the CNN model, NL-SGS-D2was utilized to enhance the feature extraction capability of model by eliminating the background noise. Under the optimal hyperparameter settings, calibration model achieved a determination coefficient (R2C) of 0.9982 and root mean square error (RMSEC) of 0.0181 during validation, along with a 16% performance enhancement enabled by the stepwise hybrid preprocessing strategy. The LODs (0.36 μg/g) and LOQs (1.10 μg/g) of the proposed method met the requirements for 3-MCPD detection in oil samples by the Commission Regulation issued of EU. The method proposed by CNN model with hybrid preprocessing was superior to the traditional model, and contributed to the quality monitoring of edible oil processing industry.
研究领域:
食品科学
·
分析化学
·
人工智能
✓ 已验证
DOI
PubMed
高影响因子
INFRARED PHYSICS & TECHNOLOGY
·
2023
·
IF 3.3
Yingchao Xue,Chengyun Zhu,Hui Jiang
This study introduces a fast analytical approach for detecting chlorpyrifos residues in corn oil, based on a one-dimensional convolutional neural network (1D-CNN) structure for near-infrared (NIR) spectral deep learning models. Data augmentation algorithms were introduced to effectively expand the database of corn oil samples obtained in the experiment, ensuring sufficient training of the deep learning network. A well-designed 1D-CNN structure was implemented to create a multivariate calibration model. This model can be used to quantify chlorpyrifos residues in corn oil. The study found that using data augmentation with a 1D-CNN model led to improved performance compared to using the original spectra calibration in the model. Particularly, the 1D-CNN model’s best overall performance was achieved using data augmented with varying degrees of signal-to-noise ratio (SNR) algorithms, with a prediction coefficient of determination ( R P 2 ) of 0.9492 and a relative percent deviation (RPD) of 4.4569. The study demonstrates the feasibility of using NIR spectral deep learning models for quantitative analysis of pesticide residues in edible oils. Furthermore, data augmentation methods can effectively improve the accuracy of deep learning algorithms in NIR spectral chemometrics model calibration.
研究领域:
食品科学
·
分析化学
·
人工智能
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）