A feasibility research on the application of machine vision technology in appearance quality inspection of Xuesaitong dropping pills

Hou, Yizhe; Cai, Xiang; Miao, Peiqi; Li, Shunan; Shu, Chengren; Li, Pian; Li, Wenlong; Zheng, Li

doi:10.1016/j.saa.2021.119787

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…The standard variance spectrum of repeat spectra (SVSRS) of NIR spectra was obtained from six samples of Epimedii Folium and was used to evaluate the stability and reproducibility of the measured spectral conditions 14 . In the Empty Â 4 condition, the light energy far exceeded the sample absorption and an error was reported, so this condition was not investigated.…”

Section: Optimization Of Conditions For Nir Spectroscopymentioning

confidence: 99%

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

Chen,

Zhang,

Feng

et al. 2024

Phytochemical Analysis

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IntroductionThere are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time‐consuming analysis methods.ObjectiveNear‐infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium.Materials and methodsQuantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K‐nearest neighbor (KNN), back‐propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high‐performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints.ResultsQualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory.ConclusionThe method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

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Section: Optimization Of Conditions For Nir Spectroscopymentioning

confidence: 99%

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

Chen,

Zhang,

Feng

et al. 2024

Phytochemical Analysis

Self Cite

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“…With the deepening of the development of machine vision inspection technology, it has also been widely used in many fields such as metallurgy, electronics, food and so on [18].…”

Section: Related Workmentioning

confidence: 99%

Ceramic Art Image Design Based on Neural Network and Particle Image Algorithm

Zhang

Liu

2023

Preprint

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In recent years, scientists have developed a new type of algorithm called convolutional neural network algorithm in the field of neural networks. This algorithm not only has a powerful image recognition function, but also can distinguish and arrange data images. At the same time, the algorithm's recognition and processing functions are also very powerful, able to identify relatively hidden images and process a very large image library in a short time. The research content of this article is the application and development of ceramic image creation based on the classification effect of neural network and the characteristics of quantum particle swarm algorithm. And according to the principles, standards, characteristics of neural network classification and the characteristics and technology of particle swarm algorithm, the traditional LB G algorithm and an improved LB G algorithm are discussed, and simulation experiments are carried out. During the experiment, the staff analyzed and optimized the specific process of the quantum particle swarm algorithm through a large number of calculations and simulation experiments. And according to the classification of neural network and quantum particle swarm algorithm, the researchers also proposed a set of practical ceramic image design methods. Through the inspection and comparison of the design results, the researchers preliminarily judged that the design method is not only practical, but also high Many advantages such as recognition, high accuracy and good visual experience. At the same time, the staff also optimized the method based on the preliminary design results.

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“…Quality control in the manufacturing process is an important issue to guarantee the quality of end-products of botanical drugs [ 1 , 2 ]. In 2004, the U.S. Food and Drug Administration (FDA) issued the process analytical technology (PAT) industry guide, pointing out that a variety of methods exist as an overall quality-control system for PAT, which are used to monitor key quality attributes of raw materials and intermediates in real time, and to provide a reliable guarantee for the quality of end-products [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets

et al. 2022

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Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method. In the establishing of the CNN calibration model, the spectral characteristic bands were screened out by a competitive adaptive reweighted sampling (CARS) algorithm. The performance of the CNN model is evaluated by root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), coefficient of determination of calibration (Rc2), coefficient of determination of cross-validation (Rcv2), and coefficient of determination of validation (Rp2). The Rp2 values for soluble solid, salvianolic acid B, danshensu, ferulic acid, and rosmarinic acid are 0.9415, 0.9246, 0.8458, 0.8667, and 0.8491, respectively. The established model was used for the analysis of three batches of unknown samples from the manufacturing process of Guanxinning tablets. As the results show, Raman spectroscopy is faster and more convenient than that of conventional methods, which is helpful for the implementation of process analysis technology (PAT) in the manufacturing process of Guanxinning tablets.

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A feasibility research on the application of machine vision technology in appearance quality inspection of Xuesaitong dropping pills

Cited by 14 publications

References 31 publications

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

Ceramic Art Image Design Based on Neural Network and Particle Image Algorithm

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets

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