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Li H, Gui X, Wang P, Yue Y, Li H, Fan X, Li X, Liu R. Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology. Food Chem 2024; 440:138210. [PMID: 38118320 DOI: 10.1016/j.foodchem.2023.138210] [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: 09/23/2023] [Revised: 11/13/2023] [Accepted: 12/11/2023] [Indexed: 12/22/2023]
Abstract
Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.
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Affiliation(s)
- Haiyang Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xinjing Gui
- Henan University of Chinese Medicine, Zhengzhou, China; The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Panpan Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Yousong Yue
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Han Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuehua Fan
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuelin Li
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China
| | - Ruixin Liu
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China.
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Ceniti C, Spina AA, Piras C, Oppedisano F, Tilocca B, Roncada P, Britti D, Morittu VM. Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy. Foods 2023; 12:2917. [PMID: 37569186 PMCID: PMC10418805 DOI: 10.3390/foods12152917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.
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Affiliation(s)
- Carlotta Ceniti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Anna Antonella Spina
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Cristian Piras
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Francesca Oppedisano
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Bruno Tilocca
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Paola Roncada
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Domenico Britti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
| | - Valeria Maria Morittu
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
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Wei L, Ding Y, Chen J, Yang L, Wei J, Shi Y, Ma Z, Wang Z, Chen W, Zhao X. Quantitative analysis of fertilizer using laser-induced breakdown spectroscopy combined with random forest algorithm. Front Chem 2023; 11:1123003. [PMID: 36711235 PMCID: PMC9880321 DOI: 10.3389/fchem.2023.1123003] [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: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023] Open
Abstract
Chemical fertilizers are important for effectively improving soil fertility, promoting crop growth, and increasing grain yield. Therefore, methods that can quickly and accurately measure the amount of fertilizer in the soil should be developed. In this study, 20 groups of soil samples were analyzed using laser-induced breakdown spectroscopy, and partial least squares (PLS) and random forest (RF) models were established. The prediction performances of the models for the chemical fertilizer content and pH were analyzed as well. The experimental results showed that the R 2 and root mean square error (RMSE) of the chemical fertilizer content in the soil obtained using the full-spectrum PLS model were .7852 and 2.2700 respectively. The predicted R 2 for soil pH was .7290, and RMSE was .2364. At the same time, the full-spectrum RF model showed R 2 of .9471 (an increase of 21%) and RMSE of .3021 (a decrease of 87%) for fertilizer content. R 2 for the soil pH under the RF model was .9517 (an increase of 31%), whereas RMSE was .0298 (a decrease of 87%). Therefore, the RF model showed better prediction performance than the PLS model. The results of this study show that the combination of laser-induced breakdown spectroscopy with RF algorithm is a feasible method for rapid determination of soil fertilizer content.
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Affiliation(s)
- Lai Wei
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yu Ding
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China,*Correspondence: Yu Ding,
| | - Jing Chen
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Linyu Yang
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Jinyu Wei
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yinan Shi
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Zigao Ma
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Zhiying Wang
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Wenjie Chen
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xingqiang Zhao
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
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Yousuff M, Babu R. Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space. EARTH SCIENCE INFORMATICS 2022; 16:825-844. [PMID: 36575666 PMCID: PMC9782283 DOI: 10.1007/s12145-022-00917-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
Spectroscopy is a methodology for gaining knowledge of particles, especially biomolecules, by quantifying the interactions between matter and light. By examining the level of light absorbed, reflected or released by a specimen, its constituents, properties, and volume can be determined. Spectra obtained through spectroscopy procedures are quick, harmless and contactless; hence nowadays preferred in chemometrics. Due to the high dimensional nature of the spectra, it is challenging to build a robust classifier with good performance metrics. Many linear and nonlinear dimensionality reduction-based classification models have been previously implemented to overcome this issue. However, they lack in capturing the subtle details of the spectra into the low dimension space or cannot efficiently handle the nonlinearity present in the spectral data. We propose a graph-based neural network embedding approach to extract appropriate features into latent space and circumvent the spectrums' nonlinearity problem. Our approach performs dimensionality reduction into two phases: constructing a nearest neighbor graph and producing almost linear embedding using a fully connected neural network. Further, the low dimensional embedding is subjected to classification using the Random Forest algorithm. In this paper, we have implemented and compared our technique with four nonlinear dimensionality techniques widely used for spectral data analysis. In this study, we have considered five different spectral datasets belonging to specific applications. The various classification performance metrics of all the techniques are evaluated. The proposed approach is able to perform competitively well on six different low-dimensional spaces for each dataset with an accuracy score above 95% and Matthew's correlation coefficient value close to 1. The trustworthiness score of almost 1 show that the presented dimensionality reduction approach preserves the closest neighbor structure of high dimensional spectral inputs into latent space.
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Affiliation(s)
- Mohamed Yousuff
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Vellore, 632014 Tamilnadu India
| | - Rajasekhara Babu
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Vellore, 632014 Tamilnadu India
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A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression. ATMOSPHERE 2022. [DOI: 10.3390/atmos13071044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented “first Five & last Three” model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods.
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Zhou Y, Gao J. Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway? Front Psychiatry 2022; 13:1037503. [PMID: 36405901 PMCID: PMC9667021 DOI: 10.3389/fpsyt.2022.1037503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni's test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD.
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Affiliation(s)
- Yu Zhou
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
| | - Jing Gao
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
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