1
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França L, Grassi S, Pimentel MF, Amigo JM. A single model to monitor multistep craft beer manufacturing using near infrared spectroscopy and chemometrics. FOOD AND BIOPRODUCTS PROCESSING 2021. [DOI: 10.1016/j.fbp.2020.12.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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2
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Zuo Y, Yang J, Li C, Deng X, Zhang S, Wu Q. Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2020; 2020:8847277. [PMID: 33204575 PMCID: PMC7657684 DOI: 10.1155/2020/8847277] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/10/2020] [Accepted: 10/21/2020] [Indexed: 06/11/2023]
Abstract
Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients (R p 2) were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches' results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.
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Affiliation(s)
- Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China
| | - Jing Yang
- School of Basic Medical Sciences, Wuhan University, 299 Bayi Rd, Wuhan, Hubei 430072, China
| | - Chen Li
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China
| | - Xuehua Deng
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China
| | - Shengsheng Zhang
- Innovation Laboratory, The Third Experiment Middle School, Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Qing Wu
- Innovation Laboratory, The Third Experiment Middle School, Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
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3
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A Review on the Application of Chemometrics and Machine Learning Algorithms to Evaluate Beer Authentication. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01864-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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4
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Wei G, Li Y, Zhang Z, Chen Y, Chen J, Yao Z, Lao C, Chen H. Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms. PeerJ 2020; 8:e9087. [PMID: 32377459 PMCID: PMC7194094 DOI: 10.7717/peerj.9087] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 04/08/2020] [Indexed: 11/20/2022] Open
Abstract
Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0–20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination (R2), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy (Rc2 = 0.835, RP2 = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.
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Affiliation(s)
- Guangfei Wei
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
| | - Yu Li
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China
| | - Zhitao Zhang
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
| | - Yinwen Chen
- Department of Foreign Languages, Northwest A&F University, Yangling, China
| | - Junying Chen
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
| | - Zhihua Yao
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
| | - Congcong Lao
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
| | - Huifang Chen
- College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China
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Liu Y, Zhou S, Han W, Liu W, Qiu Z, Li C. Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Anal Chim Acta 2019; 1086:46-54. [PMID: 31561793 DOI: 10.1016/j.aca.2019.08.026] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 08/06/2019] [Accepted: 08/14/2019] [Indexed: 01/23/2023]
Abstract
Fusion of spectral and spatial information has been proved to be an effective approach to improve model performance in near-infrared hyperspectral data analysis. Regardless, most of the existing spectral-spatial classification methods require fairly complex pipelines and exact selection of parameters, which mainly depend on the investigator's experience and the object under test. Convolutional neural network (CNN) is a powerful tool for representing complicated data and usually works with few "hand-engineering", making it an appropriate candidate for developing a general and automatic approach. In this paper, a two-branch convolutional neural network (2B-CNN) was developed for spectral-spatial classification and effective wavelengths (EWs) selection. The proposed network was evaluated by three classification data sets, including herbal medicine, coffee bean and strawberry. The results showed that the 2B-CNN obtained the best classification accuracies (96.72% in average) when compared with support vector machine (92.60% in average), one dimensional CNN (92.58% in average), and grey level co-occurrence matrix based support vector machine (93.83% in average). Furthermore, the learned weights of the two-dimensional branch in 2B-CNN were adopted as the indicator of EWs and compared with the successive projections algorithm. The 2B-CNN models built with wavelengths selected by the weight indicator achieved the best accuracies (96.02% in average) among all the examined EWs models. Different from the conventional EWs selection method, the proposed algorithm works without any additional retraining and has the ability to comprehensively consider the discriminative power in spectral domain and spatial domain.
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Affiliation(s)
- Yisen Liu
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
| | - Songbin Zhou
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China.
| | - Wei Han
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
| | - Weixin Liu
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
| | - Zefan Qiu
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
| | - Chang Li
- Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
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Chapman J, Gangadoo S, Truong VK, Cozzolino D. Spectroscopic approaches for rapid beer and wine analysis. Curr Opin Food Sci 2019. [DOI: 10.1016/j.cofs.2019.09.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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7
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Feng L, Zhu S, Chen S, Bao Y, He Y. Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2934. [PMID: 31277225 PMCID: PMC6651745 DOI: 10.3390/s19132934] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 06/24/2019] [Accepted: 07/02/2019] [Indexed: 11/17/2022]
Abstract
Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Shuangshuang Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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8
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da Silva Oliveira V, Honorato RS, Honorato FA, Pereira CF. Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics. Forensic Sci Int 2018; 286:121-127. [DOI: 10.1016/j.forsciint.2018.03.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/28/2018] [Accepted: 03/01/2018] [Indexed: 12/13/2022]
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9
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10
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Pereira HV, Amador VS, Sena MM, Augusti R, Piccin E. Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers. Anal Chim Acta 2016; 940:104-12. [DOI: 10.1016/j.aca.2016.08.002] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 08/02/2016] [Accepted: 08/03/2016] [Indexed: 01/05/2023]
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11
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Mannina L, Marini F, Antiochia R, Cesa S, Magrì A, Capitani D, Sobolev AP. Tracing the origin of beer samples by NMR and chemometrics: Trappist beers as a case study. Electrophoresis 2016; 37:2710-2719. [DOI: 10.1002/elps.201600082] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 06/15/2016] [Accepted: 06/24/2016] [Indexed: 12/27/2022]
Affiliation(s)
- Luisa Mannina
- Dipartimento di Chimica e Tecnologie del Farmaco; Sapienza Università di Roma; Rome Italy
- Istituto di Metodologie Chimiche, CNR; Laboratorio di Risonanza Magnetica Nucleare “Annalaura Segre,”; Monterotondo Rome Italy
| | - Federico Marini
- Dipartimento di Chimica; Sapienza Università di Roma; Rome Italy
| | - Riccarda Antiochia
- Dipartimento di Chimica e Tecnologie del Farmaco; Sapienza Università di Roma; Rome Italy
| | - Stefania Cesa
- Dipartimento di Chimica e Tecnologie del Farmaco; Sapienza Università di Roma; Rome Italy
| | - Antonio Magrì
- Dipartimento di Chimica; Sapienza Università di Roma; Rome Italy
| | - Donatella Capitani
- Istituto di Metodologie Chimiche, CNR; Laboratorio di Risonanza Magnetica Nucleare “Annalaura Segre,”; Monterotondo Rome Italy
| | - Anatoly P. Sobolev
- Istituto di Metodologie Chimiche, CNR; Laboratorio di Risonanza Magnetica Nucleare “Annalaura Segre,”; Monterotondo Rome Italy
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12
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Ghasemi-Varnamkhasti M, Goli R, Forina M, Mohtasebi SS, Shafiee S, Naderi-Boldaji M. Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2016. [DOI: 10.1080/10942912.2015.1118386] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | - Reza Goli
- Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord, Iran
| | - Michele Forina
- Department of Drug and Food Chemistry and Technology, University of Genoa, Genoa, Italy
| | | | - Sahameh Shafiee
- Department of Agricultural Machinery Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mojtaba Naderi-Boldaji
- Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord, Iran
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13
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Evaluation of the suitability of electronic nose based on fast GC for distinguishing between the plum spirits of different geographical origins. Eur Food Res Technol 2016. [DOI: 10.1007/s00217-016-2680-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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14
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Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen–thawed fish muscle. Food Chem 2016; 197:855-63. [DOI: 10.1016/j.foodchem.2015.11.019] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 10/07/2015] [Accepted: 11/04/2015] [Indexed: 01/08/2023]
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15
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Śliwińska M, Wiśniewska P, Dymerski T, Wardencki W, Namieśnik J. Application of Electronic Nose Based on Fast GC for Authenticity Assessment of Polish Homemade Liqueurs Called Nalewka. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0448-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Using UV–Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup. Food Chem 2016; 192:374-9. [DOI: 10.1016/j.foodchem.2015.07.022] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 11/20/2014] [Accepted: 07/07/2015] [Indexed: 01/21/2023]
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17
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Diniz PHGD, Pistonesi MF, Alvarez MB, Band BSF, de Araújo MCU. Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA). J Food Compost Anal 2015. [DOI: 10.1016/j.jfca.2014.11.012] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Cheng JH, Sun DW. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. FOOD BIOPROCESS TECH 2015. [DOI: 10.1007/s11947-014-1457-9] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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19
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Pessoa C, Ranzan C, Trierweiler L, Trierweiler J. Development of Ant Colony Optimization (ACO) Algorithms Based on Statistical Analysis and Hypothesis Testing for Variable Selection. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.09.084] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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20
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Giovenzana V, Beghi R, Guidetti R. Rapid evaluation of craft beer quality during fermentation process by vis/NIR spectroscopy. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.06.017] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Tan C, Chen H, Lin Z, Wu T, Wang L, Zhang K. Classification of Liquor Using Near-Infrared Spectroscopy and Chemometrics. ANAL LETT 2014. [DOI: 10.1080/00032719.2014.938343] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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22
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Xiong Z, Sun DW, Dai Q, Han Z, Zeng XA, Wang L. Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9853-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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23
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Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella). FOOD BIOPROCESS TECH 2014. [DOI: 10.1007/s11947-014-1325-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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24
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Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9809-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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25
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Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis. Food Chem 2014; 155:279-86. [PMID: 24594186 DOI: 10.1016/j.foodchem.2014.01.060] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Revised: 01/15/2014] [Accepted: 01/18/2014] [Indexed: 11/20/2022]
Abstract
This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer.
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Cheng JH, Sun DW, Zeng XA, Pu HB. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. INNOV FOOD SCI EMERG 2014. [DOI: 10.1016/j.ifset.2013.10.013] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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27
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Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem 2013; 141:389-96. [DOI: 10.1016/j.foodchem.2013.02.094] [Citation(s) in RCA: 158] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 02/03/2013] [Accepted: 02/23/2013] [Indexed: 11/22/2022]
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28
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de Araújo Gomes A, Galvão RKH, de Araújo MCU, Véras G, da Silva EC. The successive projections algorithm for interval selection in PLS. Microchem J 2013. [DOI: 10.1016/j.microc.2013.03.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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29
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Freitas SKB, Nascimento ECLD, Dionízio AGG, Gomes ADA, Araújo MCUD, Galvão RKH. A flow-batch analyzer using a low cost aquarium pump for classification of citrus juice with respect to brand. Talanta 2013; 107:45-8. [DOI: 10.1016/j.talanta.2012.12.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2012] [Revised: 11/29/2012] [Accepted: 12/21/2012] [Indexed: 10/27/2022]
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30
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Soares SFC, Gomes AA, Araujo MCU, Filho ARG, Galvão RKH. The successive projections algorithm. Trends Analyt Chem 2013. [DOI: 10.1016/j.trac.2012.09.006] [Citation(s) in RCA: 142] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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31
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Pontes MJC, Gomes AA, Galvão RKH, Araújo MCU. Internal and External Validation in SPA-LDA: A Comparative Study Involving Diesel/Biodiesel Blends. ACTA ACUST UNITED AC 2012. [DOI: 10.1255/nirn.1313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Márcio José Coelho Pontes
- Universidade Federal da Paraíba, Departamento de Química—Laboratório de Automação e Instrumentaçáo em Química Analítica/Quimiometria (LAQA), Caixa Postal 5093, CEP 58051-970—João Pessoa, PB, Brazil
| | - Adriano Araújo Gomes
- Universidade Federal da Paraíba, Departamento de Química—Laboratório de Automação e Instrumentaçáo em Química Analítica/Quimiometria (LAQA), Caixa Postal 5093, CEP 58051-970—João Pessoa, PB, Brazil
| | | | - Mário César Ugulino Araújo
- Universidade Federal da Paraíba, Departamento de Química—Laboratório de Automação e Instrumentaçáo em Química Analítica/Quimiometria (LAQA), Caixa Postal 5093, CEP 58051-970—João Pessoa, PB, Brazil
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Screening analysis of biodiesel feedstock using UV–vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm. Talanta 2012; 97:579-83. [DOI: 10.1016/j.talanta.2012.04.056] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2012] [Revised: 04/25/2012] [Accepted: 04/28/2012] [Indexed: 11/24/2022]
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