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Barragán-Hernández W, Mahecha-Ledesma L, Burgos-Paz W, Olivera-Angel M, Angulo-Arizala J. Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches. J Anim Sci 2021; 98:5939743. [PMID: 33099624 DOI: 10.1093/jas/skaa342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/19/2020] [Indexed: 02/06/2023] Open
Abstract
This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.
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Affiliation(s)
- Wilson Barragán-Hernández
- Red de Ganadería y Especies Menores, Centro de Investigación El Nus, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), San Roque, Antioquia, Colombia
| | - Liliana Mahecha-Ledesma
- Facultad de ciencias agrarias, Grupo de investigación en ciencias animales-GRICA, Universidad de Antioquia, Medellín, Colombia
| | - William Burgos-Paz
- Red de Ganadería y Especies Menores, Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), Mosquera, Cundinamarca, Colombia
| | - Martha Olivera-Angel
- Facultad de ciencias agrarias, Grupo de investigación Biogénesis, Universidad de Antioquia, Medellín, Colombia
| | - Joaquín Angulo-Arizala
- Facultad de ciencias agrarias, Grupo de investigación en ciencias animales-GRICA, Universidad de Antioquia, Medellín, Colombia
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3
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A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:7686724. [PMID: 32695153 PMCID: PMC7368966 DOI: 10.1155/2020/7686724] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 06/10/2020] [Accepted: 06/12/2020] [Indexed: 11/18/2022]
Abstract
The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.
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6
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Mu H, Gao H, Chen H, Fang X, Zhou Y, Wu W, Han Q. Study on the Volatile Oxidation Compounds and Quantitative Prediction of Oxidation Parameters in Walnut (
Carya cathayensis
Sarg.) Oil. EUR J LIPID SCI TECH 2019. [DOI: 10.1002/ejlt.201800521] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Honglei Mu
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Haiyan Gao
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Hangjun Chen
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Xiangjun Fang
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Yongjun Zhou
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Weijie Wu
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
| | - Qiang Han
- Food Science InstituteZhejiang Academy of Agricultural ScienceKey Laboratory of Post‐Harvest Handling of Fruits of the Ministry of AgricultureKey Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang ProvinceHangzhouZhejiang310021China
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7
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Jiang H, Mei C, Li K, Huang Y, Chen Q. Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 204:73-80. [PMID: 29906647 DOI: 10.1016/j.saa.2018.06.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 06/02/2018] [Accepted: 06/04/2018] [Indexed: 06/08/2023]
Abstract
This study aimed to investigate the potential of FT-NIR spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.
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Affiliation(s)
- Hui Jiang
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, PR China; School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Congli Mei
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Kangji Li
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yonghong Huang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
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9
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Kucha CT, Liu L, Ngadi MO. Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review. SENSORS 2018; 18:s18020377. [PMID: 29382092 PMCID: PMC5855493 DOI: 10.3390/s18020377] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/07/2018] [Accepted: 01/10/2018] [Indexed: 12/31/2022]
Abstract
Fat is one of the most important traits determining the quality of pork. The composition of the fat greatly influences the quality of pork and its processed products, and contribute to defining the overall carcass value. However, establishing an efficient method for assessing fat quality parameters such as fatty acid composition, solid fat content, oxidative stability, iodine value, and fat color, remains a challenge that must be addressed. Conventional methods such as visual inspection, mechanical methods, and chemical methods are used off the production line, which often results in an inaccurate representation of the process because the dynamics are lost due to the time required to perform the analysis. Consequently, rapid, and non-destructive alternative methods are needed. In this paper, the traditional fat quality assessment techniques are discussed with emphasis on spectroscopic techniques as an alternative. Potential spectroscopic techniques include infrared spectroscopy, nuclear magnetic resonance and Raman spectroscopy. Hyperspectral imaging as an emerging advanced spectroscopy-based technology is introduced and discussed for the recent development of assessment for fat quality attributes. All techniques are described in terms of their operating principles and the research advances involving their application for pork fat quality parameters. Future trends for the non-destructive spectroscopic techniques are also discussed.
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Affiliation(s)
- Christopher T Kucha
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
| | - Li Liu
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
| | - Michael O Ngadi
- Department of Bioresource Engineering, McGill University, Macdonald Campus 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, QC H9X 3V9, Canada.
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10
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Pigani L, Vasile Simone G, Foca G, Ulrici A, Masino F, Cubillana-Aguilera L, Calvini R, Seeber R. Prediction of parameters related to grape ripening by multivariate calibration of voltammetric signals acquired by an electronic tongue. Talanta 2017; 178:178-187. [PMID: 29136810 DOI: 10.1016/j.talanta.2017.09.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/07/2017] [Accepted: 09/10/2017] [Indexed: 10/18/2022]
Abstract
An electronic tongue (ET) consisting of two voltammetric sensors, namely a poly-ethylendioxythiophene modified Pt electrode and a sonogel carbon electrode, has been developed aiming at monitoring grape ripening. To test the effectiveness of device and measurement procedures developed, samples of three varieties of grapes have been collected from veraison to harvest of the mature grape bunches. The derived musts have been then submitted to electrochemical investigation using Differential Pulse Voltammetry technique. At the same time, quantitative determination of specific analytical parameters for the evaluation of technological and phenolic maturity of each sample has been performed by means of conventional analytical techniques. After a preliminary inspection by principal component analysis, calibration models were calculated both by partial least squares (PLS) on the whole signals and by the interval partial least squares (iPLS) variable selection algorithm, in order to estimate physico-chemical parameters. Calibration models have been obtained both considering separately the signals of each sensor of the ET, and by proper fusion of the voltammetric data selected from the two sensors by iPLS. The latter procedure allowed us to check the possible complementarity of the information brought by the different electrodes. Good predictive models have been obtained for estimation of pH, total acidity, sugar content, and anthocyanins content. The application of the ET for fast evaluation of grape ripening and of most suitable harvesting time is proposed.
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Affiliation(s)
- L Pigani
- Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via G. Campi, 103, 41125 Modena, Italy; Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy.
| | - G Vasile Simone
- Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via G. Campi, 103, 41125 Modena, Italy; Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
| | - G Foca
- Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy; Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
| | - A Ulrici
- Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy; Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
| | - F Masino
- Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy; Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
| | - L Cubillana-Aguilera
- Institute of Research on Electron Microscopy and Materials, Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar, University of Cadiz, República Saharaui, S/N, 11510 Puerto Real, Cadiz, Spain
| | - R Calvini
- Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
| | - R Seeber
- Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via G. Campi, 103, 41125 Modena, Italy; Centro Interdipartimentale BIOGEST-SITEIA, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
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