Development of chemometric models using Vis-NIR and Raman spectral data fusion for assessment of infant formula storage temperature and time |
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| Affiliation: | 1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China;2. Forest Products Development Center, Auburn University, Auburn, AL, USA;3. School of Forestry and Wildlife, Auburn University, Auburn, AL, USA;4. National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, 222000, China;1. Dpto. de Nutrición y Bromatología, Toxicología y Medicina Legal, Facultad de Farmacia, Universidad de Sevilla, C/P. García González n°2, E-41012 Sevilla, Spain;2. Department of Applied Science and Technology (DISAT), Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10129 Torino, TO, Italy;3. Chemometrics and Analytical Techniques, Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark;4. Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125 Modena, Italy;1. Department of Chemical Engineering and Materials Science, University of California Irvine, 916 Engineering Tower, Irvine, CA 92697-2575, USA;2. Pacific Northwest National Laboratory, Richland, WA 99354, USA;3. Department of Chemistry, University of California Irvine, 1102 Natural Sciences 2, Irvine, CA 92697-2025, USA;1. School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, PR China;2. School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Xiyuan Road 279, Suzhou 215008, Jiangsu, PR China;3. Laboratory Services Department, Food and Drugs Authority, Accra, Ghana;4. Gaoyou Qinyou Egg Products Co. LTD, Gaoyou, China |
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| Abstract: | This study evaluated the potential of Vis-NIR and Raman spectral data fusion combined with PLS and SVM chemometric models developed using a large dataset (n = 1700) of commercial infant formula (IF) samples to (i) discriminate between different IF storage temperature (20, 37 °C) and (ii) predict IF storage time (0–12 months). Three interval-based PLS variable selection methods (forward interval PLS (FiPLS), backward interval PLS (BiPLS) and synergy interval PLS (SiPLS)) and SVM-recursive feature elimination (SVM-RFE) methods were compared for model development. The best IF storage temperature discrimination model was developed using SVM classification (SVMC) and Vis-NIR spectra (400–2498 nm) (AccuracyCV = 99.82%, AccuracyP = 100%). SVM regression (SVMR) models developed using medium level data fusion (features selected by SVM-RFE) had the lowest root mean square error (RMSE) values for IF samples stored at either temperature, 20 °C or 37 °C (RMSECV = 0.7–0.8, RMSEP = 0.6–0.9).Industrial relevanceSpectroscopic technologies, including Vis-NIR and Raman spectroscopy have been widely applied for process analysis and increasingly for on-line process monitoring in areas of chemicals, food processing, agriculture and pharmaceuticals, etc. Due to their rapid measurement and minimal or no sample preparation, they are highly suitable for in-line process monitoring. This study demonstrates that Vis-NIR and Raman process analytical tools either individually or combined may be employed for quality assessment and process control of IF manufacture. |
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