Near-Infrared (NIR) Spectroscopy to Differentiate Longissimus thoracis et lumborum (LTL) Muscles of Game Species

Dumalisile, Pholisa; Manley, Marena; Hoffman, Louwrens C.; Williams, Paul J.

doi:10.1007/s12161-020-01739-x

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…To further eliminate the influence of the environmental factors on the original spectra (OS), continuous wavelet transform (CWT), minmax normalization (Minmax), standard normal variate (SNV) and multiplicative scattering correction (MSC) were used as four preprocessing algorithms in MATLAB (MATLAB R2016a, Mathworks) for spectra correction. The CWT algorithm was used to correct the baseline drift and eliminate high-frequency noise; the Minmax algorithm was chosen to strengthen the data; the SNV and MSC algorithms were used to correct the scattering and eliminate the effects caused by the inhomogeneity of tea powder particle size and the nonconstant light range [21][22][23][24]. The choice of wavelet parameters (wavelet basis and decomposition scale) in CWT was crucial and directly determined the merits of the subsequent models [25].…”

Section: Spectral Pretreatmentmentioning

confidence: 99%

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Zhang

Dai

Zhang

et al. 2023

Foods

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Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

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